USRE39302E1 - Intelligent help system - Google Patents

Intelligent help system Download PDF

Info

Publication number
USRE39302E1
USRE39302E1 US09/375,867 US37586799A USRE39302E US RE39302 E1 USRE39302 E1 US RE39302E1 US 37586799 A US37586799 A US 37586799A US RE39302 E USRE39302 E US RE39302E
Authority
US
United States
Prior art keywords
user
rules
help information
knowledge base
data
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Expired - Lifetime
Application number
US09/375,867
Inventor
Charles D. Lanier
Richard J. Wolf
Leticia Villegas Butner
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Samsung Electronics Co Ltd
Original Assignee
Samsung Electronics Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Samsung Electronics Co Ltd filed Critical Samsung Electronics Co Ltd
Priority to US09/375,867 priority Critical patent/USRE39302E1/en
Assigned to AST COMPUTERS, LLC reassignment AST COMPUTERS, LLC RELEASE OF SECURITY INTEREST Assignors: ARI SERVICE, INC., AS SUCCESSOR IN INTEREST BY MERGER TO AST RESEARCH, INC.
Assigned to ARI SERVICE, INC. reassignment ARI SERVICE, INC. ADOPTION OF RESOLUTIONS BY WRITTEN CONSENT OF BOARD OF DIRECTORS OF AST INNOVATIONS, INC. AND ATTACHED BILL OF SALE TRANSFERRING ALL ASSETS OF AST INNOVATIONS INC. TO ARI SERVICES, INC. Assignors: AST INNOVATIONS, INC., A DELAWARE CORPORATION
Assigned to SAMSUNG ELECTRONICS AMERICA, INC. reassignment SAMSUNG ELECTRONICS AMERICA, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: ARI SERVICE, INC.
Assigned to SAMSUNG ELECTRONICS CO., LTD. reassignment SAMSUNG ELECTRONICS CO., LTD. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: SAMSUNG ELECTRONICS AMERICA, INC.
Application granted granted Critical
Publication of USRE39302E1 publication Critical patent/USRE39302E1/en
Anticipated expiration legal-status Critical
Expired - Lifetime legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/451—Execution arrangements for user interfaces
    • G06F9/453—Help systems
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10S706/00—Data processing: artificial intelligence
    • Y10S706/902—Application using ai with detail of the ai system
    • Y10S706/911—Nonmedical diagnostics
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10S706/00—Data processing: artificial intelligence
    • Y10S706/902—Application using ai with detail of the ai system
    • Y10S706/927—Education or instruction

Definitions

  • This invention relates to help systems for computers; more specifically, it relates to help systems that aid a user of a computer by providing context sensitive help.
  • Computer-aided help system have been developed to provide on-line assistance to computer users.
  • those systems display help information on the display screen of the computer.
  • Simple help systems always start with the same display, regardless of the circumstances, and the user must enter specific information to find help for his or her particular situation.
  • More advanced help systems display context-sensitive help.
  • Context-sensitive help systems determine what particular part of an application program the user is in. Then help information is displayed that is relevant to this user location.
  • help systems represent an advancement over simple help systems, they have numerous limitations. Such systems are usually tightly coupled to an application program; they must rely on the application program to keep track of and store the context. Further, since these systems are limited to displaying help information based upon program location, they will always return the same help information for a given location regardless of how the user got there. While such systems provide the convenience of on-line help, the help information they provide is nothing more than a user's manual correlated with a given program screen or function. As a result, these help systems tend to be of limited utility to the user who cannot specifically identify the problem or who has “lost his way.”
  • the invention recognizes a need for an intelligent help system which processes information specific to the user's history, such as tasks he or she has successfully executed (and how many times) or has had previous help with, and information which defines a state of a machine and a state of a programmed application.
  • an intelligent help system for aiding the user of a computer program is provided by maintaining an historic queue and using artificial intelligence techniques to select help information based on user-directed events and the current state of the system.
  • user-directed activities are monitored and stored in the historical queue inside a knowledge base.
  • System states are also monitored and stored.
  • the knowledge base is then used by an inference engine to isolate the specific kind of help that a user needs.
  • the user is given assistance upon request which is appropriate to that user's level of understanding or experience and the current activities that he or she has executed.
  • FIG. 1 is a block diagram of a computer system in which the invention may be embodied.
  • FIG. 2 is a block diagram of a computer software system used in the preferred embodiment.
  • FIG. 3A illustrates the processing of user-directed events and system states into help information.
  • FIG. 3B is a flow chart of the general methods of the help system.
  • FIG. 4 is a flow chart of a query by the monitoring device.
  • FIGS. 5A-B are a flow chart of the methods of the event interpreters.
  • FIG. 6 is a flow chart of the methods for processing events.
  • FIG. 7 is a flow chart of the methods for identifying a task.
  • FIG. 8 illustrates the rule taxonomy of the invention.
  • FIG. 9 illustrates frames and slots for the storage of knowledge.
  • FIGS. 10A-B are a flow chart of the methods for proving rules.
  • FIG. illustrates the operation of the display engine.
  • the preferred embodiment of the invention is implemented on a computer system 100 having a central processor 102 , a system memory 103 , a display device 105 , a keyboard 106 , a mouse 107 , a disk memory 108 , an I/O controller 101 , and interconnecting means 110 , such as a system bus.
  • a Tandy 1000 series computer (Tandy Corporation of Ft. Worth, Tex.) is used as the system 100 .
  • a computer software system 200 is shown for programming the computer system of FIG. 1 .
  • Software system 200 is stored in system memory 103 and on disk memory 108 .
  • System 200 programs the central processor 102 to display a graphic user interface (GUI) on display monitor 105 .
  • GUI graphic user interface
  • help system 204 is implemented in the Tandy DeskMate environment 203 which provides a software interface 205 between a user 206 , a computer application 202 , and an operating system 201 . It will be apparent, however, that one of ordinary skill in the art, informed by this specification, could implement the invention in other operating environments.
  • an artificial intelligence (AI) paradigm is used to deal with knowledge which may be vast and uncertain, leading to multiple solutions for a given situation.
  • An AI model has the ability to learn or infer more knowledge from what it already knows. Thus, if a user requests help and the help system cannot reach a solution, the system can get further information from the user and then remember the situation; the next time that that situation occurs a solution can be given without querying the user again.
  • help system 300 of the preferred embodiment comprises a monitoring device 320 for collecting data generated in response to user-directed events and system states 310 , a knowledge base 330 for storing data 331 along with a help information database 335 used to determine the best help to give, an inference engine 340 for interpreting data 331 and help information database 335 in knowledge base 330 , and a display engine 350 for presenting appropriate help information 360 on display device 105 .
  • Data 331 comprises an historical queue 332 and a state data 333
  • help information database 335 comprises a plurality of rules 334 and text 336 .
  • FIG. 3B is a flow chart illustrating the general methods of help system 300 .
  • user-directed events and system states are monitored.
  • User-directed events are the activities that a user performs in an application program, for example, saving a file in a paint application or copying a block of text in a word-processing application.
  • the system state comprises a machine state and an application-specific state.
  • the information collected in step 351 is stored as facts or data 331 of knowledge base 350 .
  • sequential user-directed events are stored in historical queue 332 and knowledge about the system is stored as state data 333 .
  • step 353 if a user requests help (e.g., pressing F1 key), then in step 355 inference engine 340 tests known data 331 against help system rules 334 . However, if no help is requested in step 353 , the routine loops back to the monitoring step 351 .
  • help e.g., pressing F1 key
  • Knowledge base 330 stores heuristics in the form of rules.
  • Rules 334 such as those used in step 355 , are premise-conclusion statements predefined by an application developer which guide inference engine 340 in selecting an output. For example, suppose a user is in a Text (word processing) listbox and no files are selected. A rule that would check this is:
  • Monitoring device 320 tracks or monitors user input, it has processing capabilities. It may assert data in knowledge base 330 after identifying a sequence of one or more events or states. It may remove or retract data concerning states which are no longer true, or reasserts a new value for old data. It keeps track of the number of times an activity has successfully been completed by a user.
  • Monitoring device 320 monitors different types of information, including machine states, application specific states, and historical information.
  • the machine state includes current system level, such as within an application or accessory, running a component, or at the desktop interface.
  • An accessory is an application that may “pop up” over another application. Examples of accessories include a pop-up calculator, calendar, or alarm.
  • a component is a graphic element that a user interacts with to display and accept information from a user. For example, the components in a dialogue box include radio buttons, push buttons, and edit fields.
  • the menubar is also a component.
  • Monitoring device 320 also monitors application-specific states (application specifics), including information unique to an application, and component states, including information within the current machine state which is either application specific or general; since components such as radio buttons are generated in the same way, regardless of whether the component is used at the application level or general level, information about component states is also generated in a uniform manner. For example in all applications which have a menubar, choices are obtained by selecting an item off the menu.
  • application-specific states application specifics
  • component states including information unique to an application
  • component states including information within the current machine state which is either application specific or general; since components such as radio buttons are generated in the same way, regardless of whether the component is used at the application level or general level, information about component states is also generated in a uniform manner. For example in all applications which have a menubar, choices are obtained by selecting an item off the menu.
  • Monitoring device 320 also tracks historical information which indicates the completion of a task for which help has been defined. This data is stored in historical queue 332 . A successful completion indicates that the user no longer needs help in performing that task. More specific help information, rather than general information, can be given as the user gains more experience with a program.
  • monitoring device 320 adds a dialogue box entry (CMP_DLG_BOX) into historical queue 332 and then stores corresponding editfield, listbox, radiobutton, iconbutton and checkbox information.
  • CMP_DLG_BOX dialogue box entry
  • Each entry into historical queue 332 can be of variable length, depending on the type of entry made.
  • a unique “return code” is assigned to each component, thus facilitating the distinction between components.
  • a far pointer to an entry's structure e.g., dialogue box, component, and/or menubar structures
  • the format used can be of variable length.
  • a subentry flag is defined to indicate when there is a subentry. For example, a component may still be running while the menubar is processed, therefore, the menubar is part of a single entry. If the flag is set to a value of true, then any subentry data is stored. Another flag is defined to indicate the user keystrokes that occurred during the entry. This enables the system to determine, for example, whether the user has begun entering data into a dialogue box, or if he chose help immediately. The name of the box and menu entries, such as DLGBOX, MSGBOX, LISTBOX, or MENU, is stored to distinguish one box from another. A third flag is defined to indicate the user keystrokes that occurred before invoking help. This enables the system to determine what the user was doing in the application.
  • Variables which manipulate historical queue 332 are defined as follows:
  • the data structure of the queue may be summarized as follows:
  • the monitoring of information occurs at different places in software system 200 . All state changes that result in the execution of dialogue and message boxes are monitored.
  • DeskMate environment 203 commands are entered by the user through a menubar and are processed by event interpreters, which check for menubar changes.
  • the application programs themselves may indicate state changes in their respective working areas.
  • DeskMate environment 203 is divided into the following hierarchy of level changes, thus simplifying the monitor's task of updating information:
  • the variable “level” takes the value of DeskTop, Menubar, Dlg_box, Message_box, Info_box, or Component.
  • the monitoring can obtain the state changes by getting the address calls from DeskMate environment 203 which indicate a function call to an application program or resource.
  • FIG. 4 illustrates how monitoring device 320 queries for the address of calls it is interested in.
  • applications call core service routines (CSR) to run components and dialogue, information, and message boxes.
  • CSR core service routines
  • a menubar interpreter handles the processing of the menubar. Therefore, independently of an application, the calls which change the structures that the application is using can be monitored to keep track of information changes.
  • step 401 an entry call is made to a core service routine.
  • step 402 the component type is identified and then compared with a list of components that monitoring device 320 is interested in.
  • step 403 if the component is listed in the table, then in step 404 , monitoring device 320 takes the application's parameters and its structure pointer to copy data from within the application's structures. The structures are not modified.
  • step 405 monitoring device 320 again accesses data in the structures, this time for the purpose of historical information updates.
  • program is defined to indicate which particular program is running. Program takes the value of the program name, which is the same identifier for help information database 335 associated with the application or accessory.
  • Two event interpreters pick up menubar changes.
  • the menu selected is important for the state, while the item returned is important for the history.
  • a high-priority event interpreter picks up the menubar changes when help is requested, and a low priority event interpreter will make all changes from the menubar's return code.
  • the high-priority interpreter examines events first before any further processing by the DeskMate system.
  • the low-priority interpreter examines events after they are processed by the DeskMate system.
  • FIGS. 5A-B illustrate the method of the events interpreters.
  • the application registers the menubar with monitoring device 320 .
  • the state information is updated according to what level the system is operating at (indicated by the variable “level”). If level equals zero in step 504 , then the menubar is the last thing changed, therefore a menubar update is needed.
  • the menubar interpreter makes an update. But if “level” is not zero, then step 505 is skipped.
  • the low priority interpreter checks the event type.
  • step 508 the low priority interpreter does a history update, performed by taking the return code and comparing its value with the menubar menus.
  • the string corresponding to the return code is the item of interest.
  • step 509 the routine loops back to step 501 to await another F 1 keystroke.
  • Application-specific information is obtained by having the application assert data into the current state data 333 in knowledge base 330 .
  • the application accomplishes this by making a call to an “Assert” function with the parameters “variable” and “value,” which are pointers to strings.
  • the variable should match a variable in the rule premise, i.e., the rule must be defined beforehand.
  • An application should assert any historical information related to its unique state configuration.
  • Application data are removed from the current state data 333 by the application once those data are no longer true by calling a “Retract” function.
  • the applications may directly assert data into knowledge base 330 , they do not determine what help to give. Instead, they supply the inputs for this determination.
  • Historical information is obtained by using a user's I.D. to index the user's unique historical information. This historical information is updated at the end of a task completion. For example, when the user has successfully executed a copying function, monitoring device 320 recognizes this by checking that the user has selected “text” and then selected “copy” from the file menu. Having determined that the user has mastered this task, monitoring device 320 updates the user's historical information. For historical information associated with the application's specific data, the application updates the information itself by calling the function UpdateHistory with a parameter pointing to a string representing the activity just completed.
  • FIG. 6 summarizes the method for processing the events from the event interpreters.
  • events are processed by examining the mouse coordinates and event type and value.
  • events are identified by trying to “fire” or trigger a rule that would make a data assertion or retraction into knowledge base 330 . These rules represent all conditions that must be true for data to be asserted or retracted. If a rule fires in step 603 , then the event is identified, step 604 . If the rule does not fire, then the event is not identified, step 605 .
  • FIG. 7 illustrates the approach used in step 602 ( FIG. 6 ) to identify a task (sequence of events).
  • a key or mouse event is examined.
  • the event if the event does not match the first premise line in any rule associated with the system level, then the event is discarded in step 704 . Otherwise, in step 703 , all rules that fire are placed on an Agenda, which represents the most likely task(s).
  • the rules are approved or disproved by examining the next key or mouse events that come in.
  • step 706 If all the rules in the Agenda fail at step 706 , then the first key analyzed is discarded and the next key is used to search for new rules, step 708 ; otherwise in step 707 , the Agenda is cleared and the procedure loops to step 701 to examine the next key/mouse event.
  • Monitoring device 320 attempts to assert data which apply to a given level.
  • Each application or accessory can be considered as a distinct object (as a level) which performs certain activities, some of which are common to other objects. Therefore, it is convenient to divide the monitor's data structure into “frames.”
  • a frame contains the activities that each application is capable of, with current values and historical information.
  • the frames have slots for storing the data used by the rules or a pointer to another frame.
  • user input and system state are analyzed by monitoring device 320 for passage as data to knowledge base 330 for storage.
  • Commands are defined for controlling this flow of data to and from knowledge base 330 .
  • Data are stored in frames through the Assert command. Old information is deleted with the Retract command.
  • a query is made to knowledge base 330 to attain information through a Query command. Previous data are asserted by a Reassert command.
  • Knowledge base 330 comprises formal (traditional data base information) and informal (heuristic) knowledge which is rule based.
  • FIG. 8 demonstrates the rule taxonomy.
  • At the lower level of the rule hierarchy 800 is a single rule 804 defining a pattern-to-action goal.
  • the pattern is known as the left-hand side of a rule, while the action is the right-hand side.
  • the rules use the common logical operators AND, OR, and NOT, as well as Boolean operators such as IF, THEN, and ELSE.
  • the key word TEST indicates that a comparison needs to be made.
  • the right-hand side of a rule can have an ELSE clause, an assertion, a retraction, another rule, or a procedure call.
  • ELSE c b could be an assertion which would add data b into data 331 ; multiple data assertions are possible. A retraction would delete data b from data 331 : IF a THEN (RETRACT b).
  • Linked Rules 803 are a linking between rules which share the same conclusion. This is a design implementation intended to make the inference process easier by knowing alternative solution paths.
  • Rule Groups 802 group rules with a common purpose. For example, rules with a conclusion indicating what specific help to give are all rules determining the goal state. A group can have an (optional) priority identifier so the most important or most specific rules can be tried out first when searching. This does not imply that a rule group will be left out of a search.
  • Rules Classes 801 are a further conceptualization of rules. They separate individual knowledge bases, each having a unique identifier by which it is distinguished. This identifier can be used by a set of control rules which use the machine state information to indicate knowledge base access.
  • each rule also contains formal or informal knowledge.
  • the informal knowledge becomes formal when a rule fires successfully.
  • the formal knowledge is stored in and accessed from knowledge base 330 in frame structures.
  • Frames can be made up of other frames, which can also be shared. With this data structure, it is possible to access only the frame associated with the current state information when monitoring device 320 is updating the user's historical information.
  • the frame's slots are like any linked list, except they represent actions and attributes of an object or concepts that the frames represent. Since frames indicate the relationship between a user's activity and the associated heuristics used to interpret that activity, it is easy to access only relevant information.
  • FIG. 9 illustrates a frame 901 with its related slots 902 .
  • Rule Classes 801 are made up of pointers to the variables and values in the frames.
  • the slots which contain another frame are really another group within a rule class.
  • the slots contain the rules. The successful completion of a frame yields a solution.
  • rules may be coded as data structures (illustrated in the C language):
  • the inputs to knowledge base 330 are the data collected b monitoring device 320 .
  • the outputs of knowledge base 330 are data 331 and rules 334 which inference engine 340 selects for examination and help information text 336 which display engine 350 processes.
  • Rules 334 are predefined by application developer. The frames indicate the relationship between the user's activity and the associated heuristics used to interpret that activity, thus making it easier to access only the information needed.
  • the knowledge-based rules are structured so that obvious associations between rules can be incorporated within knowledge base 330 simplifying the design and the inferences from those rules.
  • Inference engine 340 interprets data 331 and rules 334 in knowledge base 330 to give a help solution to the user, or it intelligently asks for more information in order to obtain a solution.
  • inference engine 340 operates using a backward-chaining method. This method starts with a goal state (a particular kind of help) and tries to prove it by reaching initial known data.
  • step 1001 knowledge base 330 is accessed according to the system state's level.
  • step 1002 depending on the system state, a group of rules (goal) is selected from knowledge base 330 and used as an hypothesis.
  • step 1003 an attempt is made to prove each rule's conclusion by proving its premise. If, in step 1004 , the premise examined is the conclusion of another rule, it is pushed onto a stack (last-in first-out structure in system memory 103 ) and an attempt is made to prove the other rule's premise in step 1005 .
  • step 1006 if a premise is proved, it is asserted into a working fact or data list at step 1007 . Otherwise if the premise fails, a search is made in step 1008 for a rule with the same conclusion, which inference engine 340 will then try to prove.
  • step 1009 when all the premises in the rule have been proved, the rule has fired and the conclusion can be asserted at step 1010 .
  • step 1011 this process is repeated until there are no more goal states to analyze or until no solution is found. In the latter case, knowledge base 330 is incomplete, therefore, inference engine 340 queries the user for more information or else explains to the user that no help is defined for the particular scenario.
  • the inputs to inference engine 340 are data 331 and rules 334 contained in knowledge base 330 . Some of rules 334 control other knowledge bases which can be selected. Inference engine 340 first examines rules 334 to select the proper rule class 801 . Since the groups 802 under the class 801 indicate if they are goal states, engine 340 can also indicate which rules will be used as hypotheses. Final output from inference engine 340 is a help “tag” that indicates a particular help solution. In addition, the engine creates a temporary output of data asserted while proving the rules.
  • Display engine 350 is an interface between the output of inference engine 340 and help text 336 giving the user easy access to the most useful help topics.
  • Display engine 350 provides general to in-depth help to a user. It processes the inference engine's help tag to provide context-sensitive help.
  • the help information itself has tags which are used by display engine 350 to locate further help information. This allows a user to select specific help from a subset of help information.
  • the help information data structure used by display engine 350 is defined for each application/accessory and system resource interfaces. Help that is repeated across multiple applications is divided into groups. The help information which is given is individualized for novice, intermediate, and advanced users. A display format is used which allows a user to select a single item of help from a suggested list, allow the user to continue selecting help until he wants to quit, and allow the user to search through the Help Topics for a particular topic.
  • help structure in C language
  • a help structure in C language
  • Help information database 335 is a database containing text 336 and rules 334 fields, i.e., help information text 336 is linked to help information rules 334 .
  • a tag 1102 is provided for representing the solution that a rule produces.
  • Display engine 350 matches tag 1102 with a solution tag 1101 from inference engine 340 .
  • the corresponding text (from text 336 ) is the actual text sent as help information 360 to display device 105 .
  • Grp is a rule group.
  • Group 0 is always the group in which rules are placed that will give a solution. If these rules cause any other rules to fire, these rules are in another group.
  • Rule# is the number of the rule.
  • the rules are sorted by rule number, because one may want to try to fire one rule before another.
  • PorC# is a premise or conclusion number.
  • the records in the table are also sorted by PorC#. The conclusion is number 0 because it is the first thing pulled out of the table when inference engine 340 starts to fire a rule.
  • Var# is an identifier for the engine to do faster searching. Each new variable added to the table has a unique identifier.
  • the variables are DeskMate components, like a dialog box.
  • Var the variable field, is a string representing a variable for which a value is expected such as “running.cmp” (DISABLED FIELD).
  • Value is the string field which contains a value. If the value is the current one for the variable, then the premise line succeeds.
  • Bind is a binding variable. If a rule has a binding, the variable will bind to the currently known value of the variable. This eliminates repetition of rules that do the same exact thing. Only premises can bind.
  • KeyW is a number indicating the negation (NOT) of a premise, or a TEST or CALL. TEST will do number comparisons, and assumes the value string field is a numerical value. CALL is used to execute a pre-defined function. The parameters of the function are placed in the value field. Q# is the number in queue 332 for which a premise line test applies. If the queue number equals 0, it is assumed the premise does not use predefined data.

