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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 7/9/2026 has been entered.
Claims 1, 3, 6-13, 17-20 are pending, claims 1, 3, 6, 7, 9, 11, 13, 17, 19, and 20 are amended, claims 2, 4-5, and 14-16 are cancelled.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Step 1: The claims 1, 3, 6-12 are a system, claims 13, 17-19 are a method, and claims 20 is a computer readable medium. Thus, each independent claim, on its face, is directed to one of the statutory categories of 35 U.S.C. §101. However, the claims 1, 3, 6-13, 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong 1: The independent claims (1, 13 and 20, taking claim 1 as a representative claim) recite:
An electronic device, comprising: circuitry configured to:
receive first history information associated with a set of users for a first item of a set of items;
receive second history information associated with the set of items for a specific user of the set of users;
receive first correlation information associated with the set of items for the specific user of the set of users;
determine first similarity information associated with each user of the set of users with respect to remaining users of the set of users;
determine, based on the received first correlation information, second similarity information associated with each item of the set of items with respect to remaining items of the set of items;
apply a user sequence header on the received first correlation information associated with the set of items for the specific user of the set of users, wherein the user sequence header corresponds to at least one second item of the set of items;
receive social network information associated with each user of the set of users with respect to remaining users of the set of users;
determine first neighborhood information associated with the specific user of the set of users based on the first similarity information, wherein the first neighborhood information identifies one or more neighboring users of the set of users whose similarity scores satisfy a threshold criterion;
execute a convolution summation on the received first history information, the determined first similarity information, the received social network information, and the determined first neighborhood information to generate a convolution result;
apply a linear projection to the convolution result to generate an integrated user representation;
determine a first embedding associated with each user of the set of users for the first item, based on the integrated user representation;
determine a second embedding associated with each item of the set of items for the specific user, based on the received second history information and the determined second similarity information;
apply a first transformer model on the determined first embedding, wherein the first transformer model corresponds to a shared Bidirectional Encoder Representations from Transformers (BERT) modeland the determined first embedding corresponds to an integrated representation generated from heterogeneous data sources including the first history information, the first similarity information, and the social network information;
apply a second transformer model on the determined second embedding;
determine the at least one second item from the set of items based on the application of the second transformer model;
generate a masked-user sequence corresponding to the first item based on a masking operation of a user from the set of users associated with the first item;
reconstruct a masked user from the masked-user sequencebased on contextual information encoded in the first embedding;
train the shared BERT model based on minimization of a loss associated with the reconstruction of the masked user from the first embedding;
render, based on the trained shared BERT model, first recommendation information including the reconstructed masked user, wherein the first recommendation information is generated based on the integrated user representation;
and render, based on the determined at least one second item, second recommendation information including the determined at least one second item for the specific user.
These limitations, except for the italicized portions, under their broadest reasonable interpretations, recite certain methods of organizing human activity for managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as well as commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). The claimed invention recites steps for receiving historical information about a user and user item interactions, determining an embedding for the user item interactions, applying a transformer model to determine and user-item pair. The steps under its broadest reasonable interpretation specifically fall under sales activities. The Examiner notes that although the claim limitations are summarized, the analysis regarding subject matter eligibility considers the entirety of the claim and all of the claim elements individually, as a whole, and in ordered combination.
Prong 2: This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of
An electronic device, comprising: circuitry configured to: (claim 1)
A method, comprising: in an electronic device: (claim 13)
A non-transitory computer-readable medium having stored thereon, computer-executable instructions that when executed by an electronic device, causes the electronic device to execute operations, the operations comprising: (claim 20)
apply a user sequence header on the received first correlation information associated with the set of items for the specific user of the set of users, wherein the user sequence header corresponds to at least one second item of the set of items;
apply a first transformer model on the determined first embedding, wherein the first transformer model corresponds to a shared Bidirectional Encoder Representations from Transformers (BERT) modeland the determined first embedding corresponds to an integrated representation generated from heterogeneous data sources including the first history information, the first similarity information, and the social network information;
apply a second transformer model on the determined second embedding;
train the shared BERT model based on minimization of a loss associated with the reconstruction of the masked user from the first embedding;
render, based on the trained shared BERT model, first recommendation information including the reconstructed masked user, wherein the first recommendation information is generated based on the integrated user representation;
The additional elements emphasized above are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of processing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The limitations not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application – MPEP 2106.05(f).
