CN101005490A - Method for providing personalized service facing final user - Google Patents
Method for providing personalized service facing final user Download PDFInfo
- Publication number
- CN101005490A CN101005490A CN 200610001678 CN200610001678A CN101005490A CN 101005490 A CN101005490 A CN 101005490A CN 200610001678 CN200610001678 CN 200610001678 CN 200610001678 A CN200610001678 A CN 200610001678A CN 101005490 A CN101005490 A CN 101005490A
- Authority
- CN
- China
- Prior art keywords
- service
- user
- personalized
- end user
- correlation
- 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
Images
Landscapes
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Information Transfer Between Computers (AREA)
Abstract
The method comprises: capturing the current execution state; at service network condition, according to the service attribute and the service calling history of the user, calculating the service dependence and the user's favorite service so as to dynamically select service resources and recommend the personality service for end user.
Description
Technical field
The present invention relates to technical field of the computer network, particularly relate to a kind of service rendering method of the personalization towards the end user, be applicable to the grid resource manager part.
Background technology
For the user provides a kind of transparent way to use Service Source under the distributional environment is that service grid environment is wished a target reaching.The effect of grid resource manager part is exactly: be used for organizing in the face of physical resource at logical layer.Physical resource is registered by certain mode, increases the information such as attribute, description of resource, has just formed a basic element in the explorer, to basic element do further tissue, classification can form the service level element with higher abstraction level.
At present, we consider that mainly web serves the special physical resource of this class, and the standardization that Web service itself is had makes it progressively become the basic element of structure Distributed Application under the service-oriented computing pattern.Under this background, the more and more enterprises business is packaged into Web service and issue, and this makes that the quantity of Web service sharply increases on the internet.At this moment, a result who adopts the grid resource manager part to form when logical organization is carried out in service to web is exactly: wherein have the service of magnanimity.At this moment, if the user is directly presented in all services, the user will be easy to " losing " wherein, be difficult to find the service that is fit to oneself.
Simultaneously, the service of autonomous management also is among the continuous variation, is embodied in:
The service autonomous adding and withdraw from; The attribute information, incidence relation of service be along with factors vary such as use, for example, portrays its during attribute with the average time of implementation of service time of implementation, and the value of this attribute changes with the execution each time of serving; Correlation between the change of Service Properties value and then the influence service or the like;
If the grid resource manager part can and use preference according to user's customized information, choose suitable Service Source dynamically, form the personalized service space, and push suitable service to the user will be user-friendly, raise the efficiency.This also is the desired purpose that reaches of the present invention.
Summary of the invention
The present invention is intended to the problem set forth in the technical solution background, a kind of service rendering method of personalization has been proposed, this method is a foundation with Service Properties and the existing history of calling to service of user, calculation services correlation and user's use preference, and choose suitable Service Source in view of the above dynamically.With the method is the base configuration grid resource manager, can recommend to satisfy the Service Source of its individual demand to the end user.
To achieve these goals, according to the present invention, proposed a kind of personalized service rendering method towards the end user, described method comprises step: catch current executing state; Under the service grid environment environment, be foundation with Service Properties and user to the history of calling of serving, calculation services correlation and user's use preference; And, dynamically choose Service Source according to current executing state and service correlation that calculates and user's use preference, recommend to satisfy the service of its individual demand to the end user.
Preferably, described service grid environment environment is open, and it is non-definite being embodied in its border, the adding that Service Source can be autonomous or withdraw from, and set of service is dynamic change.
Preferably, described Service Properties comprises input, output and the not function attribute of service.
Preferably, described user is a dynamical evolution to the history of calling of service.
Preferably, described service correlation is to calculate generation according to Service Properties and the existing history of calling to service of user.
Preferably, user's use preference derives from the existing history of calling to service.
Preferably, the described executing state that is meant at current of dynamically choosing is chosen service.
Preferably, described recommendation just be meant under the uncertain situation of user's request can be initiatively present the service of satisfying its individual demand for the user.
Preferably, described current executing state comprises the output parameter of current service of calling and current service of calling.
Preferably, described step of catching current executing state comprises: obtain the descriptor of the service of current execution, comprise the name of service and input, output semanteme (Fig. 1, SP1).