Abstract

An intelligent help system which processes information specific to a user and a system state is described. The system incorporates a monitoring device to determine which events to store as data in an historical queue. These data, as well as non-historical data (e.g., system state), are stored in a knowledge base. An inference engine tests rules against the knowledge base data, thereby providing a help tag. A display engine links the help tag with an appropriate solution tag to provide help text for display.

Description

COPYRIGHT NOTICE
A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever.
FIELD OF THE INVENTION
This invention relates to help systems for computers; more specifically, it relates to help systems that aid a user of a computer by providing context sensitive help.
BACKGROUND OF THE INVENTION
In order to operate a computer effectively, a user must master a number of commands and data formats. One usually accomplishes this by spending hours reading printed user documentation and/or by using trial and error techniques.
Computer-aided help system have been developed to provide on-line assistance to computer users. In response to a request by a user, those systems display help information on the display screen of the computer. Simple help systems always start with the same display, regardless of the circumstances, and the user must enter specific information to find help for his or her particular situation. More advanced help systems display context-sensitive help. Context-sensitive help systems determine what particular part of an application program the user is in. Then help information is displayed that is relevant to this user location.
While such context-sensitive help systems represent an advancement over simple help systems, they have numerous limitations. Such systems are usually tightly coupled to an application program; they must rely on the application program to keep track of and store the context. Further, since these systems are limited to displaying help information based upon program location, they will always return the same help information for a given location regardless of how the user got there. While such systems provide the convenience of on-line help, the help information they provide is nothing more than a user's manual correlated with a given program screen or function. As a result, these help systems tend to be of limited utility to the user who cannot specifically identify the problem or who has “lost his way.”
SUMMARY OF THE INVENTION
The invention recognizes a need for an intelligent help system which processes information specific to the user's history, such as tasks he or she has successfully executed (and how many times) or has had previous help with, and information which defines a state of a machine and a state of a programmed application.
According to the invention, an intelligent help system for aiding the user of a computer program is provided by maintaining an historic queue and using artificial intelligence techniques to select help information based on user-directed events and the current state of the system. In particular, user-directed activities are monitored and stored in the historical queue inside a knowledge base. System states are also monitored and stored. The knowledge base is then used by an inference engine to isolate the specific kind of help that a user needs. Thus, the user is given assistance upon request which is appropriate to that user's level of understanding or experience and the current activities that he or she has executed.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a block diagram of a computer system in which the invention may be embodied.
FIG. 2 is a block diagram of a computer software system used in the preferred embodiment.
FIG. 3A illustrates the processing of user-directed events and system states into help information.
FIG. 3B is a flow chart of the general methods of the help system.
FIG. 4 is a flow chart of a query by the monitoring device.
FIGS. 5A-B are a flow chart of the methods of the event interpreters.
FIG. 6 is a flow chart of the methods for processing events.
FIG. 7 is a flow chart of the methods for identifying a task.
FIG. 8 illustrates the rule taxonomy of the invention.
FIG. 9 illustrates frames and slots for the storage of knowledge.
FIGS. 10A-B are a flow chart of the methods for proving rules.
FIG. illustrates the operation of the display engine.
 DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT
Referring to FIG. 1, the preferred embodiment of the invention is implemented on a computer system 100 having a central processor 102, a system memory 103, a display device 105, a keyboard 106, a mouse 107, a disk memory 108, an I/O controller 101, and interconnecting means 110, such as a system bus. In the preferred embodiment, a Tandy 1000 series computer (Tandy Corporation of Ft. Worth, Tex.) is used as the system 100.
Referring to FIG. 2, a computer software system 200 is shown for programming the computer system of FIG. 1. Software system 200 is stored in system memory 103 and on disk memory 108. System 200 programs the central processor 102 to display a graphic user interface (GUI) on display monitor 105. In the preferred embodiment, help system 204 is implemented in the Tandy DeskMate environment 203 which provides a software interface 205 between a user 206, a computer application 202, and an operating system 201. It will be apparent, however, that one of ordinary skill in the art, informed by this specification, could implement the invention in other operating environments.
In the preferred embodiment, an artificial intelligence (AI) paradigm is used to deal with knowledge which may be vast and uncertain, leading to multiple solutions for a given situation. An AI model has the ability to learn or infer more knowledge from what it already knows. Thus, if a user requests help and the help system cannot reach a solution, the system can get further information from the user and then remember the situation; the next time that that situation occurs a solution can be given without querying the user again.
Referring to FIG. 3A, help system 300 of the preferred embodiment comprises a monitoring device 320 for collecting data generated in response to user-directed events and system states 310, a knowledge base 330 for storing data 331 along with a help information database 335 used to determine the best help to give, an inference engine 340 for interpreting data 331 and help information database 335 in knowledge base 330, and a display engine 350 for presenting appropriate help information 360 on display device 105. Data 331 comprises an historical queue 332 and a state data 333, while help information database 335 comprises a plurality of rules 334 and text 336.
FIG. 3B is a flow chart illustrating the general methods of help system 300. In step 351, user-directed events and system states are monitored. User-directed events are the activities that a user performs in an application program, for example, saving a file in a paint application or copying a block of text in a word-processing application. The system state comprises a machine state and an application-specific state. In step 352, the information collected in step 351 is stored as facts or data 331 of knowledge base 350. Specifically, sequential user-directed events are stored in historical queue 332 and knowledge about the system is stored as state data 333. In step 353 if a user requests help (e.g., pressing F1 key), then in step 355 inference engine 340 tests known data 331 against help system rules 334. However, if no help is requested in step 353, the routine loops back to the monitoring step 351.
Knowledge base 330 stores heuristics in the form of rules. Rules 334, such as those used in step 355, are premise-conclusion statements predefined by an application developer which guide inference engine 340 in selecting an output. For example, suppose a user is in a Text (word processing) listbox and no files are selected. A rule that would check this is:
if
no files
running.listbox TEXT
then
help running text

