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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 .
DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 08, 2026 has been entered.
Claims 1-14 and 21-24 (claims 15-20 are canceled) are pending in the application. In response to the Restriction Requirement of May 28, 2025, claims 8-14 and 21-24 have been withdrawn.
Thus, claims 1-7 are pending for this office action.
This action is response to the response filed on January 08, 2026.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-7 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Shukla et al (US 20180246972 A1).
With respect to claim 1, Shukla et al teaches
receiving, via at least one processor, a content item containing content ([0038] user, receiving a plurality of web documents (e.g., links to websites, social networking sites, and other online content) for online content);
determining, via the at least one processor, a value for the content item based on values of one or more other content items associated with the content (FIG. 5, at 508, score of an interest by a particular amount based on user interest on a website. [0038] receiving a plurality of web documents (e.g., links to websites, social networking sites, and other online content) for online content, ranking the plurality of web documents based on a document score and a user signal, and generating a content feed that includes at least a subset of the plurality of web documents based on the ranking);
monitoring, via the at least one processor, online activity related to user interaction with the content item ([0081] monitors a user's online activity and a component 214 that monitors a user's in-app behavior (e.g., monitors a user's activity within/while using the app, such as client application 224). in FIG. 2, determines a user's interests (e.g., learns a user's interests));
updating, via the at least one processor, the value for the content item in real-time based on the online activity related to user interaction ([0201] graph data store 1720 to provide real-time updating for delivering timely results utilized by search and feed system 1700. [0208] to provide in near real-time online content. [0212] to perform updating of the graph data store so that changes in the online world can be reflected in near real-time updates in the disclosed graph data structure. [0324] user's interactions via the user's application activity logs); and
displaying, via the at least one processor, the value for the content item as the value changes in real-time ([0201] graph data store 1720 to provide real-time updating for delivering timely results utilized by search and feed system 1700. [0208] to provide in near real-time online content. [0212] updating of the graph data store so that changes in the online world real-time updates. [0087] monitors a user's online activity including, demographic information, psychographic information, personal tastes (e.g., user preferences), an interest graph, and a user graph).
With respect to claim 2, Shukla et al teaches determining that the content item is a new content item; and upon determining that the content item is a new content item, extracting features from the content item ([0029] FIG. 22 for identifying new content aggregated from online sources).
With respect to claim 3, Shukla et al teaches identifying value curves of a one or more plurality of content items associated with the new content item, the value curves providing predictions of values of the content item over time; and combining the identified value curves to produce a value curve for the new content item ([0087] monitors a user's online activity including, demographic information, psychographic information, personal tastes (e.g., user preferences), an interest graph, and a user graph).
With respect to claim 4, Shukla et al teaches the value curves are identified using one or more machine learning models ([0046] provided to a machine learning model. [0253] e.g., a neural network machine learning system).
With respect to claim 5, Shukla et al teaches classifying each value curve of content items by associating with a class associated with each value curve of content items by an output layer neuron ([0253] e.g., a neural network machine learning system).
With respect to claim 6, Shukla et al teaches generating a set of reference value curves for an initial set of content items at operation, wherein the set of reference value curves is expressed as a set of polynomial functions ([0087] monitors a user's online activity including, demographic information, psychographic information, personal tastes (e.g., user preferences), an interest graph, and a user graph).
With respect to claim 7, Shukla et al teaches updating continually the value curve of the new content item and the value of the new content item, wherein the value curve is expressed as a polynomial function ([0087] monitors a user's online activity including, demographic information, psychographic information, personal tastes (e.g., user preferences), an interest graph, and a user graph).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ISAAC M WOO whose telephone number is (571)272-4043. The examiner can normally be reached 9:00 to 5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tony Mahmoudi can be reached on 571-272-4078. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ISAAC M WOO/Primary Examiner, Art Unit 2163