Preferably, described use preference according to current executing state and service correlation that calculates and user, the step of dynamically choosing Service Source comprises: with current executing state is input, use preference to be the condition filter service with service correlation and user, the results set that obtains constitutes the current personalized service space of user.
Preferably, the step of described recommendation service comprises: to the ordering of the service in the personalized service space, select one group of the highest service of rank to present to the user by the service correlation.
Preferably, the calculation procedure of service correlation is as follows: determine the parameter matching relation: at two service parameter set, determine the corresponding relation between the element in two groups of parameters according to the semantic close principle of parameter; The calculating parameter similarity: right to parameter with corresponding relation, calculate similarity; Calculation services correlation: the correlation that reaches the similarity calculation services of respectively organizing corresponding parameter according to the parameter corresponding relation that has obtained.
Preferably, the user uses the calculation procedure of preference as follows: the preliminary treatment of user's use historical record: the existing use of analysis user is historical, and once landing to logging off is a unit, organizes by the call sequence of service; Determine the follow-up relation of forerunner between the service: for any two services that occur in the historical record unit, if service execution before the another one service, and do not have other services between the two, then the two has the follow-up relation of forerunner; The user uses the adjustment of preference: when user's use history increases, calculate each service in all uses, have the possibility of the follow-up relation of forerunner.
Description of drawings
Below in conjunction with the detailed description of preferred embodiment of accompanying drawing to being adopted, above-mentioned purpose of the present invention, advantage and feature will become apparent by reference, wherein:
Fig. 1 is that personalized service of the present invention presents the process schematic diagram;
Fig. 2 is a service relevance algorithms flow chart of the present invention; And
Fig. 3 is that user of the present invention uses the preference algorithm flow chart.
Embodiment
The present invention is foundation with Service Properties and user to the history of calling of serving in present popular service describing mode, adopts the correlation of diverse ways calculation services and user's use preference, and the principle of various computational methods is as follows:
Serve the principle of obtaining of correlation: the process of calling service can be regarded the process that an information is obtained as, and whenever calling the output information set that obtains after the service will become the reference point that next step information is obtained.
The user uses the principle of obtaining of preference: have certain incidence relation between the service of calling in a use, this relation has been portrayed the preference that the user uses service from the angle of user's use habit, and it can be used as a foundation of recommendation service.
Thus,, proposed a kind of service rendering method of the personalization towards the end user, belonged to computer application field according to the present invention.The present invention proposes a personalized service rendering method that is applied in the grid resource manager part, this method is according to user's customized information and use preference thereof, choose suitable Service Source dynamically, form the personalized service space, and push suitable service to the user.The grid resource manager part that with the method is base configuration has following characteristics: take into full account user's customized information and use preference, the service of satisfying its demand is provided to the end user.The present invention is according to relation between the historical calculation services of user's use and user's use preference, and filtration and recommendation service in view of the above, and concrete steps are as follows: historical analysis and the preliminary treatment of the existing use of (1) user; (2) according to using historical calculation services correlation; (3) according to the use preference of using the historical user of calculating; (4) according to the use preference filtering services of serving correlation and user; (5) form user's personalized service space and dynamically adjustment; (6) catch current executing state; (7) recommend suitable service according to current executing state to the user.The present invention can be widely used in resource organizations and the management under the grid environment, pushes the resource that satisfies its demand for the user.
The preferred embodiments of the present invention are described below with reference to the accompanying drawings.