This rule attempts to “fire” by proving its premise, “no files.” First, it checks the known data 331 in knowledge base 330. If this is not in data 331, it checks for other rules with this premise as their conclusion. Next, if in step 356 a match is found, the corresponding rule fires in step 357. In step 358, in response to the particular rule that fired, a conclusion is asserted and appropriate help information 360 is displayed. The format of rules in this embodiment is described hereinafter with reference to FIG. 8.
Monitoring device 320 and its functions will now be described in detail. While monitoring device 320 tracks or monitors user input, it has processing capabilities. It may assert data in knowledge base 330 after identifying a sequence of one or more events or states. It may remove or retract data concerning states which are no longer true, or reasserts a new value for old data. It keeps track of the number of times an activity has successfully been completed by a user.
Monitoring device 320 monitors different types of information, including machine states, application specific states, and historical information. The machine state includes current system level, such as within an application or accessory, running a component, or at the desktop interface. An accessory is an application that may “pop up” over another application. Examples of accessories include a pop-up calculator, calendar, or alarm. A component is a graphic element that a user interacts with to display and accept information from a user. For example, the components in a dialogue box include radio buttons, push buttons, and edit fields. The menubar is also a component.
Monitoring device 320 also monitors application-specific states (application specifics), including information unique to an application, and component states, including information within the current machine state which is either application specific or general; since components such as radio buttons are generated in the same way, regardless of whether the component is used at the application level or general level, information about component states is also generated in a uniform manner. For example in all applications which have a menubar, choices are obtained by selecting an item off the menu.
Monitoring device 320 also tracks historical information which indicates the completion of a task for which help has been defined. This data is stored in historical queue 332. A successful completion indicates that the user no longer needs help in performing that task. More specific help information, rather than general information, can be given as the user gains more experience with a program.
The structure of historical queue 332 will now be described in detail. For each entry, an entry type is stored. For example, when a user runs a dialogue box, monitoring device 320 adds a dialogue box entry (CMP_DLG_BOX) into historical queue 332 and then stores corresponding editfield, listbox, radiobutton, iconbutton and checkbox information. Each entry into historical queue 332 can be of variable length, depending on the type of entry made. A unique “return code” is assigned to each component, thus facilitating the distinction between components. A far pointer to an entry's structure (e.g., dialogue box, component, and/or menubar structures) is stored to allow direct access to that structure. Following this, dialogue and component information is copied. The format used can be of variable length. A subentry flag is defined to indicate when there is a subentry. For example, a component may still be running while the menubar is processed, therefore, the menubar is part of a single entry. If the flag is set to a value of true, then any subentry data is stored. Another flag is defined to indicate the user keystrokes that occurred during the entry. This enables the system to determine, for example, whether the user has begun entering data into a dialogue box, or if he chose help immediately. The name of the box and menu entries, such as DLGBOX, MSGBOX, LISTBOX, or MENU, is stored to distinguish one box from another. A third flag is defined to indicate the user keystrokes that occurred before invoking help. This enables the system to determine what the user was doing in the application.
Variables which manipulate historical queue 332 are defined as follows:
  • ihm_HelpQ: the 500 byte queue, there is one in each task data area of the Core Service Routine (CSR).
  • ihm_TOP: the physical start of the HelpQ buffer.
  • ihm_ENDQ: the physical end of the HelpQ buffer.
  • ihm_start_ptr: pointer to the first entry in the queue, (which is last historically).
  • ihm_cur_ptr: pointer to the first byte of the last entry in the HelpQ.
  • ihm_end ptr: pointer to the last byte of data entered into the HelpQ.
  • ihm_save_ptr: the ptr value of the last entry made, saved because an invalid ptr is entered into the HelpQ as a NULL.
  • ihm_menu_ptrl: double word pointer to the current menubar. Updated each time mb draw is called.
The data structure of the queue may be summarized as follows:
  • dw— offset of previous entry
  • dw— type
  • dw— return code
  • dw— struct ptr— offset
  • dw— struct ptr— segment
  • db— # subentries
  • db— length of name
  • db(? )— name of dlg box, msg box, menu, listbox
  • db— keyflag— any mouse or key events before entry?
  • db(? )— subentry data
  • db— keyflag— any mouse or key events during entry?
  • dw— length of information copied or 0
In the preferred embodiment, the monitoring of information occurs at different places in software system 200. All state changes that result in the execution of dialogue and message boxes are monitored. In DeskMate environment 203, commands are entered by the user through a menubar and are processed by event interpreters, which check for menubar changes. In addition, the application programs themselves may indicate state changes in their respective working areas.
DeskMate environment 203 is divided into the following hierarchy of level changes, thus simplifying the monitor's task of updating information:
1. Top Level to application/accessory or menubar/component;
2. Application to accessory or menubar/component or return to top level;
3. Accessory to menubar/component or return to application or top level.
Specific information is required as to the following states: application is running or has quit; which application is running; accessory is running or has quit; which accessory is running; dialogue box is running or has quit; message box is running or has quit; component is running or has quit; menubar menu has been pulled down or has returned; user at desk top; if user not at top level, the level attained prior to the current one. The variable “level” takes the value of DeskTop, Menubar, Dlg_box, Message_box, Info_box, or Component. The monitoring can obtain the state changes by getting the address calls from DeskMate environment 203 which indicate a function call to an application program or resource.
FIG. 4 illustrates how monitoring device 320 queries for the address of calls it is interested in. In DeskMate environment 203, applications call core service routines (CSR) to run components and dialogue, information, and message boxes. A menubar interpreter handles the processing of the menubar. Therefore, independently of an application, the calls which change the structures that the application is using can be monitored to keep track of information changes.
Thus, the steps are as follows. In step 401, an entry call is made to a core service routine. In step 402, the component type is identified and then compared with a list of components that monitoring device 320 is interested in. In step 403, if the component is listed in the table, then in step 404, monitoring device 320 takes the application's parameters and its structure pointer to copy data from within the application's structures. The structures are not modified. Upon exit of the service and before control is returned to the application, at step 405 monitoring device 320 again accesses data in the structures, this time for the purpose of historical information updates.
Since a distinction may be made between applications and accessories, a variable “program” is defined to indicate which particular program is running. Program takes the value of the program name, which is the same identifier for help information database 335 associated with the application or accessory.
Two event interpreters pick up menubar changes. The menu selected is important for the state, while the item returned is important for the history. A high-priority event interpreter picks up the menubar changes when help is requested, and a low priority event interpreter will make all changes from the menubar's return code. The high-priority interpreter examines events first before any further processing by the DeskMate system. On the other hand, the low-priority interpreter examines events after they are processed by the DeskMate system.
FIGS. 5A-B illustrate the method of the events interpreters. In step 501, the application registers the menubar with monitoring device 320. At step 502, if the user has selected the F1 (help) key, then at step 503 the state information is updated according to what level the system is operating at (indicated by the variable “level”). If level equals zero in step 504, then the menubar is the last thing changed, therefore a menubar update is needed. At step 505, the menubar interpreter makes an update. But if “level” is not zero, then step 505 is skipped. At step 506, the low priority interpreter checks the event type. If, at step 507, it is menubar (level=menubar), then in step 508 the low priority interpreter does a history update, performed by taking the return code and comparing its value with the menubar menus. The string corresponding to the return code is the item of interest. At step 509, the routine loops back to step 501 to await another F1 keystroke.
The rules associated with the functions of the above components are written such that if a component has a title string, this string is used to identify its help source. If a component does not have a title string, then the name of the component will be used as its identifier.
Since all applications use the features of DeskMate environment 203, standard representation of data 331 and rules 334 is possible. For example, the following variables can be used to indicate events and states:
    • menu.selected
    • menu.item.selected
    • dlg.box.running
    • dlg.box.focus
    • msg.box.running
    • cmp.running
    • listbox.item.selected
    • checkbox.item.selected
Application-specific information is obtained by having the application assert data into the current state data 333 in knowledge base 330. The application accomplishes this by making a call to an “Assert” function with the parameters “variable” and “value,” which are pointers to strings. The variable should match a variable in the rule premise, i.e., the rule must be defined beforehand. An application should assert any historical information related to its unique state configuration. Application data are removed from the current state data 333 by the application once those data are no longer true by calling a “Retract” function. Although the applications may directly assert data into knowledge base 330, they do not determine what help to give. Instead, they supply the inputs for this determination.
Historical information is obtained by using a user's I.D. to index the user's unique historical information. This historical information is updated at the end of a task completion. For example, when the user has successfully executed a copying function, monitoring device 320 recognizes this by checking that the user has selected “text” and then selected “copy” from the file menu. Having determined that the user has mastered this task, monitoring device 320 updates the user's historical information. For historical information associated with the application's specific data, the application updates the information itself by calling the function UpdateHistory with a parameter pointing to a string representing the activity just completed.
FIG. 6 summarizes the method for processing the events from the event interpreters. At step 601, events are processed by examining the mouse coordinates and event type and value. In step 602, events are identified by trying to “fire” or trigger a rule that would make a data assertion or retraction into knowledge base 330. These rules represent all conditions that must be true for data to be asserted or retracted. If a rule fires in step 603, then the event is identified, step 604. If the rule does not fire, then the event is not identified, step 605.
FIG. 7 illustrates the approach used in step 602 (FIG. 6) to identify a task (sequence of events). In step 701, a key or mouse event is examined. In step 702, if the event does not match the first premise line in any rule associated with the system level, then the event is discarded in step 704. Otherwise, in step 703, all rules that fire are placed on an Agenda, which represents the most likely task(s). At step 705, the rules are approved or disproved by examining the next key or mouse events that come in. If all the rules in the Agenda fail at step 706, then the first key analyzed is discarded and the next key is used to search for new rules, step 708; otherwise in step 707, the Agenda is cleared and the procedure loops to step 701 to examine the next key/mouse event.
Since the information monitored is dependent upon the machine state level, the data generated are divided according those levels. Monitoring device 320 attempts to assert data which apply to a given level. Each application or accessory can be considered as a distinct object (as a level) which performs certain activities, some of which are common to other objects. Therefore, it is convenient to divide the monitor's data structure into “frames.” A frame contains the activities that each application is capable of, with current values and historical information. The frames have slots for storing the data used by the rules or a pointer to another frame.
In the preferred embodiment, user input and system state are analyzed by monitoring device 320 for passage as data to knowledge base 330 for storage. Commands are defined for controlling this flow of data to and from knowledge base 330. Data are stored in frames through the Assert command. Old information is deleted with the Retract command. A query is made to knowledge base 330 to attain information through a Query command. Previous data are asserted by a Reassert command.
Knowledge base 330 comprises formal (traditional data base information) and informal (heuristic) knowledge which is rule based. FIG. 8 demonstrates the rule taxonomy. At the lower level of the rule hierarchy 800 is a single rule 804 defining a pattern-to-action goal. The pattern is known as the left-hand side of a rule, while the action is the right-hand side. The rules use the common logical operators AND, OR, and NOT, as well as Boolean operators such as IF, THEN, and ELSE. In addition, the key word TEST indicates that a comparison needs to be made. The right-hand side of a rule can have an ELSE clause, an assertion, a retraction, another rule, or a procedure call. For example, in the rule:
IF a THEN b, ELSE c
b could be an assertion which would add data b into data 331; multiple data assertions are possible. A retraction would delete data b from data 331:
IF a THEN (RETRACT b).
“Another rule” may be imbedded as follows:
IF a then b, IF (TEST (=bc)) THEN d.
An example of a procedure call would be:
IF a THEN (CALL Help Tutorial(a)).
Linked Rules 803 are a linking between rules which share the same conclusion. This is a design implementation intended to make the inference process easier by knowing alternative solution paths. Rule Groups 802 group rules with a common purpose. For example, rules with a conclusion indicating what specific help to give are all rules determining the goal state. A group can have an (optional) priority identifier so the most important or most specific rules can be tried out first when searching. This does not imply that a rule group will be left out of a search. Rules Classes 801 are a further conceptualization of rules. They separate individual knowledge bases, each having a unique identifier by which it is distinguished. This identifier can be used by a set of control rules which use the machine state information to indicate knowledge base access.
The premise of each rule also contains formal or informal knowledge. The informal knowledge becomes formal when a rule fires successfully. The formal knowledge is stored in and accessed from knowledge base 330 in frame structures. Frames can be made up of other frames, which can also be shared. With this data structure, it is possible to access only the frame associated with the current state information when monitoring device 320 is updating the user's historical information. The frame's slots are like any linked list, except they represent actions and attributes of an object or concepts that the frames represent. Since frames indicate the relationship between a user's activity and the associated heuristics used to interpret that activity, it is easy to access only relevant information. FIG. 9 illustrates a frame 901 with its related slots 902.
Rule Classes 801 are made up of pointers to the variables and values in the frames. The slots which contain another frame are really another group within a rule class. In a frame-based reasoning system, one selects the frame to prove by filling in slot values. The slots contain the rules. The successful completion of a frame yields a solution.
BEGIN.RULECLASS
<id>
BEGIN.RULEGROUP
<id>
IF
( <variable> IS <value> )
( <Value> )
( NOT( <variable> IS <value> ))
( TEST (<variable> EQ <#> ))
THEN
( <value> )
END.RULEGROUP
END.RULECLASS
By way of illustration and not limitation, the rules may be coded as data structures (illustrated in the C language):
/*------------------------------------------------------------------------ */
/* Premise is a structure containing a single premise */
/* of a rule. It is made up of the string itself, the type */
/* of data it is, and the possible values it can hold. */
/*------------------------------------------------------------------------ */
struct Premise
{
char *pVar;
char *pValueSet;
char *pValue;
};
/*------------------------------------------------------------------------ */
/* Premises is a structure for maintaining a linked */
/* list of all the premises (strings) in a single rule */
/*------------------------------------------------------------------------ */
struct Premises
{
struct Premise *pPremise;
int Size;
struct Premises *pNext;
};
/*------------------------------------------------------------------------ */
/* RuleClass is a structure of all RuleGroups that can */
/* associated with one another in searching */
/*------------------------------------------------------------------------ */
struct RuleClass
{
struct RuleGroup *pRuleGroup;
int NumberOfGroups;
int ID;
struct RuleClass *pNext;
};
/*------------------------------------------------------------------------ */
/* RuleGroup is a structure containing all rules that */
/* are considered to be associated in searching */
/*------------------------------------------------------------------------ */
struct RuleGroup
{
struct Rules *pRules;
int NumSubGroups;
int Priority;
struct RuleGroup *pNext;
};
.N
/*------------------------------------------------------------------------ */
/* Rules is a structure containing all the rules within */
/* a Rule Group. They can be singular or associated */
/* with one another. */
/*------------------------------------------------------------------------ */
struct Rules
{
struct LinkedRules *pLinkedRules;
char *pConclusion;
struct Rules *pNext;
};
/*------------------------------------------------------------------------ */
/* LinkedRules is a structure for maintaining a linked */
/* list of rules which all have the same conclusion. A */
/* rule is made up of premises (structure) a conclusion */
/* (string) and a why statement (string) explaining */
/* why a rule succeeded. */
/*------------------------------------------------------------------------ */
struct LinkedRules
{
struct Premises *pAllPremises;
struct LinkedRules *pNext;
};
/*------------------------------------------------------------------------ */
/* Query is a structure for maintaining a linked list */
/* of all the queries for information (strings) in the */
/* knowledge base. */
/*------------------------------------------------------------------------ */
struct Query
{
char *pFact;
char *pAsk;
char *pSet;
struct Query *pNext;
};
The inputs to knowledge base 330 are the data collected b monitoring device 320. The outputs of knowledge base 330 are data 331 and rules 334 which inference engine 340 selects for examination and help information text 336 which display engine 350 processes. Rules 334 are predefined by application developer. The frames indicate the relationship between the user's activity and the associated heuristics used to interpret that activity, thus making it easier to access only the information needed. The knowledge-based rules are structured so that obvious associations between rules can be incorporated within knowledge base 330 simplifying the design and the inferences from those rules.
Inference engine 340 interprets data 331 and rules 334 in knowledge base 330 to give a help solution to the user, or it intelligently asks for more information in order to obtain a solution. In the preferred embodiment, inference engine 340 operates using a backward-chaining method. This method starts with a goal state (a particular kind of help) and tries to prove it by reaching initial known data.
As FIGS. 10A-B illustrate, the steps used by inference engine 340 are as follows. In step 1001, knowledge base 330 is accessed according to the system state's level. In step 1002, depending on the system state, a group of rules (goal) is selected from knowledge base 330 and used as an hypothesis. Next in step 1003 an attempt is made to prove each rule's conclusion by proving its premise. If, in step 1004, the premise examined is the conclusion of another rule, it is pushed onto a stack (last-in first-out structure in system memory 103) and an attempt is made to prove the other rule's premise in step 1005. In step 1006, if a premise is proved, it is asserted into a working fact or data list at step 1007. Otherwise if the premise fails, a search is made in step 1008 for a rule with the same conclusion, which inference engine 340 will then try to prove. In step 1009, when all the premises in the rule have been proved, the rule has fired and the conclusion can be asserted at step 1010. In step 1011, this process is repeated until there are no more goal states to analyze or until no solution is found. In the latter case, knowledge base 330 is incomplete, therefore, inference engine 340 queries the user for more information or else explains to the user that no help is defined for the particular scenario.
The inputs to inference engine 340 are data 331 and rules 334 contained in knowledge base 330. Some of rules 334 control other knowledge bases which can be selected. Inference engine 340 first examines rules 334 to select the proper rule class 801. Since the groups 802 under the class 801 indicate if they are goal states, engine 340 can also indicate which rules will be used as hypotheses. Final output from inference engine 340 is a help “tag” that indicates a particular help solution. In addition, the engine creates a temporary output of data asserted while proving the rules.
Display engine 350 is an interface between the output of inference engine 340 and help text 336 giving the user easy access to the most useful help topics. Display engine 350 provides general to in-depth help to a user. It processes the inference engine's help tag to provide context-sensitive help. In addition, the help information itself has tags which are used by display engine 350 to locate further help information. This allows a user to select specific help from a subset of help information.
The help information data structure used by display engine 350 is defined for each application/accessory and system resource interfaces. Help that is repeated across multiple applications is divided into groups. The help information which is given is individualized for novice, intermediate, and advanced users. A display format is used which allows a user to select a single item of help from a suggested list, allow the user to continue selecting help until he wants to quit, and allow the user to search through the Help Topics for a particular topic.
The structure by which the help information is accessed and stored is classified according to application/accessory or system component (requires specific name identification, not type), experience level of user, kind (autodetect or user-invoked), and topic identifier. For example, a help structure (in C language) can be:
struct Help Source {
int Subject;
int Kind;
int HelpLevel;
char *pTopicString };

