/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 § 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 1-20 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1, in step 1 of the 101 analysis set forth in MPEP 2106, the claim recites An electronic device. The claim recites a device which is interpreted as a machine. A machine is one of the four statutory categories of invention.
In Step 2A, Prong 1 of the 101 analysis set forth in MPEP 2106, the examiner has determined that the following limitations recite a process that, under broadest reasonable interpretation, covers a mental process or mathematical concept but for the recitation of generic computer components:
determine a first set of user embeddings and a first set of item embeddings based on the received collaborative filtering graph; (i.e., the broadest reasonable interpretation includes a step of evaluation and judgement and could be performed mentally or with pen and paper like labeling users and items, which is either a mental process of evaluation/judgement (MPEP 2106)).
determine a second set of user embeddings and a second set of item embeddings… (i.e., the broadest reasonable interpretation includes a step of evaluation and judgement and could be performed mentally or with pen and paper like labeling users and items, which is either a mental process of evaluation/judgement (MPEP 2106)).
construct a hypergraph from the received collaborative filtering graph; (i.e., the broadest reasonable interpretation includes a step of observation, evaluation, and judgement and could be performed mentally or with pen and paper like creating a graph from another graph, which is either a mental process of observation/evaluation/judgement (MPEP 2106)).
determine a third set of user embeddings and a third set of item embeddings based on the constructed hypergraph; (i.e., the broadest reasonable interpretation includes a step of observation, evaluation, and judgement and could be performed mentally or with pen and paper like labeling users and items based on a graph, which is either a mental process of observation/evaluation/judgement (MPEP 2106)).
determine a first contrastive loss based on the determined second set of user embeddings and the determined third set of user embeddings; (i.e., the broadest reasonable interpretation includes mathematical calculation of a contrastive loss function, a mathematical calculation is considered a mathematical concept (MPEP 2106)).
determine a second contrastive loss based on the determined second set of item embeddings and the determined third set of item embeddings; (i.e., the broadest reasonable interpretation includes mathematical calculation of a contrastive loss function, a mathematical calculation is considered a mathematical concept (MPEP 2106)).
determine a collaborative filtering score based on the determined first contrastive loss and the determined second contrastive loss; (i.e., the broadest reasonable interpretation includes a step of evaluation and judgement and could be performed mentally or with pen and paper like applying a score based on two values, which is either a mental process of evaluation/judgement (MPEP 2106)).
determine a recommendation of an item for a user based on the determined collaborative filtering score; (i.e., the broadest reasonable interpretation includes a step of evaluation and judgement and could be performed mentally or with pen and paper like recommending items with the highest score, which is either a mental process of evaluation/judgement (MPEP 2106)).
If the claim limitations, under their broadest reasonable interpretation, covers activities classified under Mental processes: concepts performed in the human mind (including observation, evaluation, judgement, or opinion) (see MPEP 2106.04(a)(2), subsection (III)) or Mathematical concepts: mathematical relationships, mathematical formulas or equations, or mathematical calculations (see MPEP 2106.04(a)(2), subsection (I)). Accordingly, the claim recites an abstract idea.
In Step 2A, Prong 2 of the 101 analysis, set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
An electronic device, comprising: circuitry configured to: (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
receive a collaborative filtering graph corresponding to a set of users and a set of items associated with the set of users; (i.e., the broadest reasonable interpretation of receiving a data instance is mere data gathering, which is an insignificant extra solution activity (MPEP 2106.05(g))).
apply a semantic clustering model on each of the determined first set of user embeddings and the determined first set of item embeddings; (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
…based on the application of the semantic clustering model; (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
and render the determined recommended item on a display device. (i.e., the broadest reasonable interpretation of displaying an output is mere data outputting, which is an insignificant extra solution activity (MPEP 2106.05(g))).
Since the claim does not contain any other additional elements, that amount to integration into a practical application, the claim is directed to an abstract idea.
In Step 2B of the 101 analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception:
Regarding limitations (X) and (XIII), under the broadest reasonable interpretation, recite steps of mere data gathering/outputting, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering/outputting as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018)).
