CN105653645B - Network information attention degree evaluation method and device - Google Patents
Network information attention degree evaluation method and device Download PDFInfo
- Publication number
- CN105653645B CN105653645B CN201511001437.8A CN201511001437A CN105653645B CN 105653645 B CN105653645 B CN 105653645B CN 201511001437 A CN201511001437 A CN 201511001437A CN 105653645 B CN105653645 B CN 105653645B
- Authority
- CN
- China
- Prior art keywords
- user set
- target
- user
- network information
- sample
- 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.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/955—Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the invention provides a method and a device for evaluating network information attention. The method comprises the following steps: counting a first user set of exposure target network information; counting a second user set exposing the target network information in the sample base; calculating a first proportion of target people in the second user set, and calculating a second proportion of target people in the third user set; and calculating the probability of the target network information being exposed by the target crowd according to the first proportion and the second proportion. According to the embodiment of the invention, the first proportion of the target population in the second user set and the second proportion of the target population in the user set left by the first user set except the second user set are calculated by counting the first user set exposing the target network information and the second user set exposing the target network information in the sample library, and the probability of the target network information being exposed by the target population is calculated according to the first proportion and the second proportion, so that the network information attention degree evaluation precision is improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of computers, in particular to a method and a device for evaluating network information attention.
Background
With the development of computer technology, a user can browse a webpage or use an APP to expose and view network information through an intelligent television of a television, and equipment identification information is used as data stored on a local terminal of the user to record the behavior of browsing certain network information by the user.
The number of times that a certain network information is browsed every day may be thousands of, and thousands of pieces of equipment identification information are correspondingly generated, because the workload of calculating the proportion of a target group from among thousands of pieces of equipment identification information is large, a sample library is established in the prior art, and comprises equipment identification information corresponding to a plurality of sample users respectively, whether the sample user browses the target network information or not is judged according to the network information browsed by the sample user recorded by each piece of equipment identification information in the sample library, the number of the sample users browsing the target network information in the sample library is counted, and the proportion of the target group in the sample users browsing the target network information is taken as the probability that the target network information is browsed by the target group.
Because the number of the sample users in the sample library is less, the number of the sample users browsing the target network information in the sample library is less, and the probability that the target network information is browsed by the target crowd is evaluated by using less sample users, the probability that the evaluated target network information is browsed by the target crowd is not accurate, and the evaluation precision of the network information attention is reduced.
Disclosure of Invention
The embodiment of the invention provides a method and a device for evaluating network information attention degree, which are used for improving the evaluation precision of the network information attention degree.
One aspect of the embodiments of the present invention is to provide a method for evaluating network information attention, including:
counting a first user set of exposure target network information;
counting a second user set in a sample library, wherein the second user set exposes the target network information, and the sample library comprises a plurality of sample users;
calculating a first proportion of target people in the second user set, and calculating a second proportion of target people in a third user set, wherein the third user set is a user set left by the first user set except the second user set;
and calculating the probability of the target network information being exposed by the target crowd according to the first proportion and the second proportion.
Another aspect of the embodiments of the present invention is to provide a network information attention degree evaluation apparatus, including:
the statistical module is used for counting a first user set of exposure target network information; counting a second user set in a sample library, wherein the second user set exposes the target network information, and the sample library comprises a plurality of sample users;
the calculating module is used for calculating a first proportion of the target population in the second user set and calculating a second proportion of the target population in a third user set, wherein the third user set is a user set left by the first user set except the second user set; and calculating the probability of the target network information being exposed by the target crowd according to the first proportion and the second proportion.
According to the method and the device for evaluating the network information attention, the first proportion of the target population in the second user set and the second proportion of the target population in the user set except the second user set in the first user set are calculated by counting the first user set exposing the target network information and the second user set exposing the target network information in the sample base, and the probability of the target network information being exposed by the target population is calculated according to the first proportion and the second proportion.
Drawings
Fig. 1 is a flowchart of a network information attention degree evaluation method according to an embodiment of the present invention;
fig. 2 is a structural diagram of a network information attention degree evaluation apparatus according to an embodiment of the present invention.
