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Número de publicaciónUS20090067590 A1
Tipo de publicaciónSolicitud
Número de solicitudUS 12/268,894
Fecha de publicación12 Mar 2009
Fecha de presentación11 Nov 2008
Fecha de prioridad14 Ene 2005
También publicado comoUS7450698, US20060159240
Número de publicación12268894, 268894, US 2009/0067590 A1, US 2009/067590 A1, US 20090067590 A1, US 20090067590A1, US 2009067590 A1, US 2009067590A1, US-A1-20090067590, US-A1-2009067590, US2009/0067590A1, US2009/067590A1, US20090067590 A1, US20090067590A1, US2009067590 A1, US2009067590A1
InventoresRobert R. Bushey, Benjamin Anthony Knott, John Mills Martin
Cesionario originalSbc Knowledge Ventures, L.P.
Exportar citaBiBTeX, EndNote, RefMan
Enlaces externos: USPTO, Cesión de USPTO, Espacenet
System and method of utilizing a hybrid semantic model for speech recognition
US 20090067590 A1
Resumen
A system includes a network interface, a speech input conversion component, and a routing module. Speech input is received in connection with a call. At least a segment of the speech input is transformed into a first textual format. A first list of entries is generated based, at least partially, on consideration of the first textual format. The first list includes at least one action with a corresponding confidence level and at least one object with another corresponding confidence level. An entry of the first list having a higher corresponding confidence level is selected, and a second textual format is output. A second list is generated based, at least partially, on consideration of the selected entry and the second textual format. A routing option is suggested based on the selected entry and a pairing entry in the second list.
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Reclamaciones(20)
1. A system, comprising:
a network interface configured to receive a speech input in connection with a call;
a speech input conversion component configured to:
transform at least a segment of the speech input into a first textual format;
generate a first list of entries based, at least partially, on consideration of the first textual format, the first list comprising at least one action having a corresponding confidence level and at least one object having another corresponding confidence level;
select an entry of the first list having a higher corresponding confidence level;
output a second textual format;
generate a second list based, at least partially on consideration of the selected entry and the second textual format; and
a routing module configured to suggest a routing option for the call based on the selected entry and an associated pairing entry in the second list.
2. The system of claim 1, wherein the speech input conversion component is further configured to re-process the speech input to create an object list when an action is the selected entry.
3. The system of claim 1, wherein the speech input conversion component is further configured to re-process the speech input to create an action list when an object is the selected entry.
4. The system of claim 2, wherein the speech input conversion component is further configured to:
include an associated confidence level with an object entry in the object list; and
select an object as the pairing entry based on the associated confidence level.
5. The system of claim 3, wherein the speech input conversion component is further configured to:
re-process the speech input to produce the action list with confidence levels; and
select an action based on the confidence levels in the action list.
6. The system of claim 1, wherein the speech input conversion component is further configured to compare the first textual format to a list of word strings and to assign a probability to at least one word string included in the list of word strings.
7. The system of claim 6, wherein the speech input conversion component is further configured to assign an appropriate confidence level to the at least one word string.
8. The system of claim 1, wherein the entry selected is one of a verb and an adverb-verb combination.
9. The system of claim 1, wherein the entry selected is one of a noun or an adjective-noun combination.
10. The system of claim 1, wherein the speech input conversion component is further configured to utilize a synonym table to assist in converting the speech input into action and objects.
11. A method, comprising:
receiving a speech input in connection with a call;
processing the speech input to generate a first action list and an object list;
assigning a first confidence level to each action of the first action list and to each object of the object list;
selecting a particular object with a high confidence level from the object list; and
removing at least one action from the first action list, wherein the at least one action is inconsistent with the particular object.
12. The method of claim 11, further comprising assigning a second confidence level to each remaining action of the first action list based on the particular object.
13. The method of claim 12, further comprising:
re-processing the speech input to generate a second action list;
assigning a second confidence level to each action of the second action list;
selecting a particular action with a high confidence level from the second action list; and
suggesting a routing option for the call based on the particular action and the particular object.
14. The method of claim 13, further comprising routing the call to a destination.
15. The method of claim 13, wherein the first confidence level and the second confidence level are assigned based on a predetermined likelihood of reflecting an intent of a caller.
