CN107592346A - User classification method based on user behavior analysis - Google Patents
User classification method based on user behavior analysis Download PDFInfo
- Publication number
- CN107592346A CN107592346A CN201710772803.2A CN201710772803A CN107592346A CN 107592346 A CN107592346 A CN 107592346A CN 201710772803 A CN201710772803 A CN 201710772803A CN 107592346 A CN107592346 A CN 107592346A
- Authority
- CN
- China
- Prior art keywords
- user
- access
- information
- time
- duration
- 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.)
- Granted
Links
Landscapes
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present embodiments relate to a kind of user classification method based on user behavior analysis, including：Server sends PUSH message to user terminal, and the PUSH message includes the link information of recommendation service content；The user terminal is identified according to access of the link information to the recommendation service content, and generates access record；The record that accesses includes the ID, service content ID and temporal information of user；According to ID, the access record of the user in preset period of time is counted, obtains the access times in the preset period of time and total access duration；According to the access times and total user's mark information for accessing duration and generating the user；The corresponding relation established between the user's mark information and the ID of the user, and store.
Description
Technical field
The present invention relates to communication technical field, more particularly to a kind of user classification method based on user behavior analysis.
Background technology
User behavior analysis, refer in the case where obtaining website visiting amount master data, relevant data are counted,
Analysis, therefrom find that user accesses the rule of website, and these rules are combined with net marketing strategy etc., so as to find mesh
The problem of there may be in preceding network marketing activity, and provide foundation further to correct or reformulating net marketing strategy.
With the development of the communication technology, the application based on user behavior analysis is also more and more extensive.
In the personalized customization of application, most products all or using allowing user to select label, are carried out on the market at present
Content push, the basic behavioural habits of user are not known about, cause user's position inaccurate, can not accurately determine user couple
The interest-degree of product content is runed, causes content recommendation not to be inconsistent with user's actual need, precision is poor.
The content of the invention
It is an object of the invention to provide a kind of user classification method based on user behavior analysis, by being supervised to user behavior
The data obtained are surveyed to be analyzed, can in further detail, the behavioural habits of user are well understood, it is right and by quantitative analysis
User carries out exhaustive division, more contributes to push the accuracy of content, improves business conversion ratio.
To achieve the above object, the invention provides a kind of user classification method based on user behavior analysis, including：
Server sends PUSH message to user terminal, and the PUSH message includes the link information of recommendation service content；
The user terminal is identified according to access of the link information to the recommendation service content, and generates access note
Record；The record that accesses includes user I D, service content I D and temporal information of user；
According to ID, the access record of the user in preset period of time is counted, is obtained in the preset period of time
Access times and total access duration；
According to the access times and total user's mark information for accessing duration and generating the user；
The corresponding relation established between the user's mark information and the user I D of the user, and store.
Preferably, it is described according to ID, the access record of the user in preset period of time is counted, is obtained described pre-
If the access times in the period specifically include：
According to the ID, it is determined that the temporal information that the access of the user records in preset period of time；
Extract the date and time information that the temporal information includes；
Access record to identical date and time information merges statistical disposition, obtains the access time in the preset period of time
Number.
Preferably, it is described according to ID, the access record of the user in preset period of time is counted, is obtained described pre-
If total access duration in the period specifically includes：
According to the ID, it is determined that the temporal information that the access of the user records in preset period of time；
The access duration of single reference is extracted according to the temporal information；
The access duration of each single reference in the preset period of time is added up, obtains total access duration.
Preferably, the user's mark information according to the access times and total access duration generation user is specific
Including：
First threshold and Second Threshold are determined according to the preset period of time；
Determine whether the access times reach first threshold；
If being not reaching to first threshold, the mark of generation first；
If reaching first threshold, determine whether total access duration reaches Second Threshold；
If reaching Second Threshold, the mark of generation second；
If being not reaching to Second Threshold, the mark of generation the 3rd.
It is further preferred that in the user's mark that the user is generated according to the access times and total access duration
Before information, in addition to：
Obtain total access duration of multiple users in the preset period of time；
The average value of total access duration of the multiple user in the preset period of time is calculated, obtains second threshold
Value.
