CN102722379A - Software recommendation method and system - Google Patents
Software recommendation method and system Download PDFInfo
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- CN102722379A CN102722379A CN2011100790410A CN201110079041A CN102722379A CN 102722379 A CN102722379 A CN 102722379A CN 2011100790410 A CN2011100790410 A CN 2011100790410A CN 201110079041 A CN201110079041 A CN 201110079041A CN 102722379 A CN102722379 A CN 102722379A
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Abstract
The invention relates to a software recommendation method and system. The method comprises the following steps of: acquiring user software installation information; calculating an association value between non-installed software and software installed by a user according to the user software installation information; and recommending the non-installed software to the user according to the association value. By the method, the association value between the non-installed software and the software installed by the user is calculated according to the user software installation information, and the non-installed software is recommended to the user according to the association value; the installed software of the user and the association value between the non-installed software and the installed software are combined, so that the software can be adaptively recommended according to a software installation environment in a user computer; therefore, the non-installed software can be intelligently and individually recommended to the user and well meet the requirement of the user; and demands of different users can be met.
Description
[technical field]
The present invention relates to a kind of software administration technical field, relate in particular to a kind of software recommend method and system.
[background technology]
At present, computer control software mostly is built-in with software management module, and software is put into different categories.When the user clicks certain classification, represent software matrix to such software ordering of user, recommend software to the user.
But; The ordering of traditional software tabulation is mostly with himself product and there is the software of investment relation to put preceding and with the postpone of rival's product, in addition; Secondly before also can putting for the product of the businessman of promotion expense some, just consider the factors such as scoring, issuing time, download of software.Therefore, when the user recommends software, represent to user's software matrix all basic identical; Machinery and do not have an individual difference; Because different user is different to the requirement of software, thereby causes satisfying requirements of different users, the user obtains required software need expend user's plenty of time and energy; Increased user's burden.
[summary of the invention]
In view of this, be necessary the software recommend method that a kind of personalization is provided, can meets the needs of different users.
A kind of software recommend method comprises the steps:
Obtain the user software mount message;
According to the user software mount message, calculate not install software and the user relating value of install software;
Recommend not install software according to said relating value to the user.
Preferably, the said user software mount message that obtains is specially for obtaining user's install software title: obtain user's install software title through user ID to the software matrix in high in the clouds.
Preferably, said according to the user software mount message, calculate not install software and user the step of the relating value of install software be specially:
According to all software matrixs that backup to high in the clouds, the pulling matrix between the software for calculation;
According to said pulling matrix and user's software matrix, calculate each not relating value of install software and user's software matrix.
Preferably; Said according to the user software mount message; Calculate not install software and user the step of the relating value of install software be specially: according to all software matrixs that backup to high in the clouds and user's software matrix; Calculate the user all install software each not the pulling value and the totalling of install software are obtained the not relating value of the software matrix of install software and user.
Preferably; Said according to the user software mount message; Calculate not install software and user the step of the relating value of install software be specially: according to all software matrixs that backup to high in the clouds and user's software matrix, calculate not install software and the user relating value of one or more popular software in the install software.
Preferably, saidly recommend not according to said relating value to the user that the step of install software is specially: install software according to the size of user's install software relating value,, to sorting for a short time and forming recommendation list recommendation list is not represented to the user by big.
Preferably, saidly recommend not according to said relating value to the user that the step of install software is specially:, will recommend the user greater than the not install software of particular value with user's install software relating value according to the relating value size.
A kind of software commending system comprises:
The mount message acquisition module is used to obtain the user software mount message;
The relating value computing module is used for the mount message according to said user software, calculates not install software and the user relating value of install software;
The software recommending module, the user recommends not install software according to said relating value to the user.
Preferably, said mount message acquisition module is used to obtain user's install software title, and said mount message acquisition module is used for obtaining user's install software title through user ID to the software matrix in high in the clouds.
Preferably, said relating value computing module is used for according to all software matrixs that backup to high in the clouds, the pulling matrix between the software for calculation; According to said pulling matrix and user's software matrix, calculate each not relating value of install software and user's software matrix.
