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 therefore, subject to the conditions and requirements of this title.
Claims 1-17 and 19-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims recite mathematical concepts; taking claim 1 as exemplary of claims 1, 14-15, 17 and 21, and based on the plain meaning of the words in the claims, the broadest reasonable interpretation is obtaining and processing data representing a program and represented as a graph and generating a representation of the data by using a graph neural network; thus, the mathematical concepts are written in prose form. This judicial exception is not integrated into a practical application because the claimed features are recited at a high level of generality such that there are no meaningful details of a digital circuit design recited beyond the mathematical concepts of a program and a graph representing the digital circuit design for processing the data using a graph neural network (see, for example, [0022] for specific types of circuits). Claims 2-12 and 19-20 recite further mathematical concepts in prose form for a prediction neural network to obtain, process and generate data; there are no meaningful details of hardware verification or test recited beyond data and mathematical concepts as similarly described above. Also, the generically recited system (see claims 17, 19-20) and one or more non-transitory computer-readable storage media (see claim 21) do not add meaningful imitations to the abstract idea because they amount to simply implementing the abstract idea on or with a computer. The claims not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no meaningful details recited regarding a digital circuit (see, for example, [0022]), and adding insignificant extra solution activity, such as manufacturing as recited in claims 13 and 16, is not indicative of integration into a practical application as recited; the generically recited features are well-understood, routine and conventional. Thus, given a broadest reasonable interpretation of the high level of generality claimed, the claims are rejected as being directed to an abstract idea without significantly more.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. D. Bieber et al. [“Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks”] appear to disclose the closest prior art for constructing embedding of source code that capture information about program semantics, graph neural networks and control flow graphs (see entire document). Also, P. Philipp et al. disclose “Analysis of Control Flow Graphs Using Graphs Using Graph Convolutional Neural Networks” (see entire document).
T. Roddenberry et al. [“HodgeNet: Graph Neural Networks for Edge Data”] disclose analysis of flow data on graphs (see entire document). F. Scarselli et al. disclose “The Graph Neural Network Model” (see entire document). Y. Ma et al. [“High Performance Graph Convolutional Networks with Applications in Testability Analysis”] disclose test point insertion (see entire document).
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/LEIGH M GARBOWSKI/ Primary Examiner, Art Unit 2851
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