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 1-10 & 13-20 have been amended.
Claims 1-20 are pending.
Response to Arguments
Applicant's arguments filed May 20, 2026 have been fully considered but they are not persuasive. See Examiner’s response below.
With respect to rejections under 35 U.S.C. 102, applicant appears to assert that ZHANG does not teach “the first recommended content comprises a first content material”. Examiner respectfully disagrees. ZHANG teaches [0011] utilization of a recommendation model to obtain a target resource [0012, FIG. 4], wherein the target resource is indicative of content material. Thus, ZHANG does in fact teach “the first recommended content comprises a first content material”.
With respect to rejections under 35 U.S.C. 102, applicant appears to assert that ZHANG does not teach “analysis information”. Examiner respectfully disagrees. ZHANG teaches [0040, FIG. 4] that the content information provided is based upon a reinforcement learning process, wherein this reinforcement learning process is indicative of analysis information. Thus, ZHANG does in fact teach [FIG. 4] displaying of “analysis information”.
With respect to rejections under 35 U.S.C. 102, applicant appears to assert that ZHANG does not teach “the terminal device displays the first recommendation content”. Examiner respectfully disagrees and maintains his position with respect to the same. ZHANG teaches and illustrates [FIG. 4, 0051, 0165] pushed recommended content to a display page which is based on machine learning analysis. Thus, ZHANG does in fact teach “the terminal device displays the first recommendation content”.
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).
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 first 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 first information comprises at least one of attribute information of a historically delivered content 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 first recommended content according to the first information and a content recommendation model, wherein the first recommended content comprises a first content material and analysis information for the first 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.)
ZHANG teaches [0011] utilization of a recommendation model to obtain a target resource [0012, FIG. 4], wherein the target resource is indicative of content material.
ZHANG teaches [0040, FIG. 4] that the content information provided is based upon a reinforcement learning process, wherein this reinforcement learning process is indicative of analysis information.
and pushing the first recommended content to the terminal device such that the terminal device displays the first recommended content in the content material page (ZHANG illustrates and teaches [FIG. 4, 0051, 0165] pushing recommended content to a display page.)
ZHANG teaches and illustrates [FIG. 4, 0051, 0165] pushed recommended content to a display page which is based on machine learning analysis.
As per Claim 2, ZHANG teaches:
wherein the obtaining a first recommended content according to the first information and a content recommendation model comprises: acquiring candidate content materials matched with the first information from a content material library through the content recommendation model, and determining the first 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 at least one of category information or delivery object information of the historically delivered content; (ZHANG teaches [0057] historically delivered content.)
the acquiring candidate content materials matched with the first information from a content material library through the content recommendation model comprises at least one of: 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; 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 first 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 first 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 first 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 first information from a content material library through the content recommendation model comprises: acquiring a first content material matched with the first 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 first recommended content according to the first information and a content recommendation model comprises: obtaining second candidate recommended contents according to the first 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 first recommended contents; and the pushing the first recommended content to the terminal device such that the terminal device displays the first recommended content in the content material page comprises: pushing the plurality of groups of first recommended contents to the terminal device such that the terminal device displays the plurality of groups of first 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 first 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 first recommended content sent by the terminal device, updating and training the content recommendation model according to the interaction feedback information and the first 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.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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.
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/JOSHUA BULLOCK/Primary Examiner, Art Unit 2153 July 14, 2026
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