Prosecution Insights
Last updated: April 19, 2026
Application No. 18/983,097

CONTENT RECOMMENDATION METHOD, STORAGE MEDIUM AND ELECTRONIC DEVICE

Non-Final OA §102
Filed
Dec 16, 2024
Examiner
BULLOCK, JOSHUA
Art Unit
2153
Tech Center
2100 — Computer Architecture & Software
Assignee
BEIJING YOUZHUJU NETWORK TECHNOLOGY CO., LTD.
OA Round
1 (Non-Final)
82%
Grant Probability
Favorable
1-2
OA Rounds
3y 2m
To Grant
99%
With Interview

Examiner Intelligence

Grants 82% — above average
82%
Career Allow Rate
522 granted / 634 resolved
+27.3% vs TC avg
Strong +16% interview lift
Without
With
+16.5%
Interview Lift
resolved cases with interview
Typical timeline
3y 2m
Avg Prosecution
28 currently pending
Career history
662
Total Applications
across all art units

Statute-Specific Performance

§101
15.9%
-24.1% vs TC avg
§103
32.5%
-7.5% vs TC avg
§102
39.6%
-0.4% vs TC avg
§112
5.4%
-34.6% vs TC avg
Black line = Tech Center average estimate • Based on career data from 634 resolved cases

