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 .
DETAILED ACTION
This action is a responsive to the application filed on 04/12/2024.
Claims 1-6 are pending.
Claims 1-6 are rejected.
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 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1 and 5-6 are respectively drawn to a system, method, and non-transitory computer readable storage medium, hence each falls under one of four categories of statutory subject matter (Step 1). Nonetheless, the claims are directed to a judicially recognized exception of an abstract idea without significantly more.
Claims 1 and 5-6 recite the following or analogous limitations “entering a plurality of data samples individually to a…model and extracting a plurality of features of each of the data samples from the…model; normalizing the plurality of features to a plurality of normalized features that fall within a certain numerical value range; selecting, based on the plurality of normalized features, at least one data sample, which is part of the plurality of data samples, from the plurality of data samples;”. These limitations, as claimed, under its broadest reasonable interpretation, can be evaluated in a human mind, except for the recitation of generic computer components (using artificial intelligence/machine learning, a computer including one or more microprocessors, and a non-transitory computer readable storage medium) (Step 2A). Other than reciting “a non-transitory computer-readable recording medium”, “a computer”, “a processor”, “a memory”, “machine learning model”, “and training the machine learning model by using the at least one data sample” to perform the exceptions, nothing in the claims preclude the steps from practically being performed in the human mind. For example, a human expert can:
mentally/with the aid of pen and paper entering a plurality of data samples individually to a…model and extracting a plurality of features of each of the data samples from the…model (e.g. by thinking of/writing out remembered data instances input into a calculation and determining representations of the input instances from the calculation)
mentally/with the aid of pen and paper normalizing the plurality of features to a plurality of normalized features that fall within a certain numerical value range (e.g. by thinking of/writing out converting the representations to lower dimensional representations between upper and lower boundary values)
mentally/with the aid of pen and paper selecting, based on the plurality of normalized features, at least one data sample, which is part of the plurality of data samples, from the plurality of data samples (e.g. by thinking of/writing out choosing a data instance based on the lower dimensional representations)
Thus, the claims recite a mental process (Step 2A, Prong 1).
Claims 1 and 5-6 include additional elements, “a non-transitory computer-readable recording medium”, “a computer”, “a processor”, “a memory”, “machine learning model”, “and training the machine learning model by using the at least one data sample”, however the recitations of these elements are at a high level of generality, and adding the words “apply it” (or an equivalent) with the judicial exception (i.e., “training the machine learning model by using the at least one data sample”), or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (i.e., “a non-transitory computer-readable recording medium”, “a computer”, “a processor”, “a memory”) (see MPEP 2106.05(f)); and generally link the use of the judicial exception to a particular technological environment or field of use (i.e., “machine learning model”) (see MPEP 2106.05(h)). Hence, each of the additional limitations or in combination do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (Step 2A, Prong 2). The additional elements in the claim do not amount to significantly more than an abstract idea. Furthermore, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of using “a non-transitory computer-readable recording medium”, “a computer”, “a processor”, “a memory”, “machine learning model”, “and training the machine learning model by using the at least one data sample” to perform the steps of the independent claims amounts to no more than mere adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, and generally link the use of the judicial exception to a particular technological environment or field of use, as these cannot provide an inventive concept. (STEP 2B). As such, claims 1, 13, and 17 are not patent eligible.
Dependent claims 2-4 are also ineligible for the same reasons given with respect to claims 1 and 5-6. The dependent claims describe additional mental processes:
mentally/with the aid of pen and paper wherein the…model…updates, based on a connection relationship among a plurality of nodes indicated by graph data, a feature of each of the plurality of nodes, and wherein the plurality of features are features that have passed through the…network (claim 3) (e.g. by mentally/writing out the calculation updating lower dimensional representation value based on semantic distances between the instances in a second calculation)
mentally/with the aid of pen and paper wherein the selecting includes transforming the plurality of normalized features into a plurality of principal component features by executing principal component analysis, and selecting the at least one data sample, based on a distance between an individual pair of the plurality of principal component features (claim 3) (e.g. by mentally/writing out converting the lower dimensional representations into semantically linked instances and choosing instances for updating the calculation based on the semantic length)
mentally/with the aid of pen and paper wherein the…model predicts molecular energy from molecular data indicating a molecule including a plurality of atoms, and wherein the plurality of features are features calculated for the plurality of atoms (claim 4) (e.g. by mentally/writing out the calculation outputting a value representing molecular structure and arrangements of atoms, and the representations being atomic electron counts)
Again, the dependent claims continued to cover the performance of the limitation in the mind as inherited from the independent claims (Step 2A, Prong 1). The dependent claim 2 recitation of “wherein the machine learning model includes a graph neural network” and “graph neural network”, claim 4 recitation of “machine learning model”, are again recited at a high level and amount to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)); and these do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (Step 2A, Prong 2). The additional element in the claims do not amount to significantly more than an abstract idea. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements to perform the steps of in the dependent claims and perform the steps of the claims amount to no more than generally linking the use of the judicial exception to a particular technological environment or field of use; however, these cannot provide an inventive concept. (STEP 2B). As such, dependent claims 2-4 do not amount to significantly more than an abstract idea nor provide any inventive concept, therefore are not patent eligible.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-6 are rejected under 35 U.S.C. 103 as being unpatentable over Mahmoud et al (“Node classification with graph neural network based centrality measures and feature selection”, 2023) hereinafter Mahmoud, in view of Taguchi et al (US Pub 20240096443) hereinafter Taguchi.
