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 .
Preliminary Amendment
The preliminary amendment filed 8/26/2025 has been entered. Claims 15-16 have been amended, Claims 17-21 have been added. Claims 14 has been canceled.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claims 1-2, 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al. (US 2024/0137620 A1), hereinafter “Zhao”, and in view of Hao et al. (US 2023/0009814 A1), hereinafter “Hao”.
As per claim 1, Zhao teaches a method for recommendation comprising:
“acquiring a sequence of objects interacted by a user historically, sequence of objects being sorted in an order of interaction time” at [0015], [0030];
(Zhao teaches acquiring sequential inputs that are organized based on time. For example, a history of user behavior over time may be input as the sequential inputs, history of content that has been classified as being interacted with by user accounts, such as content has been watched. The sequential inputs are inputted into a recommendation engine to output recommend content to the user)
“generating a sequence of object distributions based on a plurality of probability distributions of a plurality of objects in the sequence of objects” at [0053];
(Zhao teaches generating a sequence of object distribution [p1, p2, … Pn] and [y1, y2, … yn] based on the plurality of probability distributions of a plurality of objects in the sequential input. One distribution is the Softmax output p and the other distribution is a vector y=[0, 0, 1, 0])
“generating a set of
(Zhao teaches measuring a difference (i.e., “distance”) between the output and the original embedding (as only y3=1))
“generating a recommendation result of recommending an object to the user based on the sequence of object distributions and the set of
(Zhao teaches the recommendation engine generate recommended content using the recommendation engine)
Zhao does not teach generating a set of “maximum mean discrepancy (MMD)” distances as claimed. However, Hao teaches a similar method for training information recommendation model including the steps of generating a set of maximum mean discrepancy (MMD) distances between a first distribution and a second distribution at [0090]-[0093]. Thus, it would have been obvious to one of ordinary skill in the art to combine Hao with Zhao’s teaching using the MMD distances to train the model sot that “a fairly good output is generated through mutual game learning between the discriminative model and the generative model, so that the generative model has higher prediction accuracy, and a generated pseudo sample has a better effect, thereby further improving the recommendation effect during information recommendation”, as suggested by Hao at [0095].
As per claim 2, Zhao and Hao teach the method of claim 1 discussed above. Hao also teaches: wherein “determining the MMD distance between the probability distributions of each pair of objects in the sequence of object distributions comprises: generating a sequence of sample embeddings by sampling a probability distribution of each object in the sequence of object distributions” at [0046]-[0053], [0081]-[0069].
Claims 15-17 recite similar limitations as in claims 1-2 and are therefore rejected by the same reasons.
Allowable Subject Matter
Claims 3-13, 18-21 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
Examiner's Note: Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KHANH B PHAM whose telephone number is (571)272-4116. The examiner can normally be reached Monday - Friday, 8am to 4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sanjiv Shah can be reached at (571)272-4098. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KHANH B PHAM/Primary Examiner, Art Unit 2166
August 24, 2026
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