DETAILED ACTION
Notice of Pre-AIA or AIA Status
1.The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
2.The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
3.Claim(s) 1,3-12 and 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over Bradley (US Pat.No.11,087,365) in view of Muth (US Pat.No.12,316,610).
4. Regarding claims 1,8 and 10 Bradley teaches a computer-implemented method and a system comprising: receiving, by a client device and from multiple content platforms across a trust boundary, a plurality of digital component selection factors that each correspond to a digital component of a plurality of digital components, wherein the trust boundary defines one or more computing devices which can access user data; receiving, by the client device, a request for a digital component for presentation at the client device, wherein the request includes request data; generating, by the client device, a user data factor based on the request for the digital component; in response to receiving the request for the digital component, transmitting, by the client device and to at least one of the multiple content platforms across the trust boundary, a non-private contextual request that includes the request data and information about generating the user data factor (Fig.1, Col.3, lines.35-67; Col.2, linses.1-20 teaches a user may be using a device 110 to access digital content. For example, the user may use the device 110 to access news, videos, images, articles, or other digital content. The device 110 may request content from one or more content delivery servers 120 over one or more wired or wireless networks. Specifically, the device 110 may send a request for content at a first communication 114. The content delivery server 120 may receive the request for content from the device 110. At a second communication 122, the content delivery server 120 may request content and/or a content identifier from one or more content selection servers 130 for an available content delivery slot. Available content delivery slots may be locations on digital content at which content can be delivered, such as locations on a webpage, positions within a mobile app (e.g., banner slots, interstitial slots, popup slots, pop-under slots, etc.), or other suitable locations where digital content can be presented. The content selection server 130 may be one of a number of, or multiple, servers that receive the request for the content identifier from the content delivery server 120. The request for the content identifier from the content delivery server 120 may include a request for the content that was requested by the user device. The second communication 122 may include contextual information, such as a website of the content that was requested by the device 110, time and date information, etc., user information, such as a user identifier for the user that is operating or otherwise associated with the device, location information for the content delivery slot (e.g., for videos or audio content, a location could represent a playback position, such as before or after the requested content, etc.), and other information);
after transmitting the non-private contextual request, receiving, by the client device and from the at least one of the multiple content platforms across the trust boundary, a contextual response that includes a contextual selection factor that corresponds to the information about generating the user data factor; selecting, by the client device, one of the plurality of the digital components based on the corresponding digital component selection factors, the received contextual selection factor, and the user data factor; and providing, by the client device, the selected one of the digital components for presentation (Col.4, lines.10-20 teaches the content data, such as image files, video files, text files, and other data may be stored at the cache memory data 140. Data stored at the cached memory data 140 may be selected based at least in part on geographic location, predicted user interactions, previously presented content, content rankings, and/or other factors.
Col.6, lines.27-35 and lines.39-48 teaches the content selection server 130 may extract user information, contextual information, and/or other information from the request or the second communication 122, and may use the extracted information to select content for presentation to the user for which the request is provided. The content selection server 130 may select content and/or a product identifier based at least in part on historical user interaction data for a particular user. Historical user interaction data may include clickstream data for a user or group of related users. Clickstream data may include data related to user browse interactions, such as clicks, views, timings, and other information. The historical user interaction data may include information related to user interactions such as purchases, clicks, views, hovers, likes, shares, comments, and other user interactions).
Bradley teaches all the above claimed limitations but fails to teach the trust boundary defines one or more computing devices which can access private user data without the private user data being transmitted to untrusted third party devices.
Muth teaches teach the trust boundary defines one or more computing devices which can access private user data without the private user data being transmitted to untrusted third party devices; and the user data factor remains private and does not cross the trust boundary (Col.55, lines.40-45 teaches the privacy network is a neutral Internet service that allows to safely verify users to identity, protects privacy, and enforces policies on the use of your information and files. It allows users to authenticate and prove facts about yourself and verify users' right to access records and online content without exposing privacy sensitive information).
Therefore, it would have been obvious to ordinary skill in the art before the invention was filed to modify Bradley to include the trust boundary defines one or more computing devices which can access private user data without the private user data being transmitted to untrusted third party devices; and the user data factor remains private and does not cross the trust boundary as taught by Muth such a setup would analyze user activity and records to protect users from identity theft & cyber-security fraud. Locate and authorize users' access to records, accounts digital media and other electronic content.
