DETAILED ACTION
This action is in response to the claims filed 12/19/2023 for Application number 18/545,762. Claims 1-20 are currently pending.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/19/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1,
Step 1 Analysis: Claim 1 is directed to a process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 1 recites, in part, The limitations of:
associate first datapoints of a first feature with a first node; can be considered to be an evaluation in the human mind
associate second datapoints of a second feature with a second node; can be considered to be an evaluation in the human mind
construct a graph from the first datapoints and the second datapoints can be considered to be an evaluation in the human mind
perform a comparison of a graph accuracy with a baseline accuracy can be considered to be an evaluation in the human mind
These limitations as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements - “interface circuitry” “machine readable instructions” and “programmable circuitry to at least one of instantiate or execute the machine readable instructions to:..”. Thus, this element in the claim is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B Analysis: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of utilizing “interface circuitry” “machine readable instructions” and “programmable circuitry to at least one of instantiate or execute the machine readable instructions to:..”. to perform the steps of the claimed process amount 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. The claim is not patent eligible.
Regarding claim 2, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the programmable circuitry is to store the association of the datapoints in response to the comparison of the graph accuracy being more accurate than the baseline accuracy. This limitation is an insignificant extra-solution activity and thus the judicial exception is not integrated into a practical application. The claim as a whole is directed to an abstract idea.
The claim does not include any additional elements that amount to significantly more than the judicial exception. This limitation is just a nominal or tangential addition to the claim, and is also well-understood, routine and conventional as evidenced by MPEP §2106.05(d)(II)(iv), “Storing and retrieving information in memory”. This limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, this additional element represents an insignificant extra-solution activity which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 3, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the programmable circuitry is to subsequently associate the datapoints in response to the comparison of the graph accuracy being less accurate than the baseline accuracy. This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 4, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the programmable circuitry is further to:
associate third datapoints of a third feature with the first node;
associate the third datapoints of the third feature with the second node; and append the third datapoints of the third feature to either the first node or the second node based on a comparison of a first node score and a second node score. This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 5, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the programmable circuitry is to associate third datapoints with an edge feature that connects the first node and the second node in response to:
i) a graph accuracy of an association of the third datapoints with the first node being less accurate than the baseline accuracy; and
ii) a graph accuracy of an association of the third datapoints with the second node being less accurate than the baseline accuracy.
These limitations amount to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 6, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the programmable circuitry is further to remove user identifiable information from a dataset that includes the first datapoints and the second datapoints. This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 7, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the programmable circuitry is further to determine a type of the datapoints of the dataset as belonging to either categorical features, binary features, or numerical features. This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 8, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the programmable circuitry is further to: determine a similarity of individual datapoints included in the dataset; and
generate a similarity graph based on the similarity of the individual datapoints. These limitations amount to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 9, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the programmable circuitry is further to: train the similarity graph and a bipartite graph to generate a trained similarity graph and a trained bipartite graph; and
perform graph neural network inference based on a combination of the trained similarity graph and the trained bipartite graph. These limitations amount to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 10, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the programmable circuitry is further to augment the datapoints based on results of the graph neural network inference. This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Claim 11 recites features similar to claim 1 and is rejected for at least the same reasons therein. Claim 11 additionally requires analysis for “non-transitory machine readable storage medium comprising instructions to cause programmable circuitry to at least…” however this is an additional element that amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
Regarding Claims 12-14, they recite features similar to claim 2-4 and are rejected for at least the same reasons therein.
Regarding Claim 15, it recites features similar to claim 1and is rejected for at least the same reasons therein.
Regarding Claims 16-20, they recite features similar to claim 6-9 (Claims 19 and 20 recite similar limitations of claim 9) and are rejected for at least the same reasons therein.
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.
Claims 1-3, 11-13, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. ("US 20260134278 A1", hereinafter "Chen") in view of Lee et al. ("ATripartite-Graph Based Recommendation Framework for Price-Comparison Services", hereinafter "Lee").