It is further preferred that before the total access duration for obtaining multiple users in the preset period of time, it is described
Method also includes：
The information of the user characteristics attribute of the user is determined according to the user I D of the user；
Match query is carried out in subscriber information management database according to the user characteristics attribute of the user, it is determined that described
Multiple users.
Preferably, after the server sends PUSH message to user terminal, methods described also includes：
The user terminal generates PUSH message prompt message according to the PUSH message, and shows.
It is further preferred that methods described also includes：
The user terminal receives the out code to the PUSH message prompt message of user's input, and generation refusal carries
Show feedback information, be sent to the server；The user I D of the refusal prompting feedback information including the user and described push away
Send service content I D corresponding to recommendation service content in message.
User classification method provided in an embodiment of the present invention based on user behavior analysis, by being obtained to user behavior monitoring
Data analyzed, can in further detail, the behavioural habits of user are well understood, and by quantitative analysis, to user
Exhaustive division is carried out, more contributes to push the accuracy of content, improves business conversion ratio.
Brief description of the drawings
Fig. 1 is the schematic diagram of the user classification method provided in an embodiment of the present invention based on user behavior analysis.
Embodiment
Below by drawings and examples, technical scheme is described in further detail.
The present invention provides a kind of user classification method that can be used in application service, by monitoring what is obtained to user behavior
Data are analyzed, so as to carry out exhaustive division to user, to ensure the accuracy of application service PUSH message and validity.
Fig. 1 is the flow chart of the user classification method provided in an embodiment of the present invention based on user behavior analysis, is tied below
Close shown in Fig. 1, the user classification method provided in implementing to the present invention illustrates.
User classification method provided in an embodiment of the present invention based on user behavior analysis comprises the following steps：
Step 110, server sends PUSH message to user terminal；
Specifically, PUSH message includes the link information of recommendation service content.User terminal pushes away according to PUSH message generation
Message notifying information is sent, and is shown.
Step 120, user terminal is identified according to access of the link information to recommendation service content, and generates access record；
Specifically, after user terminal displays PUSH message prompt message, the instruction action of user's input is identified,
Instruction action may include initiating the action accessed, it is also possible to the action including closing prompt message.
If recognize the chain of the action, then the recommendation service content in PUSH message prompt message of initiating to access
Information is connect, is linked to recommendation service content, while generates access record.Wherein, accessing record includes user I D of user, clothes
Business content I D and temporal information.
If user terminal receives the out code to PUSH message prompt message of user's input, generation refusal prompting is anti-
Feedforward information, server is sent to, so that server is recorded., can be according to counting on if user frequently closes prompting
Data determine that user loses interest in for content recommendation, then can adjust content recommendation or stop continuing to recommend the user.Wherein,
Service content I D corresponding to recommendation service content in user I D and PUSH message of the refusal prompting feedback information including user.
Step 130, according to user I D, the access record of the user in preset period of time is counted, is obtained in preset period of time
Access times and total access duration；
Specifically, the statistics on access times, can be counted according to the date, i.e. statistic of user accessing
Number of days.Such as can be according to user I D, it is determined that the temporal information that the access of user records in preset period of time；When then extracting
Between the date and time information that includes of information；Finally the access record to identical date and time information merges statistical disposition, obtains pre-
If the access times in the period.
It is of course also possible to directly added up according to the access times actually occurred.
, can be according to user I D, it is determined that the access record of user in preset period of time on total statistics for accessing duration
Temporal information；Then the access duration of single reference is extracted according to temporal information；Finally to each single reference in preset period of time
Access duration added up, obtain always accessing duration.
Step 140, according to access times and total user's mark information for accessing duration generation user；
Specifically, user can be divided according to the requirement that whether access reach access times and access duration,
User can be at least divided into three classes, to distinguish the liveness of different user.
One specific implementation procedure may include steps of：
Step 141, first threshold and Second Threshold are determined according to preset period of time；
Step 142, determine whether access times reach first threshold；
If being not reaching to first threshold, step 143, the mark of generation first are performed；
If reaching first threshold, step 144 is performed, it is determined that always accessing whether duration reaches Second Threshold；
If reaching Second Threshold, step 145, the mark of generation second are performed；
If being not reaching to Second Threshold, step 146, the mark of generation the 3rd are performed.