Regarding claims 1 and 5-6, Mahmoud teaches a non-transitory computer-readable recording medium storing therein a computer program that causes a computer to execute a process comprising; machine learning method comprising; and an information processing apparatus comprising: a memory configured to store a plurality of data samples and a machine learning model; and a processor coupled to the memory and the processor configured to execute a process including (section 1 teaches measuring the “memory size” when performing the steps of the invention; wherein the memory is known to be communicatively coupled to one or more processors executing code for performing the embodiments of the disclosure in a computing system):
entering a plurality of data samples individually to a machine learning model and extracting a plurality of features of each of the data samples from the machine learning model (section 2.5 teaches “Starting with all features as input, GNNFC (machine learning model) learns to identify significant features (extracting) while reducing the impact of unimportant features”);
normalizing the plurality of features to a plurality of normalized features that fall within a certain numerical value range (sections 2.4-2.5 teach “When the batch size is small, BR [batch renormalization] introduces two more parameters that restrict the estimated mean and variance of BN within a given range (fall within a certain numerical value range), decreasing their drift” and “BR is used to normalize node features during training (normalizing the plurality of features)” iteratively after “the first step”);
selecting, based on the plurality of normalized features, at least one data sample, which is part of the plurality of data samples, from the plurality of data samples (section 2.5 teaches “In GNNFC we calculate the Chi-square between each feature and the target and choose the features with the best Chi-square scores. We specify (selecting) additional features with regard to graph centrality measurements such as betweenness and closeness since graph centralities have been used to characterize various properties of graphs”); and
training the machine learning model by using the at least one data sample (section 2.5 teaches “After calculating these features, we merge them with the selected features obtained from input features, and then input the new feature matrix for GNN as shown in Figure 1.”).
Mahmoud at least implies normalizing the plurality of features to a plurality of normalized features that fall within a certain numerical value range (see mappings above); however, Taguchi teaches normalizing the plurality of features to a plurality of normalized features that fall within a certain numerical value range (paragraphs 0046 and 0061 teach normalizing a training set for a machine learning model, where “normalized value can be a number from 0 to 1, a number from −1 to −1, a normalized value between 0 and 100, or any other numerical range”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to implement Taguchi’s teachings of molecular dynamics simulations of normalizing input data into specific numerical ranges into Mahmoud‘s teaching of GNNFC batch renormalization and choosing features for training order to “optimize the topological space covered by the blueprints to be explored” (Taguchi, paragraphs 0104 and 0164).
Regarding claim 2, the combination of Mahmoud and Taguchi teach all the claim limitations of claim 1 above; and further teach wherein the machine learning model includes a graph neural network that updates, based on a connection relationship among a plurality of nodes indicated by graph data, a feature of each of the plurality of nodes, and wherein the plurality of features are features that have passed through the graph neural network (Mahmoud, sections 2.3 and 2.5 teach “in our model graph neural network using feature selection based centrality measures (GNNFC), we'll calculate several features based on centrality measures”; wherein “in a message passing step (passed through the GNN) a node's representation is iteratively updated by aggregating its neighbors’ representations (updates, based on a connection relationship among a plurality of nodes indicated by graph data, a feature of each of the plurality of nodes). A node's representation captures the structural information inside its k – hop network neighborhood after 𝑘 iterations of aggregation”).
Regarding claim 3, the combination of Mahmoud and Taguchi teach all the claim limitations of claim 1 above; and further teach wherein the selecting includes transforming the plurality of normalized features into a plurality of principal component features by executing principal component analysis, and selecting the at least one data sample, based on a distance between an individual pair of the plurality of principal component features (Mahmoud, sections 2.3, 2.3.2, and 2.5 teach “in our model graph neural network using feature selection based centrality measures (GNNFC), we'll calculate several features based on centrality measures (distance)… Let's define the ‘Geodesic distance’ between two nodes in a graph to better understand this measure” for finding the centrality measures (executing principal component analysis); wherein “We specify additional features with regard to graph centrality measurements (selecting the at least one data sample, based on a distance between an individual pair of the plurality of principal component features) such as betweenness and closeness since graph centralities have been used to characterize various properties of graphs”).
Regarding claim 4, the combination of Mahmoud and Taguchi teach all the claim limitations of claim 1 above; and further teach wherein the machine learning model predicts molecular energy from molecular data indicating a molecule including a plurality of atoms, and wherein the plurality of features are features calculated for the plurality of atoms (Taguchi, paragraphs 0046 and 0061 teach normalizing a training set for a machine learning model for predicting “molecular dynamics simulations” including atom selection and comparison of molecule predictions in polypeptides).
Mahmoud and Taguchi are combinable for the same rationale as set forth above with respect to claims 1 and 5-6.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Reh et al (US Pub 20200293564) teach training machine learning algorithms on input entries, where a “computing server may normalize the range of the features”.
Conclusion
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/C.M./Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123
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