16. A method, comprising:
receiving a speech input in connection with a call;
processing the speech input to generate a first object list and an action list;
assigning a first confidence level to each object of the first object list and to each action of the action list;
selecting a particular action with a high confidence level from the action list; and
removing at least one object from the first object list, wherein the at least one object is inconsistent with the particular action.
17. The method of claim 16, further comprising assigning a second confidence level to each remaining object of the first object list based on the particular action.
18. The method of claim 17, further comprising:
re-processing the speech input to generate a second object list;
assigning a second confidence level to each object of the second object list; and
selecting a particular object with a high confidence level from the second object list;
suggesting a routing option for the call based on the particular action and the particular object.
19. The method of claim 18, further comprising routing the call to a destination.
20. The method of claim 16, wherein the first confidence level and the second confidence level are assigned based on a predetermined likelihood of reflecting an intent of a caller.
Descripción
    CLAIM OF PRIORITY
  • [0001]
    This application is a continuation application of, and claims priority to, U.S. patent application Ser. No. 11/036,204, filed Jan. 14, 2005, the contents of which are expressly incorporated herein by reference in their entirety.
  • FIELD OF THE DISCLOSURE
  • [0002]
    The present disclosure relates generally to speech recognition and, more particularly, to a system and method of utilizing a hybrid semantic model for speech recognition.
  • BACKGROUND
  • [0003]
    Many speech recognition systems utilize specialized computers that are configured to process human speech and carry out some task based on the speech. Some of these systems support “natural language” type interactions between users and automated call routing (ACR) systems. Natural language call routing allows callers to state the purpose of the call “in their own words.”
  • [0004]
    A goal of a typical ACR application is to accurately determine why a customer is calling and to quickly route the customer to an appropriate agent or destination for servicing. Research has shown that callers prefer speech recognition systems to keypad entry or touchtone menu driven systems.
  • [0005]
    As suggested above, natural language ACR systems attempt to interpret the intent of the customer based on the spoken language. When a speech recognition system partially misinterprets the caller's intent significant problems can result. A caller who is misrouted is generally an unhappy customer. Misrouted callers often terminate the call or hang-up when they realize that there has been a mistake. If a caller does not hang up they will typically talk to an operator who tries to route the call. Routing a caller to an undesired location and then to a human operator leads to considerable inefficiencies for a business. Most call routing systems handle a huge volume of calls and, even if a small percentage of calls are mishandled, the costs associated with the mishandled calls can be significant.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • [0006]
    FIG. 1 illustrates a simplified configuration of a telecommunication system;
  • [0007]
    FIG. 2 is a general diagram that illustrates a method of routing calls;
  • [0008]
    FIG. 3 is a flow diagram that illustrates a method of processing and routing calls;
  • [0009]
    FIG. 4 is a table that depicts speech input and mapped synonym terms; and
  • [0010]
    FIG. 5 is a table illustrating action-object pairs and call destinations relating to the action-object pairs.
  • DETAILED DESCRIPTION OF THE DRAWINGS
  • [0011]
    The present disclosure is directed generally to integrating speech enabled automated call routing with action-object technology. Traditional automatic call routing systems assign a correct destination for a call 50% to 80% of the time. Particular embodiments of the disclosed system and method using action-object tables may achieve a correct destination assignment 85 to 95% of the time. In some embodiments, a semantic model may be used to create an action-object pair that further increases call routing accuracy while reducing costs. In particular implementations, the correct call destination routing rate may approach the theoretical limit of 100%. Due to higher effective call placement rates, the number of abandoned calls (e.g., caller hang-ups prior to completing their task) may be significantly reduced, thereby reducing operating costs and enhancing customer satisfaction.
  • [0012]
    In accordance with the teachings of the present disclosure, a call may be routed based on a selectable action-object pair. In practice, a call is received from a caller and a received speech input is converted into text or “text configurations,” which may be the same as, similar to, or can be associated with, known actions and objects. Generally, objects are related to nouns and actions are related to verbs. The converted text may be compared to tables of known text configurations representing objects and actions. A confidence level may be assigned to the recognized actions and objects based on text similarities and other rules. An action-object list may be created that contains recognized actions and objects and their confidence levels. In some embodiments, the entry (action or object) in the list with the highest confidence level may be selected as a dominant item. If an action is dominant a system incorporating teachings disclosed herein may look for a complementary object. Likewise, if an object is dominant, the system may look for a complementary action.