Detailed Description
Fig. 1 is a flowchart of a method for evaluating network information attention according to an embodiment of the present invention. The embodiment of the invention provides a network information attention degree evaluation method aiming at the problems that the number of sample users in a sample library is small, the number of sample users browsing target network information in the sample library is small, the probability of browsing the target network information by a target crowd is evaluated by using fewer sample users, the evaluated probability of browsing the target network information by the target crowd is not accurate, the network information attention degree evaluation accuracy is reduced, and the method specifically comprises the following steps:
s101, counting a first user set of exposure target network information;
in the embodiment of the invention, a user browses a webpage or uses an APP (application) through the smart television and exposes the interested network information, the number of times of exposure of target network information and the user exposing the target network information can be counted through equipment identification information by the user through the network information of each exposure of the smart television, and specifically, all users exposing the target network information are used as a first user set.
Step S102, counting a second user set exposing the target network information in a sample library, wherein the sample library comprises a plurality of sample users;
the embodiment of the invention also establishes a sample library which comprises a plurality of sample users, wherein the sample users can be randomly selected users, the target network information is not always exposed, whether the sample users expose the target network information or not is judged according to the equipment identification information corresponding to the sample users, and the sample users which expose the target network information in the sample library form a second user set.
Step S103, calculating a first proportion of target people in the second user set, and calculating a second proportion of target people in a third user set, wherein the third user set is a user set left by the first user set except the second user set;
the target population may be specifically a woman of a certain age, or a user population conforming to other characteristics, preferably, the target population selected in the embodiment of the present invention is a woman of 20 to 30 years old, and the first proportion of the target population in the second user set is calculated, that is, the proportion of the women of 20 to 30 years old in the sample users who expose the target network information in the sample library is calculated; and calculating a second proportion of target people in the third user set, namely calculating the proportion of women aged 20 to 30 years in the users exposed with the target network information except the hit sample user in the sample library, wherein the hit sample user in the sample library is the sample user exposed with the target network information in the sample library.
And step S104, calculating the probability of the target network information being exposed by the target crowd according to the first proportion and the second proportion.
The calculating the probability of the target network information being exposed by the target crowd according to the first proportion and the second proportion comprises: and calculating a weighted sum of the first proportion and the second proportion, and taking the weighted sum as the probability that the target network information is exposed by the target crowd.
For example, the first proportion of the sample users exposed to the target network information in the sample library occupied women aged 20 to 30 is 40%, the second proportion of the users exposed to the target network information except for the hit sample users in the sample library occupied women aged 20 to 30 is 50%, and the weighted sum of the first proportion of 40% and the second proportion of 50% is calculated as 40% 0.6+ 50% 0.4 to 44% according to preset weights such as 0.6 and 0.4, and the weighted sum is used as the probability that the target network information is exposed to the target population, that is, the probability that the target network information is exposed to women aged 20 to 30 is 44%.
According to the embodiment of the invention, the first proportion of the target population in the second user set and the second proportion of the target population in the user set except the second user set in the first user set are calculated by counting the first user set exposing the target network information and the second user set exposing the target network information in the sample library, and the probability of the target network information being exposed by the target population is calculated according to the first proportion and the second proportion.
On the basis of the above embodiment, the first user set includes first device identification information corresponding to a user who has been exposed to the target network information, the first device identification information includes user behavior information, the sample library includes second device identification information corresponding to each sample user, and the second device identification information includes sample user behavior information and sample user basic information.
The calculating a second proportion of the target population in the third user set comprises: judging whether the user behavior information corresponding to each first device identification information in the third user set is the same as the sample user behavior information corresponding to any second device identification information in the sample library, and if so, taking the sample user basic information corresponding to the second device identification information as the user basic information corresponding to the first device identification information; and counting a second proportion of the target population in the third user set according to the user basic information corresponding to the first device identification information in the third user set.
The first user set comprises first equipment identification information corresponding to users who have exposed the target network information, the third user set is a user set which is left by the first user set except the second user set, the third user set comprises first equipment identification information corresponding to users who have exposed the target network information but do not belong to the sample library, the first equipment identification information comprises user behavior information, and the user behavior information can be specifically entertainment information, news information, series videos, movie videos and the like of users who have exposed the target network information. The sample library includes second device identification information corresponding to each sample user, where the second device identification information includes sample user behavior information and sample user basic information, for example, the second device identification information corresponding to a sample user in the sample library includes not only a record of the sample user exposing certain network information, but also basic information of the sample user, such as age and gender.