Examiner uses Berkheimer: Option 2, a citation to one or more of the court decisions discussed in MPEP 2106.05(d)(II) as noting well-understood, routine, and conventional nature of the additional elements:
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II).
Regarding limitation (IX), under the broadest reasonable interpretation, merely recite steps that apply generic computer components to perform a judicial exception, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Similarly, limitations (XI) and (XII), under the broadest reasonable interpretation, merely recite steps that apply a generic clustering model to perform a judicial exception, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Considering additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 2, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 2 recites wherein the circuitry is further configured to: apply a graph neural network model on the received collaborative filtering graph, wherein each of the first set of user embeddings and the first set of item embeddings is further determined based on the application of the graph neural network model. Under the broadest reasonable interpretation, the limitations merely recite steps that apply a generic graph neural network to perform judicial exceptions, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Therefore, claim 2 does not solve the deficiencies of claim 1.
Regarding claim 3, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 3 recites wherein the circuitry is further configured to: determine a set of user-to-item correlations, a set of item-to-user correlations, and a set of user-to-user correlations based on the constructed hypergraph; Under the broadest reasonable interpretation, the limitations recite making correlations between items and users on a graph which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. Claim 3 also recites determine a fourth set of user embeddings based on the determined set of user-to-item correlations and the set of user-to-user correlations;. Under the broadest reasonable interpretation, the limitations recite labeling users based on multiple data correlations which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. Claim 3 also recites apply a first set of hypergraph convolution network (HGCN) models on the determined fourth set of user embeddings; determine the third set of user embeddings based on the application of the first set of HGCN models;. Under the broadest reasonable interpretation, the limitations merely recite steps that apply a generic HGCN model to perform judicial exceptions, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Claim 3 also recites determine a fourth set of item embeddings based on the determined set of item- to-user correlations;. Under the broadest reasonable interpretation, the limitations recite labeling items based on a data correlation which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. Claim 3 also recites apply a second set of HGCN models on the determined fourth set of item embeddings; and determine the third set of item embeddings based on the application of the second set of HGCN models. Under the broadest reasonable interpretation, the limitations merely recite steps that apply a generic HGCN model to perform judicial exceptions, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 3 does not solve the deficiencies of claim 1.
Regarding claim 4, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 4 recites wherein the semantic clustering model corresponds to a spectral clustering model configured for dimensionality reduction. Under the broadest reasonable interpretation, the limitations merely recite steps that apply a generic spectral clustering model to perform judicial exceptions, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Therefore, claim 4 does not solve the deficiencies of claim 1.
Regarding claim 5, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 5 recites determine a fifth set of user embeddings based on the first contrastive loss and third set of user embeddings; and determine a fifth set of item embeddings based on the second contrastive loss and third set of item embeddings. Under the broadest reasonable interpretation, the limitations recite labeling users using past user labels and labeling items using past item labels and loss values which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 5 does not solve the deficiencies of claim 1.
Regarding claim 6, it is dependent upon claim 5 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 6 recites wherein the circuitry is further configured to: determine final user embeddings based on the determined fifth set of user embeddings; and determine final item embeddings based on the determined fifth set of item embeddings, Under the broadest reasonable interpretation, the limitations recite labeling users using past user labels and labeling items using past item labels which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. Claim 6 also recites wherein the determination of the collaborative filtering score is further based on the determined final user embeddings and the determined final item embeddings. Under the broadest reasonable interpretation, the limitations recite determining a score based on user and item labels which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 6 does not solve the deficiencies of claim 5.
Regarding claim 7, it is dependent upon claim 6 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 7 recites wherein each of the determined final user embeddings and the determined final item embeddings corresponds to a concatenation of at least one of a collaborative view, a hypergraph view, or a semantic view. Under the broadest reasonable interpretation, the limitations recite labeling users and items by combining views which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 7 does not solve the deficiencies of claim 6.
Regarding claim 8, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 8 recites wherein the constructed hypergraph corresponds to a multiplex bipartite graph with homogenous edges. Under the broadest reasonable interpretation, the limitations recite constructing a graph with constraints which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 8 does not solve the deficiencies of claim 1.
