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 .
The instant application having Application No. 19/316,582 filed on 09/02/2024 is presented for examination by the Examiner. Claims 1-20 are currently pending in the present application.
Drawings
The drawings filed 09/02/2025 are accepted for examination purposes.
Information Disclosure Statement
As required by M.P.E.P. 609, the Applicant's submission of the Information Disclosure Statement dated 09/02/2025 is acknowledged by the Examiner and the cited references have been considered in the examination of the claims now pending.
Claim Rejections - 35 USC § 102
5. 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
6. Claims 1-2, 11-12, and 20 rejected under 35 U.S.C. 102(a)(2) as being anticipated by Chujie Zheng (US-20250348740-A1).
As per claim 1, Zheng teaches “A method for content recommendation, comprising”:
“determining a plurality of sets of candidate recommended content from a content library using a plurality of content screening strategies, the plurality of content screening strategies being based on different content ranking criteria, respectively,” (fig. 8, [0022], [0027], [0032], [0054]);
“determining, using a trained machine learning model, a recommendation score of each piece of candidate recommended content in the plurality of sets of candidate recommended content relative to a target user based on user reference information of the target user and content reference information of the plurality of sets of candidate recommended content,” ([0016], [0021]-[0022], [0057]-[0058]); and
“determining, based on the recommendation score corresponding to each piece of candidate recommended content in the plurality of sets of candidate recommended content, a set of target
recommended content from the plurality of sets of candidate recommended content for providing
to the target user,’ ([0021]-[0022], [0059]-[0061]).
As per claim 2, Zheng further shows “wherein determining the plurality of sets of candidate
recommended content from the content library using the plurality of content screening strategies
comprises:
for each of the plurality of content screening strategies, ([0032], [0054])
ranking recommended content in the content library based on a content ranking criterion
corresponding to the content screening strategy,” ([0032], [0054], [0057); and
“selecting a set of candidate recommended content in the content library based on the ranking result,” ([0059]-[0061]).
As per claim 11, Zheng teaches “An electronic device, comprising”:
“at least one processor,” (fig. 10); and
at least one memory coupled to the at least one processor and storing instructions for
execution by the at least one processor, the instructions, when executed by the at least one
processor, causing the electronic device to perform a method for content recommendation,
comprising:
determining a plurality of sets of candidate recommended content from a content library using a plurality of content screening strategies, the plurality of content screening strategies being based on different content ranking criteria, respectively,” (fig. 8, [0022], [0027], [0032], [0054]);
“determining, using a trained machine learning model, a recommendation score of each piece of candidate recommended content in the plurality of sets of candidate recommended content relative to a target user based on user reference information of the target user and content reference information of the plurality of sets of candidate recommended content,” ([0016], [0021]-[0022], [0057]-[0058]); and
“determining, based on the recommendation score corresponding to each piece of candidate recommended content in the plurality of sets of candidate recommended content, a set of target
recommended content from the plurality of sets of candidate recommended content for providing
to the target user,” ([0021]-[0022], [0059]-[0061]).
As per claim 12, Zheng further shows “wherein determining the plurality of sets of candidate
recommended content from the content library using the plurality of content screening strategies
comprises:
for each of the plurality of content screening strategies, ([0032], [0054])
“ranking recommended content in the content library based on a content ranking criterion corresponding to the content screening strategy,” ([0032], [0054], [0057); and
“selecting a set of candidate recommended content in the content library based on the ranking result,” ([0059]-[0061]).
As per claim 20, Zheng teaches “A non-transitory computer readable storage medium with a computer program stored thereon, wherein the computer program, when executed by a processor, implements a method for content recommendation, comprising:
determining a plurality of sets of candidate recommended content from a content library using a plurality of content screening strategies, the plurality of content screening strategies being based on different content ranking criteria, respectively,” (fig. 8, [0022], [0027], [0032], [0054]);
“determining, using a trained machine learning model, a recommendation score of each piece of candidate recommended content in the plurality of sets of candidate recommended content relative to a target user based on user reference information of the target user and content reference information of the plurality of sets of candidate recommended content,” ([0016], [0021]-[0022], [0057]-[0058]); and
“determining, based on the recommendation score corresponding to each piece of candidate
recommended content in the plurality of sets of candidate recommended content, a set of target
recommended content from the plurality of sets of candidate recommended content for providing
to the target user,” ([0021]-[0022], [0059]-[0061]).
Allowable Subject Matter
7. Claims 3-10 and 13-19 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
8. The prior art made of record, listed on PTO 892 provided to Applicant is considered to have relevancy to the claimed invention. Applicant should review each identified reference carefully before responding to this office action to properly advance the case in light of the prior art.
Contact Information
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to KIM T NGUYEN whose telephone number is (571)270-1757. The examiner can normally be reached on Mon-Thurs 6-4:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kavita Stanley can be reached on (571)272-8352. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Jul. 29, 2026
/KIM T NGUYEN/Primary Examiner, Art Unit 2153
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