Judging whether the user behavior information corresponding to each first device identification information in the third user set is the same as the sample user behavior information corresponding to any second device identification information in the sample library, if so, for example, the user behavior information corresponding to the first device identification information corresponding to a certain user in the third user set is an exposure drama video and an online shopping of korean brand cosmetics, the behavior information of a certain sample user in the sample library is also an exposure drama video and an online shopping of korean brand cosmetics, and meanwhile, the sample library records that the sample user is a 25-year-old woman, taking the 25-year-old woman as the basic information of the three-way user behavior information of the third user set, namely, the exposure drama video and the online shopping of korean brand cosmetics, and according to the method, the basic information of users having the same sample user behavior information stored in the sample library in the third user set can be counted, in the embodiment of the present invention, since the number of the first device identification information included in the third user set is much larger than the number of the second device identification information in the sample library, the third user set can estimate that the number of the first device identification information of the user basic information is smaller than the total number of the first device identification information in the third user set, and calculate the second proportion of the target population in the third user set according to the estimated user basic information.
After the calculating the probability of the target network information being exposed by the target crowd according to the first proportion and the second proportion, the method further includes:
calculating an iGRP value and a Reach value according to the probability, wherein,
PV1 represents the total number of times the target network information is exposed, PV2 represents the number of users who exposed the target network information, TA% represents the probability, W represents the stable rate of the target network information existing in the network, and T represents the total number of the target population in the sample base.
In the embodiment of the invention, after the probability TA% of the target network information exposed by the target crowd is calculated according to the first proportion and the second proportion, the TA% is respectively substituted into the formula
And
and calculating an iGRP value and a Reach value, and measuring the attention of the network information through three values of TA%, iGRP and Reach.
According to the embodiment of the invention, the weighted sum of the first proportion and the second proportion is calculated, and the weighted sum is used as the probability of exposure of the target network information by the target crowd, so that the evaluation precision of the probability of exposure of the target network information by the target crowd is improved, and the evaluation precision of the network information attention is further improved.
Fig. 2 is a structural diagram of a network information attention degree evaluation apparatus according to an embodiment of the present invention. The network information attention degree evaluation device provided by the embodiment of the present invention may execute the processing flow provided by the network information attention degree evaluation method embodiment, as shown in fig. 2, the network information attention degree evaluation device 20 includes a statistics module 21 and a calculation module 22, where the statistics module 21 is configured to count a first user set of exposure target network information; counting a second user set in a sample library, wherein the second user set exposes the target network information, and the sample library comprises a plurality of sample users; the calculating module 22 is configured to calculate a first proportion of the target people in the second user set, and calculate a second proportion of the target people in a third user set, where the third user set is a user set remaining from the first user set except the second user set; and calculating the probability of the target network information being exposed by the target crowd according to the first proportion and the second proportion.
According to the embodiment of the invention, the first proportion of the target population in the second user set and the second proportion of the target population in the user set except the second user set in the first user set are calculated by counting the first user set exposing the target network information and the second user set exposing the target network information in the sample library, and the probability of the target network information being exposed by the target population is calculated according to the first proportion and the second proportion.
On the basis of the above embodiment, the first user set includes first device identification information corresponding to a user who has been exposed to the target network information, the first device identification information includes user behavior information, the sample library includes second device identification information corresponding to each sample user, and the second device identification information includes sample user behavior information and sample user basic information.
The calculation module 22 is specifically configured to determine whether user behavior information corresponding to each piece of first device identification information in the third user set is the same as sample user behavior information corresponding to any one piece of second device identification information in the sample library, and if so, take sample user basic information corresponding to the second device identification information as user basic information corresponding to the first device identification information; and counting a second proportion of the target population in the third user set according to the user basic information corresponding to the first device identification information in the third user set.
The calculating module 22 is specifically configured to calculate a weighted sum of the first proportion and the second proportion, and use the weighted sum as a probability that the target network information is exposed by the target people.
The calculation module 22 is further configured to calculate iGRP values and Reach values according to the probabilities, wherein,
PV1 represents the total number of times the target network information is exposed, PV2 represents the number of users who exposed the target network information, TA% represents the probability, W represents the stable rate of the target network information existing in the network, and T represents the total number of the target population in the sample base.
The network information attention degree evaluation apparatus provided in the embodiment of the present invention may be specifically configured to execute the method embodiment provided in fig. 1, and specific functions are not described herein again.
According to the embodiment of the invention, the weighted sum of the first proportion and the second proportion is calculated, and the weighted sum is used as the probability of exposure of the target network information by the target crowd, so that the evaluation precision of the probability of exposure of the target network information by the target crowd is improved, and the evaluation precision of the network information attention is further improved.