  • [0013]
    In some implementations, when an action is dominant, remaining actions may be masked and the confidence level of the complementary objects in the action-object list may be adjusted. Conversely, if an object is dominant, the remaining objects may be masked and the confidence level of complementary actions in the action-object list may be adjusted. An adjustment to an assigned confidence level may be based, for example, on the likelihood that the prospective complement in the action-object list is consistent with the dominant entry. Depending upon implementation details, a call may be routed based on a dominant action and a complementary object or a dominant object and a complementary action.
  • [0014]
    Referring now to FIG. 1, an illustrated communications system 100 that includes a call routing support system is shown. Communications system 100 includes a speech-enabled call routing system (SECRS) 118, such as an interactive voice response system having a speech recognition module. Communications system 100 also includes a plurality of potential call destinations. Illustrative call destinations shown include service departments, such as billing department 120, balance information 122, technical support 124, employee directory 126, and new customer service departments 128. In practice, communication network 116 may receive calls from a variety of callers, such as the illustrated callers 110, 112, and 114. In a particular embodiment, communication network 116 may be a public telephone network, a wireless telephone network, a voice over Internet protocol (VoIP) type network, or some other network capable of supporting communication. As depicted, SECRS 118 may include components, such as a processor 142, memory 143, a synonym table 144, and a routing module 140. Depending upon implementation details, SECRS 118 may be coupled to and may route calls to various destinations across a LAN, an Intranet, an extranet, the Public Internet, and/or some other communication link or network, as shown. In addition, SECRS 118 may route calls to an agent, such as the illustrated live operator 130.
  • [0015]
    An illustrative embodiment of SECRS 118 may be a call center having a plurality of agent terminals attached. Thus, while only a single operator 130 is shown in FIG. 1, it should be understood that a plurality of different agent terminals or types of terminals may be coupled to SECRS 118, such that a variety of agents may service incoming calls. Moreover, and as indicated above, SECRS 118 may be operable as an automated call routing system.
  • [0016]
    In a particular embodiment, action-object routing module 140 includes an action-object lookup table for matching action-object pairs to desired call routing destinations. This process may be better understood through consideration of FIG. 2. Referring to FIG. 2, an illustrative block diagram of SECRS 118 is depicted. In this particular embodiment, processor 142 in SECR 118 includes an acoustic processing model 210, semantic processing model 220, and action-object routing table 230. In a first conversion, acoustic model 210 may receive speech input 202 and provide text as its output 204. Semantic model 220 may receive text 204 directly or indirectly from acoustic model 210 and produce an action-object table. The action(s) and object(s) in the action-object table may be ordered or ranked according to a confidence level. The confidence level may be used to indicate how likely a given action or object reflects a correct and useable customer instruction.
  • [0017]
    When a speech input conversion creates a dominant action (e.g., an action has the highest confidence level in the action-object list), a system like SECRS 118 of FIG. 1 may initiate a secondary conversion that creates an object list from the initial speech input. The call may then be routed based on several criteria, such as the overall highest confidence level in the action-object list (a dominant list entry) and the highest confidence level complimentary term from the secondary conversion (a complement to the dominant entry).
  • [0018]
    In practice, the secondary conversion or a second list can be generated that may take the initial speech received from the caller and processes the initial speech a second time. During the second conversion the semantic model 220 may look specifically for consistent objects while ignoring actions if an action had the highest overall confidence level. In such a case, the high scoring action may have been selected, the actions may have been masked, and objects that are inconsistent with the selected action may be tagged as invalid. Examples of invalid action-object combinations can be understood by referring to FIG. 5, where objects are listed on the left of the chart, and actions are listed across the top of the chart. For example, if the action of “acquire” has the highest confidence level in the action-object list then during the secondary conversion, objects such as “bill,” “payment,” “other providers,” “coupon specials” “name/number” and “store locations” may be masked or tagged as invalid selections.
  • [0019]
    If the speech input conversion creates a dominant object, a secondary conversion may be initiated to create an action list to assist in selecting a complementary action. The secondary conversion may take the initial speech received from the caller and processes the initial speech a second time. It may also rely on an output from the processing performed in connection with the earlier conversion. During the second conversion, semantic model 220 may look specifically for actions while ignoring objects. The confidence levels of actions may also be adjusted based on actions that are inconsistent with the selected object. Thus, in either case a call may be routed based on a dominant entry and a valid complement to the dominant entry.