Office Action

§102
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 . Claims 1-20 are pending. Claim Rejections - 35 USC § 102 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 person shall be entitled to a patent unless – (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. Claim(s) 1-3, 5-7, 9-13, & 19-20 is/are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over ZHANG et al. (US Pub. No. 2022/0284327 A1). In respect to Claim 1, ZHANG teaches: a content recommendation method, comprising: receiving a content request sent by a terminal device, (ZHANG teaches [0053] a resource request.) wherein the content request carries target information for matching with recommended contents, (ZHANG teaches [0053] the target receiving pushed resources, wherein a target recommendation model [0011] carries information for matching recommended contents.) and the target information comprises attribute information of a historically delivered content and/or historical interaction information in a content material page displayed by the terminal device; (ZHANG TEACHES [0057] historically delivered resources associated with target information.) obtaining a target recommended content according to the target information and a content recommendation model, wherein the target recommended content comprises a target content material and analysis information for the target content material, and the content recommendation model is used for matching with recommended contents according to inputted information; (ZHANG teaches [0053] the target receiving pushed resources, wherein a target recommendation model [0011] carries information for matching recommended contents.) and pushing the target recommended content to the terminal device such that the terminal device displays the target recommended content in the content material page (ZHANG illustrates and teaches [FIG. 4, 0051, 0165] pushing recommended content to a display page.) As per Claim 2, ZHANG teaches: wherein the obtaining a target recommended content according to the target information and a content recommendation model comprises: acquiring candidate content materials matched with the target information from a content material library through the content recommendation model, and determining the target recommended content according to the candidate content materials, wherein the content material library is used for storing content materials and analysis information for the content materials (ZHANG teaches [0098] a resource library for matching materials with the target information.) As per Claim 3, ZHANG teaches: wherein the attribute information of the historically delivered content comprises category information and/or delivery object information of the historically delivered content; (ZHANG teaches [0057] historically delivered content.) the acquiring candidate content materials matched with the target information from a content material library through the content recommendation model comprises: taking the content materials matched with the category information of the historically delivered content in the content material library as the candidate content materials through the content recommendation model; and/or, taking the content materials matched with the delivery object information of the historically delivered content in the content material library as the candidate content materials through the content recommendation model (ZHANG teaches [0067] categorical information associated with historical content.) As per Claim 5, ZHANG teaches: wherein the historical interaction information comprises at least one selected from the group consisting of historical material usage information, historical material browsing information and historical material collection information, and the acquiring candidate content materials matched with the target information from a content material library through the content recommendation model comprises: determining historical interaction materials according to the historical interaction information; and determining content materials matched with the historical interaction materials in the content material library as the candidate content materials through the content recommendation model (ZHANG teaches [0053] the target receiving pushed resources, wherein a target recommendation model [0011] carries information for matching recommended contents.) (ZHANG teaches [0067] categorical information associated with historical content.) As per Claim 6, ZHANG teaches: wherein the acquiring candidate content materials matched with the target information from a content material library through the content recommendation model comprises: determining initial candidate content materials meeting an interaction index condition from the content material library, and determining content materials matched with the target information in the initial candidate content material as the candidate content materials, through the content recommendation model, wherein the interaction index condition comprises at least one selected from the group consisting of a historical delivery index condition, an interaction feedback index condition and a content level index condition (ZHANG teaches [0059] conditions associated with historical resources.) As per Claim 7, ZHANG teaches: wherein the acquiring candidate content materials matched with the target information from a content material library through the content recommendation model comprises: acquiring a first content material matched with the target information and a second content material similar to the first content material from the content material library through the content recommendation model and taking the first content material and the second content material as the candidate content materials (ZHANG teaches [0046] first and second target recommendations.) As per Claim 9, ZHANG teaches: wherein the obtaining a target recommended content according to the target information and a content recommendation model comprises: obtaining second candidate recommended contents according to the target information and the content recommendation model; (ZHANG teaches [0046] first and second target recommendations.) and grouping the second candidate recommended contents according to analysis information corresponding to content materials in the second candidate recommended contents to obtain a plurality of groups of target recommended contents; and the pushing the target recommended content to the terminal device such that the terminal device displays the target recommended content in the content material page comprises: pushing the plurality of groups of target recommended contents to the terminal device such that the terminal device displays the plurality of groups of target recommended contents in groups in the content material page (ZHANG illustrates and teaches [FIG. 4, 0051, 0165] pushing recommended content to a display page.) As per Claim 10, ZHANG teaches: wherein the content recommendation method further comprises: acquiring an initial content material, wherein the initial content material comprises a delivered content and a multi-interest content; and analyzing the initial content material to obtain analysis information for the initial content material and storing the initial content material and analysis information for the initial content material in a content material library, wherein the content material library is used for matching with the target recommended content by the content recommendation model (ZHANG teaches [0067] categorical information associated with historical content.) As per Claim 11, ZHANG teaches: wherein the analyzing the initial content material to obtain analysis information for the initial content material comprises: analyzing the initial content material to obtain at least one selected from the group consisting of content type information, content object information, a content generation strategy, content tag information and delivery object information of the initial content material as analysis information for the initial content material (ZHANG teaches [0067] categorical information associated with historical content.) As per Claim 12, ZHANG teaches: wherein the analyzing the initial content material to obtain analysis information for the initial content material comprises: analyzing the initial content material through a content analysis model to obtain at least one selected from the group consisting of content structure information, a content recommendation reason and content topic information of the initial content material as analysis information for the initial content material, wherein the content analysis model is used for analyzing the inputted content material (ZHANG [0011]) As per Claim 13, ZHANG teaches: wherein the content recommendation method further comprises: after receiving interaction feedback information for the target recommended content sent by the terminal device, updating and training the content recommendation model according to the interaction feedback information and the target recommended content to obtain a new content recommendation model (ZHANG [0040, 0118]) Claim 19 is the media claim corresponding to method claim 1, therefore is rejected for the same reasons noted previously. Claim 20 is the device claim corresponding to method claim 1, therefore is rejected for the same reasons noted previously. Allowable Subject Matter Claims 4, 8, & 14-18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSHUA BULLOCK whose telephone number is (571)270-1395. The examiner can normally be reached 8:00 am - 4:00 pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kavita Stanley can be reached at 571-272-8352. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /JOSHUA BULLOCK/Primary Examiner, Art Unit 2153 February 18, 2026
Read full office action

Prosecution Timeline

Dec 16, 2024
Application Filed
Feb 18, 2026
Non-Final Rejection — §102 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

1-2
Expected OA Rounds
82%
Grant Probability
99%
With Interview (+16.5%)
3y 2m
Median Time to Grant
Low
PTA Risk
Based on 634 resolved cases by this examiner. Grant probability derived from career allow rate.

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