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 responsive to the following communication: Non-Provisional Application filed Feb. 28, 2024.
Claims 1-12 are pending in the case. Claims 1, 5 and 9 are independent claims.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Rejections - 35 U.S.C. § 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.
As to claim 1:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “inputting a plurality of pieces of data to a machine learning model, and acquiring a plurality of prediction results of the plurality of pieces of data” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “generating one or more pieces of data based on first data of which the prediction result indicates a first group among the plurality of pieces of data” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “executing clustering of the plurality of pieces of data and the one or more pieces of data based on a plurality of features of the plurality of pieces of data and the one or more pieces of data, which are obtained based on a parameter of the machine learning model” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “updating the parameter of the machine learning model based on training data including the plurality of pieces of data and the one or more pieces of data for which results of the clustering are used as ground truth labels.” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “[a] non-transitory computer-readable recording medium storing a machine learning program for causing a computer to execute a process” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “updating the parameter of the machine learning model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No
As to claim 5:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “inputting a plurality of pieces of data to a machine learning model, and acquiring a plurality of prediction results of the plurality of pieces of data” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “generating one or more pieces of data based on first data of which the prediction result indicates a first group among the plurality of pieces of data” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “executing clustering of the plurality of pieces of data and the one or more pieces of data based on a plurality of features of the plurality of pieces of data and the one or more pieces of data, which are obtained based on a parameter of the machine learning model” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “updating the parameter of the machine learning model based on training data including the plurality of pieces of data and the one or more pieces of data for which results of the clustering are used as ground truth labels.” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “[a] machine learning method implemented by a computer,” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “updating the parameter of the machine learning model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No
As to claim 9:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “inputting a plurality of pieces of data to a machine learning model, and acquiring a plurality of prediction results of the plurality of pieces of data” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “generating one or more pieces of data based on first data of which the prediction result indicates a first group among the plurality of pieces of data” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “executing clustering of the plurality of pieces of data and the one or more pieces of data based on a plurality of features of the plurality of pieces of data and the one or more pieces of data, which are obtained based on a parameter of the machine learning model” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “updating the parameter of the machine learning model based on training data including the plurality of pieces of data and the one or more pieces of data for which results of the clustering are used as ground truth labels.” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “[a] machine learning apparatus comprising a control unit configured to perform processing” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “updating the parameter of the machine learning model” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No
Claims 2-4 are dependent on claim 1 and includes all the limitations of claim 1. The claims recite additional limitations, but do not otherwise add any meaningful limits beyond the abstract idea. Claims 6-8 are dependent on claim 5 and includes all the limitations of claim 5. The claims recite additional limitations, but do not otherwise add any meaningful limits beyond the abstract idea. Claims 10-12 are dependent on claim 9 and includes all the limitations of claim 9. The claims recite additional limitations, but do not otherwise add any meaningful limits beyond the abstract idea.
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 1-12 are rejected under 35 U.S.C. 103 as being unpatentable over Lee (hereinafter Lee) U.S. Patent Publication No. 2021/0097400 in view of Song (hereinafter Song) U.S. Patent Publication No. 2020/0065656.
With respect to independent claim 1, Lee teaches a non-transitory computer-readable recording medium storing a machine learning program for causing a computer to execute a process comprising:
inputting a plurality of pieces of data to a machine learning model, and acquiring a plurality of prediction results of the plurality of pieces of data (see e.g., Para [43]-[54] – “each of the classifier instances 208 receive the extra unlabeled data samples 312 and generate a prediction for the received samples.” The system processes datasets and generates predictions);
generating one or more pieces of data based on first data of which the prediction result indicates a first group among the plurality of pieces of data (see e.g., Para [41]-[46][56]-[63] – “The oversampling module 204 may be invoked for oversampling the latent space learned by the VAE, to generate additional “no good” samples. The random generation module 206 may be invoked for generating supplemental samples in the latent space 206 using a random input. According to one embodiment, the randomly generated data samples are unlabeled data samples.”” the oversampling module 502 is configured to sample attributes from instances in the minority class (“no good” class) for generating synthetic samples (e.g. the oversampled dataset 318).”);
updating the parameter of the machine learning model based on training data including the plurality of pieces of data and the one or more pieces of data (see e.g., Para [45]-[56] – “208 is used for generating annotations for the extra unlabeled data samples 312 and generate an annotated dataset 316 … each classifier instance 208 may distilled into the student classifier 210. Considering an aggregate of predictions of the various model instances helps reduce error of the trained student model even if each of the individual model instances, when considered independently, may be prone to errors. Use of an ensemble mechanism may be desirable, therefore, to steadily achieve stability of the trained student model 210. ““the student classifier 210 using: i) the original input dataset 200; ii) annotated dataset 316; and/or iii) oversampled dataset 318. The trained student classifier 210 may then be used as a binomial classifier to classify a newly manufactured product as, for example, “good” or “no good,” based on new trace data acquired for the product.”).