  • [0020]
    The results of a reiterative speech recognition process may be provided to action-object routing table 230. Routing table 230 may receive action-object pairs 206 and produce a call routing destination 208. Based on the call routing destination 208, a call received at a call routing network like SECRS 118 may be routed to a final destination, such as the billing department 120 or the technical support service destination 124 depicted in FIG. 1. In a particular embodiment, the action-object routing table 230 may be a look up table or a spreadsheet, such as a Microsoft Excel™ spreadsheet.
  • [0021]
    Referring to FIG. 3, an illustrative embodiment of a method of processing a call using an automated call routing system such as the system of FIG. 1 is illustrated. The method starts at 300 and proceeds to step 302 where a speech input signal, such as a received utterance, is received or detected. Using phonemes or some other effective techniques, the received speech input may be converted into a plurality of word strings or text in accordance with an acoustic model, as shown at steps 304 and 306. In a particular embodiment, probability values may be assigned to word strings based on established rules and the content and coherency of the word string. At step 308, the word strings may be parsed into objects and actions. Objects generally represent nouns and adjective-noun combinations, while actions generally represent verbs and adverb-verb combinations. The actions and objects are assigned confidence values or probability values based on how likely they are to reflect the intent of the caller. In a particular embodiment a probability value or confidence level for the detected action and the detected object is determined utilizing a priority value of the word string used to create the selected action and the selected object.
  • [0022]
    In some cases, many possible actions and objects may be detected or created from the word strings. A method incorporating teachings of the present disclosure may attempt to determine and select a most probable action and object from a list of preferred objects and actions. To aid in this resolution, a synonym table such as the synonym table of FIG. 4 may be utilized to convert detected actions and objects into actions and objects that the system expects and/or is configured to “listen for.” Thus, detected objects and actions may be converted to expected actions and objects and assigned a confidence level. The process may also utilize the synonym table, for example, to adjust confidence levels of the actions and objects. The synonym table may store natural language phrases and their relationship with a set of actions and objects. In practice, natural language spoken by the caller may be compared to the natural language phrases in the table. Using the synonym table, the system and method may map portions of the natural phrases to detected objects and maps portions of the natural spoken phrase to detected actions. Thus, the word strings can be converted into expected objects and actions, at step 308. In summary, at step 310 multiple actions and multiple objects can be detected and provided with a confidence level according to the likelihood that a particular action or object identifies a customer's intent and thus will lead to a successful routing of the call.
  • [0023]
    The confidence level may be assigned to an action and/or an object based on many criteria, such as the textual similarities, business rules, etc., in step 310. Confidence levels may also be assigned based on a combination of factors, and some of these factors may not involved speech recognition. For example, in a particular example, if a caller does not currently have service, a caller's number (caller ID) may be utilized to assign a high confidence level to the action “acquire” and a low confidence value the actions “change” or “cancel.” In the event that a confidence level for an action-object pair is below a predetermined level, the call may be routed to a human operator or agent terminal.
  • [0024]
    An action-object list may be utilized at step 312 to select a dominant entry. If an action is selected as the dominant entry at step 334, other actions in the action-object list may be masked and objects that are inconsistent with the selected action may be tagged as invalid at step 336. The process of invalidating objects based on a dominant action can be further explained by referring to FIG. 5 where objects are listed on the left side of the chart and actions are listed across the top of the chart. For example if the action of “cancel” has the highest confidence level in the action-object list, the objects described as “bill,” “payment,” “other providers,” “coupon specials” “name/number” and “store locations” may be masked or tagged as invalid selections because a caller would not likely want to, for example, “cancel-store locations.” Thus, the method may ignore objects and invalid actions when a dominant object has been selected. The entries at the intersection of valid action-object illustrate routing destinations or phone extension where a call is routed when the system determines a dominant entry and it's complement.
  • [0025]
    Based on a dominant action, the confidence level of the objects can be adjusted at step 338. The caller's input of the utterance may be sent through the acoustic model, again in step 340, and the acoustic model may create and store word strings, as shown in step 342. Word strings may be parsed into objects using the semantic model in step 344, and an object list may be formed where each object in the list is assigned a confidence level in step 346. When a list is sufficiently complete, the object having the highest confidence level may be selected to complement the dominant action and an action-object pair may be created at step 330.