Preferably; Said relating value computing module is used for according to backuping to all software matrixs in high in the clouds and user's software matrix; Calculate the user all install software each not the pulling value and the totalling of install software are obtained the not relating value of the software matrix of install software and user.
Preferably, said relating value computing module is used for according to backuping to all software matrixs in high in the clouds and user's software matrix, calculates not install software and the user relating value of one or more popular software in the install software.
Preferably, said software recommending module not install software is represented recommendation list to the user to sorting for a short time and forming recommendation list by big according to big or small with user's install software relating value, recommends not install software to the user.
Preferably, said software recommending module will be recommended the user greater than the not install software of particular value with user's install software relating value according to the relating value size.
Above-mentioned software recommend method and system; According to user installation information; Calculate not install software and the user relating value of install software, and recommend not install software to the user, in conjunction with user install software and the not install software and the relating value of install software according to relating value; According to the adaptive recommendation software of software installation environment in the user computer; Thereby intelligent, personalized recommends not install software to the user, makes the not install software to user's recommendation can well meet user's requirement, has satisfied requirements of different users.
[description of drawings]
Fig. 1 is the process flow diagram of software recommend method among the embodiment;
Fig. 2 calculates not install software and the user method flow diagram of the relating value of install software among the embodiment;
Fig. 3 is the structural representation of software commending system among the embodiment.
[embodiment]
Below in conjunction with accompanying drawing, specific embodiments of the invention is described in detail.
Fig. 1 is the process flow diagram of software recommend method among the embodiment.This method comprises:
S10: obtain the user software mount message.
The user software mount message comprises install software title, set-up time, installation site, software type or the like.Among this embodiment, obtaining the user software mount message is to obtain user's title of install software, and step is specially: the software matrix of (server end) obtains user's install software title to high in the clouds through user ID.Behind the user installation software, can backup in the software matrix corresponding of high in the clouds with this ID (being ID).For example, (userID, software matrix).Software matrix is the title of all softwares of installing on the subscriber set, separates with comma between each dbase.For example, (userId_1, " Tencent QQ, QQ computer house keeper, Jinshan anti-virus software .... "), the expression ID be userId_1 user installation Tencent QQ, QQ computer house keeper, softwares such as Jinshan anti-virus software.Obtain user's install software title through software matrix, thereby know mounted all softwares of user.
S20:, calculate not install software and the user relating value of install software according to the user software mount message.
For can be accurately and personalizedly recommend not install software to the user, meet the needs of different users, among this embodiment, through user's install software and not install software and the relating value that has between the install software recommend not install software for the user.
According to all software matrixs that backup to high in the clouds,, be designated as the pulling value of software A to B with the ratio that software B has been installed among the user that software A has been installed.
As shown in Figure 2, calculate not install software and user the method for the relating value of install software be specially:
S21: according to all software matrixs that backup to high in the clouds, the pulling matrix between the software for calculation.
According to all software matrixs that backup to high in the clouds, calculate in all softwares the pulling value between the software in twos, make up the pulling matrix according to the pulling value.
For example: according to all software matrixs that backup to high in the clouds, always total N money software.
For all software; Consider that it makes up in twos; For example; Software i and software j combination; Statistics has been installed the ratio of among the user of software i software j being installed in all backup to the software matrix in high in the clouds; Obtain the pulling value of software i to j; Obtain all pulling values between the software in twos with this; And then the pulling matrix
that obtains between all softwares is N * N matrix, and wherein:
expression software i is to the pulling value of j.
Through the pulling matrix, obtained all softwares pulling value between any two, can Reaction Fast Query Software pulling value between any two, can calculate not install software and the user relating value of install software fast for all users.
S22:, calculate the relating value that not install software and user software are tabulated according to pulling matrix and software matrix.
Among this embodiment, all softwares (being mounted all softwares of user) in the user software tabulation to the pulling value totalling of a certain not install software, are designated as this not install software and user software relating value of tabulating.For the specific user, according to its software matrix and pulling matrix, calculate not install software and user during the relating value of install software, calculate the relating value of all softwares during install software and this user software are not tabulated.