Accordingly, these additional elements when considered individually or as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The independent claims are directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A Prong two, the additional elements in the claims amount to no more than mere instructions to apply the judicial exception using a generic computer component.
Even when considered as an ordered combination, the additional elements of claim 1, 13, and 20 do not add anything that is not already present when they are considered individually. Therefore, under Step 2B, there are no meaningful limitations in claims 1, 13, and 20 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (see MPEP 2106.05).
As such, independent claims 1, 13, and 20 are ineligible.
Dependent claims 3, 6-12 and 17-19 when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. §101 because the additional recited limitations fail to establish that the claims are not directed to the same abstract idea of Independent Claims 1, 13 and 19 without significantly more.
Claim 3 recites wherein the second transformer model corresponds to the shared BERT model. The limitation merely further limits that abstract idea and does not integrate the judicial exception into a practical application.
Claim 6 recites wherein the first correlation information corresponds to a masked item from the set of items, for the specific user, and the circuitry is further configured to train the second transformer model based on the masked item corresponding to the specific user. The limitation merely further limits that abstract idea and does not integrate the judicial exception into a practical application.
Claim 7 recites wherein the circuitry is further configured to determine second neighborhood information associated with the set of items for the specific user of the set of users, based on the first correlation information, the determination of the at least one second item from the set of items is further based on the determined second The limitation merely further limits that abstract idea and does not integrate the judicial exception into a practical application.
Claim 8 recites wherein the circuitry is further configured to: receive second correlation information associated with the set of users for the first item of the set of items; and determine the first similarity information
Claim 9 recites wherein the circuitry is further configured to apply an item sequence header on the received second correlation information associated with the set of users for the first item of the set of items, and the item sequence header corresponds to the reconstructed masked user. The limitation merely further limits that abstract idea and does not integrate the judicial exception into a practical application.
Claim 10 recites wherein the second correlation information
Claim 11 recites wherein the circuitry is further configured to determine second neighborhood information associated with the set of users for the first item of the set of items, based on the second correlation information, the reconstruction of the masked user is further based on the determined second neighborhood information, and the second neighborhood information is indicative of each user of the set of users correlated with the first item. The limitation merely further limits that abstract idea and does not integrate the judicial exception into a practical application.
Claim 12 recites wherein the social network information includes at least one of: a set of relationships between the set of users on a set of social network platforms, or a set of preferences corresponding to the set of items for each user of the set of users. The limitation merely further limits that abstract idea and does not integrate the judicial exception into a practical application.
Claims 17-19 recite parallel claim language and therefore are also rejected for the reasons set forth above.
For these reasons claims 1, 3, 6-13, 17-20 are rejected under 35 USC 101.
Subject Matter Free of Prior Art
Claims 1, 3, 6-13, 17-20 are determined to have overcome the prior art of rejection and are free of prior art, however the claims remain rejected under 35 USC 101, as set forth above. The subject matter of claims previously pending claims 5 and 16 and intervening claims 2, 4, 14 and 15 is now recited in claims 1, 13 and now 20. For these reasons the previous reasons for “subject matter free of prior art now applies to claims 1, 13, 20 and their dependent claims.
The closest art of record was found to be as follows:
Narahara (US 20050144499) shows the labeling of the matrix in Figure 4 and Figure 5 “recommended user” “similar user” vs. Programs labeled by category as headers that correspond to the groups of users.