  • [0026]
    If at step 312 it is determined that an object has the highest confidence level or is dominant then a search for a complementary action may be conducted. Objects remaining in the action-object list and action that are inconsistent with the selected object may be masked or tagged as invalid, as shown in step 316. Thus such a method may ignore objects and invalid actions in the search for a complementary action when a dominant object has been elected.
  • [0027]
    Based on the dominant object, the confidence level of listed actions may be adjusted at step 318. The original caller input may be sent through the acoustic model, again in step 320 and the acoustic model may create and store word strings as in step 322. Words strings may then be parsed into objects using the semantic model in step 324 and an actions list may be formed where actions in the list is assigned a confidence level at step 326. The action having the highest confidence level (at step 328) may be selected to complement the dominant object and an action-object pair may be passed at step 330. The call may then be routed at step 331, the process ending at 332.
  • [0028]
    In practice, it may be beneficial to convert word strings such as “I want to have” to an action such as “get.” This substantially reduces the size of the action and object tables. As shown in FIG. 4, differently expressed or “differently spoken” inputs that have the same or similar caller intent may be converted to a single detected action-object, and/or action-object pair. Further, improper and informal sentences as well as slang may be connected to an action-object pair that may not bear phonetic resemblance to the words uttered by the caller. With a mapped lookup table such as the table in FIG. 4, speech training and learning behaviors found in conventional call routing systems may not be required. The tables in the present disclosure may be updated easily, leading to a lower cost of system maintenance.
  • [0029]
    The above-disclosed subject matter is to be considered illustrative, and not restrictive, and the appended claims are intended to cover all such modifications, enhancements, and other embodiments that fall within the true spirit and scope of the present invention. Thus, to the maximum extent allowed by law, the scope of the present invention is to be determined by the broadest permissible interpretation of the following claims and their equivalents, and shall not be restricted or limited by the foregoing detailed description.
Citas de patentes
Patente citada Fecha de presentación Fecha de publicación Solicitante Título
US5416830 *28 Dic 199316 May 1995Octel Communications CorporationIntegrated voice meassaging/voice response system
US5497373 *22 Mar 19945 Mar 1996Ericsson Messaging Systems Inc.Multi-media interface
US5522046 *3 Jun 199428 May 1996Ncr CorporationCommunication system uses diagnostic processors and master processor module to identify faults and generate mapping tables to reconfigure communication paths in a multistage interconnect network
US5632002 *28 Dic 199320 May 1997Kabushiki Kaisha ToshibaSpeech recognition interface system suitable for window systems and speech mail systems
US5754639 *3 Nov 199519 May 1998Lucent TechnologiesMethod and apparatus for queuing a call to the best split
US5754978 *27 Oct 199519 May 1998Speech Systems Of Colorado, Inc.Speech recognition system
US6028601 *1 Abr 199722 Feb 2000Apple Computer, Inc.FAQ link creation between user's questions and answers
US6038293 *3 Sep 199714 Mar 2000Mci Communications CorporationMethod and system for efficiently transferring telephone calls
US6038305 *1 Ago 199714 Mar 2000Bell Atlantic Network Services, Inc.Personal dial tone service with personalized caller ID
US6049594 *24 Jul 199711 Abr 2000At&T CorpAutomatic vocabulary generation for telecommunications network-based voice-dialing
US6064731 *29 Oct 199816 May 2000Lucent Technologies Inc.Arrangement for improving retention of call center's customers
US6173266 *6 May 19989 Ene 2001Speechworks International, Inc.System and method for developing interactive speech applications
US6173289 *14 Mar 19979 Ene 2001Novell, Inc.Apparatus and method for performing actions on object-oriented software objects in a directory services system