In above process, first threshold is the threshold value of access times, such as the access times in 30 days, and optimal is
By merging cumulative access day, number of days as defined in arrival, that is, it is active to think user, is concern and sense to content recommendation
Interest.
Second Threshold is the threshold value for accessing duration, and the mode that again may be by setting fixed value carries out threshold value really
It is fixed.
It is, of course, also possible to dynamic threshold value is set to, by the data statistics to a range of multiple users, with flat
Relevant parameter of the average as threshold value.
For example in a specific example, total access duration of multiple users in preset period of time can be obtained, then
By calculating the average value of total access duration of multiple users in preset period of time, Second Threshold is obtained.Certainly, in preferable side
In case, acquired multiple users are not random or arbitrarily choose, but can determine user's according to the ID of user
The information of user characteristics attribute；Then inquiry is carried out in subscriber information management database according to the user characteristics attribute of user
Match somebody with somebody, to determine selected multiple users.That is selection has the other users of same or similar characteristic attribute with user
Reference data of the data as threshold calculations.Specific characteristic attribute can include but is not limited to：Age, sex, occupation,
Area and combinations thereof, etc..
Step 150, the corresponding relation established between user's mark information and the ID of user, and store.
In the incidence relation it is determined that after the user's mark information of user, established between user's mark information and ID, from
And the label information of user is can determine that by ID, pushed away so as to carry out corresponding content according to user's mark information
Send.
In order to be better understood from said process, it is illustrated with a specific example.
In a specific example, recommendation service content is the Healthy Curriculum for being supplied to user to be checked.Prompting letter
Breath is shown in user terminal.If user clicks on the link information of recommendation service content, it is linked to server and obtains in course
Appearance starts to practise, and reports record by user terminal, including the course ID of user's exercise, start to practise time, in practice pages
Duration of face residence time etc..Recommend if user closes, client records simultaneously report service course ID.Wherein, push away daily
The content for recommending course can be all different, can also include refresher course and newly-increased course two parts, the wherein content of refresher course
It is the newly-increased course of proxima luce (prox. luc).
The gathered data of one month by a definite date is carried out to user, is recorded according to accessing, is calculated in all 30 days and practise course
Number of days is more than the user of 10 days, and practises the user that course number of days is less than or equal to 10 days in 30 days, exercise in 30 days is less than etc.
These users in 10 days are designated as user type 1.
For user of the exercise more than 10 days in 30 days, these users are calculated in this 30 days, are stopped in the exercise of all pages
The total duration stayed, and calculate the average duration for the other users that there is same or similar characteristic attribute with user.Total duration is small
User type 2 is designated as in the user equal to average duration.The user that total duration is more than to average duration is designated as user type 3.
Further, it is also possible to according to user for the specifically chosen type come further refined user of course content.Such as
For the user of user type 2 and user type 3, the number ratio of refresher course and newly-increased course is selected come further by it
It is finely divided.If selecting the number of refresher course to be more than newly-increased course, user type a is designated as, otherwise be designated as type b.
Therefore by the method for the embodiment of the present invention, user can effectively be classified based on user behavior.
User classification method provided in an embodiment of the present invention based on user behavior analysis, by being obtained to user behavior monitoring
Data analyzed, can in further detail, the behavioural habits of user are well understood, and by quantitative analysis, to user
Exhaustive division is carried out, more contributes to push the accuracy of content, improves business conversion ratio.
Professional should further appreciate that, each example described with reference to the embodiments described herein
Unit and algorithm steps, it can be realized with electronic hardware, computer software or the combination of the two, it is hard in order to clearly demonstrate
The interchangeability of part and software, the composition and step of each example are generally described according to function in the above description.
These functions are performed with hardware or software mode actually, application-specific and design constraint depending on technical scheme.
Professional and technical personnel can realize described function using distinct methods to each specific application, but this realization
It is it is not considered that beyond the scope of this invention.
