DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to communication filed on 23 January 2026. Claims 1-4, 6, 8-11, 13, 15-18, 20, and 22-25 are pending in the case. Claims 1, 8, and 15 were amended. Claims 7, 14, and 21 were cancelled. Claims 24, and 25 were added. Claims 1, 8, and 15 are the independent claims. This action is non-final.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on January 23rd, 2026 has been entered.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, 6, 8-9, 13, 15-16, 20, and 22-25 are rejected under 35 U.S.C. 103 as being unpatentable over Joshi (US 2019/0251083 A1) in view of Lundbaek (US 2022/0171873 A1) in view of Jacobs et al. (US 2019/0114672 A1), further in view of Cogley et al. (US 2024/0160836 A1).
Regarding claim 1, Joshi teaches a computer-implemented method comprising:
obtaining, from a client device and during a browsing session conducted by a user of the client device, a plurality of contextual features relating to context within which the browsing session is conducted, wherein the plurality of contextual features do not include any personally-identifiable data of the user (see Joshi, Paragraphs [0018], [0032]-[0033], “The search engine 110 enables retrieving and using contextual data associated with previous user sessions in conversational searches. … In addition, certain data may be treated in one or more ways before it is stored or used, so that personally identifiable information is removed. For example, a user's identity may be treated so that no personally identifiable information can be determined for the user, or a user's geographic location may be generalized where location information is obtained (such as to a city, ZIP code, or state level), so that a particular location of a user cannot be determined. Thus, the user may have control over how information is collected about the user and used by a content server. … The contextual data for each session may include the one or more queries, one or more search results responsive to the queries, and metadata associated with the queries and the search results.” [Contextual data (i.e., contextual features) associated with previous user sessions (i.e., browsing session) may be retrieved (i.e., obtained), where in the contextual data may not include personally identifiable information (i.e., personally-identifiable data).]);
However, Joshi does not explicitly teach:
generating, using a trained contextual model and based on the plurality of contextual features, an audience interest profile applicable to the user of the client device
Lundbaek teaches:
generating, using a trained contextual model and based on the plurality of contextual features, an audience interest profile applicable to the user of the client device (see Lundbaek, Paragraph [0089], “The term “trained user resource interest model” refers to one or more algorithmic model(s), statistical model(s), and/or machine learning model(s) that is/are configured to generate data representing an affinity a user profile has for a particular context of content represented by context data corresponding to one or more search result(s). In an example context, a trained user resource interest model is configured to generate data representing whether a user profile prefers or has a particular affinity towards a particular context associated with a search result. In some embodiments, a trained user resource interest model may determine the user has a preference (e.g., an “interest”) in a first context (e.g., interpretive dance) over a second context (e.g., law).” [A trained user resource interest model (i.e., contextual model) may be used to generate data representing whether a user profile prefers or has a particular affinity towards a particular context associated with a search result (i.e., audience interest profile).]),
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Joshi (teaching retrieving context from previous sessions) in view of Lundbaek (teaching apparatuses, methods, and computer program products for privacy-preserving personalized data searching and privacy-preserving personalized data search training), and arrived at a method that generates an audience interest profile using a machine learning model. One of ordinary skill in the art would have been motivated to make such a combination for the purposes of improving accuracy (see Lundbaek, Paragraph [0245]). In addition, both the references (Joshi and Lundbaek) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as data systems. The close relation between both the references highly suggests an expectation of success.