US6173399 *12 Jun 19979 Ene 2001Vpnet Technologies, Inc.Apparatus for implementing virtual private networks
US6175621 *4 Nov 199716 Ene 2001At&T Corp.Priority call on busy
US6353608 *16 Jun 19985 Mar 2002Mci Communications CorporationHost connect gateway for communications between interactive voice response platforms and customer host computing applications
US6366658 *7 May 19982 Abr 2002Mci Communications CorporationTelecommunications architecture for call center services using advanced interactive voice responsive service node
US6366668 *11 Mar 19992 Abr 2002Avaya Technology Corp.Method of routing calls in an automatic call distribution network
US6381329 *3 Nov 199930 Abr 2002TeleraPoint-of-presence call center management system
US6385584 *30 Abr 19997 May 2002Verizon Services Corp.Providing automated voice responses with variable user prompting
US6389400 *3 May 199914 May 2002Sbc Technology Resources, Inc.System and methods for intelligent routing of customer requests using customer and agent models
US6510414 *5 Oct 199921 Ene 2003Cisco Technology, Inc.Speech recognition assisted data entry system and method
US6519562 *25 Feb 199911 Feb 2003Speechworks International, Inc.Dynamic semantic control of a speech recognition system
US6526126 *2 Mar 200125 Feb 2003Distributed Software Development, Inc.Identifying an unidentified person using an ambiguity-resolution criterion
US6529871 *25 Oct 20004 Mar 2003International Business Machines CorporationApparatus and method for speaker verification/identification/classification employing non-acoustic and/or acoustic models and databases
US6546087 *16 Feb 20018 Abr 2003Siemens Information & Communication Networks, Inc.Method and system for enabling queue camp-on for skills-based routing
US6553112 *11 Mar 199822 Abr 2003Fujitsu LimitedCall center system
US6553113 *9 Jul 199922 Abr 2003First Usa Bank, NaSystem and methods for call decisioning in a virtual call center integrating telephony with computers
US6570967 *7 Jun 199527 May 2003Ronald A. Katz Technology Licensing, L.P.Voice-data telephonic interface control system
US6571240 *2 Feb 200027 May 2003Chi Fai HoInformation processing for searching categorizing information in a document based on a categorization hierarchy and extracted phrases
US6678360 *25 Ago 200013 Ene 2004Ronald A. Katz Technology Licensing, L.P.Telephonic-interface statistical analysis system
US6678718 *29 Ago 199713 Ene 2004Aspect Communications CorporationMethod and apparatus for establishing connections
US6690788 *15 Sep 200010 Feb 2004Avaya Inc.Integrated work management engine for customer care in a communication system
US6694012 *30 Ago 199917 Feb 2004Lucent Technologies Inc.System and method to provide control of music on hold to the hold party
US6697460 *30 Abr 200224 Feb 2004Sbc Technology Resources, Inc.Adaptive voice recognition menu method and system
US6700972 *25 Ago 19992 Mar 2004Verizon Corporate Services Group Inc.System and method for processing and collecting data from a call directed to a call center
US6704404 *11 Jul 20009 Mar 2004Netcall PlcCallback telecommunication system and method
US6707789 *3 Nov 199916 Mar 2004At&T Corp.Flexible SONET ring with integrated cross-connect system
US6714631 *31 Oct 200230 Mar 2004Sbc Properties, L.P.Method and system for an automated departure strategy
US6714643 *24 Feb 200030 Mar 2004Siemens Information & Communication Networks, Inc.System and method for implementing wait time estimation in automatic call distribution queues
US6721416 *13 Jun 200013 Abr 2004International Business Machines CorporationCall centre agent automated assistance
US6731722 *13 Jun 20024 May 2004Callfx.ComAutomated transaction processing system
US6842504 *13 Ago 200211 Ene 2005Sbc Properties, L.P.System and method for the automated analysis of performance data
US6847711 *13 Feb 200325 Ene 2005Sbc Properties, L.P.Method for evaluating customer call center system designs
US6853722 *29 Abr 20028 Feb 2005Sbc Technology Resources, Inc.System and method for automating customer slamming and cramming complaints
US6853966 *30 Abr 20028 Feb 2005Sbc Technology Resources, Inc.Method for categorizing, describing and modeling types of system users
US6859529 *25 Feb 200222 Feb 2005Austin Logistics IncorporatedMethod and system for self-service scheduling of inbound inquiries