The method that is described with reference to the embodiments described herein can use hardware, computing device the step of algorithm
Software module, or the two combination are implemented.Software module can be placed in random access memory (RAM), internal memory, read-only storage
(ROM), electrically programmable ROM, electrically erasable ROM, register, hard disk, moveable magnetic disc, CD-ROM or technical field
In any other form of storage medium well known to interior.
Above-described embodiment, the purpose of the present invention, technical scheme and beneficial effect are carried out further
Describe in detail, should be understood that the embodiment that the foregoing is only the present invention, be not intended to limit the present invention
Protection domain, within the spirit and principles of the invention, any modification, equivalent substitution and improvements done etc., all should include
Within protection scope of the present invention.
Claims (8)
1. a kind of user classification method based on user behavior analysis, it is characterised in that methods described includes：
Server sends PUSH message to user terminal, and the PUSH message includes the link information of recommendation service content；
The user terminal is identified according to access of the link information to the recommendation service content, and generates access record；
The record that accesses includes the ID, service content ID and temporal information of user；
According to ID, the access record of the user in preset period of time is counted, obtains the access in the preset period of time
Number and total access duration；
According to the access times and total user's mark information for accessing duration and generating the user；
The corresponding relation established between the user's mark information and the ID of the user, and store.
2. user classification method according to claim 1, it is characterised in that it is described according to ID, count when default
The access record of the user, the access times obtained in the preset period of time specifically include in section：
According to the ID, it is determined that the temporal information that the access of the user records in preset period of time；
Extract the date and time information that the temporal information includes；
Access record to identical date and time information merges statistical disposition, obtains the access times in the preset period of time.
3. user classification method according to claim 1, it is characterised in that it is described according to ID, count when default
The access record of the user, the total access duration obtained in the preset period of time specifically include in section：
According to the ID, it is determined that the temporal information that the access of the user records in preset period of time；
The access duration of single reference is extracted according to the temporal information；
The access duration of each single reference in the preset period of time is added up, obtains total access duration.
4. user classification method according to claim 1, it is characterised in that described according to the access times and total access
The user's mark information that duration generates the user specifically includes：
First threshold and Second Threshold are determined according to the preset period of time；
Determine whether the access times reach first threshold；
If being not reaching to first threshold, the mark of generation first；
If reaching first threshold, determine whether total access duration reaches Second Threshold；
If reaching Second Threshold, the mark of generation second；
If being not reaching to Second Threshold, the mark of generation the 3rd.
5. user classification method according to claim 4, it is characterised in that described according to the access times and total visit
Before asking the user's mark information that duration generates the user, in addition to：
Obtain total access duration of multiple users in the preset period of time；
The average value of total access duration of the multiple user in the preset period of time is calculated, obtains the Second Threshold.
6. user classification method according to claim 5, it is characterised in that obtain multiple users described default described
Before total access duration in period, methods described also includes：
The information of the user characteristics attribute of the user is determined according to the ID of the user；
Match query is carried out in subscriber information management database according to the user characteristics attribute of the user, determined the multiple
User.
7. user classification method according to claim 1, it is characterised in that the server sends to user terminal and pushed
After message, methods described also includes：
The user terminal generates PUSH message prompt message according to the PUSH message, and shows.