6. Regarding claims 3, 14 Bradley teaches the computer-implemented method and the system, wherein the user data factor is generated by a trained machine learning model using information from the digital component request (Col.5, lines.36-40 teaches the ML model is trained/generated based on user-specific information (e.g., demographic, purchase history, etc.), and contextual information (e.g., time of day, website content, keywords, etc.).
7. Regarding claims 4, 15 Bradley teaches the computer-implemented method and the system, wherein the trained machine learning model is trained at least in part on the client device using historical user data (Col.5, lines.36-40 teaches generating an MI model based on historical user interaction data) .
8. Regarding claims 5, 16 Bradley teaches the computer-implemented method and the system,, wherein the trained machine learning model is collaboratively trained with at least one other client device and the historical user data remains on the client device (Col.4, lines.23-57 and Col.5, lines.36-40 teaches generate/train an ML model based on user profile information, historical user interaction information, and historical user content rating information).
9. Regarding claims 6, 17 Bradley teaches the computer-implemented method and the system,, wherein selecting one of the digital components comprises: for each of the digital components, generating a score based on the corresponding digital component selection factors, the contextual selection factor, and the user data factor (Col.4, lines.23-57 teaches the content selection server 130 may be in communication with cached memory data 140 that may be stored locally or remotely at one or more high-speed datastores. Data stored at the cached memory data 140 may be selected based at least in part on geographic location, predicted user interactions, previously presented content, content rankings, and/or other factors. The cached memory data 140 may include product information and content data that may be associated with respective product identifiers or content identifiers. For example, Data File 78.2 may be associated with product identifier 1223. The respective data files may include creative materials, such as images, video, audio, and the like, product information, such as pricing, user ratings, options or features, and other product information, and may include different or additional information. Some data files may include prioritization data that is indicative of a priority of presentation for the creative materials and/or the product information (e.g., for dynamic content, pricing information may be presented before rating information, or a video may be presented before an image, etc.
Col.5, lines.9-40 teaches the content selection server 130 will determine a number of eligible users for a particular product identifier. The users may have recently interacted with the product identifier or related webpage (e.g., clicked on a link, added the camera to a digital shopping cart, read product reviews, etc.), the users may satisfy targeting criteria associated with the particular product identifier (e.g., certain age range, certain demographic, certain location, etc.), or may otherwise be determined to be eligible for presentation of content associated with the product identifier. The content selection server 130 may rank the product identifiers based at least in part on the number of eligible users for the respective product identifiers. The content selection server 130 will determine a probability of action for specific users for specific content. Such probabilities may be used for rankings or ranking scores. Probabilities may be determined, for example, by logistic regressions, redundancy models, or other models. The models used to determine probability may consider several inputs, including user-specific information (e.g., demographic, purchase history, etc.), and contextual information (e.g., time of day, website content, keywords, etc.));
and selecting the digital component with the greatest score (Col.5, lines.51-63 teaches the content selection server 130 may cache the highest ranked or some of the highest ranked product identifiers and the respective product information based at least in part on the number of eligible users. Therefore, the cached data may have a relatively high probability of being served or selected for presentation to users, since the cached data may be associated with the highest number of users. The content selection server 130 may actively cache content and/or product identifiers based at least in part on a number of eligible users, determinations of users that have made purchases and may no longer be eligible, predicted user interactions, predicted device interactions, and the like).
10. Regarding claims 7, 18 Bradley teaches the computer-implemented method and the system,, wherein the digital component selection factor, the contextual selection factor, and the user data factor each comprise embedding vectors of equal dimensions (Col.4, lines.23-57 teaches the content selection server 130 may be in communication with cached memory data 140 that may be stored locally or remotely at one or more high-speed datastores. Data stored at the cached memory data 140 may be selected based at least in part on geographic location, predicted user interactions, previously presented content, content rankings, and/or other factors. The cached memory data 140 may include product information and content data that may be associated with respective product identifiers or content identifiers. For example, Data File 78.2 may be associated with product identifier 1223. The respective data files may include creative materials, such as images, video, audio, and the like, product information, such as pricing, user ratings, options or features, and other product information, and may include different or additional information. Some data files may include prioritization data that is indicative of a priority of presentation for the creative materials and/or the product information (e.g., for dynamic content, pricing information may be presented before rating information, or a video may be presented before an image, etc.