Regarding claim 9, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 9 recites wherein a first edge type in the hypergraph corresponds to an interaction between a first user and a subset of first items associated with the first user, and a second edge type in the hypergraph corresponds to an interaction between a subset of second users and a second item associated with each of the subset of second users. Under the broadest reasonable interpretation, the limitations recite constructing a graph with constraints which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 9 does not solve the deficiencies of claim 1.
Regarding claim 10, in step 1 of the 101 analysis set forth in MPEP 2106, the claim recites A method, comprising:. The claim recites a method. A method is one of the four statutory categories of invention. For the Step 2A/2B analyses, since claim 10 is similar to claim 1 it is rejected under the same rationales as claim 1.
The additional limitation below fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception.
in an electronic device: (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
Considering additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claims 11-18, the claims are similar to claims 2-9 and rejected under the same rationales.
Regarding claim 19, in step 1 of the 101 analysis set forth in MPEP 2106, the claim recites A non-transitory computer-readable medium. The claim recites a non-transitory computer-readable medium which is interpreted as an article of manufacture. An article of manufacture is one of the four statutory categories of invention. For the Step 2A/2B analyses, since claim 19 is similar to claim 1 it is rejected under the same rationales as claim 1.
The additional limitation below fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception.
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: (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
Considering additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.\
Regarding claim 20, the claim is similar to claim 8 and rejected under the same rationales.
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, 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, 5-11, and 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over Xia, et al., Non-Patent Literature “Hypergraph Contrastive Collaborative Filtering” (“Xia”) in view of Lin, et al., Non-Patent Literature “Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning” (“Lin”).
Regarding claim 1, Xia discloses
An electronic device, comprising: circuitry configured to: (Xia, pg. 7 col. 1, “We use Adam optimizer with the learning rate of 1𝑒−3 and 0.96 decay ratio for model inference; training a model is interpreted as using an electronic device with circuitry (i.e. An electronic device, comprising: circuitry configured to:).”).
receive a collaborative filtering graph corresponding to a set of users and a set of items associated with the set of users; (Xia, pg. 2 col. 2 and see Figure 2, “We let U = {𝑢1,...,𝑢𝑖,...,𝑢𝐼} (|U| = 𝐼) and V = {𝑣1,...,𝑣𝑗,...,𝑣𝐽 } (|V| = 𝐽) represent the set of users and items, respectively [receive a collaborative filtering graph]. The interaction matrix A ∈ R𝐼×𝐽 indicates the implicit relationships between each user in U and his/her consumed items [corresponding to a set of users and a set of items associated with the set of users;].”).
determine a first set of user embeddings and a first set of item embeddings based on the received collaborative filtering graph; (Xia, pg. 3 col. 2, “Following the common collaborative filtering paradigm, we first represent each user 𝑢𝑖 and item 𝑣𝑗 with the embedding vectors e(𝑢) 𝑖 ∈R𝑑 and e(𝑣) 𝑗 ∈ R𝑑, respectively (𝑑 denotes the embedding dimensionality). We further define E(𝑢) ∈R𝐼×𝑑 and E(𝑣) ∈R𝐽×𝑑 to represent the embeddings corresponding to users and items [determine a first set of user embeddings and a first set of item embeddings based on the received collaborative filtering graph;].”).
…determine a second set of user embeddings and a second set of item embeddings…; (Xia, pg. 4 col. 1, “By integrating multiple embedding propagation layers, we refine the user/item representations to aggregate local neighborhood in formation for contextual embedding generation […determine a second set of user embeddings and a second set of item embeddings…;].”).
construct a hypergraph from the received collaborative filtering graph; (Xia, pg. 3 col. 2, “we propose a new hyper graph neural network with global dependency structure learning [construct a hypergraph] to comprehensively capture global collaborative effects for graph neural CF paradigm [from the received collaborative filtering graph;].”).
