DETAILED ACTION
This Office Action is sent in response to Applicant’s Communication received 3/11/2024 for application number 18/601,523.
Claims 1-20 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 .
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. Independent claim 1 (representative of independent claims 14 and 20) recites:
A graph processing method performed by a computing device comprising processing hardware and storage hardware, the method comprising: generating, by the processing hardware, and storing in the storage hardware, messages based on node states of nodes of input graph data stored in the storage hardware using a neural message generating model stored in the storage hardware; generating, by the processing hardware, aggregated messages by aggregating the messages; updating the node states of the nodes by applying a first neural update model and a second neural update model to the aggregated messages; and outputting an inference with respect to the input graph data based on the updated node states.
(2A, prong 1) The underlined portions of the claim recite an abstract idea, specifically a mathematical equation and calculation. Applicant’s specification discloses that generating, aggregating, and updating are performed by equation 1:
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see para, 0050-51 as filed), and the inference output (i.e. task result) is determined by iterating equation 1 (para. 0051), and that the first and second update models may be different functions
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(2A, prong 2) This judicial exception is not integrated into a practical application. The claims recite the additional element of (a) “processing hardware” and “storage hardware.” This claim is a mere instruction to apply the mathematical equation and calculation on an abstract computer because it only adds generic computer hardware to the abstract idea after-the-fact. Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not integrate the abstract idea into a practical application because the additional elements only add mere instructions to apply the exception to the abstract idea.
(2B) The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional element (a) is a mere instruction to apply the exception as explained above. Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not amount to significantly more than the abstract idea itself because the additional elements only add mere instructions to apply the exception to the abstract idea; that is to say they only state running the equation on a generic computer.
Dependent claim 2 recites the messages are generated using a model based on node states, node features, and node edges. This is part of the recited mathematical formula:
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Dependent claim 3 recites the first and second update models use different parameters or architecture. This adds to the recited mathematical calculations; (i.e. that equation 1 uses two different parameters for
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Dependent claims 4-5 and 15-16 recite the updating uses a fusion of the outputs of the first and second update models using a fusion coefficient. The specification states that this is performed using equation 2 (see para. 0056-57 as filed).
Dependent claims 6-8 and 17-18 recite the updating selects one of the neural update models using a probability model that generates probability values for selecting one of the update models using input graph data. The specification states that this may be performed using equation 3 (see para. 0063-64 as filed).
Dependent claims 9-10 and 190 recite updating the nodes based on a current assignment state, the state determining based on testing task performance. The specification states this may be performed using equations 4 and 5 (para. 0070-71 and 74-74 as filed).
Dependent claims 11-12 recite determining node states and outputting task results using an encoder and decoder. Applicant’s specification states the encoder determines vectors based on graph data, and the decoder maps node states to a score (para. 0054 as filed), which are mathematical calculations.
Dependent claims 13 recites the graph data is a graph neural network. Applicant’s specification states that the graph neural network “may include/be a graph” (para. 0048 as filed). Making calculations on a graph is still mathematical calculations.
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.
Claim(s) 1-3, 13-14, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Corso et al., Principal Neighbourhood Aggregation for Graph Nets (NPL [U], see Notice of References Cited) in view of Luan et al., Revisiting Heterophily For Graph Neural Networks (NPL [V], see Notice of References Cited).
In reference to claim 1, Corso teaches a graph processing method performed by a computing device comprising processing hardware and storage hardware, the method comprising: generating, by the processing hardware, and storing in the storage hardware, messages based on node states of nodes of input graph data stored in the storage hardware using a neural message generating model stored in the storage hardware (nodes messages are generated based on node state, pages 2-3); generating, by the processing hardware, aggregated messages by aggregating the messages (messages are aggregated, pages 3-5); updating the node states of the nodes by applying a first neural update model … to the aggregated messages (MLP applied to aggregated messages to determine update to node, page 5, fig. 2); and outputting an inference with respect to the input graph data based on the updated node states (a graph task can be performed, pages 6-7).
However, Corso does not explicitly teach a second neural update model.
Luan teaches a second neural update model (see pages 1 and 5-7: Luan teaches node-wise adaptive channel mixing, so each node has its own learned update function).
It would have been obvious to one of ordinary skill in art, having the teachings of Corso and Luan before the earliest effective filing date, to modify the update function of Corso to include the second update model of Luan.
One of ordinary skill in the art would have been motivated to modify the update function of Corso to include the second update model of Luan because it can help GNN perform better on certain tasks (Luan, page 1).
In reference to claim 2, Luan further teaches the graph processing method of claim 1, wherein the messages are generated by executing the neural message generating model based on the node states of the nodes, embedding features of the nodes, and edges of the nodes (state, features, and edges of nodes, see pages 2-3).
In reference to claim 3, Luan further teaches the graph processing method of claim 1, wherein the first neural update model has a different architecture or different parameters than the second neural update model (different parameters, pages 1 and 5-7).
In reference to claim 13, Corso further the teaches the graph processing method of claim 1, wherein the input graph data comprises a graph neural network (GNN, see page 1).
In reference to claim 14, this claim is directed to a device associated with the method claimed in claim 1 and is therefore rejected under a similar rationale.
In reference to claim 20, this claim is directed to a non-transitory computer-readable medium associated with the method claimed in claim 1 and is therefore rejected under a similar rationale.
Allowable Subject Matter
Claims 4-12 and 15-19 would be allowable if rewritten 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew T. Chiusano whose telephone number is (571)272-5231. The examiner can normally be reached M-F, 10am-6pm.
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/ANDREW T CHIUSANO/Primary Examiner, Art Unit 2144
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