In summary, in the embodiments of the present invention, a first user set exposing target network information and a second user set exposing the target network information in a sample library are counted, a first ratio of target groups in the second user set and a second ratio of target groups in the first user set except the second user set are calculated, and a probability that the target network information is exposed by the target group is calculated according to the first ratio and the second ratio, so that compared with a case where fewer sample users evaluate a probability that the target network information is browsed by the target group, accuracy of evaluating a network information attention is improved; by calculating the weighted sum of the first proportion and the second proportion, the weighted sum is used as the probability of exposure of the target network information by the target crowd, so that the evaluation precision of the probability of exposure of the target network information by the target crowd is improved, and the evaluation precision of the network information attention is further improved.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute some steps of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
It is obvious to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be performed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to perform all or part of the above described functions. For the specific working process of the device described above, reference may be made to the corresponding process in the foregoing method embodiment, which is not described herein again.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (6)
1. A network information attention degree evaluation method is characterized by comprising the following steps:
counting a first user set of exposure target network information;
counting a second user set in a sample library, wherein the second user set exposes the target network information, and the sample library comprises a plurality of sample users;
calculating a first proportion of target people in the second user set, and calculating a second proportion of target people in a third user set, wherein the third user set is a user set left by the first user set except the second user set;
calculating the probability of the target network information being exposed by the target crowd according to the first proportion and the second proportion;
the first user set comprises first device identification information corresponding to users exposed by the target network information, the first device identification information comprises user behavior information, the sample library comprises second device identification information corresponding to each sample user, and the second device identification information comprises sample user behavior information and sample user basic information;
the calculating a second proportion of the target population in the third user set comprises:
judging whether the user behavior information corresponding to each first device identification information in the third user set is the same as the sample user behavior information corresponding to any second device identification information in the sample library, and if so, taking the sample user basic information corresponding to the second device identification information as the user basic information corresponding to the first device identification information;
and counting a second proportion of the target population in the third user set according to the user basic information corresponding to the first device identification information in the third user set.
2. The method of claim 1, wherein said calculating the probability of the target network information being exposed to the target population based on the first ratio and the second ratio comprises:
and calculating a weighted sum of the first proportion and the second proportion, and taking the weighted sum as the probability that the target network information is exposed by the target crowd.
3. The method according to any one of claims 1-2, wherein after calculating the probability of the target network information being exposed to the target population according to the first ratio and the second ratio, further comprising:
calculating an iGRP value and a Reach value according to the probability, wherein,
PV1 represents the total number of times the target network information is exposed, PV2 represents the number of users who exposed the target network information, TA% represents the probability, W represents the stable rate of the target network information existing in the network, and T represents the total number of the target population in the sample base.
4. A network information attention degree evaluation device, comprising:
the statistical module is used for counting a first user set of exposure target network information; counting a second user set in a sample library, wherein the second user set exposes the target network information, and the sample library comprises a plurality of sample users;
the calculating module is used for calculating a first proportion of the target population in the second user set and calculating a second proportion of the target population in a third user set, wherein the third user set is a user set left by the first user set except the second user set; calculating the probability of the target network information being exposed by the target crowd according to the first proportion and the second proportion;
the first user set comprises first device identification information corresponding to users exposed by the target network information, the first device identification information comprises user behavior information, the sample library comprises second device identification information corresponding to each sample user, and the second device identification information comprises sample user behavior information and sample user basic information;
the calculation module is specifically configured to determine whether user behavior information corresponding to each piece of first device identification information in the third user set is the same as sample user behavior information corresponding to any one piece of second device identification information in the sample library, and if so, take sample user basic information corresponding to the second device identification information as user basic information corresponding to the first device identification information; and counting a second proportion of the target population in the third user set according to the user basic information corresponding to the first device identification information in the third user set.
5. The device according to claim 4, wherein the calculating module is specifically configured to calculate a weighted sum of the first ratio and the second ratio, and use the weighted sum as the probability that the target network information is exposed to the target crowd.