For example, for each install software k not, the software matrix of supposing the user is software (software u
1, software u
2... software u
t), t money software has been installed.The relating value that calculates not install software k and user software tabulation is:
Wherein, A is a N dimensional vector, and N is all software numbers, and the k component of A is A
k, represent the pulling value totalling of this user, i.e. the relating value of software k and this user software tabulation to software k.
In other embodiments; Can also not constitute the pulling matrix; According to all software matrixs that backup to high in the clouds and user's software matrix, calculate the user all install software each not the pulling value and the totalling of install software are obtained the relating value that not install software and user software are tabulated.
Perhaps, according to all software matrixs that backup to high in the clouds and user's software matrix, calculate not install software and the user relating value of one or more popular software in the install software.
S30: recommend not install software to the user according to relating value.
Among this embodiment, install software is not represented recommendation list to the user to sorting for a short time and forming recommendation list by big according to big or small with user's install software relating value, recommends not install software to the user.Perhaps, according to the relating value size, will recommend the user greater than the software of particular value with user's install software relating value.
In addition, a kind of software commending system also is provided.
Fig. 3 is a software commending system structural representation among the embodiment.This system comprises mount message acquisition module 100, relating value computing module 200 and software recommending module 300.
Mount message acquisition module 100 is used to obtain the user software mount message.
The user software mount message comprises install software title, set-up time, installation site, software type or the like.In in should implementing, it is to obtain user's title of install software that mount message acquisition module 100 obtains the user software mount message, is specially that the software matrix of (server end) obtains user's title of install software to high in the clouds through user ID.Behind the user installation software, can backup in the software matrix corresponding of high in the clouds with this ID.For example, (userID, software matrix).Software matrix is the title of all softwares of installing on the subscriber set, separates with comma between each dbase.For example, (userId_1, " Tencent QQ, QQ computer house keeper, Jinshan anti-virus software .... "), the expression ID be userId_1 user installation Tencent QQ, QQ computer house keeper, softwares such as Jinshan anti-virus software.Mount message acquisition module 100 obtains user's install software title through software matrix, thereby knows mounted all softwares of user.
Relating value computing module 200 is used for the mount message according to user software, calculates not install software and the user relating value of install software.
For can be accurately and personalizedly recommend not install software to the user, meet the needs of different users, among this embodiment, consider the user install software and not install software and the relevance that has between the install software recommend not install software for the user.
Among this embodiment,, be designated as the pulling value of software A, be designated as the relating value of B and A simultaneously B with the ratio that software B has been installed among the user that software A has been installed.
Relating value computing module 200 is according to all software matrixs that backup to high in the clouds, and the pulling value between the software for calculation makes up the pulling matrix according to the pulling value, according to pulling matrix and user's software matrix, calculates the relating value that not install software and user software are tabulated.
For example: according to all software matrixs that backup to high in the clouds, always total N money software.
For all software; Relating value computing module 200 considers that it makes up in twos; For example; Software i and software j combination; Relating value computing module 200 statistics has been installed the ratio of among the user of software i software j being installed in all backup to the software matrix in high in the clouds; Obtain the pulling value of software i to j; Obtain all pulling values between the software in twos with this; And then the pulling matrix
that obtains between all softwares is N * N matrix, and wherein:
expression software i is to the pulling value of j.
Through the pulling matrix, obtained all softwares pulling value between any two, can Reaction Fast Query Software pulling value between any two, can calculate not install software and the user relating value of install software fast for all users.
Among this embodiment, all softwares (being mounted all softwares of user) in the user software tabulation are designated as this not install software and user software relating value of tabulating to the pulling value totalling of a certain not install software.For the specific user, relating value computing module 200 is according to its software matrix and pulling matrix, calculates not install software and user during the relating value of install software, calculates the relating value of all softwares in the software matrix of not install software and this user.
For example, for each install software k not, the software matrix of supposing the user is software (software u
1, software u
2... software u
t), t money software has been installed.For example, for each install software k not, the software matrix of supposing the user is software (software u
1, software u
2... software u
t), t money software has been installed.The relating value that relating value computing module 200 calculates not install software k and user software tabulation is:
Wherein, A is a N dimensional vector, and N is all software numbers, and the k component of A is A
k, represent the pulling value totalling of this user, i.e. the relating value of software k and this user software tabulation to software k.