Lee does not expressly show executing clustering of the plurality of pieces of data and the one or more pieces of data based on a plurality of features of the plurality of pieces of data and the one or more pieces of data, which are obtained based on a parameter of the machine learning model and the updating based on training data including data for which results of the clustering are used as ground truth labels. However, Song teaches the above feature (see e.g. para [21]-[25] [33]-[36] and [45]-[56] – “The system obtains a batch of training items and a ground truth assignment of the training items in the batch into a plurality of clusters (step 202). The ground truth assignment assigns each training item in the batch to a respective cluster from the set of clusters ... The system determines an oracle clustering score for the ground truth assignment based on the embeddings for the training items … The system adjusts the current values of the network parameters by performing an iteration of a neural network training procedure to optimize, i.e., minimize, the clustering objective using the oracle clustering score (step 208). Generally, the training procedure determines an update to the current values of the parameters from a gradient of the clustering objective with respect to the parameters and then applies, e.g., adds, the update to the current values to determine updated values of the parameters.”). Both Lee and Song are directed to machine learning classification. Accordingly, it would have been obvious to the skilled artisan before the effective filing date of the claimed invention having Lee and Song in front of them to modify the system of Lee to include the above feature. The motivation to combine Lee and Song comes from Song. Song discloses the motivation to use clustering based supervision to improve learning performance ([21]-[25] [33]-[36] and [45]-[56]). This motivation for combination also applies to the remaining claims which depend on this combination.
With respect to dependent claim 2, the modified Lee teaches the generating includes selecting pieces of second data similar to the first data among a plurality of pieces of second data of which the plurality of prediction results indicate a second group among the plurality of pieces of data, and generating the one or more pieces of data corresponding to a feature between a feature of the first data and a second feature of the second data (see e.g., Para [61]-[63] – “the algorithm selects two or more similar instances (using a distance measure) in the latent space, and perturbs an instance one attribute at a time by a random amount within the difference to the neighboring instances.”).
With respect to dependent claim 3, the modified Lee teaches the generating includes generating the one or more pieces of data by adding noise to third data obtained by duplicating the first data (see e.g., Para [62] – “ADASYN may offset each of the data elements produced by adding to it a small random vector (or “offset”), to reduce the likelihood that the synthetic samples from the minor class may interfere with the other class (e.g., the majority class, which may be the “good” class).”).
With respect to dependent claim 4, the modified Lee teaches causing the computer to execute the process further comprising: determining whether or not to update the parameters of the machine learning model based on the prediction result and the ground truth label included in the training data (see e.g., Song Para [52]-“ computes the gradient as above if the loss for the batch, i.e., the value of the clustering loss function l(X, y*) for the batch, is greater than zero. If the loss is less than or equal to zero, the system sets the gradients to zero and does not update the current values of the network parameters.”).
Claim 5 is rejected for the similar reasons discussed above with respect to claim 1.
Claim 6 is rejected for the similar reasons discussed above with respect to claim 2.
Claim 7 is rejected for the similar reasons discussed above with respect to claim 3.
Claim 8 is rejected for the similar reasons discussed above with respect to claim 4.
Claim 9 is rejected for the similar reasons discussed above with respect to claim 1.
Claim 10 is rejected for the similar reasons discussed above with respect to claim 2.
Claim 11 is rejected for the similar reasons discussed above with respect to claim 3.
Claim 12 is rejected for the similar reasons discussed above with respect to claim 4.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PEIYONG WENG whose telephone number is (571)270-1660. The examiner can normally be reached on Mon.-Fri. 8 am to 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Matthew Ell, can be reached on (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PEI YONG WENG/Primary Examiner, Art Unit 2141
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