determine a third set of user embeddings and a third set of item embeddings based on the constructed hypergraph; (Xia, pg. 4 col. 1, “Γ(𝑢) 𝑙 ∈ R𝐼×𝑑 represents the hyper embeddings of users (𝑢𝑖 ∈ U) in hypergraph representation space under the 𝑙-th propagation layer. The hyper embeddings Γ(𝑣) 𝑙 of items(𝑣𝑗 ∈ V) can be generated in an analogous way [determine a third set of user embeddings and a third set of item embeddings based on the constructed hypergraph;].”).
determine a first contrastive loss based on the determined second set of user embeddings and the determined third set of user embeddings; determine a second contrastive loss based on the determined second set of item embeddings and the determined third set of item embeddings; (Xia, pg. 5 col. 1 and Figure 3c, “We design our contrastive learning component by maximizing the agreement between the explicit user-item interactive relationships and the implicit hypergraph based dependency; Figure 3c shows that the contrastive learning is based on comparing the local, or second embeddings, and the global, third embeddings, with two contrastive losses: one for the user and one for the item (i.e. determine a first contrastive loss based on the determined second set of user embeddings and the determined third set of user embeddings; determine a second contrastive loss based on the determined second set of item embeddings and the determined third set of item embeddings;).”).
determine a collaborative filtering score based on the determined first contrastive loss and the determined second contrastive loss; (Xia, pg. 2 col. 2 and Figure 3c, “Many existing CF approaches are designed with the various embedding functions to generate vectorized representations of users and items. Then, the similarity matching function is introduced to estimate the relevance score [determine a collaborative filtering score] between user 𝑢𝑖 and the candidate item 𝑣𝑗; Figure 3c shows that the HCCF considers user and item contrastive losses to determine collaborative filtering scores (i.e. based on the determined first contrastive loss and the determined second contrastive loss;).”).
determine a recommendation of an item for a user based on the determined collaborative filtering score; and render the determined recommended item on a display device. (Xia, abstract, “we propose a new self-supervised recommendation [determine a recommendation of an item for a user] framework Hypergraph Contrastive Collaborative Filtering (HCCF) [based on the determined collaborative filtering score;] to jointly capture local and global collaborative relations with a hypergraph enhanced cross-view contrastive learning architecture. In particular, the designed hypergraph structure learning enhances the discrimination ability of GNN-based CF paradigm, so as to comprehensively capture the complex high-order dependencies among users. Additionally, our HCCF model effectively integrates the hyper graph structure encoding with self-supervised learning to reinforce the representation quality of recommender systems; a recommender system is interpreted as having a display to provide users with recommendations (i.e. and render the determined recommended item on a display device.)”).
While Xia teaches a system for collaborative filtering recommendations using hypergraph embeddings, Xia does not explicitly teach:
apply a semantic clustering model on each of the determined first set of user embeddings and the determined first set of item embeddings;
embeddings based on the application of the semantic clustering model
Lin teaches:
apply a semantic clustering model on each of the determined first set of user embeddings and the determined first set of item embeddings; (Lin, pg. 4 col. 1, “In particular, similar users/items tend to fall in neighboring embedding space, and the prototypes are the center of clusters that represent a group of semantic neighbors. Thus, we apply a clustering algorithm [apply a semantic clustering model] on the embeddings of users and items to obtain the prototypes of users or items [on each of the determined first set of user embeddings and the determined first set of item embeddings;].”).
embeddings based on the application of the semantic clustering model (Lin, pg. 4 col. 1, “In particular, similar users/items tend to fall in neighboring embedding space, and the prototypes are the center of clusters that represent a group of semantic neighbors. Thus, we apply a clustering algorithm on the embeddings of users and items to obtain the prototypes of users or items [embeddings based on the application of the semantic clustering model].”).
Xia and Lin are both in the same field of endeavor (i.e. collaborative filtering). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Xia and Lin to teach the above limitation(s). The motivation for doing so is that collaborative filtering is improved by considering semantic characteristics of user and items (cf. Lin, pg. 4 col. 1, “we can identify the semantic neighbors by learning the latent prototype for each user and item. Based on this idea, we further propose the prototype-contrastive objective to explore potential semantic neighbors and incorporate them into contrastive learning to better capture the semantic characteristics of users and items in collaborative filtering.”).