In other embodiments; Relating value computing module 200 can also not constitute the pulling matrix; According to all software matrixs that backup to high in the clouds and user's software matrix; Calculate the user all install software each not the pulling value and the totalling of install software are obtained the relating value that not install software and user software are tabulated.
Perhaps, relating value computing module 200 is according to backuping to all software matrixs in high in the clouds and user's software matrix, calculates not install software and the user relating value of one or more popular software in the install software.
Among this embodiment, software recommending module 300 not install software is represented recommendation list to the user to sorting for a short time and forming recommendation list by big according to big or small with user's install software relating value, recommends not install software to the user.Perhaps, according to the relating value size, will recommend the user greater than the software of particular value with user's install software relating value.
Above-mentioned software recommend method and system; According to user installation information; Calculate not install software and the user relating value of install software, and recommend not install software to the user, in conjunction with user install software and the not install software and the relating value of install software according to relating value; According to the adaptive recommendation software of software installation environment in the user computer; Thereby intelligent, personalized recommends not install software to the user, makes the not install software to user's recommendation well meet user's requirement, has satisfied requirements of different users.
The above embodiment has only expressed several kinds of embodiments of the present invention, and it describes comparatively concrete and detailed, but can not therefore be interpreted as the restriction to claim of the present invention.Should be pointed out that for the person of ordinary skill of the art under the prerequisite that does not break away from the present invention's design, can also make some distortion and improvement, these all belong to protection scope of the present invention.Therefore, the protection domain of patent of the present invention should be as the criterion with accompanying claims.
Claims (14)
1. a software recommend method comprises the steps:
Obtain the user software mount message;
According to the user software mount message, calculate not install software and the user relating value of install software;
Recommend not install software according to said relating value to the user.
2. software recommend method according to claim 1 is characterized in that, the said user software mount message that obtains is specially for obtaining user's install software title: obtain user's install software title through user ID to the software matrix in high in the clouds.
3. software recommend method according to claim 2 is characterized in that, and is said according to the user software mount message, calculate not install software and user the step of the relating value of install software be specially:
According to all software matrixs that backup to high in the clouds, the pulling matrix between the software for calculation;
According to said pulling matrix and user's software matrix, calculate each not relating value of install software and user's software matrix.
4. software recommend method according to claim 2; It is characterized in that; Said according to the user software mount message; Calculate not install software and user the step of the relating value of install software be specially: according to all software matrixs that backup to high in the clouds and user's software matrix, calculate the user all install software each not the pulling value and the totalling of install software are obtained the not relating value of the software matrix of install software and user.
5. software recommend method according to claim 2; It is characterized in that; Said according to the user software mount message; Calculate not install software and user the step of the relating value of install software be specially: according to all software matrixs that backup to high in the clouds and user's software matrix, calculate not install software and the user relating value of one or more popular software in the install software.
6. according to the arbitrary described software recommend method of claim 3 to 5; It is characterized in that; Saidly recommend not according to said relating value to the user that the step of install software is specially: install software according to the size of user's install software relating value;, to sorting for a short time and forming recommendation list recommendation list is represented to the user by big.
7. according to the arbitrary described software recommend method of claim 3 to 5; It is characterized in that; Saidly recommend not according to said relating value to the user that the step of install software is specially:, will recommend the user greater than the not install software of particular value with user's install software relating value according to the relating value size.
8. a software commending system is characterized in that, comprising:
The mount message acquisition module is used to obtain the user software mount message;
The relating value computing module is used for the mount message according to said user software, calculates not install software and the user relating value of install software;
The software recommending module, the user recommends not install software according to said relating value to the user.
9. software commending system according to claim 8; It is characterized in that; Said mount message acquisition module is used to obtain user's install software title, and said mount message acquisition module is used for obtaining user's install software title through user ID to the software matrix in high in the clouds.
10. software commending system according to claim 9 is characterized in that, said relating value computing module is used for according to all software matrixs that backup to high in the clouds, the pulling matrix between the software for calculation; According to said pulling matrix and user's software matrix, calculate each not relating value of install software and user's software matrix.