The combination of Joshi, and Lundbaek further teaches:
wherein the trained contextual model is trained using training data including a set of historical contextual data aggregated from a plurality of prior browsing sessions and a corresponding set of labels indicating audience interest profiles that each represent an affinity of a particular audience interest segment to one or more content categories (see Lundbaek, Paragraph [0014], “the privacy-preserving search model utilizes a trained user resource interest model configured for identifying a set of previously engaged search results; extracting resource context data for each previously engaged search result of the set of previously engaged search results; and generating at least one of a center of interest and a center of disinterest based at least on the resource context data for each previously engaged search result.” [The trained user resource interest model uses contextual data from previously engaged search results (i.e., prior browsing sessions)]),
and wherein the set of historical contextual data does not include any personally-identifiable data of users from the plurality of prior browsing sessions (see Joshi, Paragraphs [0032]-[0033], “In addition, certain data may be treated in one or more ways before it is stored or used, so that personally identifiable information is removed. … The search engine 110 includes a session data repository 122 in the repository 114 for storing contextual data of user sessions. Each user session may include one or more queries. The contextual data for each session may include the one or more queries, one or more search results responsive to the queries, and metadata associated with the queries and the search results.” [The historical contextual data may not include personally identifiable information (i.e., personally-identifiable data).]);
However, the combination of Joshi, and Lundbaek do not explicitly teach:
identifying, based on the generated audience interest profile, a digital component for provision on the client device;
Jacobs teaches:
identifying, based on the generated audience interest profile, a digital component for provision on the client device (see Jacobs, Paragraphs [0049]-[0053], “The service provider system 106 then uses the recommendation 502 to select digital content 504 from a plurality of items of digital content 402 to be provided back to the client device 104.” [Digital content (i.e., digital component ) is selected (i.e., identifying) to be provided back to the client device.]);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Joshi (teaching retrieving context from previous sessions) in view of Lundbaek (teaching apparatuses, methods, and computer program products for privacy-preserving personalized data searching and privacy-preserving personalized data search training), further in view of Jacobs (teaching digital content control based on shared machine learning properties), and arrived at a method that identifies a digital component based on an audience interest profile. One of ordinary skill in the art would have been motivated to make such a combination for the purposes of improving accuracy and efficiency of use of computational resources (see Jacobs, Paragraph [0003]). In addition, the references (Joshi, Lundbaek and Jacobs) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as data systems. The close relation between the references highly suggests an expectation of success.
The combination of Joshi, Lundbaek, and Jacobs further teaches:
and providing, for display within a page displayed on the client device and during the browsing session, the digital component (see Jacobs, Paragraphs [0049]-[0053], “The received at least one item of digital content is output within the context of the application by the client device (block 614), e.g., as digital marketing content in conjunction with other digital content output by the application 120, items of digital content 504 for consumption such as digital images 506, digital video 508, digital audio 510, digital media 512, and so forth.” [The digital content (i.e., digital component ) may be output by the application.]);
in response to providing the digital component for display on the client device; obtaining, from the client device during the browsing session conducted by a user of the client device, subsequent contextual features related to the provided digital component; and modifying, using data relating to the subsequent contextual features, the audience interest profile of the user (see Joshi, Paragraphs [0010], [0039]-[0040], “the conversational search system determines second contextual data for the second query. The second contextual data may include the second query, the second search result, and metadata associated with the second query and the second search result. The conversational search system may store the second contextual data in the second repository and associate the second contextual data with the second identifier in the second repository. … a user session is defined as a series of search queries from a same user device or a same device identifier within a specific period of time, e.g., 1 minute, 2 minutes, or 5 minutes, after the user session starts, e.g., when receiving a first query of the queries.” Also, see Lundbaek, Paragraph [0089], Also, see Jacobs, Paragraphs [0032] [Contextual data (i.e., contextual features) may be obtained during a user session (i.e., browsing session) for additional queries, thus, implying an audience interest profile of the user may be modified accordingly after a digital component is displayed.]),
However, the combination of Joshi, Lundbaek, and Jacobs do not explicitly teach:
wherein the data relating to the subsequent contextual features is discarded after termination of the browsing session on the client device.
Cogley teaches:
wherein the data relating to the subsequent contextual features is discarded after termination of the browsing session on the client device (see Jacobs, Paragraph [0020], “The context information is discarded once the user session has been completed.” [The context information (i.e., contextual features) may be discarded after the user session has been completed (i.e., after termination of the browsing session on the client device).]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Joshi (teaching retrieving context from previous sessions) in view of Lundbaek (teaching apparatuses, methods, and computer program products for privacy-preserving personalized data searching and privacy-preserving personalized data search training) in view of Jacobs (teaching digital content control based on shared machine learning properties), further in view of Cogley (teaching context adaptive writing assistant), and arrived at a method that discards contextual information after termination of a browsing session. One of ordinary skill in the art would have been motivated to make such a combination for the purposes of improving user experience (see Cogley, Paragraph [0016]). In addition, the references (Joshi, Lundbaek, Jacobs, and Cogley) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as data systems. The close relation between the references highly suggests an expectation of success.