which can be filled by:
Subject equal to TEXT_SUBSTITUTION
Kind equal to AUTO

and pTopicString pointing to “Text Substitution.”
Referring to FIG. 11, the processing of help information in display engine 350 will now be described in detail. Help information database 335 is a database containing text 336 and rules 334 fields, i.e., help information text 336 is linked to help information rules 334. In addition, a tag 1102 is provided for representing the solution that a rule produces. Display engine 350 matches tag 1102 with a solution tag 1101 from inference engine 340. The corresponding text (from text 336) is the actual text sent as help information 360 to display device 105.
The organization of rules 334 is as follows. It contains the following fields: Grp, Rule#, PorC#, Var#, Var, Val, Bind, KeyW, and Q#. Grp is a rule group. Group 0 is always the group in which rules are placed that will give a solution. If these rules cause any other rules to fire, these rules are in another group. Rule# is the number of the rule. The rules are sorted by rule number, because one may want to try to fire one rule before another. PorC# is a premise or conclusion number. The records in the table are also sorted by PorC#. The conclusion is number 0 because it is the first thing pulled out of the table when inference engine 340 starts to fire a rule. (This is because it is doing backward chaining—start with the conclusion, and then prove the premises). Var# is an identifier for the engine to do faster searching. Each new variable added to the table has a unique identifier. The variables are DeskMate components, like a dialog box. Var, the variable field, is a string representing a variable for which a value is expected such as “running.cmp” (DISABLED FIELD). Value is the string field which contains a value. If the value is the current one for the variable, then the premise line succeeds. Bind is a binding variable. If a rule has a binding, the variable will bind to the currently known value of the variable. This eliminates repetition of rules that do the same exact thing. Only premises can bind. KeyW is a number indicating the negation (NOT) of a premise, or a TEST or CALL. TEST will do number comparisons, and assumes the value string field is a numerical value. CALL is used to execute a pre-defined function. The parameters of the function are placed in the value field. Q# is the number in queue 332 for which a premise line test applies. If the queue number equals 0, it is assumed the premise does not use predefined data.
While the invention is described in some detail with specific reference to a single preferred embodiment and certain alternatives, there is no intent to limit the invention to that particular embodiment or those specific alternatives. For example, one skilled in the art could implement such a help system in another interface environment or without any interface environment. Backward-chaining is but one of many possible AI techniques used to process data and rules, other possible techniques include forward-chaining and rule-value methods. Input device is not limited to a keyboard and a pointing device but contemplates any means by which data enters a computer, such as by voice recognition. Help information is not limited to a specific medium but instead includes any conveyance of help information, such as graphical representations. The true scope of the invention is defined not by the foregoing description but by the following claims.

Claims (48)