Col.5, lines.9-40 teaches the content selection server 130 will determine a number of eligible users for a particular product identifier. The users may have recently interacted with the product identifier or related webpage (e.g., clicked on a link, added the camera to a digital shopping cart, read product reviews, etc.), the users may satisfy targeting criteria associated with the particular product identifier (e.g., certain age range, certain demographic, certain location, etc.), or may otherwise be determined to be eligible for presentation of content associated with the product identifier. The content selection server 130 may rank the product identifiers based at least in part on the number of eligible users for the respective product identifiers. The content selection server 130 will determine a probability of action for specific users for specific content. Such probabilities may be used for rankings or ranking scores. Probabilities may be determined, for example, by logistic regressions, redundancy models, or other models. The models used to determine probability may consider several inputs, including user-specific information (e.g., demographic, purchase history, etc.), and contextual information (e.g., time of day, website content, keywords, etc.). Col.5, lines.51-63 teaches the content selection server 130 may cache the highest ranked or some of the highest ranked product identifiers and the respective product information based at least in part on the number of eligible users. Therefore, the cached data may have a relatively high probability of being served or selected for presentation to users, since the cached data may be associated with the highest number of users).
11. Regarding claim 9 Bradley teaches the computer-implemented method, wherein selecting the one of the plurality of the digital components comprises transmitting, by a trusted server and to the client device, a second contextual response that includes the corresponding digital component selection factors and the contextual selection factor, wherein the second contextual response causes the client device to select the one of the plurality of the digital components using the corresponding digital component selection factors, the contextual selection factor, and the user data factor (Col.4, lines.23-57 teaches the content selection server 130 may be in communication with cached memory data 140 that may be stored locally or remotely at one or more high-speed datastores. Data stored at the cached memory data 140 may be selected based at least in part on geographic location, predicted user interactions, previously presented content, content rankings, and/or other factors. The cached memory data 140 may include product information and content data that may be associated with respective product identifiers or content identifiers. For example, Data File 78.2 may be associated with product identifier 1223. The respective data files may include creative materials, such as images, video, audio, and the like, product information, such as pricing, user ratings, options or features, and other product information, and may include different or additional information. Some data files may include prioritization data that is indicative of a priority of presentation for the creative materials and/or the product information (e.g., for dynamic content, pricing information may be presented before rating information, or a video may be presented before an image, etc.
Col.5, lines.9-40 teaches the content selection server 130 will determine a number of eligible users for a particular product identifier. The users may have recently interacted with the product identifier or related webpage (e.g., clicked on a link, added the camera to a digital shopping cart, read product reviews, etc.), the users may satisfy targeting criteria associated with the particular product identifier (e.g., certain age range, certain demographic, certain location, etc.), or may otherwise be determined to be eligible for presentation of content associated with the product identifier. The content selection server 130 may rank the product identifiers based at least in part on the number of eligible users for the respective product identifiers. The content selection server 130 will determine a probability of action for specific users for specific content. Such probabilities may be used for rankings or ranking scores. Probabilities may be determined, for example, by logistic regressions, redundancy models, or other models. The models used to determine probability may consider several inputs, including user-specific information (e.g., demographic, purchase history, etc.), and contextual information (e.g., time of day, website content, keywords, etc.). Col.5, lines.51-63 teaches the content selection server 130 may cache the highest ranked or some of the highest ranked product identifiers and the respective product information based at least in part on the number of eligible users. Therefore, the cached data may have a relatively high probability of being served or selected for presentation to users, since the cached data may be associated with the highest number of users).
12.Claims 2 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Bradley (US Pat.No.11,087,365) in view of Moth (US Pat.No.12,316,610) as applied to claims 1,10 above and further in view of Greenzeiger (US Pub.No.2015/0220970)
13. Regarding claims 2 and 13 over Bradley in view of Muth teaches all the above claimed limitations but fails to teach the computer-implemented method and the system, wherein the information about generating the user data factor comprises a version number determined based on the user data factor.
Greenzeiger teaches the information about generating the user data factor comprises a version number determined based on the user data factor (Para:0036 teaches a policy can specify that user declared data is maintained as long as the user account is active, but user characteristic values based on location information expire after a specified period of time. In some instances, the user-profile-updater module 124 can update the user profile database 156 at least every week, or every day [every update is the new version of user data]).
Therefore, it would have been obvious to ordinary skill in the art before the invention was filed to modify Bradley in view of Muth to include the information about generating the user data factor comprises a version number determined based on the user data factor as taught by Greenzeiger such a setup would ensures that updates are controlled and structured, maintain accuracy and consistency in the data.
Conclusion
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/DEREENA T CATTUNGAL/Primary Examiner, Art Unit 2431