8. user classification method according to claim 7, it is characterised in that methods described also includes：
The user terminal receives the out code to the PUSH message prompt message of user's input, and generation refusal prompting is anti-
Feedforward information, it is sent to the server；The refusal prompting feedback information includes the ID of the user and the push disappears
Service content ID corresponding to recommendation service content in breath.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710772803.2A CN107592346B (en) | 2017-08-31 | 2017-08-31 | User classification method based on user behavior analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710772803.2A CN107592346B (en) | 2017-08-31 | 2017-08-31 | User classification method based on user behavior analysis |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107592346A true CN107592346A (en) | 2018-01-16 |
| CN107592346B CN107592346B (en) | 2020-09-01 |
Family
ID=61051238
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710772803.2A Active CN107592346B (en) | 2017-08-31 | 2017-08-31 | User classification method based on user behavior analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107592346B (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108287909A (en) * | 2018-01-31 | 2018-07-17 | 北京仁和汇智信息技术有限公司 | A kind of paper method for pushing and device |
| CN108897592A (en) * | 2018-06-22 | 2018-11-27 | 珠海市君天电子科技有限公司 | A kind of software methods of exhibiting and relevant device |
| CN109840800A (en) * | 2018-12-14 | 2019-06-04 | 深圳壹账通智能科技有限公司 | A kind of Products Show method, apparatus, storage medium and server based on integral |
| CN109934626A (en) * | 2019-03-05 | 2019-06-25 | 重庆锐云科技有限公司 | A kind of Real Estate Marketing management method and device based on public platform bean vermicelli liveness |
| CN109947821A (en) * | 2019-03-14 | 2019-06-28 | 腾讯科技（深圳）有限公司 | Generation method, display methods, device, equipment and the storage medium of report information |
| CN110058869A (en) * | 2019-03-12 | 2019-07-26 | 中国平安人寿保险股份有限公司 | Mobile application method for pushing, computer readable storage medium and terminal device |
| CN110069619A (en) * | 2019-03-16 | 2019-07-30 | 平安城市建设科技(深圳)有限公司 | Source of houses methods of exhibiting, device, equipment and computer readable storage medium |
| CN110276646A (en) * | 2019-06-25 | 2019-09-24 | 北京字节跳动网络技术有限公司 | For the data processing method of pushed information, device and electronic equipment |
| CN110933152A (en) * | 2019-11-21 | 2020-03-27 | 北京奇艺世纪科技有限公司 | Preheating method, device and system and electronic equipment |
| CN111415191A (en) * | 2020-02-19 | 2020-07-14 | 珠海格力电器股份有限公司 | User classification method and device, electronic equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101359995A (en) * | 2008-09-28 | 2009-02-04 | 腾讯科技（深圳）有限公司 | Method and apparatus providing on-line service |
| CN103870512A (en) * | 2012-12-18 | 2014-06-18 | 腾讯科技（深圳）有限公司 | Method and device for generating user interest label |
| CN104700289A (en) * | 2015-03-17 | 2015-06-10 | 中国联合网络通信集团有限公司 | Advertising method and device |
| US20150262069A1 (en) * | 2014-03-11 | 2015-09-17 | Delvv, Inc. | Automatic topic and interest based content recommendation system for mobile devices |
| CN106506328A (en) * | 2016-10-18 | 2017-03-15 | 江西博瑞彤芸科技有限公司 | The method for pushing of message |
-
2017
- 2017-08-31 CN CN201710772803.2A patent/CN107592346B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101359995A (en) * | 2008-09-28 | 2009-02-04 | 腾讯科技（深圳）有限公司 | Method and apparatus providing on-line service |
| CN103870512A (en) * | 2012-12-18 | 2014-06-18 | 腾讯科技（深圳）有限公司 | Method and device for generating user interest label |
| US20150262069A1 (en) * | 2014-03-11 | 2015-09-17 | Delvv, Inc. | Automatic topic and interest based content recommendation system for mobile devices |
| CN104700289A (en) * | 2015-03-17 | 2015-06-10 | 中国联合网络通信集团有限公司 | Advertising method and device |
| CN106506328A (en) * | 2016-10-18 | 2017-03-15 | 江西博瑞彤芸科技有限公司 | The method for pushing of message |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108287909A (en) * | 2018-01-31 | 2018-07-17 | 北京仁和汇智信息技术有限公司 | A kind of paper method for pushing and device |
| CN108287909B (en) * | 2018-01-31 | 2020-08-04 | 北京仁和汇智信息技术有限公司 | Paper pushing method and device |
| CN108897592A (en) * | 2018-06-22 | 2018-11-27 | 珠海市君天电子科技有限公司 | A kind of software methods of exhibiting and relevant device |