11. software commending system according to claim 9; It is characterized in that; Said relating value computing module is used for according to backuping to all software matrixs in high in the clouds and user's software matrix; Calculate the user all install software each not the pulling value and the totalling of install software are obtained the not relating value of the software matrix of install software and user.
12. software commending system according to claim 9; It is characterized in that; Said relating value computing module is used for according to backuping to all software matrixs in high in the clouds and user's software matrix, calculates not install software and the user relating value of one or more popular software in the install software.
13. according to the arbitrary described software commending system of claim 10 to 12; It is characterized in that; Said software recommending module not install software according to the size of user's install software relating value; Recommendation list is represented to the user to sorting for a short time and forming recommendation list by big, recommend not install software to the user.
14., it is characterized in that said software recommending module will be recommended the user greater than the not install software of particular value with user's install software relating value according to the relating value size according to the arbitrary described software commending system of claim 10 to 12.
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Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102917060A (en) * | 2012-10-19 | 2013-02-06 | 北京奇虎科技有限公司 | Application matching message sending server, and application pushing system and method |
| CN103402194A (en) * | 2013-08-02 | 2013-11-20 | 个信互动（北京）网络科技有限公司 | Method for recommending software during software update and system for implementing method |
| CN103973735A (en) * | 2013-01-30 | 2014-08-06 | 上海易销电子商务有限公司 | System for providing content for mobile equipment |
| CN104685472A (en) * | 2012-10-11 | 2015-06-03 | 汤姆逊许可公司 | Solution for distributed application life-cycle management |
| CN104750790A (en) * | 2015-03-12 | 2015-07-01 | 广东欧珀移动通信有限公司 | Software recommendation method and device |
| CN104765609A (en) * | 2015-04-03 | 2015-07-08 | 安一恒通（北京）科技有限公司 | Software related resource recommendation method, obtaining method and corresponding device |
| CN105117440A (en) * | 2015-08-11 | 2015-12-02 | 北京奇虎科技有限公司 | Method and apparatus for determining to-be-recommended application (APP) |
| WO2016041282A1 (en) * | 2014-09-19 | 2016-03-24 | 安一恒通（北京）科技有限公司 | Method, apparatus and device for providing information |
| CN106878359A (en) * | 2015-12-14 | 2017-06-20 | 百度在线网络技术（北京）有限公司 | Information-pushing method and device |
| CN108769125A (en) * | 2018-04-28 | 2018-11-06 | 广州优视网络科技有限公司 | Using recommendation method, apparatus, storage medium and computer equipment |
| CN108809987A (en) * | 2018-06-14 | 2018-11-13 | 广州任天游网络科技有限公司 | A kind of online game extension method based on big data analysis |
| CN112365290A (en) * | 2020-11-26 | 2021-02-12 | 上海触乐信息科技有限公司 | Method, device, storage medium and server for improving user retention rate of application |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101124575A (en) * | 2004-02-26 | 2008-02-13 | 雅虎公司 | Method and system for generating recommendations |
| US20080040495A1 (en) * | 2006-08-08 | 2008-02-14 | International Business Machines Corporation | System, method and program for field service of computers |
| CN101558400A (en) * | 2007-01-08 | 2009-10-14 | 网讯公司 | Methods and apparatuses for selectively processing, dynamically suggesting, and automatically initiating an application, and managing the distribution and installation of applications |
| CN101689193A (en) * | 2007-03-21 | 2010-03-31 | 雅虎公司 | in-page installer |
| CN101867594A (en) * | 2010-03-05 | 2010-10-20 | 宇龙计算机通信科技(深圳)有限公司 | Data transmission method, device and system |
| CN101937547A (en) * | 2010-09-15 | 2011-01-05 | 宇龙计算机通信科技(深圳)有限公司 | Software and/or software information pushing method, system, acquisition device, software shop service system and mobile terminal |
-
