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 .
Election/Restrictions
Applicant’s election without traverse of Group I, claims 1-9 and 13-20, in the reply filed on 11/26/2025 is acknowledged.
Claims 10-12 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a non-elected invention, there being no allowable generic or linking claim.
Claims 1-9 and 13-20 are elected.
Claims 10-12 are withdrawn.
Claims 1-9 and 13-20 are pending and rejected.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/6/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
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.
Claims 1-9 and 13-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1:
Claims 1-9 are directed to a method, which is a process. Claims 13-20 are directed to a system, which is a machine. Therefore, claims 1-9 and 13-20 are directed to one of the four statutory categories of invention.
Step 2A (Prong 1):
Claim 1 sets forth the following limitations which recite the abstract idea of providing product recommendations:
obtaining a first set of user interaction sequences associated with a source domain, the source domain being associated with a first category of items and a first group of users;
obtaining a second set of user interaction sequences associated with a target domain, the target domain being associated with a second category of items and a second group of users, the second category being different from the first category, and one or more users of the second group overlapping with one or more of the users of the first group;
performing self-supervised learning with a neural network in an embedding space to learn representations of the first and second sets of user interaction sequences, including embedding the user interaction sequences associated with the source domain and the user interaction sequences associated with the target domain in parallel, in which an encoder associated with the source domain and an encoder associated with the target domain share weights;
applying an adversarial learning component to the encoders associated with the source and target domains, the adversarial learning component including a domain discriminator that is configured to classify a particular domain according to a given user interaction sequence;
performing contrastive learning on outputs of the encoders, including (i) creating a set of augmented user interaction sequences from the first and second sets of user interaction sequences, and (ii) applying a contrastive loss function to drive the set of augmented user interaction sequences toward one or more of the user interaction sequences of the first and second sets in the embedding space; and
training the model according to cross domain recommendations for user interaction sequences that are associated with one or more of the overlapping users, the trained model being configured to predict a given category of items to be selected by a given user.
The recited limitations as a whole set forth the process for providing product recommendations. These limitations amount to certain methods of organizing human activity, including commercial or legal interactions (e.g. advertising, marketing or sales activities or behaviors).
Such concepts have been identified by the courts as abstract ideas (see: MPEP 2106).
Step 2A (Prong 2):
Examiner acknowledges that claim 1 does recite additional elements, such as a neural network, etc.
Taken individually and as a whole, claim 1 does not integrate the recited judicial exception into a practical application of the exception. The claim merely includes instruction to implement an abstract idea on a computer, or to merely use a computer as a tool to perform an abstract idea, while the additional elements do no more than generally link the use of a judicial exception to a particular field of technological environment or field of use.
Furthermore, this is also because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement a judicial exception with a particular machine, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
In view of the above, under Step 2A (Prong 2), claim 1 does not integrate the recited exception into a practical application (see again: MPEP 2106).
Step 2B:
When taken individually or as a whole, the additional elements of claim 1 do not provide an inventive concept (i.e. whether the additional elements amount to significantly more than the exception itself). As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer device to perform the receiving and determining steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Certain additional elements also recite well-understood, routine, and conventional activity (See MPEP 2106.05(d)).
Even when considered as an ordered combination, the additional elements of claim 1 do not add anything further than when they are considered individually.
In view of the above, claim 1 does not provide an inventive concept under step 2B, and is ineligible for patenting.
Dependent claims 2-9 recite further complexity to the judicial exception (abstract idea) of claim 1, such as by further defining the process for providing product recommendations. Thus, each of claims 2-9 are held to recite a judicial exception under Step 2A (Prong 1) for at least similar reasons as discussed above.
Therefore, dependent claims 2-9 do not add “significantly more” to the abstract idea. The dependent claims recite additional functions that describe the abstract idea and only generally link the abstract idea to a particularly technological environment, and applied on a generic computer. Further, the additional limitations fail to provide an improvement to the functioning of the computer, another technology, or a technical field.
Even when viewed as an ordered combination, the dependent claims simply convey the abstract idea itself applied on a generic computer and are held to be ineligible under Steps 2A/2B for at least similar rationale as discussed above regarding claim 1.
The analysis above applies to all statutory categories of invention. Regarding independent claim 13 (system), the claim recites substantially similar limitations as set forth in claim 1. As such, claim 13 and its dependent claims 14-20 are rejected for at least similar rationale as discussed above.
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 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.
(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.
Claims 1-9 and 13-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by PTO-892U.