US6871212 *14 May 200122 Mar 2005Aspect Communication CorporationMethod and apparatus for processing a telephone call
US6879683 *28 Jun 200112 Abr 2005Bellsouth Intellectual Property Corp.System and method for providing a call back option for callers to a call center
US6885734 *13 Sep 200026 Abr 2005Microstrategy, IncorporatedSystem and method for the creation and automatic deployment of personalized, dynamic and interactive inbound and outbound voice services, with real-time interactive voice database queries
US7003079 *4 Mar 200221 Feb 2006Bbnt Solutions LlcApparatus and method for monitoring performance of an automated response system
US7006605 *27 Ene 200328 Feb 2006Ochopee Big Cypress LlcAuthenticating a caller before providing the caller with access to one or more secured resources
US7027975 *8 Ago 200011 Abr 2006Object Services And Consulting, Inc.Guided natural language interface system and method
US7031444 *26 Jun 200218 Abr 2006Voicegenie Technologies, Inc.Computer-implemented voice markup system and method
US7035388 *10 Oct 200225 Abr 2006Fujitsu LimitedCaller identifying method, program, and apparatus and recording medium
US7177792 *31 May 200213 Feb 2007University Of Southern CaliforniaInteger programming decoder for machine translation
US7200614 *27 Nov 20023 Abr 2007Accenture Global Services GmbhDual information system for contact center users
US20020046030 *16 May 200118 Abr 2002Haritsa Jayant RamaswamyMethod and apparatus for improved call handling and service based on caller's demographic information
US20020049874 *18 Oct 200125 Abr 2002Kazunobu KimuraData processing device used in serial communication system
US20020059169 *13 Abr 200116 May 2002Quarterman John S.System for quickly collecting operational data for internet destinations
US20030018659 *13 Mar 200223 Ene 2003Lingomotors, Inc.Category-based selections in an information access environment
US20030026409 *31 Jul 20016 Feb 2003Sbc Technology Resources, Inc.Telephone call processing in an interactive voice response call management system
US20030035381 *16 Ago 200120 Feb 2003Yihsiu ChenNetwork-based teleconferencing capabilities utilizing data network call set-up requests
US20030069937 *12 Nov 200210 Abr 2003Khouri Joseph F.Method and apparatus for establishing connections
US20040005047 *5 Jul 20028 Ene 2004Sbc Technology Resources, Inc.Call routing from manual to automated dialog of interactive voice response system
US20040006473 *2 Jul 20028 Ene 2004Sbc Technology Resources, Inc.Method and system for automated categorization of statements
US20040032862 *31 Jul 200319 Feb 2004Nuasis CorporationHigh availability VoIP subsystem
US20040032935 *13 Ago 200219 Feb 2004Sbc Properties, L.P.System and method for the automated analysis of performance data
US20040042592 *29 Ago 20024 Mar 2004Sbc Properties, L.P.Method, system and apparatus for providing an adaptive persona in speech-based interactive voice response systems
US20040044950 *4 Sep 20024 Mar 2004Sbc Properties, L.P.Method and system for automating the analysis of word frequencies
US20040066401 *10 Sep 20038 Abr 2004Sbc Knowledge Ventures, L.P.System and method for selection of a voice user interface dialogue
US20040066416 *3 Oct 20028 Abr 2004Sbc Properties, L.P.Dynamic and adaptable system and method for selecting a user interface dialogue model
US20040073569 *27 Sep 200215 Abr 2004Sbc Properties, L.P.System and method for integrating a personal adaptive agent
US20040088285 *31 Oct 20026 May 2004Sbc Properties, L.P.Method and system for an automated disambiguation
US20040103017 *22 Nov 200227 May 2004Accenture Global Services, GmbhAdaptive marketing using insight driven customer interaction
US20050008141 *11 Jul 200313 Ene 2005Kortum Philip T.Telephone call center with method for providing customer with wait time updates
US20050015197 *25 Abr 200320 Ene 2005Shinya OhtsujiCommunication type navigation system and navigation method
US20050015744 *18 Ago 200420 Ene 2005Sbc Technology Resources Inc.Method for categorizing, describing and modeling types of system users
US20050027535 *27 Ago 20043 Feb 2005Sbc Technology Resources, Inc.Directory assistance dialog with configuration switches to switch from automated speech recognition to operator-assisted dialog
