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 .
Claim Rejections - 35 USC § 103
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1, 6, 7, 8, 13, 14, 15 and 20 are rejected under AIA 35 U.S.C. 103(a) as being unpatentable over by Li He et al. [Click-Through Rate Prediction with Multi-Modal Hypergraphs] in view of Jingying Chen et al. [US 20250246021 A1].
Regarding claim 1, Li teaches:
1. A method for training a click rate prediction model, wherein the click rate prediction model comprises a hypernetwork module and a prediction module (i.e. We propose a model to exploit the temporal user-item interactions to guide the representation learning with multi-modal features, and further predict the user click rate of the micro-video item- Abstract), and the method comprises:
obtaining sample feature information and a label value, wherein the sample feature information comprises feature information of a sample user (i.e. For each user 𝑢, we denote the user’s temporal behavior as 𝐵𝑐u responding to the current time, and sequential view user behavior as 𝐵𝑢s according to a time slot. We further utilize K(𝐵uc) and K(𝐵us) to represent the set of items in the sequential behavior, respectively- page 692, ¶2) and feature information of a target object (i.e. We also have multi-modal information associated with each item, such as visual, acoustic and textual features. For instance, we denote 𝑀 = {𝑣, 𝑎, 𝑥} as the multi-modal tuple, where 𝑣, 𝑎, and 𝑥 represent the visual, acoustic, and textual modalities, respectively- page 692, ¶1), and the label value is configured to indicate whether the sample user interacts with the target object (i.e. We also have historical interactions, such as “click” between users and items- page 692, ¶1);
obtaining a click rate prediction value of the sample user on the target object using the prediction module (i.e. we use their metadata and profiles and define an embedding matrix E𝑈 for each user u𝑗… we apply a time-aware slot window to form the input item embedding matrix
E
I
t
n
∈
R
l
×
d
… we also form an embedding matrix
E
A
t
n
∈
R
k
×
d
for each item from the entire multi-modality attribute embedding matrix M𝐴, where 𝑘 is the number of item modalities, page 693, ¶4…The function 𝑓 (・, ・, ・) is implemented with a simple bilinear network- page 695, ¶1), according to the sample feature information and the plurality of adjacent matrixes (i.e. We define the loss function 𝐿𝑈𝐼𝑃 for a single user, which will can be extended over the user set in a straightforward way. The outcome from 𝑓 (.) for each user can be constructed as a user-interest matrix F and compared with the threshold 𝛿 to output the 𝐿-dimensions vector v ∈ R1×𝐿- page 695, ¶1); and
training the click rate prediction model according to the label value and the click rate prediction value (i.e. Our goal is learning user preferences from the hypergraph structure and predicting the probability that a user clicks the recommended entities- page 692, ¶1, Interest-based User Hypergraph Generation Modeling. We aim to utilize self-supervised learning for the user-interest matrix F ∈ R𝐿×d , where 𝐿 denote the user counts and 𝑑 denote the number of multi-modalities according to items. We trained the weights {𝜃𝑎, 𝜃𝑏, 𝜃𝑐 } for each modalities).
However, Li does not teach explicitly:
obtaining a plurality of adjacent matrixes for feature interaction by processing the feature information of the target object based on the hypernetwork module.
In the same field of endeavor, Chen teaches:
obtaining a plurality of adjacent matrixes for feature interaction by processing the feature information of the target object based on the hypernetwork module (i.e. an adjacency matrix acquisition module, configured to calculate an association relationship among the cue features corresponding to the upper half facial sample image, the lower half facial sample image, and the global facial sample image, and acquire adjacency matrices corresponding to the upper half facial sample image, the lower half facial sample image, and the global facial sample image- ¶0031).
It would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Li with the teachings of Chen so that the performance of the teacher model is further improved.
Regarding claim 6, Li and Chen teach all the limitations of claim 1 and Li further teaches:
wherein training the click rate prediction model according to the label value and the click rate prediction value comprises: calculating a loss function according to the label value and the click rate prediction value; and adjusting a model parameter of the hypernetwork module and a model parameter of the prediction module according to the loss function (i.e. see section 2.31 along with equations 9-11, page 694).
Regarding claim 7, Li and Chen teach all the limitations of claim 1 and Li further teaches:
obtaining feature information of a user and feature information of a target object; inputting the feature information of the user and the feature information of the target object into a pre-trained click rate prediction model, wherein the click rate prediction model is obtained by training using the method as claimed in claim 1; and obtaining a click rate prediction value output by the click rate prediction model, and determining the click rate prediction value as a probability of the user interacting with the target object (i.e. We propose a model to exploit the temporal user-item interactions to guide the representation learning with multi-modal features, and further predict the user click rate of the micro-video item. We design a Hypergraph Click- Through Rate prediction framework (HyperCTR) built upon the hyperedge notion of hypergraph neural networks, which can yield modal-specific representations of users and micro-videos to better capture user preferences. We construct a time-aware user-item bipartite network with multi-modal information and enrich the representation of each user and item with the generated interestsbased user hypergraph and item hypergraph. Through extensive experiments on three public datasets, we demonstrate that our proposed model significantly outperforms various state-of-the-art methods- Abstract…Problem 1 Click-Through Rate Prediction Given a target user intent sequence S, and its group-aware hypergraph Ggtn and item hypergraph Gitn , both of them depending on the time sequence 𝑇 , this problem can be formulated as a function 𝑓 (𝑢, Ggtn , Gitb, 𝑖) →𝑦 for a recommended item 𝑖, where denotes 𝑦 the probability that user clicks or not- page 692, ¶7).
Regarding claim 8, apparatus claim 8 is drawn to the apparatus using/performing the same method as claimed in claim 1. Therefore, apparatus claim 8 corresponds to method claim 1, and is rejected for the same reasons of obviousness as used above.
Regarding claim 13, apparatus claim 13 is drawn to the apparatus using/performing the same method as claimed in claim 6. Therefore, apparatus claim 13 corresponds to method claim 6, and is rejected for the same reasons of obviousness as used above.
Regarding claim 14, apparatus claim 14 is drawn to the apparatus using/performing the same method as claimed in claim 7. Therefore, apparatus claim 14 corresponds to method claim 7, and is rejected for the same reasons of obviousness as used above.
Regarding claim 15, computer-readable medium storing instructions claim 15 corresponds to the same method as claimed in claim 1, and therefore is also rejected for the same reasons of obviousness as listed above.
Regarding claim 20, computer-readable medium storing instructions claim 20 corresponds to the same method as claimed in claim 6, and therefore is also rejected for the same reasons of obviousness as listed above.
Additional Prior Art Listing
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Xu, Chengfeng, et al. "Graph contextualized self-attention network for session-based recommendation." Ijcai. Vol. 19. No. 2019. 2019.
Shen, Qijie, et al. "Hierarchically fusing long and short-term user interests for click-through rate prediction in product search." Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2022.
Min, Erxue, et al. "Neighbour interaction-based click-through rate prediction via graph-masked transformer." Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2022.
Zhang, Yuyu, et al. "Sequential click prediction for sponsored search with recurrent neural networks." Proceedings of the AAAI conference on artificial intelligence. Vol. 28. No. 1. 2014.
Allowable Subject Matter
Claims 2-5, 9-12 and 16-19 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
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CLIFFORD HILAIRE
Primary Examiner
Art Unit 2488
/CLIFFORD HILAIRE/Primary Examiner, Art Unit 2488
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