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 following action is in response to the original filing of 06/23/2023.
Claims 1-20 are pending and have been considered below.
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 (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 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.
Claims 1-9 and 11-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gong et al., US 2021/0406680 A1 published 12/30/2021 [“GONG”].
Regarding claim 1, GONG discloses a computer-implemented method comprising:
receiving training data that includes groups of virtual experiences and a respective user associated with each group, wherein each group includes a first virtual experience selected by the associated user and one or more second virtual experiences rejected by the associated user from a user interface in which the first virtual experience and the one or more second virtual experiences are presented together in a ranked order (¶30: elements are virtual software merchandise, ¶35-37: presenting list of elements in ranked order, ¶38-41: training pair group of respective user includes a selected element and a not-selected element) and wherein the virtual experiences are associated with item features and the associated user is associated with user features (¶47: element features and user features);
for each group in the groups of virtual experiences:
generating feature embeddings based on the item features of the virtual experiences in the group and the user features of the associated user (¶39-40: feature embedding label training based on user attention with elements);
calculating, from a machine-learning model, a pointwise loss for each virtual experience in the group based on the feature embeddings (¶47: generating pointwise loss for each element, ¶53);
calculating, from the machine-learning model, a comparator loss for a set that includes the first virtual experience and at least one of the one or more second virtual experiences (¶48: generate pairwise loss for set of selected and not-selected elements); and
adjusting one or more parameters of the machine-learning model based on the pointwise loss and the comparator loss (¶53: update network parameters using the pairwise and pointwise losses); and
obtaining a trained machine-learning model by iteratively performing the generating, calculating the pointwise loss, calculating the comparator loss, and adjusting the one or more parameters until a stopping criterion is met (¶56: training model by updating parameters and iterating until minimized total loss, Fig. 3).
Regarding claim 2, GONG discloses the method of claim 1, wherein adjusting the one or more parameters of the machine-learning model based on the pointwise loss and the comparator loss comprises performing backpropagation using a loss value that is a linear weighted combination of the pointwise loss and the comparator loss (¶53-56).
Regarding claim 3, GONG discloses the method of claim 1, wherein the pointwise loss for the virtual experience is based on an output probability predicted by the machine-learning model (¶47).
Regarding claim 4, GONG discloses the method of claim 1, wherein the comparator loss for each group is a sum of a respective pairwise loss for each comparison of the first virtual experience and a particular one of the one or more second virtual experiences (¶48-51, EQUATION 1).
Regarding claim 5, GONG discloses the method of claim 4, wherein the respective pairwise loss includes a sum of a difference in output probabilities associated with the first virtual experience and the particular one of the one or more second virtual experiences (¶48-51, EQUATION 1).
Regarding claim 6, GONG discloses the method of claim 4, wherein the respective pairwise loss further includes a hyperparameter that defines a minimum distance (¶50).
Regarding claim 7, GONG discloses the method of claim 1, further comprising:
obtaining a sequence of user features, wherein the sequence is based on user activity on a virtual experience platform that hosts the virtual experiences (¶47: user features include historical data of activity with elements); and
performing attention modeling based on the sequence of user features, wherein the feature embeddings are based on the attention modeling (¶40).
Regarding claim 8, GONG discloses the method of claim 7, wherein the user activity includes selecting particular virtual experiences (¶47: historical browsing data of the elements by a user).
Regarding claim 9, GONG discloses the method of claim 1, wherein the stopping criterion includes one or more of:
a computational budget for training being exhausted or a change in parameter values of at least one of the one or more parameters between successive iterations falling below a threshold (¶56: minimizing total loss, ¶24).
Regarding claims 11-15, claims 11-15 recite limitations similar to claims 1-5, respectively, and is similarly rejected.
Regarding claims 16-20, claims 16-20 recite limitations similar to claims 1-5, respectively, and are similarly rejected.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over GONG in view of Suag et al., US 12,547,935 B1 filed 06/27/2022 and published 02/10/2026 [“SUAG”].
Regarding claim 10, GONG discloses the method of claim 1, wherein the ranking model is trained.
GONG fails to explicitly disclose the method further comprising:
receiving candidate user features for a candidate user and candidate item features for a plurality of candidate virtual experiences;
outputting, by the trained machine-learning model, a rank for each of the candidate virtual experiences; and
causing the candidate virtual experiences to be displayed in a second user interface in order of the rank.
SUAG discloses methods for training and deploying a model for ranking content for users to browse (col 1 lines 61-67, col 2 lines 1-4). In particular, SUAG discloses deploying a trained ranking model, such that the trained ranking model can receive, as input, candidate user features for a candidate user and candidate item features for a plurality of candidate content, and output, in user interface for the candidate user, a ranking of the candidate content (col 14 lines 29-37: deploying trained model to live to re-rank a list of products for a given user, col 4 lines 28-39: plural use devices and interfaces, col 16 lines 58-62). Therefore it would have been obvious to one having ordinary skill in the art and the teachings of GONG and SUAG before them before the effective filing of the claimed invention combine the deployed a trained rank model to act on candidate features and items to provide a ranked ordering interface for a candidate user, as suggested by SUAG, with the trained rank model of GONG. One would have been motivated to make this combination in order to aid users in discovering product recommendations in specific marketing spaces, as suggested by SUAG (col 1 lines 31-41, lines 52-60).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW L TANK whose telephone number is (571)270-1692. The examiner can normally be reached Monday-Thursday 9a-6p.
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/ANDREW L TANK/Primary Examiner, Art Unit 2141
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