Regarding claims 1 and 13, PTO-892U teaches a computer-implemented method (and related system) for training a model for cross-domain recommendations, the method comprising:
obtaining a first set of user interaction sequences associated with a source domain, the source domain being associated with a first category of items and a first group of users (p. 140, sec 2; general CDSR scenario, where each interaction sequence involves two domains, namely domain 𝑋 and domain 𝑌. Let S denote the overall interaction sequence set, where each instance (𝑆𝑋,𝑆𝑌,𝑆)𝑢 ∈ S belongs to a certain user 𝑢.);
obtaining a second set of user interaction sequences associated with a target domain, the target domain being associated with a second category of items and a second group of users, the second category being different from the first category, and one or more users of the second group overlapping with one or more of the users of the first group (p. 140, sec 2; general CDSR scenario, where each interaction sequence involves two domains, namely domain 𝑋 and domain 𝑌. Let S denote the overall interaction sequence set, where each instance (𝑆𝑋,𝑆𝑌,𝑆)𝑢 ∈ S belongs to a certain user 𝑢.);
performing self-supervised learning with a neural network in an embedding space to learn representations of the first and second sets of user interaction sequences, including embedding the user interaction sequences associated with the source domain and the user interaction sequences associated with the target domain in parallel, in which an encoder associated with the source domain and an encoder associated with the target domain share weights (p. 140, sec 3; Graphical and attentional encoder, which includes an embedding layer, a graph neural network module, and a self-attention module to generate a series of sequential representations (i.e., user representations) for each interaction sequence.);
applying an adversarial learning component to the encoders associated with the source and target domains, the adversarial learning component including a domain discriminator that is configured to classify a particular domain according to a given user interaction sequence (p. 142, sec 3.3.2; Further, it follows the min-max objective as formalized in generative adversarial network (GAN) 6], and the GAN objective is closely related to JS divergence that can be used in MI estimation.);
performing contrastive learning on outputs of the encoders, including (i) creating a set of augmented user interaction sequences from the first and second sets of user interaction sequences, and (ii) applying a contrastive loss function to drive the set of augmented user interaction sequences toward one or more of the user interaction sequences of the first and second sets in the embedding space (p. 141, sec 3.3; incorporate the cross-domain preference to make better recommendations. Hence, inspired by the infomax principle [3, 41], we develop a novel contrastive infomax objective to improve correlation between the single-domain representations and cross-domain representations.); and
training the model according to cross domain recommendations for user interaction sequences that are associated with one or more of the overlapping users, the trained model being configured to predict a given category of items to be selected by a given user (p. 140, sec 3; Sequential training objective, which includes two training objectives for single-domain and cross-domain interaction sequences to obtain single-domain and cross-domain user representations.).
Regarding claims 2 and 16, PTO-892U teaches the above method and system of claims 1 and 13. PTO 892-U also teaches further comprising:
receiving, by a processing device, user input regarding an item (p. 140, sec 2, 3);
identifying, by the processing device according to the trained model, one or more items of the given category of items (p. 140, sec 2, 3); and
causing, by the processing device, the one or more identified items to be presented to the given user (p. 140, sec 2, 3).
Regarding claim 3, PTO-892U teaches the above method of claim 1. PTO 892-U also teaches further comprising reordering at least one of the first set of user interaction sequences or the second set of user interaction sequences (p. 140, sec 3.1.3; padding technique).
Regarding claim 4, PTO-892U teaches the above method of claim 3. PTO 892-U also teaches wherein the reordering comprises:
generating a binary mask vector of either the first set of user interaction sequences or the second set of user interaction sequences (p. 140, footnote 2; constant zero vector); and
applying a random shuffling to reorder one or more non-zero values in the binary vector (p. 140, footnote 2; constant zero vector).
Regarding claim 5, PTO-892U teaches the above method of claim 1. PTO 892-U also teaches further comprising creating a nominal overlapping user based upon a first user that only has interaction sequences in the source domain and a second user that has interaction sequences in both the source domain and the target domain (p. 141, sec 3.2.1).
Regarding claim 6, PTO-892U teaches the above method of claim 5. PTO 892-U also teaches wherein the second user's interaction sequences in the source domain correlates with the first user's interaction sequences in the source domain (p. 141, sec 3.2.1).
Regarding claims 7 and 17, PTO-892U teaches the above method and system of claims 1 and 13. PTO 892-U also teaches wherein embedding the user interaction sequences associated with the source domain and the user interaction sequences associated with the target domain in parallel comprises embedding into a twin transformer encoder layer of an encoder component of the neural network (p. 143, sec 4.2.2; parallel split-joint scheme).
Regarding claims 8 and 18, PTO-892U teaches the above method and system of claims 1 and 13. PTO 892-U also teaches wherein the method includes the domain discriminator performing binary classification according to a user latent representation (p. 142, sec 3.3.1).
Regarding claims 9 and 19, PTO-892U teaches the above method and system of claims 8 and 18. PTO 892-U also teaches wherein the binary classification produces a unified user behavior sequence embedding vector (p. 142, sec 3.3.1).
Regarding claim 14, PTO-892U teaches the above system of claim 13. PTO 892-U also teaches wherein the neural network has a transformer architecture (p. 140, sec 3.1.2-3.1.3).
Regarding claim 15, PTO-892U teaches the above system of claim 13. PTO 892-U also teaches wherein the encoders are attention-based encoders (p. 140, sec 3.1).
Regarding claim 20, PTO-892U teaches the above system of claim 13. PTO 892-U also teaches wherein the domain discriminator includes a fully connected network (p. 142, sec 3.3.1).
Conclusion
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/ANAND LOHARIKAR/Primary Examiner, Art Unit 3689