6. The apparatus according to any one of claims 4-5, wherein the calculating module is further configured to calculate an iGRP value and a Reach value according to the probability, wherein,
PV1 represents the total number of times the target network information is exposed, PV2 represents the number of users who exposed the target network information, TA% represents the probability, W represents the stable rate of the target network information existing in the network, and T represents the total number of the target population in the sample base.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201511001437.8A CN105653645B (en) | 2015-12-28 | 2015-12-28 | Network information attention degree evaluation method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201511001437.8A CN105653645B (en) | 2015-12-28 | 2015-12-28 | Network information attention degree evaluation method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105653645A CN105653645A (en) | 2016-06-08 |
| CN105653645B true CN105653645B (en) | 2020-05-12 |
Family
ID=56478104
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201511001437.8A Active CN105653645B (en) | 2015-12-28 | 2015-12-28 | Network information attention degree evaluation method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105653645B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106227743B (en) * | 2016-07-12 | 2019-09-24 | 精硕科技（北京）股份有限公司 | Advertisement target group touching reaches ratio estimation method and device |
| CN106934064A (en) * | 2017-03-30 | 2017-07-07 | 常州大学怀德学院 | Network information hotspot prediction system and method based on ELM |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102750320A (en) * | 2012-05-18 | 2012-10-24 | 合一网络技术(北京)有限公司 | Method, device and system for calculating network video real-time attention |
| CN103064866A (en) * | 2011-10-24 | 2013-04-24 | 北京千橡网景科技发展有限公司 | Method and equipment for confirming attention degree of content in Internet |
| CN104991935A (en) * | 2015-07-06 | 2015-10-21 | 无锡天脉聚源传媒科技有限公司 | Website attention processing method and apparatus |
-
2015
- 2015-12-28 CN CN201511001437.8A patent/CN105653645B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103064866A (en) * | 2011-10-24 | 2013-04-24 | 北京千橡网景科技发展有限公司 | Method and equipment for confirming attention degree of content in Internet |
| CN102750320A (en) * | 2012-05-18 | 2012-10-24 | 合一网络技术(北京)有限公司 | Method, device and system for calculating network video real-time attention |
| CN104991935A (en) * | 2015-07-06 | 2015-10-21 | 无锡天脉聚源传媒科技有限公司 | Website attention processing method and apparatus |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105653645A (en) | 2016-06-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106940705B (en) | Method and equipment for constructing user portrait | |
| CN107832437B (en) | Audio/video pushing method, device, equipment and storage medium | |
| US10277480B2 (en) | Method, apparatus, and system for determining a location corresponding to an IP address | |
| CN109145210B (en) | Information recommendation method and device and storage medium | |
| CN108170692B (en) | Hotspot event information processing method and device | |
| CN107222566A (en) | Information-pushing method, device and server | |
| CN106649681B (en) | Data processing method, device and equipment | |
| TW202007178A (en) | Method, device, apparatus, and storage medium of generating features of user | |
| US20120303552A1 (en) | Techniques to track users and user metrics for a website | |
| CN109753601B (en) | Method and device for determining click rate of recommended information and electronic equipment | |
| CN109309596B (en) | Pressure testing method and device and server | |
| JP2018516421A (en) | Network access operation identification method, server, and storage medium | |
| CN110223186A (en) | User's similarity determines method and information recommendation method | |
| CN113076416A (en) | Information heat evaluation method and device and electronic equipment | |
| CN115705583A (en) | Multi-target prediction method, device, equipment and storage medium | |
| CN105956061B (en) | Method and device for determining similarity between users | |
| CN105653645B (en) | Network information attention degree evaluation method and device | |
| CN112561636A (en) | Recommendation method, recommendation device, terminal equipment and medium | |
| CN116186119A (en) | User behavior analysis method, device, equipment and storage medium | |
| CN110737691B (en) | Method and apparatus for processing access behavior data | |
| CN103810239B (en) | The statistical method and device of conversion data | |
| US20160078032A1 (en) | Method of on-line media scoring | |
| CN110020166A (en) | A kind of data analysing method and relevant device | |
| US10482105B1 (en) | External verification of content popularity | |
| CN107220269B (en) | Personalized recommendation method for geographic position sensitive app |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information |
Address after: 100041 Beijing, Shijingshan District Xing Xing street, building 30, room 3, building 9, room 9014 Applicant after: Jing Shuo Technology (Beijing) Limited by Share Ltd Address before: 100010 Beijing city Dongcheng District bamboo rod alley No. 1 9 1007 Applicant before: JINGSHUO CENTURY TECHNOLOGY (BEIJING) CO., LTD. |
|
| CB02 | Change of applicant information | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20201230 Address after: 136a, 1st floor, D-1 building, Dongsheng Science Park, 66 xixiaokou Road, Haidian District, Beijing 100080 (Dongsheng area) Patentee after: Enyike (Beijing) Data Technology Co.,Ltd. Address before: Room 9014, 9 / F, building 3, yard 30, Shixing street, Shijingshan District, Beijing 100041 Patentee before: ADMASTER TECHNOLOGY (BEIJING) Co.,Ltd. |
|
| TR01 | Transfer of patent right |