Fig. 1 is that service of the present invention pushes the process schematic diagram
Specifically comprise following three phases:
1. catch current executing state: obtain the descriptor of the service of current execution, comprise the name of service and input, output semanteme;
2. be input with current executing state, use preference to be condition filtering services from the logical services resource with service correlation and user, the results set that obtains constitutes the current personalized service space of user;
3. by the service correlation service in the personalized service space is sorted, select one group of the highest service of rank to present to the user;
Fig. 2 is a service relevance algorithms flow chart of the present invention, and its step is as follows:
SP1. determine the parameter matching relation: at first the semanteme of the output parameter of current service is put into array A by the parameter order, the semanteme of the output parameter of candidate service is put into array B by the parameter order, find out all possible corresponding relation among array A and the B then, step is as follows: (1) takes out the 1st element among the A, therefrom finds the 1st element that can mate; (2) other element among other element and the B among the A is carried out this step by passing the rule mode; (3) repeating step (1) and (2) do not have other elements in array B; (4) repeating step (1), (2), (3) do not have other elements in array A, and all corresponding relations that will obtain are at last put into object array C;
SP2. calculating parameter similarity: each element among the object array C is represented a possible corresponding relation, and the step of calculating parameter similarity is as follows: (1) takes out an element from C; (2) it is right to take out the parameter that comprises in this element; (3) each parameter is concerned the calculating similarity to pressing between the parameter semanteme, if there is not ancestors-descendants's relation between the parameter semanteme, then similarity is 0, otherwise similarity is the inverse of distance between them; (4), and the result is placed on the correspondence position among the array D to the summation of the right similarity of all parameters; (5) repeat above-mentioned steps, in array C, do not have other element;
SP3. calculation services correlation: according to the parameter corresponding relation that has obtained and respectively organize the correlation of the similarity calculation services of corresponding parameter, concrete steps are: the value among the array D is sorted, take out maximum, and the position at maximum place, the element of this position is the elements corresponding corresponding relation among the object array C.
Fig. 3 is that user of the present invention uses the preference algorithm flow chart, and concrete steps are as follows:
SP1. the user uses historical analysis and preliminary treatment: the use history of analysis user, and once landing to logging off is a unit, the order of the service of calling by the user is organized, and puts into array S;
SP2. determine the follow-up relation of forerunner between the service: for any two services that occur in the historical record unit, if a service was carried out before the another one service, and there are not other services between the two, then the two has the follow-up relation of forerunner, the step of calculating is as follows: (1) takes out two service S[i from S] and S[j] service of formation is right, wherein i is not equal to j; (2) increase an element in Hash table D, its strong value is { S[i], S[j] }, and value is 0; (3) if S[i], S[j] there is a follow-up relation of forerunner, then strong value among the Hash table D is made as 1 for the value of the element of { S[i], S[j] }; (4) step of repetition front is up to not having new service to producing;
SP3. the user uses the adjustment of preference: when user's use history increases, calculate each service in all uses, have the possibility of the follow-up relation of forerunner.Concrete steps are as follows: when new implementation produces, at first calculate service relation in this process by SP1 and SP2, then each element in the Hash table and existing use preference are compared, if this element does not exist, then this element is added and use favorites list, if this element exists, then adjust the size of its value, the algorithm of adjusting is: with original on duty with original number of times, add and this time calculate the value that generates, then divided by the total degree that adds after 1.
As mentioned above, the present invention is basic management and organization service resource in present popular service describing mode, its essence is, the preference of using with correlation between the service and user is foundation, describing mode with current executing state and service is a condition, choose suitable service dynamically, and be pushed to the user.
The obtain manner of the correlation between the described service is as follows: with the output parameter of current service as the reference point, can link algorithm with the output parameter of candidate service by service and calculate, the value that obtains is exactly the size of the correlation of current service and candidate service.
Described service can link the semanteme of algorithm according to service parameter, finds out the corresponding relation between the parameter with maximum similarity, and calculates in the correlation between the service under this corresponding relation.
It is use according to the user that described user uses preference, by using the preference algorithm computation to obtain;
Needing in the described use preference algorithm that the user is analyzed, calculated to user's use history uses preference and preference is adjusted when the use dynamical evolution;
It is a unit that the analysis of described use history to the user is once landed to logging off with the user, and service is wherein organized by the order that calls;
Described service-user uses the calculating of preference to finish with adjusting employing increment adjustment algorithm, calculates each service in all uses, has the possibility of the follow-up relation of forerunner;
Described choice of dynamical service and propelling movement are according to current executing state and the service degree of correlation and user's preferences, generate the personalized service space, and by the service correlation service are wherein sorted, and the service that priority is the highest is pushed to the user.
Compared with prior art, effect of the present invention is embodied in:
1, just can be initiatively under the uncertain situation of user's request present the service of satisfying its individual demand for the user.
2, with user personalized information and use preference to be foundation, in the uncertain set of service in border, choose suitable Service Source dynamically, form user's personalized service space.