Regarding claim 2, Xia in view of Lin teaches the electronic device according to claim 1. Xia further teaches wherein the circuitry is further configured to: apply a graph neural network model on the received collaborative filtering graph, wherein each of the first set of user embeddings and the first set of item embeddings is further determined based on the application of the graph neural network model. (Xia, pg. 3 col. 2, “Following the common collaborative filtering paradigm, we first represent each user 𝑢𝑖 and item 𝑣𝑗 with the embedding vectors e(𝑢) 𝑖 ∈R𝑑 and e(𝑣) 𝑗 ∈R𝑑, respectively (𝑑 denotes the embedding dimensionality). We further define E(𝑢) ∈R𝐼×𝑑 and E(𝑣) ∈R𝐽×𝑑 to represent the embeddings corresponding to users and items [wherein each of the first set of user embeddings and the first set of item embeddings is further determined based on the application of the graph neural network model.]. Inspired by the effectiveness of simplified graph convolutional network in LightGCN [apply a graph neural network model on the received collaborative filtering graph,]”).
Regarding claim 5, Xia in view of Lin teaches the electronic device according to claim 1. Xia further teaches:
wherein the circuitry is further configured to: determine a fifth set of user embeddings based on the first contrastive loss and third set of user embeddings; (Xia, pg. 5 col. 2 and Figure 3c, “We perform the contrastive learning between the local user embedding (z(𝑢) 𝑖,𝑙 ) and global hypergraph-guided representation Γ(𝑢) 𝑖,𝑙 . This allows the local and global dependency views to collaboratively supervise each other, which enhances the user representation; the enhanced user representation is interpreted as the fifth or final user embeddings as it is enhanced based on the training of third user embeddings, the hypergraph user embeddings, and the user contrastive loss (i.e. determine a fifth set of user embeddings based on the first contrastive loss and third set of user embeddings;).”).
and determine a fifth set of item embeddings based on the second contrastive loss and third set of item embeddings. (Xia, pg. 5 col. 2 and Figure 3c, “We perform the contrastive learning between the local user embedding (z(𝑢) 𝑖,𝑙 ) and global hypergraph-guided representation Γ(𝑢) 𝑖,𝑙 . This allows the local and global dependency views to collaboratively supervise each other, which enhances the user representation; looking at Figure 3c, the contrastive loss has the user contrastive loss Ls(u) and an item contrastive loss Ls(v) thus the fifth or final item embeddings is interpreted as having an analogous process to the fifth or final user embeddings (i.e. and determine a fifth set of item embeddings based on the second contrastive loss and third set of item embeddings.).”).
Regarding claim 6, Xia in view of Lin teaches the electronic device according to claim 5. Xia further teaches:
wherein the circuitry is further configured to: determine final user embeddings based on the determined fifth set of user embeddings; and determine final item embeddings based on the determined fifth set of item embeddings, (Xia, pg. 5 col. 2 and Figure 3c, “We perform the contrastive learning between the local user embedding (z(𝑢) 𝑖,𝑙) and global hypergraph-guided representation Γ(𝑢) 𝑖,𝑙 . This allows the local and global dependency views to collaboratively supervise each other, which enhances the user representation; the enhanced user representation is interpreted as the fifth or final user embeddings as it is enhanced based on the training of third user embeddings, the hypergraph user embeddings, and the user contrastive loss. The fifth or final item embeddings is interpreted as having an analogous process to the fifth or final user embeddings (i.e. determine final user embeddings based on the determined fifth set of user embeddings; and determine final item embeddings based on the determined fifth set of item embeddings,).”).
wherein the determination of the collaborative filtering score is further based on the determined final user embeddings and the determined final item embeddings. (Xia, abstract, “we propose a new self-supervised recommendation framework Hypergraph Contrastive Collaborative Filtering (HCCF) [wherein the determination of the collaborative filtering score] to jointly capture local and global collaborative relations with a hypergraph enhanced cross-view contrastive learning architecture; during inferencing/recommending stage, the HCCF model will have been trained based on the enhanced user/item representations to determine scores (i.e. is further based on the determined final user embeddings and the determined final item embeddings.).”).
