DETAILED ACTION
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 filed 12/14/22 has been considered.
Drawings
The drawings filed 12/14/22 have been accepted.
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 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: “Is the claim to a process, machine, manufacture or composition of matter?”
Yes, claims 1-7 are directed to either a process, machine, manufacture or composition of matter.
Regarding claim 1:
Step 2A (1): “Does the claim recite an abstract idea, law of nature, or natural phenomenon?”
“classifying input data into one or more groups based on a weight of output of each neural network module in a case where data input in training by machine learning is performed for a plurality of neural network modules”- as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. Classification is interpreted as a type of organizing data, which can be performed in the mind; and
“generating, in machine learning processing after the classification, a mini-batch of the input data such that pieces of the input data included in the same group are included in the same mini-batch” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. Generating batches is interpreted as grouping based on classification, which can be performed in the mind.
Step 2A (2): “Does the claim recite additional elements that integrate the judicial exception into a practical application?”
This judicial exceptions as recited are not integrated into a practical application. In particular, claim 1 only recites a “computer-readable recording medium” to perform the steps of ‘classifying’ and ‘generating’. The “computer-readable recording medium” in both limitations are recited at a high level of granularity (i.e. computing system performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Step 2B: “Does the claim recite additional elements that amount to significantly more than the judicial exception?”
The claim limitations reciting the abstract idea 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 element of using a computing system to perform the above steps amounts 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. Therefore, the claim limitation does not include elements that amount to significantly more. Claim 1 is not patent eligible.
Regarding claim 2, it recites, “wherein the plurality of neural network modules is included in a modular neural network.”
The additional elements of dependent claim 2 do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. MPEP 2106.05(h)(vi): Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
Mere instructions to apply an exception using a generic component cannot provide an inventive concept. Because claim 2 is directed to the abstract idea, it does not add significantly more. The claim is not patent eligible.
Regarding claim 3, it recites,
“wherein the processing of classifying includes processing of inputting the input data to the modular neural network,” As drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. Classification is interpreted as a type of organizing data, which can be performed in the mind. and
“determining a group of the input data based on a distance between a vector generated based on a weight for output of the plurality of neural network modules and reference information that represents a cluster.” As drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. Grouping is interpreted as a type of organizing data, which can be performed in the mind.
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 integration of the abstract idea into a practical application, the additional element of using a computing system to perform the ‘classifying’ and ‘determining’ steps amounts 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. Because claim 3 is directed to the abstract idea, it does not add significantly more. The claim is not patent eligible.
Regarding claim 4, it recites, “for causing the processor to execute the processing further comprising updating the reference information in a nearest neighbor feature amount direction by competitive learning.” As drafted, is a process that, under its broadest reasonable interpretation, covers performance of a mathematical calculation but for the recitation of generic computer components, where competitive learning is interpreted as an algorithm.
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 integration of the abstract idea into a practical application, the additional element of using a computing system to perform the ‘classifying’ and ‘determining’ steps amounts 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. Because claim 4 is directed to the abstract idea, it does not add significantly more. The claim is not patent eligible.
Regarding claim 5, it recites, “for causing the processor to execute the processing further comprising performing training of the neural network module by supervised machine learning by an error back propagation method that uses a sum of a classification error of the group and a distance error from the reference information as a learning loss.” As drafted, is a process that, under its broadest reasonable interpretation, covers performance of a mathematical calculation but for the recitation of generic computer components.
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 integration of the abstract idea into a practical application, the additional element of using a computing system to perform the ‘training’ step amounts 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. Because claim 5 is directed to the abstract idea, it does not add significantly more. The claim is not patent eligible.
Claims 6 and 7 recite substantially similar subject matter to claim 1 and are thus similarly rejected.
Claim Rejections - 35 USC § 102
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 6-7 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Lee United States Patent Application Publication US 2022/0400943.
Regarding claim 1, Lee discloses a non-transitory computer-readable recording medium storing an information processing program for causing a processor to execute processing comprising:
classifying input data into one or more groups based on a weight of output of each neural network module in a case where data input in training by machine learning is performed for a plurality of neural network modules (Lee, para [0095-96], construction of a NN machine learning model may include a learning (or training) stage and a classification (or operational) stage. Convolutional neural networks (CNN) are also made up of neurons that have learnable weights and biases); and
generating, in machine learning processing after the classification, a mini-batch of the input data such that pieces of the input data included in the same group are included in the same mini-batch (Lee, para [0093], computing demands may be reduced by dividing a large training set into multiple mini-batches, where the mini-batch size defines the number of training samples in one forward/backward pass. In this case, and one epoch may include multiple mini-batches).
Regarding claim 2, Lee discloses the non-transitory computer-readable recording medium according to claim 1. Lee additionally discloses wherein the plurality of neural network modules is included in a modular neural network (Lee, para [0054], multiple NN stages, Stg1 and Stg2, each including its own neural network in a modular neural network configuration).
Claims 6 and 7 recite substantially similar subject matter to claim 1 and are thus similarly rejected.
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.
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Lee United States Patent Application Publication US 2022/0400943 in view of Kosko United States Patent Application Publication US 2015/0161232.
Regarding claim 3, Lee discloses the non-transitory computer-readable recording medium according to claim 2. Lee additionally discloses wherein the processing of classifying includes processing of inputting the input data to the modular neural network (Lee, para [0054], multiple NN stages, Stg1 and Stg2, each including its own neural network in a modular neural network configuration).
Lee does not disclose:
determining a group of the input data based on a distance between a vector generated based on a weight for output of the plurality of neural network modules and reference information that represents a cluster
Kosko discloses:
determining a group of the input data based on a distance between a vector generated based on a weight for output of the plurality of neural network modules and reference information that represents a cluster (Kosko, para [0030], applies iterative clustering algorithm).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the clustering to include the methods of Kosko. The motivation for doing so would have been to reduce the time it takes to get clustering results that are closer to optimal. Also, they may also increase the chance of finding more robust clusters in the face of missing or corrupted data (Kosko, para [0050]).
Regarding claim 4, Lee in view of Kosko discloses the non-transitory computer-readable recording medium according to claim 3. Kosko additionally discloses for causing the processor to execute the processing further comprising updating the reference information in a nearest neighbor feature amount direction by competitive learning (Kosko, para [0051], within a clustering algorithm, such as k-means clustering, noise can be used to speed up convergence in competitive learning).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the clustering to include the methods of Kosko. The motivation for doing so would have been to reduce the time it takes to get clustering results that are closer to optimal. Also, they may also increase the chance of finding more robust clusters in the face of missing or corrupted data (Kosko, para [0050]).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Lee United States Patent Application Publication US 2022/0400943 in view of Kosko United States Patent Application Publication US 2015/0161232 in further view of Zhang United States Patent Application Publication US 2018/0165554.
Regarding claim 5, Lee in view of Kosko discloses the non-transitory computer-readable recording medium according to claim 3. Lee in view of Kosko does not disclose the additional limitations of claim 5.
Zhang discloses for causing the processor to execute the processing further comprising performing training of the neural network module by supervised machine learning by an error back propagation method that uses a sum of a classification error of the group and a distance error from the reference information as a learning loss (Zhang, para [0063], autoencoder used to transform unsupervised to data to supervised to allow back propagation; Zhang, para [0062], D is a loss function, such as the squared Euclidean Distance).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the learning of Lee in view of Kosko to incorporate the techniques of Zhang. The motivation for doing so would have been scalability and accuracy of autoencoders (Zhang, para [0048]).
Conclusion
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/HOPE C SHEFFIELD/Primary Examiner, Art Unit 2141