3, according to current user mode, the only service of real-time propelling movement.
4, can do real-time adjustment to correlation between the service and user's use preference.
The present invention can be widely used in the computer network environment service-oriented computing, towards aspects such as end user programming, heuristic programmings, for the user provides occupation mode more easily.
Although below show the present invention in conjunction with the preferred embodiments of the present invention, one skilled in the art will appreciate that under the situation that does not break away from the spirit and scope of the present invention, can carry out various modifications, replacement and change to the present invention.Therefore, the present invention should not limited by the foregoing description, and should be limited by claims and equivalent thereof.
Claims (14)
1. personalized service rendering method towards the end user, described method comprises step:
Catch current executing state;
Under the service grid environment environment, be foundation with Service Properties and the existing history of calling of user to service, calculation services correlation and user's use preference; And
According to current executing state and service correlation that calculates and user's use preference, dynamically choose Service Source, recommend to satisfy the service of its individual demand to the end user.
2. the personalized service rendering method towards the end user according to claim 1, it is characterized in that described service grid environment environment is open, it is non-definite being embodied in its border, Service Source can be autonomous adding or withdraw from, set of service is dynamic change.
3. the personalized service rendering method towards the end user according to claim 1 is characterized in that, described Service Properties comprises input, output and the not function attribute of service.
4. the personalized service rendering method towards the end user according to claim 1 is characterized in that, the existing history of calling to service of described user is dynamical evolution.
5. the personalized service rendering method towards the end user according to claim 1 is characterized in that, described service correlation is according to Service Properties and the user is existing generates historical calculating of calling of service.
6. the personalized service rendering method towards the end user according to claim 1 is characterized in that, user's use preference derives from the existing history of calling to service of user, generates by calculating.
7. the personalized service rendering method towards the end user according to claim 1 is characterized in that, the described executing state that is meant at current of dynamically choosing is chosen service.
8. the personalized service rendering method towards the end user according to claim 1 is characterized in that, described recommendation just be meant under the uncertain situation of user's request can be initiatively present the service of satisfying its individual demand for the user.
9. according to claim 1 or 7 described personalized service rendering methods, it is characterized in that described current executing state comprises the output parameter of current service of calling and current service of calling towards the end user.
10. the personalized service rendering method towards the end user according to claim 1, it is characterized in that, described step of catching current executing state comprises: obtain the descriptor of the service of current execution, comprise the name of service and input, output semanteme (Fig. 1, SP1).
11. the personalized service rendering method towards the end user according to claim 1, it is characterized in that, described use preference according to current executing state and service correlation that calculates and user, the step of dynamically choosing Service Source comprises: with current executing state is input, use preference to be the condition filter service with service correlation and user, the results set that obtains constitute the current personalized service space of user (Fig. 1, SP2).
12, according to claim 1 or 8 described personalized service rendering methods towards the end user, it is characterized in that, the step of described recommendation service comprises: by the service correlation to the service in personalized service space ordering, select one group of the highest service of rank present to the user (Fig. 1, SP3).
13. towards end user's personalized service rendering method, it is characterized in that according to claim 1 or 5 the calculation procedure of service correlation is as follows:
Determine parameter matching relation: at two service parameters set, according to the semantic close principle of parameter determine the corresponding relation between the element in two groups of parameters (Fig. 2, SP1);
The calculating parameter similarity: right to parameter with corresponding relation, calculate similarity (Fig. 2, SP2);
The calculation services correlation: according to the parameter corresponding relation that has obtained and respectively organize corresponding parameter the similarity calculation services correlation (Fig. 2, SP3).