1. In a computer system, a method for aiding a user of a computer program, said method operating independent of said computer program, comprising the steps of:
storing a held information database;
monitoring a series of user-directed events from an input device;
generating data indicating said series of user-directed events;
storing said generated data in a knowledge base;
storing a plurality of rules for analyzing said generated data to determine appropriate help information;
detecting a request for help information from the user;
testing said rules against said generated data using an inference engine, whereby rules which are satisfied by said data are proved rules;
selecting in response to the proved rules appropriate help information from said help information database; and
displaying said selected help information to the user.
2. The method of claim 1, wherein said monitoring step further comprises monitoring a system state.
3. The method of claim 2, wherein said monitoring a system state step further comprises monitoring a machine state, an application state, an accessory state, and a component state.
4. The method of claim 1, wherein said monitoring step further comprises the steps of:
registering an application's menubar;
checking if the user has requested help;
updating a state information; and
updating a menubar.
5. The method of claim 1, wherein said testing step comprises the step of:
(a) selecting from said plurality of rules a first group of rules corresponding to a first plurality of user-directed events;
(b) attempting to prove each rule in said first group of rules;
(c) if a rule is proved, storing said rule as a proved rule in a plurality of proved rules; and
(d) repeating steps (a)-(c) for a subsequent group of rules until a rule is proved.
6. The method of claim 5, wherein step (b) comprises attempting to match a premise with each of said first group of rules with said generated data.
7. The method of claim 5, wherein step (c) comprises:
if a rule is proved, storing said rule as a proved rule in a plurality of linked proved rules.
8. The method of claim 1, wherein said generating data step comprises generating an historical queue of said user-directed events.
9. The method of claim 1, wherein said rule storing step comprises storing premise-conclusion statements from said help information database.
10. The method of claim 1, wherein said displaying step comprises displaying textural help information to the user.
11. The method of claim 1, wherein said displaying step comprises displaying graphical help information to the user.
12. The method of claim 1, wherein said testing step comprises testing said rules against said generated data using a backward-chaining inference engine.
13. The method of claim 1, wherein said testing step comprises testing rules against said generated data using a forward-chaining inference engine.
14. In a computer system, a method for aiding a user of a computer program, said method operating independent of said computer program, comprising the steps of:
storing a help information database;
storing a knowledge base for maintaining data;
identifying a series of user-directed events;
comparing said identified series with data stored in the knowledge base;
if said identified series is unknown to said knowledge base, asserting in said knowledge base data for indicating said unknown identified series;
if said identified series contradicts said knowledge base, retracting in said knowledge base data which contradicts said identified series;
if said identified series is already known to said knowledge base, reasserting in said knowledge base data for indicating said already known identified series;
storing a plurality of rules for analyzing said knowledge base to determine appropriate help information;
detecting a request for help information from the user;
testing said rules against said knowledge from the user;
testing said rules against said knowledge base using an inference engine, whereby rules which are satisfied by data stored in the knowledge base are proved rules;
selecting in response to said testing step appropriate help information from said help information database; and
displaying said selected help information to the user.
15. A help information system for aiding a user comprising:
a computer having a processor and a memory;
a display device coupled to said computer;
an input device coupled to said computer;
monitoring means coupled to the input device for monitoring a sequence of user-directed events and for generating data indicating said events;
a knowledge base coupled to said monitoring means and stored in said memory, said knowledge base comprising said generated data, a plurality of rules for analyzing said generated data to determine appropriate help information, and a help information database for storing said appropriate help information;
inference engine means, coupled to said knowledge base, for applying said rules to said data to generate an inference engine outputs and
display engine means, coupled to said inference engine and coupled to said help information database, for interpreting said inference engine output to select appropriate help information for display by said display device to the user.
16. The system of claim 15, wherein said monitoring means further comprises means for monitoring a system state.
17. The system of claim 16, wherein said means for monitoring a system state comprises means for monitoring a machine state, an application state, an accessory state, and a component state.
18. The system of claim 15, wherein said monitoring means comprises means, stored in memory and operably coupled to the input device, for interpreting a series of user-directed events and performing a history update based on the series.
19. The system of claim 15, wherein said knowledge base further comprises an historical queue stored in memory and operably coupled to said generated data.
20. The system of claim 15, wherein said inference engine means comprises backward-chaining inference engine means.
21. The system of claim 15, wherein said inference engine means comprises forward-chaining inference engine means.
22. The system of claim 15, wherein said help information comprises textural help information.
23. The system of claim 15, wherein said help information comprises graphical help information.
24. A help information system for aiding a user comprising:
a computer having a processor and a memory;
an input device coupled to said computer;
a knowledge base, coupled to said memory, for maintaining data;
a plurality of rules, coupled to said memory, for analyzing said knowledge base;
means, coupled to said memory, for identifying a series of user-directed events from said input device;
means, coupled to said memory, for updating said knowledge base with said identified series;
means, coupled to said memory, for detecting a request for help information from the user;
a help information database, coupled to said memory, for selecting appropriate help information;
an inference engine, coupled to said memory, for testing said rules against said knowledge base to generate a help solution tag;
a display engine, coupled to said memory, for selecting help information from said help information database using said help solution tag; and
a display for displaying said selected help information to the user.
25. The system of claim 24, wherein said means for updating said knowledge base comprises programming means for instructing said processor to perform the steps of:
comparing said identified series with data stored in the knowledge base;
if said identified series is unknown to said knowledge base, asserting data in said knowledge base data for indicating said unknown identified series;
if said identified series contradicts said knowledge base, retracting in said knowledge base data which contradicts said identified series; and
if said identified series is already known to said knowledge base, reasserting in said knowledge base data for indicating said already known identified series.
26. In a computer system, a method for selecting help messages for aiding a user of a computer program comprising the steps of:
storing a help information database;
monitoring a series of user-directed events from an input device;
generating data indicating said series of use-directed events;
storing said generated data in a knowledge base;
testing said generated data against stored data, said stored data stored for the purpose of analyzing said generated data to determine appropriate help information;
storing a plurality of rules for analyzing said generated data to determine appropriate help information; and
using the data indicating said series of user-directed events stored in the knowledge base to select help information from said help information database;
wherein said using step further comprises using an inference engine to test said rules against the data stored in the knowledge base to select appropriate help information.
27. The method of claim 26, wherein said monitoring step further comprises monitoring a system state.
28. The method of claim 27, wherein said monitoring a system state step further comprises monitoring a machine state, an application state, an accessory state, and a component state.
29. The method of claim 26, wherein said monitoring step further comprises the steps of:
registering an application's menubar;
updating state information; and
updating a menubar.
30. The method of claim 26, wherein said generating data step comprises generating an historical queue of said user-directed events.
31. The method of claim 26, wherein said using step uses data indicating a series of user-directed events comprising at least two user-directed events.
32. The method of claim 26 or 31, wherein said series of user-directed events are events that are not necessarily related as being part of a particular command hierarchy.
33. A help information system for aiding a user of a computer program comprising:
a computer having a processor and a memory;
an output device coupled to said computer;
an input device coupled to said computer;
monitoring means coupled to the input device for monitoring a sequence of user-directed events and for generating data indicating said events;
a knowledge base coupled to said monitoring means and stored in said memory, said knowledge base comprising said generated data, a plurality of rules for analyzing said generated data to determine appropriate help information, and a help information database for storing said appropriate help information; and
inference engine means, coupled to said knowledge base, for applying said rules to said data to select appropriate help information for output by said output device to the user.
34. In a computer system, a method for aiding a user of a computer program comprising the steps of:
storing a help information database;
monitoring a series of user-directed events from an input device;
generating data indicating said series of user-directed events;
storing said generated data in a knowledge base;
storing a plurality of rules for analyzing said generated data to determine appropriate help information;
testing said rules against said generated data using an inference engine, whereby rules which are satisfied by said data are proved rules;
selecting in response to the proved rules appropriate help information from said help information database; and
displaying said selected help information to the user.
35. The method of claim 34 or 47, wherein said monitoring step further comprises monitoring a system state.
36. The method of claim 35, wherein said monitoring a system state step further comprises monitoring a machine state, an application state, an accessory state, and a component state.
37. The method of claim 34 or 47, wherein said monitoring step further comprises the steps of:
registering an application's menubar;
updating a state information; and
updating a menubar.
38. The method of claim 34 or 47, wherein said testing step comprises the steps of:
(a) selecting from said plurality of rules a first group of rules corresponding to a first plurality of user-directed events;
(b) attempting to prove each rule in said first group of rules;
(c) if a rule is proved, storing said rule as a proved rule in a plurality of proved rules; and
(d) repeating steps (a)-(c) for a subsequent group of rules until a rule is proved.
39. The method of claim 38, wherein step (b) comprises attempting to match a premise with each of said first group of rules with said generated data.
40. The method of claim 38, wherein step (c) comprises:
if a rule is proved, storing said rule as a proved rule in a plurality of linked proved rules.
41. The method of claim 34 or 47, wherein said generating data step comprises generating an historical queue of said user-directed events.
42. The method of claim 34 or 47, wherein said rule storing step comprises storing premise-conclusion statements from said help information database.
43. The method of claim 34 or 47, wherein said displaying step comprises displaying textural help information to the user.
44. The method of claim 34 or 47, wherein said displaying step comprises displaying graphical help information to the user.
45. The method of claim 34 or 47, wherein said testing step comprises testing said rules against said generated data using a backward-chaining inference engine.
46. The method of claim 34 or 47, wherein said testing step comprises testing rules against said generated data using a forward-chaining inference engine.
47. In a computer system, a method for aiding a user of a computer program comprising the steps of:
storing a help information database;
monitoring a series of user-directed events from an input device;
generating data indicating said series of user-directed events;
storing said generated data in a knowledge base;
storing a plurality of rules for analyzing said generated data to determine appropriate help information;
detecting a request for help information from the user;
testing said rules against said generated data using an inference engine, whereby rules which are satisfied by said data are proved rules;
selecting in response to the proved rules appropriate help information from said help information database; and
displaying said selected help information to the user.
48. In a computer system, a method for aiding a user of a computer program comprising the steps of:
storing a help information database;
storing a knowledge base for maintaining data;
identifying a series of user-directed events;
comparing said identified series with data stored in the knowledge base;
if said identified series is unknown to said knowledge base, asserting in said knowledge base data for indicating said unknown identified series;
if said identified series contradicts said knowledge base, retracting in said knowledge base data which contradicts said identified series;
if said identified series is already known to said knowledge base, reasserting in said knowledge base data for indicating said already known identified series;
storing a plurality of rules for analyzing said knowledge base to determine appropriate help information;
testing said rules against said knowledge from the user;
testing said rules against said knowledge base using an inference engine, whereby rules which are satisfied by data stored in the knowledge base are proved rules;
selecting in response to said testing step appropriate help information from said help information database;
and displaying said selected help information to the user.
US09/375,867 1990-08-02 1999-08-17 Intelligent help system Expired - Lifetime USRE39302E1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US09/375,867 USRE39302E1 (en) 1990-08-02 1999-08-17 Intelligent help system

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US07/562,046 US5103498A (en) 1990-08-02 1990-08-02 Intelligent help system
US09/375,867 USRE39302E1 (en) 1990-08-02 1999-08-17 Intelligent help system

Related Parent Applications (1)

Application Number Title Priority Date Filing Date
US07/562,046 Reissue US5103498A (en) 1990-08-02 1990-08-02 Intelligent help system

Publications (1)

Publication Number Publication Date
USRE39302E1 true USRE39302E1 (en) 2006-09-19

Family

ID=24244558

Family Applications (2)

Application Number Title Priority Date Filing Date
US07/562,046 Ceased US5103498A (en) 1990-08-02 1990-08-02 Intelligent help system
US09/375,867 Expired - Lifetime USRE39302E1 (en) 1990-08-02 1999-08-17 Intelligent help system

Family Applications Before (1)

Application Number Title Priority Date Filing Date
US07/562,046 Ceased US5103498A (en) 1990-08-02 1990-08-02 Intelligent help system

Country Status (7)

Country Link
US (2) US5103498A (en)
EP (1) EP0541712B1 (en)
AU (1) AU8401991A (en)
DE (1) DE69130766T2 (en)
HK (1) HK1012102A1 (en)
SG (1) SG48223A1 (en)
WO (1) WO1992002880A1 (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070124266A1 (en) * 2005-11-30 2007-05-31 The Boeing Company Integrating multiple information-providing systems
US8521675B2 (en) 2010-01-28 2013-08-27 International Business Machines Corporation Integrated automatic user support and assistance
US8560305B1 (en) * 2012-05-16 2013-10-15 Hristo Georgiev Logifolg