Regarding claim 7, Xia in view of Lin teaches the electronic device according to claim 7. Xia further teaches wherein each of the determined final user embeddings and the determined final item embeddings corresponds to a concatenation of at least one of a collaborative view, a hypergraph view, or a semantic view. (Xia, pg. 5 col. 1, “Cross-View Collaborative Supervision. We take different views of the same user/item as the positive pairs (z𝑖,𝑙,Γ𝑖,𝑙), and treat views of different users/items as negative pairs. By doing so, our model learns discriminative representations by contrasting the generated positive and negative instances [wherein each of the determined final user embeddings and the determined final item embeddings corresponds to a concatenation of at least one of a collaborative view].”).
Regarding claim 8, Xia in view of Lin teaches the electronic device according to claim 1. Xia further teaches wherein the constructed hypergraph corresponds to a multiplex bipartite graph with homogenous edges. (Xia, pg. 4 col. 1 and Figure 2, “Hypergraph consists of a set of vertices and hyperedges, in which each hyperedge can connect any number of vertices [6]. In our hypergraph collaborative filtering scenario, we utilize hyperedges as intermediate hubs for global-aware information passing across users and items without the hop distance limit [wherein the constructed hypergraph corresponds to a multiplex bipartite graph with homogenous edges.].”).
Regarding claim 9, Xia in view of Lin teaches the electronic device according to claim 1. Xia further teaches wherein a first edge type in the hypergraph corresponds to an interaction between a first user and a subset of first items associated with the first user, and a second edge type in the hypergraph corresponds to an interaction between a subset of second users and a second item associated with each of the subset of second users. (Xia, pg. 4 col. 1 and Figures 2 and 3, “where Λ(𝑢) ∈ R𝐻×𝑑 denotes the hyperedge-specific embeddings for users, and 𝜎 denotes the Leaky ReLU mapping. Γ(𝑢) 𝑙 ∈ R𝐼×𝑑 represents the hyper embeddings of users (𝑢𝑖 ∈ U) in hypergraph representation space under the 𝑙-th propagation layer. The hyper embeddings Γ(𝑣) 𝑙 of items (𝑣𝑗 ∈ V) can be generated in an analogous way; Figures 2 and 3 shows that each item and user has a corresponding hyperedge designations (i.e. wherein a first edge type in the hypergraph corresponds to an interaction between a first user and a subset of first items associated with the first user, and a second edge type in the hypergraph corresponds to an interaction between a subset of second users and a second item associated with each of the subset of second users.).”).
Regarding claims 10-11 and 14-18, the claims are similar to claims 1-2 and 5-9 and are rejected under the same rationales.
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Xia, et al., Non-Patent Literature “Hypergraph Contrastive Collaborative Filtering” (“Xia”) in view of Lin, et al., Non-Patent Literature “Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning” (“Lin”) and further in view of Yan, et al., Non-Patent Literature “Rating-Based Collaborative Filtering Using Spectral Clustering Algorithm” (“Yan”).
Regarding claim 4, Xia in view of Lin teaches the electronic device according to claim 1. While the combination teaches a semantic clustering model, the combination does not explicitly teach wherein the semantic clustering model corresponds to a spectral clustering model configured for dimensionality reduction.
Yan teaches wherein the semantic clustering model corresponds to a spectral clustering model configured for dimensionality reduction. (Yan, pg. 3, “Spectral clustering aims to solve the problem of grouping large amounts of high dimensional data into a small number of clusters [wherein the semantic clustering model corresponds to a spectral clustering model configured for dimensionality reduction.].”).
Xia, in view of Lin, and Yan are both in the same field of endeavor (i.e. recommender systems). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Xia, in view of Lin, and Yan to teach the above limitation(s). The motivation for doing so is that spectral clustering improves recommendation speed (cf. Yan, abstract, “In recent years, the research of spectral clustering algorithm has been a new and efficient clustering analysis algorithm. In this paper, the sparsity and the real-time problem of traditional recommendation algorithms, a new recommendation algorithm based on spectral clustering is proposed. The spectral clustering process can improve the efficiency of spectral clustering algorithm. Spectral clustering can be performed off line, which will accelerate the speed of online recommendation.”).