14., it is characterized in that the user uses the calculation procedure of preference as follows according to claim 1 or 6 described personalized service rendering methods towards the end user:
The preliminary treatment of user's use historical record: the use history of analysis user, once landing to logging off is a unit, the order of the service of calling by the user organize (Fig. 3, SP1);
Determine the follow-up relation of forerunner between the service: for any two services that occur in the historical record unit, if a service was carried out before the another one service, and do not have other services between the two, then the two have the follow-up relation of forerunner (Fig. 3, SP2);
The user uses the adjustment of preference: when user's use is historical when increasing, calculate each service in all uses, have the follow-up relation of forerunner possibility (Fig. 3, SP3).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2006100016787A CN101005490B (en) | 2006-01-20 | 2006-01-20 | Method for providing personalized service facing final user |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2006100016787A CN101005490B (en) | 2006-01-20 | 2006-01-20 | Method for providing personalized service facing final user |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN101005490A true CN101005490A (en) | 2007-07-25 |
| CN101005490B CN101005490B (en) | 2010-06-02 |
Family
ID=38704354
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2006100016787A Expired - Fee Related CN101005490B (en) | 2006-01-20 | 2006-01-20 | Method for providing personalized service facing final user |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN101005490B (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102208080A (en) * | 2010-03-29 | 2011-10-05 | 深圳市腾讯计算机系统有限公司 | Method and system for optimizing service resources |
| CN102300240A (en) * | 2011-08-26 | 2011-12-28 | 北京邮电大学 | Output performance parameter-based method for evaluating similarity of two systems |
| CN102571942A (en) * | 2011-12-27 | 2012-07-11 | 上海聚力传媒技术有限公司 | Method and device for pushing resource information and providing service in P2P (peer-to-peer) network |
| CN101414296B (en) * | 2007-10-15 | 2012-07-25 | 日电（中国）有限公司 | Self-adapting service recommendation equipment and method, self-adapting service recommendation system and method |
| CN102880640A (en) * | 2012-08-20 | 2013-01-16 | 浙江大学 | Network modeling-based service recommending method |
| WO2016155493A1 (en) * | 2015-04-01 | 2016-10-06 | 阿里巴巴集团控股有限公司 | Data processing method and apparatus |
| CN109344314A (en) * | 2018-08-20 | 2019-02-15 | 腾讯科技（深圳）有限公司 | A kind of data processing method, device and server |
| CN109389409A (en) * | 2018-09-17 | 2019-02-26 | 内蒙古易商信息科技有限公司 | Method for processing business and system |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| NO20024840L (en) * | 2002-05-31 | 2003-12-01 | Telenor Asa | Method and apparatus in a telecommunications network |
| CN1198224C (en) * | 2003-06-24 | 2005-04-20 | 南京大学 | Adaptive internet catalogue web page recommending method |
-
2006
- 2006-01-20 CN CN2006100016787A patent/CN101005490B/en not_active Expired - Fee Related
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101414296B (en) * | 2007-10-15 | 2012-07-25 | 日电（中国）有限公司 | Self-adapting service recommendation equipment and method, self-adapting service recommendation system and method |
| CN102208080A (en) * | 2010-03-29 | 2011-10-05 | 深圳市腾讯计算机系统有限公司 | Method and system for optimizing service resources |
| CN102300240A (en) * | 2011-08-26 | 2011-12-28 | 北京邮电大学 | Output performance parameter-based method for evaluating similarity of two systems |
| CN102571942A (en) * | 2011-12-27 | 2012-07-11 | 上海聚力传媒技术有限公司 | Method and device for pushing resource information and providing service in P2P (peer-to-peer) network |
| CN102880640A (en) * | 2012-08-20 | 2013-01-16 | 浙江大学 | Network modeling-based service recommending method |
| KR20170134601A (en) * | 2015-04-01 | 2017-12-06 | 알리바바 그룹 홀딩 리미티드 | Data processing method and apparatus |
| WO2016155493A1 (en) * | 2015-04-01 | 2016-10-06 | 阿里巴巴集团控股有限公司 | Data processing method and apparatus |
| US10657143B2 (en) | 2015-04-01 | 2020-05-19 | Alibaba Group Holding Limited | Recommending a data handling method for a pending data handling process |
| KR102125119B1 (en) | 2015-04-01 | 2020-06-22 | 알리바바 그룹 홀딩 리미티드 | Data handling method and device |
| CN109344314A (en) * | 2018-08-20 | 2019-02-15 | 腾讯科技（深圳）有限公司 | A kind of data processing method, device and server |