Regarding claim 1, Chen teaches An apparatus comprising: interface circuitry (¶0063]);
machine readable instructions (¶0062]); and
programmable circuitry to at least one of instantiate or execute the machine readable instructions to (¶0146):
associate first datapoints of a first feature with a first node; (“(“The feature generator 116 may receive a dataset comprising the graph data (e.g., at least one of a plurality of node data of a plurality of nodes, a plurality of edge data of a plurality of edges, a plurality of node embeddings associated with the nodes and/or a plurality of edge embeddings associated with the edges), and the graph data may be received or obtained, for example, from the dataset database 110 and/or the GNN 114.” [¶0096])
associate second datapoints of a second feature with a second node; (See ¶0096)
construct a graph from the first datapoints and the second datapoints; (“Output of the feature generator 116 may be input to the GNN 114 to train and improve the GNN 114.” [¶0098) and
However Chen fails to explicitly teach
perform a comparison of a graph accuracy with a baseline accuracy.
Lee teaches perform a comparison of a graph accuracy with a baseline accuracy.
(“In the first set of experiments, we compared the recommendation method using user & item bipartite graph, and the one using user & interest-field bipartite graph. The former is the default method (“baseline accuracy”) …Table7 shows the recall and precision with 10 or 20 recommendations by two methods.” [pg. 346, bottom para])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Chen’s teachings by performing a comparison between accuracies of a graph as taught by Lee. One would have been motivated to make this modification in order to demonstrate the improvement in accuracy by applying different strategies. [pg. 346, bottom para]
Regarding claim 2, Chen/Lee teaches The apparatus of claim 1, Lee teaches wherein the programmable circuitry is to store the association of the datapoints in response to the comparison of the graph accuracy being more accurate than the baseline accuracy. (See Table 7, pg. 347, shows a comparison of graph accuracy being “more accurate than baseline accuracy” and later uses this strategy for further experimentation)
Same motivation to combine the teachings of Chen/Lee as claim 1.
Regarding claim 3, Chen/Lee teaches The apparatus of claim 1, wherein the programmable circuitry is to subsequently associate the datapoints in response to the comparison of the graph accuracy being less accurate than the baseline accuracy. (See Table 8, evaluates the same recommendation using different graphs and uses the highest accuracy configuration afterwards which in turn implies the association of datapoints related to graphs that are less accurate than a baseline accuracy)
Claim 11 recites features similar to claim 1 and is rejected for at least the same reasons therein. Claim 11 additionally requires A non-transitory machine readable storage medium comprising instructions to cause programmable circuitry to at least (Chen, ¶0145)
Regarding claims 12 and 13, they are substantially similar to claims 2 and 3 respectively, and are rejected in the same manner, the same art, and reasoning applying.
Regarding claim 15, it is substantially similar to claim 1 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Lee and further in view of Li et al. ("Adversarial Privacy Preserving Graph Embedding against Inference Attack", hereinafter "Li").
Regarding claim 6, Chen/Lee teaches The apparatus of claim 1, however fails to explicitly teach wherein the programmable circuitry is further to remove user identifiable information from a dataset that includes the first datapoints and the second datapoints.
Li teaches wherein the programmable circuitry is further to remove user identifiable information from a dataset that includes the first datapoints and the second datapoints. (“In this paper, we propose Adversarial Privacy Graph Embedding (APGE), a graph adver sarial training framework that integrates the disentangling and purging mechanisms to remove users’ private information from learned node representations.” [Abstract])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Chen’s/Lee’s teachings in order to remove user identifiable information from node representations as taught by Li. One would have been motivated to make this modification as the proposed method preserves the structural information and utility attributes of a graph while concealing users’ private attributes from inference attacks. [Li, Abstract]
Regarding claim 16, it is substantially similar to claim 6 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Lee and further in view of Reynolds et al. ("US 20220261411 A1", hereinafter "Reynolds").
Regarding claim 7, Chen/Lee teaches The apparatus of claim 6, however fails to explicitly teach wherein the programmable circuitry is further to determine a type of the datapoints of the dataset as belonging to either categorical features, binary features, or numerical features.
Reynolds teaches wherein the programmable circuitry is further to determine a type of the datapoints of the dataset as belonging to either categorical features, binary features, or numerical features. (See ¶0056, “datatypes (e.g., string, numeric, categorical, Boolean, integer, etc.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Chen’s/Lee’s teachings in order to determine a type of datapoints as taught by Reynolds. One would have been motivated to make this modification identify characteristics of a distribution of data as well as visualization of the distribution. [¶0056, Reynolds]
Regarding claim 17, it is substantially similar to claim 7 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Claims 8-10 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Lee and further in view of Sun et al. ("US 20230153579 A1", hereinafter "Sun").
Regarding claim 8, Chen/Lee teaches The apparatus of claim 6, where Chen teaches wherein the programmable circuitry is further to: determine a similarity of individual datapoints included in the dataset (“past user-item interactions and other similar users” [¶0088]); and
However fails to explicitly teach generate a similarity graph based on the similarity of the individual datapoints.
Sun teaches generate a similarity graph based on the similarity of the individual datapoints. (“A similarity matrix is constructed that includes a similarity score for every user-user node pair. Then, for each user node, a predefined number (degree) of other user nodes (e.g. degree=10) with the highest similarity scores are selected as direct neighbors for the user-user graph” (“similarity graph”) [¶0066])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Chen’s/Lee’s teachings in order to generate a similarity graph based on the similarity of datapoints as taught by Sun. One would have been motivated to make this modification as there is a need for a GCNN-based recommender system that that is able take advantage of the intrinsic difference between node types of a graph (e.g., users and items) and the relationships between same-type nodes (e.g., user-user relationships, item-item relationships). [¶0008, Sun]
Regarding claim 9, Chen/Lee/Sun teaches The apparatus of claim 8, Sun teaches wherein the programmable circuitry is further to: train the similarity graph and a bipartite graph to generate a trained similarity graph and a trained bipartite graph; and
perform graph neural network inference based on a combination of the trained similarity graph and the trained bipartite graph. (“The Multi-GCCF system expressively models high-order information via a bipartite user-item interaction graph, integrates proximal information from the bipartite user-item interaction graph by building and processing user-user and item-item graphs, and takes into account the intrinsic difference between user nodes and item nodes when performing graph convolution on the bipartite graph.” [¶0034])
Same motivation to combine the teachings of Chen/Lee/Sun as claim 8.
Regarding claim 10, Chen/Lee/Sun teaches The apparatus of claim 9, Sun teaches wherein the programmable circuitry is further to augment the datapoints based on results of the graph neural network inference. (“Fusing the outputs h.sub.u, h.sub.v of the Bipar-GCN module 202 and the respective outputs z.sub.u, z.sub.v of the Multi-graph encoding module may in some applications enable the different dependency relationships encoded by the three graphs (bipartite graph G, user-user graph G.sub.u, and item-item graph G.sub.v) to be used to learn a relationship between target user node u.sub.A and target item node u.sub.v.” [¶0086])
Same motivation to combine the teachings of Chen/Lee/Sun as claim 8.
Regarding Claims 18-20, they recite features similar to claim 8-9 (Claims 19 and 20 recite similar limitations of claim 9) and are rejected for at least the same reasons therein.
Allowable Subject Matter
Claims 4, 5, and 14 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.
Regarding claims 4 and 14:
wherein the programmable circuitry is further to:
associate third datapoints of a third feature with the first node;
associate the third datapoints of the third feature with the second node; and
append the third datapoints of the third feature to either the first node or the second node based on a comparison of a first node score and a second node score.
No prior art was uncovered which fairly discloses all of the limitations of claims 4 and 14.
The closest prior art of record is Chen et al. (“US 20260134278 A1”) which disclose a framework for improving discrimination of graph features by a GNN, however the reference does not explicitly teach associating additional datapoints with a first/second node and appending the additional datapoints of additional features to either a first/second node based on a comparison of a first and a second node score.
Regarding claim 5:
wherein the programmable circuitry is to associate third datapoints with an edge feature that connects the first node and the second node in response to:
i) a graph accuracy of an association of the third datapoints with the first node being less accurate than the baseline accuracy; and
ii) a graph accuracy of an association of the third datapoints with the second node being less accurate than the baseline accuracy
No prior art was uncovered which fairly discloses all of the limitations of claim 5.
The closest prior art of record is Chen, however the reference as explained above regarding the cited differences between the prior art reference and claims 4 and 14, does not explicitly disclose associating additional datapoints with a first/second node, thus could not explicitly be used to teach associating additional datapoints in response to the graph accuracy of an association of an additional datapoint being less accurate than the baseline accuracy.
Conclusion
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/MICHAEL H HOANG/PRIMARY EXAMINER, Art Unit 2122