Families Citing this family (241)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5222197A (en) * 1990-06-28 1993-06-22 Digital Equipment Corporation Rule invocation mechanism for inductive learning engine
US5651100A (en) * 1990-06-28 1997-07-22 Omron Corporation Approximate reasoning apparatus
US5430836A (en) * 1991-03-01 1995-07-04 Ast Research, Inc. Application control module for common user access interface
US5586218A (en) * 1991-03-04 1996-12-17 Inference Corporation Autonomous learning and reasoning agent
WO1992015951A1 (en) * 1991-03-04 1992-09-17 Inference Corporation Case-based reasoning system
EP0576574B1 (en) * 1991-03-18 2001-10-31 Echelon Corporation Programming language structures for use in a network for communicating, sensing and controlling information
JPH0520044A (en) * 1991-07-15 1993-01-29 Personal Joho Kankyo Kyokai User interface device
US6162060A (en) * 1991-08-09 2000-12-19 Texas Instruments Incorporated System and method for the delivery, authoring, and management of courseware over a computer network
US5455949A (en) * 1991-09-06 1995-10-03 International Business Machines Corporation Method for representing and signaling run-time program conditions
JPH0831041B2 (en) * 1991-09-06 1996-03-27 インターナショナル・ビジネス・マシーンズ・コーポレイション Program condition processing method and computer system
US5627764A (en) * 1991-10-04 1997-05-06 Banyan Systems, Inc. Automatic electronic messaging system with feedback and work flow administration
US5283856A (en) * 1991-10-04 1994-02-01 Beyond, Inc. Event-driven rule-based messaging system
US5555346A (en) * 1991-10-04 1996-09-10 Beyond Corporated Event-driven rule-based messaging system
USRE48056E1 (en) 1991-12-23 2020-06-16 Blanding Hovenweep, Llc Ergonomic man-machine interface incorporating adaptive pattern recognition based control system
USRE46310E1 (en) 1991-12-23 2017-02-14 Blanding Hovenweep, Llc Ergonomic man-machine interface incorporating adaptive pattern recognition based control system
US6081750A (en) * 1991-12-23 2000-06-27 Hoffberg; Steven Mark Ergonomic man-machine interface incorporating adaptive pattern recognition based control system
US6850252B1 (en) 1999-10-05 2005-02-01 Steven M. Hoffberg Intelligent electronic appliance system and method
US6400996B1 (en) 1999-02-01 2002-06-04 Steven M. Hoffberg Adaptive pattern recognition based control system and method
US5903454A (en) * 1991-12-23 1999-05-11 Hoffberg; Linda Irene Human-factored interface corporating adaptive pattern recognition based controller apparatus
USRE47908E1 (en) 1991-12-23 2020-03-17 Blanding Hovenweep, Llc Ergonomic man-machine interface incorporating adaptive pattern recognition based control system
US10361802B1 (en) 1999-02-01 2019-07-23 Blanding Hovenweep, Llc Adaptive pattern recognition based control system and method
US8352400B2 (en) 1991-12-23 2013-01-08 Hoffberg Steven M Adaptive pattern recognition based controller apparatus and method and human-factored interface therefore
US6418424B1 (en) 1991-12-23 2002-07-09 Steven M. Hoffberg Ergonomic man-machine interface incorporating adaptive pattern recognition based control system
US6343991B1 (en) * 1997-10-01 2002-02-05 Brad A. Armstrong Game control with analog pressure sensor
US5317677A (en) * 1992-04-16 1994-05-31 Hughes Aircraft Company Matching technique for context sensitive rule application
JP2939050B2 (en) * 1992-05-26 1999-08-25 シャープ株式会社 Data communication device
US5390281A (en) * 1992-05-27 1995-02-14 Apple Computer, Inc. Method and apparatus for deducing user intent and providing computer implemented services
US5434777A (en) * 1992-05-27 1995-07-18 Apple Computer, Inc. Method and apparatus for processing natural language
US5477447A (en) * 1992-05-27 1995-12-19 Apple Computer, Incorporated Method and apparatus for providing computer-implemented assistance
DE4228281A1 (en) * 1992-08-26 1994-03-03 Koenig & Bauer Ag Process for displaying machine malfunctions
US5432940A (en) * 1992-11-02 1995-07-11 Borland International, Inc. System and methods for improved computer-based training
US5598511A (en) * 1992-12-28 1997-01-28 Intel Corporation Method and apparatus for interpreting data and accessing on-line documentation in a computer system
US6633861B2 (en) 1993-03-19 2003-10-14 Ricoh Company Limited Automatic invocation of computational resources without user intervention across a network
US6049792A (en) * 1993-03-19 2000-04-11 Ricoh Company Limited Automatic invocation of computational resources without user intervention across a network
US5546502A (en) * 1993-03-19 1996-08-13 Ricoh Company, Ltd. Automatic invocation of computational resources without user intervention
US5701399A (en) * 1993-06-09 1997-12-23 Inference Corporation Integration of case-based search engine into help database
JPH0772767A (en) * 1993-06-15 1995-03-17 Xerox Corp Interactive user support system
US5537590A (en) * 1993-08-05 1996-07-16 Amado; Armando Apparatus for applying analysis rules to data sets in a relational database to generate a database of diagnostic records linked to the data sets
US5504887A (en) * 1993-09-10 1996-04-02 International Business Machines Corporation Storage clustering and packing of objects on the basis of query workload ranking
US5521844A (en) * 1993-09-10 1996-05-28 Beloit Corporation Printing press monitoring and advising system
US5982365A (en) * 1993-11-19 1999-11-09 Apple Computer, Inc. System and methods for interactively generating and testing help systems
US5572670A (en) * 1994-01-10 1996-11-05 Storage Technology Corporation Bi-directional translator for diagnostic sensor data
US5819090A (en) * 1994-03-04 1998-10-06 Ast Research, Inc. Application control module for common user access interface
US5799292A (en) * 1994-04-29 1998-08-25 International Business Machines Corporation Adaptive hypermedia presentation method and system
WO1995034051A1 (en) * 1994-06-06 1995-12-14 Spectragraphics Corporation Method and apparatus for capturing and distributing graphical data
US5563805A (en) * 1994-08-16 1996-10-08 International Business Machines Corporation Multimedia context-sensitive real-time-help mechanism for use in a data processing system
US6578019B1 (en) 1994-11-08 2003-06-10 Canon Kabushiki Kaisha Information processing system which understands information and acts accordingly and method therefor
JP3898238B2 (en) * 1994-12-02 2007-03-28 株式会社バンダイナムコゲームス Video game apparatus and image composition method thereof
US5701400A (en) * 1995-03-08 1997-12-23 Amado; Carlos Armando Method and apparatus for applying if-then-else rules to data sets in a relational data base and generating from the results of application of said rules a database of diagnostics linked to said data sets to aid executive analysis of financial data
US6182056B1 (en) 1995-05-22 2001-01-30 Gte Data Services Incorporated Method of modeling complex navigational logic in a client/server environment
US5870727A (en) * 1995-05-22 1999-02-09 Gte Data Services Incorporated Rule-based system for the provision of complex navigational logic
US6067525A (en) 1995-10-30 2000-05-23 Clear With Computers Integrated computerized sales force automation system
JP3195226B2 (en) * 1996-03-14 2001-08-06 株式会社東芝 Business support system
US6688888B1 (en) * 1996-03-19 2004-02-10 Chi Fai Ho Computer-aided learning system and method
US5727129A (en) * 1996-06-04 1998-03-10 International Business Machines Corporation Network system for profiling and actively facilitating user activities
US8229844B2 (en) 1996-06-05 2012-07-24 Fraud Control Systems.Com Corporation Method of billing a purchase made over a computer network
US20030195846A1 (en) 1996-06-05 2003-10-16 David Felger Method of billing a purchase made over a computer network
US7555458B1 (en) 1996-06-05 2009-06-30 Fraud Control System.Com Corporation Method of billing a purchase made over a computer network
JP3698491B2 (en) * 1996-07-02 2005-09-21 株式会社ドラゴン TV type baccarat game device
US6021403A (en) 1996-07-19 2000-02-01 Microsoft Corporation Intelligent user assistance facility
US5778233A (en) * 1996-10-11 1998-07-07 International Business Machines Corporation Method and apparatus for enabling global compiler optimizations in the presence of exception handlers within a computer program
US8745493B2 (en) * 1996-10-25 2014-06-03 Karen A. McKirchy Method and apparatus for providing instructional help, at multiple levels of sophistication, in a learning application
US7607147B1 (en) * 1996-12-11 2009-10-20 The Nielsen Company (Us), Llc Interactive service device metering systems
GB2324892B (en) * 1997-03-14 1999-12-01 Interactuality Limited Process monitoring system
US6038560A (en) * 1997-05-21 2000-03-14 Oracle Corporation Concept knowledge base search and retrieval system
US6003020A (en) * 1997-10-30 1999-12-14 Sapient Health Network Intelligent profiling system
US6062862A (en) * 1997-11-12 2000-05-16 Koskinen; Robin S. Financial services product training apparatus and method related thereto
US6527556B1 (en) 1997-11-12 2003-03-04 Intellishare, Llc Method and system for creating an integrated learning environment with a pattern-generator and course-outlining tool for content authoring, an interactive learning tool, and related administrative tools
US6002400A (en) * 1997-11-19 1999-12-14 International Business Machines Corporation Method and apparatus for handles to components in graphical windows user interface
JPH11242547A (en) * 1998-02-26 1999-09-07 Nec Corp Computer user support system
US6795814B1 (en) * 1998-06-04 2004-09-21 Gateway, Inc. System and method for CMOS integration
US9792659B2 (en) * 1999-04-13 2017-10-17 Iplearn, Llc Computer-aided methods and apparatus to access materials in a network environment
IL125684A0 (en) * 1998-08-06 1999-04-11 Univ Ben Gurion Method for computer operation by an intelligent user adaptive interface
US6134019A (en) * 1998-09-03 2000-10-17 Xerox Corporation Help button service for a user interface in a digital printing scanning or facsimile system
US6219047B1 (en) 1998-09-17 2001-04-17 John Bell Training agent
US6381711B1 (en) 1998-10-20 2002-04-30 International Business Machines Corporation Method and apparatus for unified, pre-addressed, context-sensitive reporting
US6341359B1 (en) 1998-12-14 2002-01-22 International Business Machines Corporation Self-diagnosing and self correcting data entry components
US6964010B1 (en) 1998-12-14 2005-11-08 International Business Machines Corporation Formatted-item list control
US7966078B2 (en) 1999-02-01 2011-06-21 Steven Hoffberg Network media appliance system and method
JP2000262747A (en) * 1999-03-18 2000-09-26 Sega Enterp Ltd Image processing device, electronic game device and storage medium for tv game machine
US6269355B1 (en) * 1999-04-15 2001-07-31 Kadiri, Inc. Automated process guidance system and method using knowledge management system
US6573906B1 (en) 1999-04-26 2003-06-03 International Business Machines Corporation Method and system for delivering integrated user assistance information and traditional help from the same source
US6340977B1 (en) 1999-05-07 2002-01-22 Philip Lui System and method for dynamic assistance in software applications using behavior and host application models
US6421655B1 (en) * 1999-06-04 2002-07-16 Microsoft Corporation Computer-based representations and reasoning methods for engaging users in goal-oriented conversations
US7337159B2 (en) * 1999-09-30 2008-02-26 Topiary Communications, Inc. System and method for sharing of expert knowledge
US6985889B1 (en) 1999-09-30 2006-01-10 Topiary Communications, Inc. System and method for sharing of expert knowledge
GB2359215B (en) * 2000-02-08 2004-02-25 Helpmagic Com Ltd Information service
WO2001069573A1 (en) * 2000-03-16 2001-09-20 University Of Southern California Adaptable smart techniques for user interaction
US8645137B2 (en) 2000-03-16 2014-02-04 Apple Inc. Fast, language-independent method for user authentication by voice
WO2002029759A2 (en) * 2000-10-06 2002-04-11 Charles Wong Virtual training sales and support
US20020095597A1 (en) * 2001-01-13 2002-07-18 Norden Gregory L. Reusable solutions for aircraft servicing
GB2375030B (en) * 2001-04-27 2005-05-11 Ibm Changing user interface following difficulty in use
EP1255189B1 (en) * 2001-05-04 2008-10-08 Microsoft Corporation Interface control
WO2003007166A2 (en) * 2001-07-13 2003-01-23 Cadessa, L.L.C. System and method for providing help services
US7194685B2 (en) * 2001-08-13 2007-03-20 International Business Machines Corporation Method and apparatus for tracking usage of online help systems
US20030059009A1 (en) * 2001-09-24 2003-03-27 Teleware, Inc. Modular multi-media communication management system with context dependent multi-media help functionality
US6871163B2 (en) * 2002-05-31 2005-03-22 Sap Aktiengesellschaft Behavior-based adaptation of computer systems
US7239981B2 (en) 2002-07-26 2007-07-03 Arbitron Inc. Systems and methods for gathering audience measurement data
US20040145601A1 (en) * 2003-01-29 2004-07-29 International Business Machines Corporation Method and a device for providing additional functionality to a separate application
US7694223B2 (en) 2003-06-04 2010-04-06 Sony Computer Entertainment Inc. Methods and systems for recording user actions in computer programs
US20050081153A1 (en) * 2003-08-12 2005-04-14 Gbs Global Business Software And Services Limited Method for providing process-dependent data
KR20070007290A (en) * 2004-03-01 2007-01-15 코닌클리케 필립스 일렉트로닉스 엔.브이. Tutorial generation unit
US20060041499A1 (en) * 2004-08-23 2006-02-23 Transaxtions Llc Obtaining A Need With Guiding Information And Credit Worthiness Using A Competitive Process
US7543232B2 (en) * 2004-10-19 2009-06-02 International Business Machines Corporation Intelligent web based help system
US20060095315A1 (en) * 2004-10-29 2006-05-04 Sbc Knowledge Ventures L.P. Method and apparatus for a mechanized attendance management system
US8296162B1 (en) 2005-02-01 2012-10-23 Webmd Llc. Systems, devices, and methods for providing healthcare information
US20060212463A1 (en) * 2005-03-15 2006-09-21 Jensen Jens O Method and apparatus for extracting metadata information from an application
US9183752B2 (en) * 2005-07-14 2015-11-10 Red Hat, Inc. Tutorial generator with automatic capture of screenshots
JP2009505203A (en) * 2005-08-11 2009-02-05 コーニンクレッカ フィリップス エレクトロニクス エヌ ヴィ How to introduce interaction patterns and application functions
US8677377B2 (en) 2005-09-08 2014-03-18 Apple Inc. Method and apparatus for building an intelligent automated assistant
US7533339B2 (en) * 2005-12-29 2009-05-12 Sap Ag System and method for providing user help
US7526722B2 (en) * 2005-12-29 2009-04-28 Sap Ag System and method for providing user help according to user category
US7979798B2 (en) * 2005-12-30 2011-07-12 Sap Ag System and method for providing user help tips
US8166412B2 (en) * 2006-01-13 2012-04-24 Adobe Systems Incorporated Visual cue discernment on scattered data
US8099664B2 (en) * 2006-03-17 2012-01-17 Microsoft Corporation Dynamic help user interface control with secured customization
US9299039B1 (en) * 2006-08-23 2016-03-29 A9.Com, Inc. Managing task lists utilizing integrated information requests
US9318108B2 (en) 2010-01-18 2016-04-19 Apple Inc. Intelligent automated assistant
US7711462B2 (en) * 2006-12-15 2010-05-04 International Business Machines Corporation Vehicle help system and method
US8380530B2 (en) 2007-02-02 2013-02-19 Webmd Llc. Personalized health records with associative relationships
US8738606B2 (en) * 2007-03-30 2014-05-27 Microsoft Corporation Query generation using environment configuration
US8977255B2 (en) 2007-04-03 2015-03-10 Apple Inc. Method and system for operating a multi-function portable electronic device using voice-activation
US9135340B2 (en) * 2007-09-12 2015-09-15 Datalaw, Inc. Research system and method with record builder
US9330720B2 (en) 2008-01-03 2016-05-03 Apple Inc. Methods and apparatus for altering audio output signals
US8996376B2 (en) 2008-04-05 2015-03-31 Apple Inc. Intelligent text-to-speech conversion
US10496753B2 (en) 2010-01-18 2019-12-03 Apple Inc. Automatically adapting user interfaces for hands-free interaction
US20100030549A1 (en) 2008-07-31 2010-02-04 Lee Michael M Mobile device having human language translation capability with positional feedback
US8504599B1 (en) 2008-10-07 2013-08-06 Honda Motor Co., Ltd. Intelligent system for database retrieval
US9124769B2 (en) 2008-10-31 2015-09-01 The Nielsen Company (Us), Llc Methods and apparatus to verify presentation of media content
US9959870B2 (en) 2008-12-11 2018-05-01 Apple Inc. Speech recognition involving a mobile device
US10255566B2 (en) 2011-06-03 2019-04-09 Apple Inc. Generating and processing task items that represent tasks to perform
US10241752B2 (en) 2011-09-30 2019-03-26 Apple Inc. Interface for a virtual digital assistant
US10241644B2 (en) 2011-06-03 2019-03-26 Apple Inc. Actionable reminder entries
US9858925B2 (en) 2009-06-05 2018-01-02 Apple Inc. Using context information to facilitate processing of commands in a virtual assistant
US9431006B2 (en) 2009-07-02 2016-08-30 Apple Inc. Methods and apparatuses for automatic speech recognition
US8635584B2 (en) * 2009-10-13 2014-01-21 Yahoo! Inc. Tutorial systems for code creation and provenance tracking
US10553209B2 (en) 2010-01-18 2020-02-04 Apple Inc. Systems and methods for hands-free notification summaries
US10679605B2 (en) 2010-01-18 2020-06-09 Apple Inc. Hands-free list-reading by intelligent automated assistant
US10276170B2 (en) 2010-01-18 2019-04-30 Apple Inc. Intelligent automated assistant
US10705794B2 (en) 2010-01-18 2020-07-07 Apple Inc. Automatically adapting user interfaces for hands-free interaction
US8682667B2 (en) 2010-02-25 2014-03-25 Apple Inc. User profiling for selecting user specific voice input processing information
US8396815B2 (en) * 2010-04-29 2013-03-12 International Business Machines Corporation Adaptive business process automation
CN102238268B (en) * 2010-04-30 2013-10-30 腾讯科技（深圳）有限公司 Information prompting method and device
US10762293B2 (en) 2010-12-22 2020-09-01 Apple Inc. Using parts-of-speech tagging and named entity recognition for spelling correction
US9262612B2 (en) 2011-03-21 2016-02-16 Apple Inc. Device access using voice authentication
US10057736B2 (en) 2011-06-03 2018-08-21 Apple Inc. Active transport based notifications
US8994660B2 (en) 2011-08-29 2015-03-31 Apple Inc. Text correction processing
US10134385B2 (en) 2012-03-02 2018-11-20 Apple Inc. Systems and methods for name pronunciation
US9483461B2 (en) 2012-03-06 2016-11-01 Apple Inc. Handling speech synthesis of content for multiple languages
US9280610B2 (en) 2012-05-14 2016-03-08 Apple Inc. Crowd sourcing information to fulfill user requests
US9721563B2 (en) 2012-06-08 2017-08-01 Apple Inc. Name recognition system
US9495129B2 (en) 2012-06-29 2016-11-15 Apple Inc. Device, method, and user interface for voice-activated navigation and browsing of a document
US9576574B2 (en) 2012-09-10 2017-02-21 Apple Inc. Context-sensitive handling of interruptions by intelligent digital assistant
US9547647B2 (en) 2012-09-19 2017-01-17 Apple Inc. Voice-based media searching
US20140115459A1 (en) * 2012-10-24 2014-04-24 Michael Norwood Help system
KR20230137475A (en) 2013-02-07 2023-10-04 애플 인크. Voice trigger for a digital assistant
US9864498B2 (en) 2013-03-13 2018-01-09 Tobii Ab Automatic scrolling based on gaze detection
US20140247232A1 (en) 2013-03-01 2014-09-04 Tobii Technology Ab Two step gaze interaction
US9368114B2 (en) 2013-03-14 2016-06-14 Apple Inc. Context-sensitive handling of interruptions
WO2014144579A1 (en) 2013-03-15 2014-09-18 Apple Inc. System and method for updating an adaptive speech recognition model
US9443231B2 (en) 2013-03-15 2016-09-13 Legacy Media, Llc Provision of predefined communications
AU2014233517B2 (en) 2013-03-15 2017-05-25 Apple Inc. Training an at least partial voice command system
WO2014197334A2 (en) 2013-06-07 2014-12-11 Apple Inc. System and method for user-specified pronunciation of words for speech synthesis and recognition
WO2014197336A1 (en) 2013-06-07 2014-12-11 Apple Inc. System and method for detecting errors in interactions with a voice-based digital assistant
US9582608B2 (en) 2013-06-07 2017-02-28 Apple Inc. Unified ranking with entropy-weighted information for phrase-based semantic auto-completion
WO2014197335A1 (en) 2013-06-08 2014-12-11 Apple Inc. Interpreting and acting upon commands that involve sharing information with remote devices
EP3937002A1 (en) 2013-06-09 2022-01-12 Apple Inc. Device, method, and graphical user interface for enabling conversation persistence across two or more instances of a digital assistant
US10176167B2 (en) 2013-06-09 2019-01-08 Apple Inc. System and method for inferring user intent from speech inputs
AU2014278595B2 (en) 2013-06-13 2017-04-06 Apple Inc. System and method for emergency calls initiated by voice command
DE112014003653B4 (en) 2013-08-06 2024-04-18 Apple Inc. Automatically activate intelligent responses based on activities from remote devices
GB2518203A (en) 2013-09-13 2015-03-18 Ibm GUI configuration
US10558262B2 (en) 2013-11-18 2020-02-11 Tobii Ab Component determination and gaze provoked interaction
US10317995B2 (en) 2013-11-18 2019-06-11 Tobii Ab Component determination and gaze provoked interaction
US9620105B2 (en) 2014-05-15 2017-04-11 Apple Inc. Analyzing audio input for efficient speech and music recognition
US10592095B2 (en) 2014-05-23 2020-03-17 Apple Inc. Instantaneous speaking of content on touch devices
US9502031B2 (en) 2014-05-27 2016-11-22 Apple Inc. Method for supporting dynamic grammars in WFST-based ASR
US9785630B2 (en) 2014-05-30 2017-10-10 Apple Inc. Text prediction using combined word N-gram and unigram language models
AU2015266863B2 (en) 2014-05-30 2018-03-15 Apple Inc. Multi-command single utterance input method
US10289433B2 (en) 2014-05-30 2019-05-14 Apple Inc. Domain specific language for encoding assistant dialog
US9430463B2 (en) 2014-05-30 2016-08-30 Apple Inc. Exemplar-based natural language processing
US9715875B2 (en) 2014-05-30 2017-07-25 Apple Inc. Reducing the need for manual start/end-pointing and trigger phrases
US9842101B2 (en) 2014-05-30 2017-12-12 Apple Inc. Predictive conversion of language input
US9734193B2 (en) 2014-05-30 2017-08-15 Apple Inc. Determining domain salience ranking from ambiguous words in natural speech
US10078631B2 (en) 2014-05-30 2018-09-18 Apple Inc. Entropy-guided text prediction using combined word and character n-gram language models
US9633004B2 (en) 2014-05-30 2017-04-25 Apple Inc. Better resolution when referencing to concepts
US10170123B2 (en) 2014-05-30 2019-01-01 Apple Inc. Intelligent assistant for home automation
US9760559B2 (en) 2014-05-30 2017-09-12 Apple Inc. Predictive text input
US9338493B2 (en) 2014-06-30 2016-05-10 Apple Inc. Intelligent automated assistant for TV user interactions
US10659851B2 (en) 2014-06-30 2020-05-19 Apple Inc. Real-time digital assistant knowledge updates
US10446141B2 (en) 2014-08-28 2019-10-15 Apple Inc. Automatic speech recognition based on user feedback
US9818400B2 (en) 2014-09-11 2017-11-14 Apple Inc. Method and apparatus for discovering trending terms in speech requests
US10789041B2 (en) 2014-09-12 2020-09-29 Apple Inc. Dynamic thresholds for always listening speech trigger
US9606986B2 (en) 2014-09-29 2017-03-28 Apple Inc. Integrated word N-gram and class M-gram language models
US9646609B2 (en) 2014-09-30 2017-05-09 Apple Inc. Caching apparatus for serving phonetic pronunciations
US9886432B2 (en) 2014-09-30 2018-02-06 Apple Inc. Parsimonious handling of word inflection via categorical stem + suffix N-gram language models
US10074360B2 (en) 2014-09-30 2018-09-11 Apple Inc. Providing an indication of the suitability of speech recognition
US10127911B2 (en) 2014-09-30 2018-11-13 Apple Inc. Speaker identification and unsupervised speaker adaptation techniques
US9668121B2 (en) 2014-09-30 2017-05-30 Apple Inc. Social reminders
US10552013B2 (en) 2014-12-02 2020-02-04 Apple Inc. Data detection
US9711141B2 (en) 2014-12-09 2017-07-18 Apple Inc. Disambiguating heteronyms in speech synthesis
US11568177B2 (en) * 2015-01-19 2023-01-31 Kabushiki Kaisha Toshiba Sequential data analysis apparatus and program
US9865280B2 (en) 2015-03-06 2018-01-09 Apple Inc. Structured dictation using intelligent automated assistants
US10567477B2 (en) 2015-03-08 2020-02-18 Apple Inc. Virtual assistant continuity
US9721566B2 (en) 2015-03-08 2017-08-01 Apple Inc. Competing devices responding to voice triggers
US9886953B2 (en) 2015-03-08 2018-02-06 Apple Inc. Virtual assistant activation
US9899019B2 (en) 2015-03-18 2018-02-20 Apple Inc. Systems and methods for structured stem and suffix language models
US9842105B2 (en) 2015-04-16 2017-12-12 Apple Inc. Parsimonious continuous-space phrase representations for natural language processing
JP6438346B2 (en) * 2015-05-12 2018-12-12 日本電信電話株式会社 Operation support device, operation support program, and operation support method
US10083688B2 (en) 2015-05-27 2018-09-25 Apple Inc. Device voice control for selecting a displayed affordance
US10127220B2 (en) 2015-06-04 2018-11-13 Apple Inc. Language identification from short strings
US10101822B2 (en) 2015-06-05 2018-10-16 Apple Inc. Language input correction
US11025565B2 (en) 2015-06-07 2021-06-01 Apple Inc. Personalized prediction of responses for instant messaging
US10255907B2 (en) 2015-06-07 2019-04-09 Apple Inc. Automatic accent detection using acoustic models
US10186254B2 (en) 2015-06-07 2019-01-22 Apple Inc. Context-based endpoint detection
US10747498B2 (en) 2015-09-08 2020-08-18 Apple Inc. Zero latency digital assistant
US10671428B2 (en) 2015-09-08 2020-06-02 Apple Inc. Distributed personal assistant
US9697820B2 (en) 2015-09-24 2017-07-04 Apple Inc. Unit-selection text-to-speech synthesis using concatenation-sensitive neural networks
US11010550B2 (en) 2015-09-29 2021-05-18 Apple Inc. Unified language modeling framework for word prediction, auto-completion and auto-correction
US10366158B2 (en) 2015-09-29 2019-07-30 Apple Inc. Efficient word encoding for recurrent neural network language models
US11587559B2 (en) 2015-09-30 2023-02-21 Apple Inc. Intelligent device identification
US10691473B2 (en) 2015-11-06 2020-06-23 Apple Inc. Intelligent automated assistant in a messaging environment
US10049668B2 (en) 2015-12-02 2018-08-14 Apple Inc. Applying neural network language models to weighted finite state transducers for automatic speech recognition
US10223066B2 (en) 2015-12-23 2019-03-05 Apple Inc. Proactive assistance based on dialog communication between devices
US10446143B2 (en) 2016-03-14 2019-10-15 Apple Inc. Identification of voice inputs providing credentials
US9934775B2 (en) 2016-05-26 2018-04-03 Apple Inc. Unit-selection text-to-speech synthesis based on predicted concatenation parameters
US9972304B2 (en) 2016-06-03 2018-05-15 Apple Inc. Privacy preserving distributed evaluation framework for embedded personalized systems
US10249300B2 (en) 2016-06-06 2019-04-02 Apple Inc. Intelligent list reading
US10049663B2 (en) 2016-06-08 2018-08-14 Apple, Inc. Intelligent automated assistant for media exploration
DK179588B1 (en) 2016-06-09 2019-02-22 Apple Inc. Intelligent automated assistant in a home environment
US10586535B2 (en) 2016-06-10 2020-03-10 Apple Inc. Intelligent digital assistant in a multi-tasking environment
US10509862B2 (en) 2016-06-10 2019-12-17 Apple Inc. Dynamic phrase expansion of language input
US10067938B2 (en) 2016-06-10 2018-09-04 Apple Inc. Multilingual word prediction
US10490187B2 (en) 2016-06-10 2019-11-26 Apple Inc. Digital assistant providing automated status report
US10192552B2 (en) 2016-06-10 2019-01-29 Apple Inc. Digital assistant providing whispered speech
DK179049B1 (en) 2016-06-11 2017-09-18 Apple Inc Data driven natural language event detection and classification
DK201670540A1 (en) 2016-06-11 2018-01-08 Apple Inc Application integration with a digital assistant
DK179343B1 (en) 2016-06-11 2018-05-14 Apple Inc Intelligent task discovery
DK179415B1 (en) 2016-06-11 2018-06-14 Apple Inc Intelligent device arbitration and control
US10579400B2 (en) * 2016-11-11 2020-03-03 International Business Machines Corporation Path-sensitive contextual help system
US10593346B2 (en) 2016-12-22 2020-03-17 Apple Inc. Rank-reduced token representation for automatic speech recognition
DK179745B1 (en) 2017-05-12 2019-05-01 Apple Inc. SYNCHRONIZATION AND TASK DELEGATION OF A DIGITAL ASSISTANT
DK201770431A1 (en) 2017-05-15 2018-12-20 Apple Inc. Optimizing dialogue policy decisions for digital assistants using implicit feedback