| CN109344314B (en) * | 2018-08-20 | 2021-11-16 | 腾讯科技（深圳）有限公司 | Data processing method and device and server |
| CN109389409A (en) * | 2018-09-17 | 2019-02-26 | 内蒙古易商信息科技有限公司 | Method for processing business and system |
| CN109389409B (en) * | 2018-09-17 | 2021-10-15 | 内蒙古易商信息科技有限公司 | Service processing method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN101005490B (en) | 2010-06-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101005490B (en) | Method for providing personalized service facing final user | |
| Krishnapuram et al. | Low-complexity fuzzy relational clustering algorithms for web mining | |
| Han et al. | An ontology-enhanced cloud service discovery system | |
| Shin et al. | Context-aware recommendation by aggregating user context | |
| JP5615932B2 (en) | Search method and system | |
| US20110055202A1 (en) | Predictive data caching | |
| CN103593434A (en) | Application recommendation method and device and server equipment | |
| Afify et al. | A semantic-based software-as-a-service (saas) discovery and selection system | |
| CN103699534B (en) | The display methods and device of data object in system directory | |
| Vadeyar et al. | Farthest first clustering in links reorganization | |
| CN105243080B (en) | The adaptive adaption system of resource perception based on ESB frameworks | |
| Ansari et al. | Mountain density-based fuzzy approach for discovering web usage clusters from web log data | |
| Mittal et al. | A hybrid approach of personalized web information retrieval | |
| CN110990706B (en) | Corpus recommendation method and device | |
| Brunner et al. | Network-aware summarisation for resource discovery in P2P-content networks | |
| Gao et al. | A semantic approach to recommendation system based on user ontology and spreading activation model | |
| CN107077485A (en) | System and method for handling figure | |
| Samir et al. | Context-based web service discovery framework with qos considerations | |
| Horincar et al. | Online refresh strategies for content based feed aggregation | |
| Chen et al. | COWES: Web user clustering based on evolutionary web sessions | |
| KR100779086B1 (en) | Method for service modeling using Bayesian network and status information in distributed environment and system thereof | |
| CN103324640A (en) | Method and device for determining search result file, as well as equipment | |
| US20170041277A1 (en) | Method for distributing a message | |
| EP2813046A1 (en) | System and method for determining a reputation mechanism | |
| Zhang et al. | Heterogeneous information assisted bandit learning: Theory and application |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| ASS | Succession or assignment of patent right |
Owner name: BEIJING FENGHUICAIZHI INTELLECTUAL PROPERTY CONSUL Free format text: FORMER OWNER: INSTITUTE OF COMPUTING TECHNOLOGY, CHINESE ACADEMY OF SCIENCES Effective date: 20121105 |
|
| C41 | Transfer of patent application or patent right or utility model | ||
| COR | Change of bibliographic data |
Free format text: CORRECT: ADDRESS; FROM: 100080 HAIDIAN, BEIJING TO: 100193 HAIDIAN, BEIJING |
|
| TR01 | Transfer of patent right |
Effective date of registration: 20121105 Address after: 100193 Beijing City, Haidian District Zhongguancun Software Park incubator room 2A2330 Patentee after: Beijing Feng Hui Choi intellectual property consultant limited liability company Address before: 100080 Haidian District, Zhongguancun Academy of Sciences, South Road, No. 6, No. Patentee before: Institute of Computing Technology, Chinese Academy of Sciences |
|
| ASS | Succession or assignment of patent right |
Owner name: BEIJING ZHONGCHUANG XINCE SCIENCE + TECHNOLOGY CO. Free format text: FORMER OWNER: BEIJING FORTUNEHIGH INTELLECTUAL LAW FIRM Effective date: 20121225 |
|
| C41 | Transfer of patent application or patent right or utility model | ||
| COR | Change of bibliographic data |
Free format text: CORRECT: ADDRESS; FROM: 100193 HAIDIAN, BEIJING TO: 100081 HAIDIAN, BEIJING |
|
| TR01 | Transfer of patent right |
Effective date of registration: 20121225 Address after: 100081 Beijing, Zhongguancun, South Street, Beijing, No. 18, International Building, block C, layer 12, layer Patentee after: Beijing Zhongchuang Xince Science & Technology Co., Ltd. Address before: 100193 Beijing City, Haidian District Zhongguancun Software Park incubator room 2A2330 Patentee before: Beijing Feng Hui Choi intellectual property consultant limited liability company |
|
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20100602 Termination date: 20170120 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |