DETAILED ACTION
This action is in response to claims filed 08 December 2023 for application 18534295 filed 08 December 2023. Currently claims 1-10 are 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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Objections
Claim 1 is objected to because of the following informalities: It appears an article has been left off the beginning of the preamble, it should be “A multi-view hyperbolic-hyperbolic…” or equivalent. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3, 6 and 7 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 3 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential elements, such omission amounting to a gap between the elements. See MPEP § 2172.01. The omitted elements are: n and d variables are undefined in the claim.
Claim 6 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential elements, such omission amounting to a gap between the elements. See MPEP § 2172.01. The omitted elements are: u and d.
Claim 7 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential elements, such omission amounting to a gap between the elements. See MPEP § 2172.01. The omitted elements are: m and γ.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In step 1, claims 1-10 are directed to the statutory category of a method.
In step 2a prong 1, claim 1 recites, in part, constructing views from a network topology, mapping features, and mapping an embedding. The limitations of constructing and mapping are processes that, under its broadest reasonable interpretation, covers performance of the limitations in the. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
In step 2a prong 2, this judicial exception is not integrated into a practical application. No additional elements have been identified, thus, the judicial exception is not integrated into a practical application.
In step 2b, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, either alone or in combination.
Claims 2-10 recite further limitations of constructing views using pagerank, mapping to a Lorentz model, aggregating neighbor information, embedding using a pooling layer and weighting the views to fuse into a unified representation, extracting features, calculating an Einstein midpoint, projecting the hyperbolic embedding, performing pooling, weighting and summing the hyperbolic node embedding. These limitations amount to the same abstract idea of mental processes identified above. No further additional elements have been identified that would amount to a practical application in step 2a prong 1 or significantly more than the abstract idea itself in step 2b.
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 (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 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.
Claim(s) 1, 4 and 5 is/are rejected under 35 U.S.C. 102(A)(1) as being anticipated by Zhang et al. (Hyperbolic Graph Attention Network).
Regarding claim 1, Zhang discloses: multi-view hyperbolic-hyperbolic graph representation learning method, comprising:
constructing two views from a network topology and node features, mapping the node features from an Euclidean space to a hyperbolic space, and inputting hyperbolic node embedding representations and three views into a hyperbolic-hyperbolic graph convolution module respectively, wherein the hyperbolic-hyperbolic graph convolution module comprises a linear transformation layer, a neighbor aggregation layer and an activation layer (Fig 2, Table 1, note: HAT model has node aggregation layers h’, linear transformation layer and activation layer §4.4, “We can find that these methods leverages different manifold to design the graph convolutional layers. Specifically, HAT leverages the Poincaré ball manifold, while HGCN uses the hyperboloid manifold.” P7 §4.6 ¶2); and
mapping, by a hyperbolic attention fusion module, hyperbolic node embedding representations of the three views into unified hyperbolic node embedding for a downstream task, wherein the hyperbolic attention fusion module comprises a view attention layer and an embedding fusion layer (Fig 2 hyperbolic projection (embedding) and hyperbolic attention are fused into final prediction of a downstream task).
Regarding claim 4, Zhang discloses: The method according to claim 1, wherein the hyperbolic-hyperbolic graph convolution module is configured for aggregating neighbor information of nodes, and comprises a hyperbolic-hyperbolic linear transformation layer, a neighbor aggregation layer and an activation layer, wherein
the hyperbolic node embedding after linear transformation is preserved in the hyperbolic space by the hyperbolic-hyperbolic linear transformation layer;
the neighbor information of the nodes is aggregated to a central node by the hyperbolic neighbor aggregation layer; and
aggregated hyperbolic node embedding is non-linearly mapped by the hyperbolic activation layer to improve a network expression capability (Figure 2).
Regarding claim 5, Zhang discloses: The method according to claim 1, wherein the hyperbolic attention fusion module comprises a view attention layer and an embedding fusion layer, wherein
hyperbolic node embedding of each view is input into a pooling layer by the view attention layer to obtain a hyperbolic graph embedding of each view, and the hyperbolic graph embeddings of views are concatenated, the concatenated hyperbolic graph embedding is mapped to the hyperbolic space via exponential mapping and input into a multilayer perceptron (MLP) layer, so as to obtain an attention score of each view; and
hyperbolic node embeddings of the three views are weighted and fused into a unified hyperbolic node representation by the embedding fusion layer based on the attention score of each view (Figure 2, concatenation §4.2).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Gastieger et al. (PREDICT THEN PROPAGATE: GRAPH NEURAL NETWORKS MEET PERSONALIZED PAGERANK), Volkovs et al. (US 20220270155), Lei et al. (US 20220237447), and Peng et al. (Hyperbolic Deep Neural Networks: A Survey) both disclose GNN with hyperbolic transformations with PageRank and/or Lorentz models, however, neither discloses the specific limitations of the claims.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC NILSSON whose telephone number is (571)272-5246. The examiner can normally be reached M-F: 7-3.
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/ERIC NILSSON/Primary Examiner, Art Unit 2151
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