Regarding claim 2, Joshi in view of Lundbaek in view of Jacobs, further in view of Cogley teaches all the limitations of claim 1. Lundbaek further teaches:
training a plurality of models using a set of training data including sets of contextual features and label data including audience interest profiles corresponding to the sets of contextual features; determining that a performance of at least one of the models meets a set of evaluation criteria; in response to determining that the performance of the model meets the set of evaluation criteria, deploying one of the plurality of models as the trained model (see Lundbaek, Paragraph [0014], “the learning to rank model 412 may utilize any of a number of evaluation metrics and utilize any of a myriad of processing approach(es).” [Evaluation metrics may be utilized.]).
Regarding claim 6, Joshi in view of Lundbaek in view of Jacobs, further in view of Cogley teaches all the limitations of claim 1. Joshi further teaches:
wherein modifying the audience interest profile of the user is conducted in real-time during the browsing session (see Joshi, Paragraph [0083], “In a later user session Y, e.g., at 6:00 pm, Jul. 1, 2013, the user inputs a new voice query. When the user starts to input the voice query, the user interface 150 a changes to a user interface 150 b. The voice query is recognized and converted into a transcription, e.g., by the speech-to-text engine 126. The transcription is transmitted, e.g., in real time or after completion of the voice query, to the user device 106 for displaying on the use interface 150 b, e.g., a string 152 b of “Tell me about the restaurant from this morning.”” [A transcription may be transmitted in real-time.]).
Regarding claims 8, 9, 13, 15-16, and 20, Joshi in view of Lundbaek in view of Jacobs, further in view of Cogley teaches all of the elements of claims 1, 2, and 6 in method form. Joshi also discloses a system [0007], and a computer readable media [0134]. Therefore, the supporting rationale of the rejection to claims 1, 2, and 6 applies equally as well to those elements of claims 8, 9, 13, 15-16, and 20.
Regarding claim 22, Joshi in view of Lundbaek in view of Jacobs, further in view of Cogley teaches all the limitations of claim 1. Joshi further teaches:
wherein the plurality of contextual features relating to context within which the browsing session is conducted comprises features representing at least one of: (i) data characterizing the URL or web page accessed during the browsing session, (ii) data descriptive of the type of browser used to access the data, (iii) data descriptive of a location of the browsing session, (iv) data descriptive of a time at which the browsing session is conducted, or (v) any estimated or actual demographics provided by the user (see Lundbaek, Paragraph [0052], “the session tags include a location tag indicating the location of a user when the user session occurs.” [The session tags (i.e., contextual features comprising data descriptive of a location of the browsing session) is obtained.]).
Regarding claim 23, Joshi in view of Lundbaek in view of Jacobs, further in view of Cogley teaches all the limitations of claim 1. Joshi, and Lundbaek further teaches:
updating the trained contextual model using the subsequent contextual features after the current browsing session concludes (see Joshi, Paragraphs [0010], [0039]-[0040], Also, see Lundbaek, Paragraph [0089], “The term “trained user resource interest model” refers to one or more algorithmic model(s), statistical model(s), and/or machine learning model(s) that is/are configured to generate data representing an affinity a user profile has for a particular context of content represented by context data corresponding to one or more search result(s). In an example context, a trained user resource interest model is configured to generate data representing whether a user profile prefers or has a particular affinity towards a particular context associated with a search result. In some embodiments, a trained user resource interest model may determine the user has a preference (e.g., an “interest”) in a first context (e.g., interpretive dance) over a second context (e.g., law).” [A trained user resource interest model (i.e., contextual model) may be updated after obtained additional contextual information.]).
Regarding claim 24, Joshi in view of Lundbaek in view of Jacobs, further in view of Cogley teaches all the limitations of claim 1. Joshi, Lundbaek, and Jacobs further teaches:
generating, using the trained contextual model and based on the subsequent contextual features, an additional interest profile applicable to the user of the client device; and combining the audience interest profile and the additional audience interest profile to generate a modified audience interest profile (see Joshi, Paragraphs [0010], [0039]-[0040], Also, see Lundbaek, Paragraph [0089], [0198], “generate an updated context cluster by adding data associated with the user-selected search result to the context cluster. In this regard, the updated context cluster associated with the center of interest or center of disinterest may be updated to include the resource context data for the new user-selected search result. In some embodiments, context data associated with the user-selected search result is added to the context cluster. In this regard, one or more context clusters may be generated and/or updated to reflect each user-selected search result as a previously-selected search result. In this regard, the updated context cluster may represent the interests of the user profile local to the user device.” Also, see Jacobs, Paragraphs [0032] [The user profile (i.e., audience interest profile) may be modified by adding new interests of the user.]).