US20050041796 *23 Sep 200424 Feb 2005Sbc Technology Resources, Inc.Call routing from manual to automated dialog of interactive voice response system
US20050047578 *12 Oct 20043 Mar 2005Sbc Properties, L.P.Method for evaluating customer call center system designs
US20050055216 *4 Sep 200310 Mar 2005Sbc Knowledge Ventures, L.P.System and method for the automated collection of data for grammar creation
US20050058264 *25 Oct 200417 Mar 2005Sbc Technology Resources, Inc.System and method for processing complaints
US20050075894 *3 Oct 20037 Abr 2005Sbc Knowledge Ventures, L.P.System, method & software for a user responsive call center customer service delivery solution
US20050078805 *7 Dic 200414 Abr 2005Sbc Properties, L.P.System and method for the automated analysis of performance data
US20050080630 *10 Oct 200314 Abr 2005Sbc Knowledge Ventures, L.P.System and method for analyzing automatic speech recognition performance data
US20050080667 *8 Oct 200314 Abr 2005Sbc Knowledge Ventures, L.P.System and method for automated customized content delivery for web sites
US20060018443 *23 Jul 200426 Ene 2006Sbc Knowledge Ventures, LpAnnouncement system and method of use
US20060023863 *28 Jul 20042 Feb 2006Sbc Knowledge Ventures, L.P.Method and system for mapping caller information to call center agent transactions
US20060026049 *28 Jul 20042 Feb 2006Sbc Knowledge Ventures, L.P.Method for identifying and prioritizing customer care automation
US20060036437 *12 Ago 200416 Feb 2006Sbc Knowledge Ventures, LpSystem and method for targeted tuning module of a speech recognition system
US20060039547 *18 Ago 200423 Feb 2006Sbc Knowledge Ventures, L.P.System and method for providing computer assisted user support
US20060050865 *7 Sep 20049 Mar 2006Sbc Knowledge Ventures, LpSystem and method for adapting the level of instructional detail provided through a user interface
US20060062375 *23 Sep 200423 Mar 2006Sbc Knowledge Ventures, L.P.System and method for providing product offers at a call center
US20070019800 *3 Jun 200525 Ene 2007Sbc Knowledge Ventures, LpCall routing system and method of using the same
US20070025528 *7 Jul 20051 Feb 2007Sbc Knowledge Ventures, L.P.System and method for automated performance monitoring for a call servicing system
US20070025542 *1 Jul 20051 Feb 2007Sbc Knowledge Ventures, L.P.System and method of automated order status retrieval
US20070047718 *25 Ago 20051 Mar 2007Sbc Knowledge Ventures, L.P.System and method to access content from a speech-enabled automated system
US20080008308 *6 Dic 200410 Ene 2008Sbc Knowledge Ventures, LpSystem and method for routing calls
Citada por
Patente citante Fecha de presentación Fecha de publicación Solicitante Título
US8676581 *22 Ene 201018 Mar 2014Microsoft CorporationSpeech recognition analysis via identification information
US87512326 Feb 201310 Jun 2014At&T Intellectual Property I, L.P.System and method for targeted tuning of a speech recognition system
US88246593 Jul 20132 Sep 2014At&T Intellectual Property I, L.P.System and method for speech-enabled call routing
US90886521 Jul 201421 Jul 2015At&T Intellectual Property I, L.P.System and method for speech-enabled call routing
US91129724 Oct 201218 Ago 2015Interactions LlcSystem and method for processing speech
US935086210 Jul 201524 May 2016Interactions LlcSystem and method for processing speech
US936811125 Abr 201414 Jun 2016Interactions LlcSystem and method for targeted tuning of a speech recognition system
US20110184735 *22 Ene 201028 Jul 2011Microsoft CorporationSpeech recognition analysis via identification information
Clasificaciones
Clasificación de EE.UU.379/88.14, 704/E15.001, 379/88.03, 704/275
Clasificación internacionalG10L11/00, H04M11/00, H04M1/64
Clasificación cooperativaH04M3/42204, H04M3/51, G10L15/08, H04M2203/2011, G10L15/1815, H04M2201/60
Clasificación europeaG10L15/18S, H04M3/42H
Eventos legales
FechaCódigoEventoDescripción
11 Nov 2008ASAssignment
Owner name: AT&T INTELLECTUAL PROPERTIES I, L.P., NEVADA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:BUSHEY, ROBERT R;KNOTT, BENJAMIN ANTHONY;MARTIN, JOHN MILLS;REEL/FRAME:021818/0689
Effective date: 20050308
26 Ene 2017ASAssignment
Owner name: NUANCE COMMUNICATIONS, INC., MASSACHUSETTS
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:AT&T INTELLECTUAL PROPERTY I, L.P.;REEL/FRAME:041504/0952
Effective date: 20161214