HybridBERT4Rec discloses ITEM/USER MASKING In CF-HybridBERT4rec, we aim to extract the target item representation which contains the similarity level between all neighbors and the target user. For each target item, there is a user sequence (see Fig. 6) that includes all users who have rated a target item, including the target user. Thus, we assume that other users besides target user are neighbors. In training, we utilize a random user masking to user sequence, aiming to allow the model to reconstruct the masked user as close to its original embedding as possible. After training is complete, we receive the network which is able to construct the next user representation based on the characteristics of users who interact with the target item. Therefore, in testing, we construct the target item representation by masking the target user and adding it to the end of the user sequence. The target item representation contains a comparison value between each neighboring user in the sequence and the target user. In other words, the target item representation represents the similarity level between all neighbors and the target user.
However, the updated search and previously cited references in the Final Office Action (dated 4/9/2026) of Ahmed, Rossi, and “GUIM” does not disclose the invention as claimed.
It was found that no references alone or in combination, neither anticipates, reasonable teaches, nor renders obvious the features of Applicant’s invention of the above claims. Therefore, none of the cited references disclose or render obvious each and every feature of the claimed invention and the claimed invention is determined to be free of the prior art. Although individually the claimed features could be taught, any combination of references would teach the claimed limitations using a piecemeal analysis, since references would only be combined and deemed obvious based on knowledge gleaned from the applicant's disclosure. Such a reconstruction is improper (i.e., hindsight reasoning). See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). The examiner emphasizes that it is the interrelationship of the limitations that renders these claims free of the prior art/additional art.
Therefore, it is hereby asserted by the Examiner that, in light of the above, that the claims are free of prior art as the references do not anticipate the claims and do not render obvious any further modification of the references to a person of ordinary skill in art.
Relevant Art Not Cited
Hsieh (US 20210174164) discloses processing embeddings through a matchmaking neural network training to map user embeddings to relevant content
Kopru (US20230101174) discusses determining similarities in combinations of user-item, item-item, query- item
Xu US20230206076 discloses the user to item mapping in Figure 1
CN110162706 discloses the construction of a user history behavior matrix showing user-item interaction for determining recommendations (see particularly claim 1)
Response to Arguments
With respect to the remarks directed to 35 USC 103 filed 7/9/2026, the rejection has been withdrawn in view of the claim amendments.
Applicant's arguments filed 7/9/2026 with respect to the rejection under 35 USC 101, have been fully considered but they are not persuasive.
With respect to the remarks directed to prong 1, as shown in the updated rejection, the limitations such a the generation of neighborhood information, threshold based user selection, convolution aggregation of heterogeneous data sources, linear projection into integrated user representation, masked user reconstruction are part of the abstract idea and the transformer model training has been considered as an additional element. The limitations indicated as part of the abstract idea are part of the data and organization of data making up the abstract idea of determining recommendations for a user. Additionally, the training architecture for a neural network model is shown as an additional element, discussed further in prong 2.
With respect to the remarks directed to prong 2, the examiner maintains the additional element of the neural network models and training are performed at a high level of generality, like that of the Recentive case, in which the requirements that the machine learning model be “iteratively trained” or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement. Recentive’s own representations about the nature of machine learning vitiate this argument: Iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning [see page 12 of the decision]. The details claimed directed to the issue described regarding cold start are rooted in the abstract idea with the additional elements of applying the machine learning recited at a high level of generality. The machine learning itself is not being improved beyond the continued learning of the model in order to improve the output (i.e. the recommendations). The examiner asserts the data set is being improved for the instances of sparse conditions and not the machine learning technology itself.
With respect to the remarks directed to Step 2B, the reasons discussed are maintained here. Further, the examiner asserts that the novelty of the invention lies in the combination of limitations which are directed to the abstract idea. More particularly it is noted that the consideration for 35 USC 102/103 and 35 USC 101 are different and one does not turn with the other. The improvement, however lies in the abstract idea, not in the implementation of the additional elements considered alone or in combination.
For at least these reasons the claims remain rejected under 35 USC 101.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VICTORIA E. FRUNZI whose telephone number is (571)270-1031. The examiner can normally be reached Monday- Friday 7-4 (EST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Marissa Thein can be reached at (571) 272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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VICTORIA E. FRUNZI
Primary Examiner
Art Unit TC 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 9/25/2026