Regarding claim 25, Joshi in view of Lundbaek in view of Jacobs, further in view of Cogley teaches all the limitations of claim 24. Jacobs further teaches:
generating, from a plurality of digital components and based on the modified audience interest profile, a subset of digital components; identifying, based on the modified audience interest profile, a digital component for provision on the client device from the subset of digital components (see Jacobs, Paragraphs [0049]-[0053], “The received at least one item of digital content is output within the context of the application by the client device (block 614), e.g., as digital marketing content in conjunction with other digital content output by the application 120, items of digital content 504 for consumption such as digital images 506, digital video 508, digital audio 510, digital media 512, and so forth.” [The digital content (i.e., digital component ) may be output for the modified audience interest profile.]).
Claims 3, 4, 10, 11, 17, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Joshi in view of Lundbaek in view of Jacobs in view of Cogley, further in view of He et al. (US 2019/0266262 A1).
Regarding claim 3, Joshi in view of Lundbaek in view of Jacobs, further in view of Cogley teaches all the limitations of claim 2. However, the combination of Joshi, Lundbaek, Jacobs, and Cogley do not explicitly teach:
wherein determining that a performance of the model meets a set of evaluation criteria further comprises applying one or more filters with a minimum preset relevance value, wherein the preset relevance value is numerical value based on a divergence between a predetermined mapping of two content categories.
He teaches:
wherein determining that a performance of the model meets a set of evaluation criteria further comprises applying one or more filters with a minimum preset relevance value, wherein the preset relevance value is numerical value based on a divergence between a predetermined mapping of two content categories (see He, Paragraph [0044], “the filtering can be applied to eliminate other identified textual content, images, or text-image pairs, including, for example, establishing a minimum relevance threshold that can be based on a distance, in the multidimensional space, between vectors encoded from such images or textual content and the input image vector, and other like aggregation.” [A filter may be applied by establishing a minimum relevance threshold.]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Joshi (teaching retrieving context from previous sessions) in view of Lundbaek (teaching Apparatuses, methods, and computer program products for privacy-preserving personalized data searching and privacy-preserving personalized data search training) in view of Jacobs (teaching digital content control based on shared machine learning properties) in view of Cogley (teaching context adaptive writing assistant), further in view of He (teaching increasing inclusiveness of search result generation through tuned mapping of text and images into the same high-dimensional space), and arrived at a method that applies a filter with minimum relevance value. One of ordinary skill in the art would have been motivated to make such a combination for the purposes of improving accurate results (see He, Paragraph [0001]). In addition, the references (Joshi, Lundbaek, Jacobs, Cogley, and He) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as data systems. The close relation between the references highly suggests an expectation of success.
Regarding claim 4, Joshi in view of Lundbaek in view of Jacobs in view of Cogley, further in view of He teaches all the limitations of claim 3. Lundbaek, and Zhang further teaches:
receiving, from a third party, a new content category and minimum relevance value; creating a new content category mapping based on the new content category; and establishing a new filter based on the new content category and minimum relevance value (see Lundbaek, Paragraph [0166], “new context data for a particular electronic resource may be generated and compared to a particular value to determine whether to add the new context data to an existing context cluster.” [New context data may be generated.] Also, see He, Paragraph [0044], “the filtering can be applied to eliminate other identified textual content, images, or text-image pairs, including, for example, establishing a minimum relevance threshold that can be based on a distance, in the multidimensional space, between vectors encoded from such images or textual content and the input image vector, and other like aggregation.” [A filter may be applied by establishing a minimum relevance threshold.]).
Regarding claims 10, 11, 17, and 18, Joshi in view of Lundbaek in view of Jacobs in view of Cogley, further in view of He teaches all of the elements of claims 3, and 4 in method form. Joshi also discloses a system [0007], and a computer readable media [0134]. Therefore, the supporting rationale of the rejection to claims 3, and 4 applies equally as well to those elements of claims 10, 11, 17, and 18.
Response to Arguments
Applicant’s Arguments, filed January 23rd, 2026, have been fully considered, but are moot in light of the new grounds of rejection.
Conclusion
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/HUSAM TURKI SAMARA/
Examiner, Art Unit 2161
/APU M MOFIZ/Supervisory Patent Examiner, Art Unit 2161