Citations (21)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4156284A (en) * 1977-11-21 1979-05-22 General Electric Company Signal processing apparatus
US4648044A (en) 1984-06-06 1987-03-03 Teknowledge, Inc. Basic expert system tool
US4658370A (en) 1984-06-07 1987-04-14 Teknowledge, Inc. Knowledge engineering tool
JPS6398741A (en) 1986-10-15 1988-04-30 Fujitsu Ltd Sequential execution control system for plural expert systems
JPS63124148A (en) 1986-11-13 1988-05-27 Nec Corp System for managing knowledge base file
US4860214A (en) 1987-01-22 1989-08-22 Ricoh Company, Ltd. Inference system
US4873661A (en) * 1987-08-27 1989-10-10 Yannis Tsividis Switched neural networks
US4885757A (en) * 1987-06-01 1989-12-05 Texas Instruments Incorporated Digital adaptive receiver employing maximum-likelihood sequence estimation with neural networks
US4903226A (en) * 1987-08-27 1990-02-20 Yannis Tsividis Switched networks
US4931926A (en) * 1985-10-07 1990-06-05 Sharp Kabushiki Kaisha Inputting system and an editing system in an inquiry-and-answer system
US4942367A (en) * 1989-09-29 1990-07-17 General Electric Company Auto-zeroing switched-capacitance buffer amplifiers with minus-unity voltage gain
US4962342A (en) * 1989-05-04 1990-10-09 Synaptics, Inc. Dynamic synapse for neural network
US4964077A (en) 1987-10-06 1990-10-16 International Business Machines Corporation Method for automatically adjusting help information displayed in an online interactive system
US4988891A (en) * 1989-05-09 1991-01-29 Mitsubishi Denki Kabushiki Kaisha Semiconductor neural network including photosensitive coupling elements
US5008810A (en) * 1988-09-29 1991-04-16 Process Modeling Investment Corp. System for displaying different subsets of screen views, entering different amount of information, and determining correctness of input dependent upon current user input
US5021988A (en) * 1989-04-27 1991-06-04 Mitsubishi Denki Kabushiki Kaisha Semiconductor neural network and method of driving the same
US5023833A (en) * 1987-12-08 1991-06-11 California Institute Of Technology Feed forward neural network for unary associative memory
US5039870A (en) * 1990-05-21 1991-08-13 General Electric Company Weighted summation circuits having different-weight ranks of capacitive structures
US5167010A (en) 1989-08-03 1992-11-24 Westinghouse Electric Corp. Expert advice display processing system
US5239617A (en) 1990-01-05 1993-08-24 International Business Machines Corporation Method and apparatus providing an intelligent help explanation paradigm paralleling computer user activity
US5485544A (en) 1989-02-17 1996-01-16 Hitachi, Ltd. History sensitive help control method and system