Regarding claim 12, the claim is similar to claim 4 and rejected under the same rationales.
Claims 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Xia, et al., Non-Patent Literature “Hypergraph Contrastive Collaborative Filtering” (“Xia”) in view of Lin, et al., Non-Patent Literature “Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning” (“Lin”) and further in view of Chen, et al., US Pre-Grant Publication US20220076314A1 (“Chen”).
Regarding claim 19, the claim is similar to claim 1. While Xia in view of Lin teaches a system for collaborative filtering recommendations using hypergraph embeddings and semantic clustering, the combination does not explicitly teach the additional limitations 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:
Chen teaches 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: (Chen, ⁋83, “The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention [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:].”).
Xia, in view of Lin, and Chen are both in the same field of endeavor (i.e. recommender systems). Xia, in view of Lin, teaches a base method for recommending items based on graph embeddings. Chen teaches a known technique of using a computer to perform recommender functions. It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Xia, in view of Lin, and Chen to teach the above limitation(s). The motivation for doing so is that applying Chen’s known technique of using a computer to perform recommender functions to Xia’s, in view of Lin, base system of recommending items based on graph embeddings would yield predictable results.
Regarding claim 20, Xia in view of Lin and Chen teaches the non-transitory computer-readable medium according to claim 19. Xia further teaches wherein the constructed hypergraph corresponds to a multiplex bipartite graph with homogenous edges. (Xia, pg. 4 col. 1 and Figure 2, “Hypergraph consists of a set of vertices and hyperedges, in which each hyperedge can connect any number of vertices [6]. In our hypergraph collaborative filtering scenario, we utilize hyperedges as intermediate hubs for global-aware information passing across users and items without the hop distance limit [wherein the constructed hypergraph corresponds to a multiplex bipartite graph with homogenous edges.].”).
Allowable Subject Matter
Claims 3 and 12 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101 set forth in this Office action and to include all of the limitations of the base claim and any intervening claims. The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 3, Below are the closest cited references, each of which disclose various aspects of the claimed invention:
Lin, et al., CN113672811A discloses a system for hypergraph convolution collaborative filtering recommendation that uses hypergraphs to visualize the correlations between user and item sets. However, even though Lin teaches the use of a hypergraph convolution network for user and item, Lin does not explicitly teach using the hypergraph convolution network to also find correlations between user to user or creating fourth sets of user and item embeddings.
Jia, et al., “Hypergraph Convolutional Network for Group Recommendation” discloses a system that uses a member-level hypergraph convolutional network to learn group members’ personal preferences by capturing cross-group collaborative connections among users and items. However, even though Jia teaches the use of a hypergraph convolution network for group user and item correlations, Jia does not explicitly teach using the hypergraph convolution network to also find correlations between user to user or creating fourth sets of user and item embeddings.
Ji, et al., “Dual Channel Hypergraph Collaborative Filtering” discloses a system that uses a dual channel hypergraph collaborative filtering framework to learn the representation of users and items so that these two types of data can be interconnected while still maintaining their specific properties. However, even though Ji teaches the use of a embeddings for user and item, Ji does not explicitly teach using a hypergraph convolution network to find user to user correlations or creating fourth sets of user and item embeddings based on the correlation.
While the above prior arts disclose the aforementioned concepts, however, none of the prior arts, individually or in reasonable combination, discloses all the limitations in the manner recited in claim 3. Specifically, the claim requires three specific correlations: user-to-item, item-to-user, and user-to-user correlations. Then, the claim requires that a fourth set of user and items embeddings be created from the user-to-item and item-to-user correlations by using a hypergraph convolutional network. While the references cited above mention aspects of a hypergraph convolutional network, user and item correlations, and embeddings, they do not recite all the elements and necessary steps in claim 3. Therefore, claim 3 is allowable over the prior art.
Regarding claim 12, the claim is similar to claim 3 and allowable over the prior art under the same rationales.
Conclusion
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/N.S.W./Examiner, Art Unit 2148 /MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148