| CN109840800A (en) * | 2018-12-14 | 2019-06-04 | 深圳壹账通智能科技有限公司 | A kind of Products Show method, apparatus, storage medium and server based on integral |
| CN109934626A (en) * | 2019-03-05 | 2019-06-25 | 重庆锐云科技有限公司 | A kind of Real Estate Marketing management method and device based on public platform bean vermicelli liveness |
| CN110058869A (en) * | 2019-03-12 | 2019-07-26 | 中国平安人寿保险股份有限公司 | Mobile application method for pushing, computer readable storage medium and terminal device |
| CN109947821A (en) * | 2019-03-14 | 2019-06-28 | 腾讯科技（深圳）有限公司 | Generation method, display methods, device, equipment and the storage medium of report information |
| CN110069619A (en) * | 2019-03-16 | 2019-07-30 | 平安城市建设科技(深圳)有限公司 | Source of houses methods of exhibiting, device, equipment and computer readable storage medium |
| CN110276646A (en) * | 2019-06-25 | 2019-09-24 | 北京字节跳动网络技术有限公司 | For the data processing method of pushed information, device and electronic equipment |
| CN110933152A (en) * | 2019-11-21 | 2020-03-27 | 北京奇艺世纪科技有限公司 | Preheating method, device and system and electronic equipment |
| CN111415191A (en) * | 2020-02-19 | 2020-07-14 | 珠海格力电器股份有限公司 | User classification method and device, electronic equipment and storage medium |
| CN111415191B (en) * | 2020-02-19 | 2024-02-13 | 珠海格力电器股份有限公司 | User classification method, device, electronic equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107592346B (en) | 2020-09-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107592346A (en) | User classification method based on user behavior analysis | |
| CN107483613A (en) | A kind of information-pushing method | |
| US20230162216A1 (en) | Systems, methods, and articles of manufacture to measure online audiences | |
| US20210227037A1 (en) | Methods and apparatus for adjusting model threshold levels | |
| AU2011374955B2 (en) | Methods and apparatus to analyze and adjust demographic information | |
| KR102347083B1 (en) | Methods and apparatus to estimate demographics of users employing social media | |
| CN109767259A (en) | Based on operation event promotion method, apparatus, equipment and the medium for burying point data | |
| US20170011420A1 (en) | Methods and apparatus to analyze and adjust age demographic information | |
| US20120303552A1 (en) | Techniques to track users and user metrics for a website | |
| CN103544212A (en) | Content recommending method and system | |
| Fiebig et al. | Preferences for new and existing contraceptive products | |
| CA3112126A1 (en) | Methods and apparatus to monitor media presentations | |
| WO2020164269A1 (en) | User-group-based message pushing method and apparatus, and computer device | |
| CN105989074A (en) | Method and device for recommending cold start through mobile equipment information | |
| US20180307733A1 (en) | User characteristic extraction method and apparatus, and storage medium | |
| US9798760B2 (en) | Application retention metrics | |
| Grover et al. | Digital footprints: Predicting personality from temporal patterns of technology use | |
| Rizoiu et al. | Evolution of privacy loss in Wikipedia | |
| CN107749888A (en) | A kind of statistical method of user behavior data | |
| US20200058037A1 (en) | Reporting of media consumption metrics | |
| CN111159541B (en) | Method and device for determining account behavior preference | |
| Díaz Ferreyra et al. | Learning from online regrets: from deleted posts to risk awareness in social network sites | |
| CN106874293B (en) | Data processing method and device | |
| CN109784987A (en) | A kind of picture copyright revenue method and device | |
| US9525687B2 (en) | Template for customer attributes |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20200916 Address after: 100020 Beijing, East Third Ring Road, North Road, a building, floor 1702, room 17, room 26 Patentee after: Beijing Borui Tongyun Technology Co.,Ltd. Address before: 330000 Jiangxi province Nanchang City Airport Economic Zone Confucianism Le Hu Street No. 399 Patentee before: JIANGXI BORUITONGYUN TECHNOLOGY Co.,Ltd. |
|
| TR01 | Transfer of patent right | ||
| CB03 | Change of inventor or designer information |
Inventor after: Shi Lihong Inventor after: You Fenglei Inventor after: Wang Kunpeng Inventor after: Li Jingna Inventor before: Shi Lihong Inventor before: Wang Kunpeng Inventor before: Li Jingna |
|
| CB03 | Change of inventor or designer information |