Patent Citations (22)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4156284A (en) * 1977-11-21 1979-05-22 General Electric Company Signal processing apparatus
US4648044A (en) 1984-06-06 1987-03-03 Teknowledge, Inc. Basic expert system tool
US4658370A (en) 1984-06-07 1987-04-14 Teknowledge, Inc. Knowledge engineering tool
US4931926A (en) * 1985-10-07 1990-06-05 Sharp Kabushiki Kaisha Inputting system and an editing system in an inquiry-and-answer system
JPS6398741A (en) 1986-10-15 1988-04-30 Fujitsu Ltd Sequential execution control system for plural expert systems
JPS63124148A (en) 1986-11-13 1988-05-27 Nec Corp System for managing knowledge base file
US4860214A (en) 1987-01-22 1989-08-22 Ricoh Company, Ltd. Inference system
US4885757A (en) * 1987-06-01 1989-12-05 Texas Instruments Incorporated Digital adaptive receiver employing maximum-likelihood sequence estimation with neural networks
US4873661A (en) * 1987-08-27 1989-10-10 Yannis Tsividis Switched neural networks
US4903226A (en) * 1987-08-27 1990-02-20 Yannis Tsividis Switched networks
US4964077A (en) 1987-10-06 1990-10-16 International Business Machines Corporation Method for automatically adjusting help information displayed in an online interactive system
US5023833A (en) * 1987-12-08 1991-06-11 California Institute Of Technology Feed forward neural network for unary associative memory
US5008810A (en) * 1988-09-29 1991-04-16 Process Modeling Investment Corp. System for displaying different subsets of screen views, entering different amount of information, and determining correctness of input dependent upon current user input
US5485544A (en) 1989-02-17 1996-01-16 Hitachi, Ltd. History sensitive help control method and system
US5021988A (en) * 1989-04-27 1991-06-04 Mitsubishi Denki Kabushiki Kaisha Semiconductor neural network and method of driving the same
US4962342A (en) * 1989-05-04 1990-10-09 Synaptics, Inc. Dynamic synapse for neural network
US4962342B1 (en) * 1989-05-04 1992-09-15 Synaptics Inc
US4988891A (en) * 1989-05-09 1991-01-29 Mitsubishi Denki Kabushiki Kaisha Semiconductor neural network including photosensitive coupling elements
US5167010A (en) 1989-08-03 1992-11-24 Westinghouse Electric Corp. Expert advice display processing system
US4942367A (en) * 1989-09-29 1990-07-17 General Electric Company Auto-zeroing switched-capacitance buffer amplifiers with minus-unity voltage gain
US5239617A (en) 1990-01-05 1993-08-24 International Business Machines Corporation Method and apparatus providing an intelligent help explanation paradigm paralleling computer user activity
US5039870A (en) * 1990-05-21 1991-08-13 General Electric Company Weighted summation circuits having different-weight ranks of capacitive structures

Non-Patent Citations (26)

* Cited by examiner, † Cited by third party
Title
"Applications and Extensions of SADT", by Douglass T Ross, pp. 147-156, from IEEE publication Computer Apr. 1985. *
"Extending State Transition Diagrams for the Specification of Human-Computer Interaction", by Anthony I Wasserman, pp. 100-114, Aug. 1985. *
"Visual Programming Environments Paradigms and Systems", by Ephraim P. Glinert, IEEE Computer Society Press Aug. 31, 1990. *
"Visual Programming Languages A Perspective And A Dimeensional Analysis", by Nan C. Shu, pp. 41-58, Aug. 19885. *
Alspector, et al., "Performance of a Stochastic Learning Microchip," from Advances in Neural Information Processing Systems I, Morgan-Kaufmann, 1989, pp. 748-760. *
Covington et al., Prolog Programming in Depth, pp. 50-55, 1988. *
DeGloria, A., "The VLSI Technology and the Design of Neural Networks," First Italian Workshop Parallel Architectures and Neural Networks, Apr. 1988, pp. 119-127. *
Erlandsen et al., "Intelligent Help Systems", Information and Software Technology, vol. 29, No. 3, Apr. 1987, pp. 115-121. *
Faggin, et al., "VLSI Implementation of Neural Networks," from an Intro. to Neural and Electronic networks, Academic Press, Inc., 1990, pp. 275-292. *
Frakes et al., "Using Expert System Components to Add Intelligent Help and Guidance to Software Tools", Information and Software Technology, vol. 31, No. 7, Sep. 1989, pp. 366-370. *
Graf, et al., "A Reconfigurable CMOS Neural Network," from 1990 IEEE Intl. Solid-State Circuits Conf., Feb. 15, 1990, 144-145. *
Graf, et al., IEEE International Solid-State Circuits Conf., 1990, "A Reconfigurable CMOS Neural Network", pp. 144-145. *
Helm, "Tandy Deskmate Intelligent Help Expert System," 1989.
Imamiya et al., "Embedding Explanation Mechanism with User Interface", SPIE vol. 635 Applications of Artificial Intelligence III (1986), pp. 650-657, Apple MacIntosh Screen Prints. *
J. Silber, "Pal: An Intelligent Help System," Proc. 3<SUP>rd </SUP>Int'l Conf. on Indus. & Engin. Appli. of Artificial Intelligence & Expert Systems (Jul. 1990), pp. 882-889.
John M. Carroll and Jean McKendree, Interface Design Issues for Advice-Giving Expert Systems, 30 Comm. of the ACM 14-32. Jan. 1987.
Kub, et al., "Programmable Analog Vector-Matrix Multipliers," IEEE Jour. Solid-State Circuits, vol. 25(1), Feb. 1990, pp. 207-214. *
M. Stehouwer and J. Bruggen, "Performing Interpretation in an Intelligent Help System," Proc. 6<SUP>th </SUP>Annual ESPRIT Conf. (Nov. 27-Dec. 1, 1989), pp. 248-257.
Mead, et al., "Analog VLSI Implementation of Neural Systems," Kluaer Academic Publishers, 1989, pp. 57-83. *
Parsayae et al., Expert Systems for Experts, pp. 66-67, 254-271, 1988. *
Prager, et al., "Reason: An Intelligent User Assistant For Interactive Environments," IBM Systems Journal, vol. 29 No. 1, pp. 141-164, Jan. 1990. *
Rossetto, et al., "Analog VLSI Synaptic Matrices as Building Blocks for Neural Networks," IEEE Micro, Dec. 1989, pp. 56-63. *
Salem, et al., "A Feed Forward Network for CMOS VLSI Implementation," Midwest Symposium Circuits & Systems 1990, 489-491. *
Schwartz, et al., "A Programmable Analog Neural Network Chip," IEEE Jour. Solid State Circuits, vol. 24(3), Apr. 1989, pp. 688-697. *
Siyilotti, et al., "A Novel Associative Memory Implemented Using Collective Computation," Conf. on Very Large Scale Integration, 1985, pp. 11-21. *
Walker et al., "A CMOS Neural Network for Pattern Association," IEEE Micro, Oct. 1989, pp. 68-74. *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070124266A1 (en) * 2005-11-30 2007-05-31 The Boeing Company Integrating multiple information-providing systems
US7475054B2 (en) * 2005-11-30 2009-01-06 The Boeing Company Integrating multiple information-providing systems
US8521675B2 (en) 2010-01-28 2013-08-27 International Business Machines Corporation Integrated automatic user support and assistance
US9009085B2 (en) 2010-01-28 2015-04-14 International Business Machines Corporation Integrated automatic user support and assistance
US8560305B1 (en) * 2012-05-16 2013-10-15 Hristo Georgiev Logifolg

Also Published As

Publication number Publication date
HK1012102A1 (en) 1999-07-23
DE69130766T2 (en) 1999-09-02
EP0541712A1 (en) 1993-05-19
EP0541712A4 (en) 1994-06-15
EP0541712B1 (en) 1999-01-13
AU8401991A (en) 1992-03-02
US5103498A (en) 1992-04-07
WO1992002880A1 (en) 1992-02-20
DE69130766D1 (en) 1999-02-25
SG48223A1 (en) 1998-04-17

Similar Documents

Publication Publication Date Title
USRE39302E1 (en) Intelligent help system
USRE37431E1 (en) Intelligent help system
US5255386A (en) Method and apparatus for intelligent help that matches the semantic similarity of the inferred intent of query or command to a best-fit predefined command intent
US5978784A (en) Computer-implemented decision management system with dynamically generated questions and answer choices
US5835900A (en) Computer-implemented decision management system with dynamically generated questions and answer choices
US6041182A (en) Automatic invocation of computational resources without user intervention
US6415275B1 (en) Method and system for processing rules using an extensible object-oriented model resident within a repository
US4972328A (en) Interactive knowledge base end user interface driven maintenance and acquisition system
Hanson et al. Interface design and multivariate analysis of UNIX command use
US6305008B1 (en) Automatic statement completion
US5542024A (en) Graphically used expert system tool background of the invention
US5594837A (en) Method for representation of knowledge in a computer as a network database system
US5754755A (en) Method and system for generating test scripts
JPH0556537B2 (en)
EP0575473A4 (en) Case-based reasoning system
JPH05197556A (en) Expert system and explanation generating method for expert system
Benbasat et al. A structured approach to designing human-computer dialogues
Imam et al. An expert code generator using rule-based and frames knowledge representation techniques
Blandford et al. Specifying user knowledge for the design of interactive systems
Ziegler et al. Formal models and techniques in human-computer interaction
Virvou Intelligence and adaptivity in human-computer interaction concerning biotechnological software users
Wachtel et al. Dialog-Based Meaning Derivation Service for Technical Language Domains
KR19980050164A (en) Object-based Library Search Method for Software Reuse
EP0453153A2 (en) Order independent rule-based program specification system
Chesson et al. Towards a Framework and Procedure for Specifying User Interfaces

Legal Events

Date Code Title Description
AS Assignment

Owner name: AST COMPUTERS, LLC, CALIFORNIA

Free format text: RELEASE OF SECURITY INTEREST;ASSIGNOR:ARI SERVICE, INC., AS SUCCESSOR IN INTEREST BY MERGER TO ASTRESEARCH, INC.;REEL/FRAME:012852/0320

Effective date: 20020503

AS Assignment

Owner name: ARI SERVICE, INC., CALIFORNIA

Free format text: ADOPTION OF RESOLUTIONS BY WRITTEN CONSENT OF BOARD OF DIRECTORS OF AST INNOVATIONS, INC. AND ATTACHED BILL OF SALE TRANSFERRING ALL ASSETS OF AST INNOVATIONS INC. TO ARI SERVICES, INC.;ASSIGNOR:AST INNOVATIONS, INC., A DELAWARE CORPORATION;REEL/FRAME:012896/0633

Effective date: 19990630

AS Assignment

Owner name: SAMSUNG ELECTRONICS AMERICA, INC., NEW JERSEY

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:ARI SERVICE, INC.;REEL/FRAME:012906/0769

Effective date: 20020516

AS Assignment

Owner name: SAMSUNG ELECTRONICS CO., LTD., KOREA, REPUBLIC OF

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:SAMSUNG ELECTRONICS AMERICA, INC.;REEL/FRAME:012937/0317

Effective date: 20020524

CC Certificate of correction