2011
- 2011-03-30 CN CN201110079041.0A patent/CN102722379B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101124575A (en) * | 2004-02-26 | 2008-02-13 | 雅虎公司 | Method and system for generating recommendations |
| US20080040495A1 (en) * | 2006-08-08 | 2008-02-14 | International Business Machines Corporation | System, method and program for field service of computers |
| CN101558400A (en) * | 2007-01-08 | 2009-10-14 | 网讯公司 | Methods and apparatuses for selectively processing, dynamically suggesting, and automatically initiating an application, and managing the distribution and installation of applications |
| CN101689193A (en) * | 2007-03-21 | 2010-03-31 | 雅虎公司 | in-page installer |
| CN101867594A (en) * | 2010-03-05 | 2010-10-20 | 宇龙计算机通信科技(深圳)有限公司 | Data transmission method, device and system |
| CN101937547A (en) * | 2010-09-15 | 2011-01-05 | 宇龙计算机通信科技(深圳)有限公司 | Software and/or software information pushing method, system, acquisition device, software shop service system and mobile terminal |
Cited By (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104685472A (en) * | 2012-10-11 | 2015-06-03 | 汤姆逊许可公司 | Solution for distributed application life-cycle management |
| US10229207B2 (en) | 2012-10-11 | 2019-03-12 | Thomson Licensing | Solution for distributed application life-cycle management |
| CN102917060A (en) * | 2012-10-19 | 2013-02-06 | 北京奇虎科技有限公司 | Application matching message sending server, and application pushing system and method |
| CN102917060B (en) * | 2012-10-19 | 2015-08-19 | 北京奇虎科技有限公司 | Application coupling message sends server, application supplying system and method |
| CN103973735A (en) * | 2013-01-30 | 2014-08-06 | 上海易销电子商务有限公司 | System for providing content for mobile equipment |
| CN103402194B (en) * | 2013-08-02 | 2016-08-31 | 浙江每日互动网络科技股份有限公司 | A kind of method recommending software when software upgrading and realize the system of the method |
| CN103402194A (en) * | 2013-08-02 | 2013-11-20 | 个信互动（北京）网络科技有限公司 | Method for recommending software during software update and system for implementing method |
| US10078508B2 (en) | 2014-09-19 | 2018-09-18 | Baidu Online Network Technology (Beijing) Co., Ltd. | Information providing method, device, and apparatus |
| WO2016041282A1 (en) * | 2014-09-19 | 2016-03-24 | 安一恒通（北京）科技有限公司 | Method, apparatus and device for providing information |
| CN104750790A (en) * | 2015-03-12 | 2015-07-01 | 广东欧珀移动通信有限公司 | Software recommendation method and device |
| CN104765609B (en) * | 2015-04-03 | 2018-12-07 | 安一恒通（北京）科技有限公司 | Software context resource recommendation method, acquisition methods and corresponding device |
| CN104765609A (en) * | 2015-04-03 | 2015-07-08 | 安一恒通（北京）科技有限公司 | Software related resource recommendation method, obtaining method and corresponding device |
| CN105117440A (en) * | 2015-08-11 | 2015-12-02 | 北京奇虎科技有限公司 | Method and apparatus for determining to-be-recommended application (APP) |
| CN106878359A (en) * | 2015-12-14 | 2017-06-20 | 百度在线网络技术（北京）有限公司 | Information-pushing method and device |
| CN106878359B (en) * | 2015-12-14 | 2020-08-04 | 百度在线网络技术（北京）有限公司 | Information pushing method and device |
| CN108769125A (en) * | 2018-04-28 | 2018-11-06 | 广州优视网络科技有限公司 | Using recommendation method, apparatus, storage medium and computer equipment |
| CN108809987A (en) * | 2018-06-14 | 2018-11-13 | 广州任天游网络科技有限公司 | A kind of online game extension method based on big data analysis |
| CN108809987B (en) * | 2018-06-14 | 2020-10-23 | 江苏果米文化发展有限公司 | Online game popularization method based on big data analysis |
| CN112365290A (en) * | 2020-11-26 | 2021-02-12 | 上海触乐信息科技有限公司 | Method, device, storage medium and server for improving user retention rate of application |
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Effective date of registration: 20151230 Address after: The South Road in Guangdong province Shenzhen city Fiyta building 518000 floor 5-10 Nanshan District high tech Zone Patentee after: Shenzhen Tencent Computer System Co., Ltd. Address before: Shenzhen Futian District City, Guangdong province 518044 Zhenxing Road, SEG Science Park 2 East Room 403 Patentee before: Tencent Technology (Shenzhen) Co., Ltd. |