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 .
Claims 1-19 are presented for examination.
Response to Amendment
Applicant’s amendment appears to have obviated the objections to the specification, drawings, and claims, as well as the rejections under 35 USC § 103. Therefore, those objections and rejections are withdrawn.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Objections
Claim 9 is objected to because of the following informalities: “trained, via the processor, is based” should be “trained, via the processor, based”. Claims 10-13 are objected to for dependency on claim 9. Appropriate correction is required.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
Claim 1
Step 1: The claim recites a method; therefore, it is directed to the statutory category of processes.
Step 2A Prong 1: The claim recites, inter alia:
[I]dentifying … training data labeled for each class: This limitation could encompass mentally identifying the training data.
[D]etermining whether to augment the training data … based on overall recognition performance indicating prediction accuracy of a deep learning model calculated in a previous epoch: This limitation could encompass mentally determining whether to augment the data based on recognition performance of the model by visually observing the model.
[A]ugmenting the training data … based on class-specific recognition performance indicating class-specific prediction accuracy of the deep learning model calculated in the previous epoch according to a determination of whether to augment the training data: This limitation could encompass mentally augmenting the training data.
[P]redicting a class: This limitation could encompass mentally predicting the class.
[C]alculating a second threshold … using the overall recognition performance, a maximum value of the class-specific recognition performance and a scale factor that determines a reflection ratio of the overall recognition performance and the maximum value of the class-specific recognition performance: This limitation is directed to the mathematical concept of calculating a threshold, a maximum value, and a scale factor.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “training the deep learning model, via the processor, based on a labeled class and the predicted class.” However, this limitation merely restricts the judicial exception to the field of use of model training. MPEP § 2106.05(h).
The claim further recites “inputting the training data or the training data that are augmented to the deep learning model according to the determination of whether to augment the training data”. This limitation recites the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Finally, the claim recites that certain steps of the method are “executed by a processor of an electronic device comprising a memory storing training data”. However, this is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of step 2A, prong 2, with the exception that the inputting limitation, in addition to being insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). As an ordered whole, the claim is directed to a mentally performable process of predicting a class using an augmented dataset. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 2
Step 1: A process, as above.
Step 2A Prong 1: The claim recites that “the determining of whether to augment the training data … comprises determining … that the training data are to be augmented in response to the overall recognition performance being greater than a first threshold that is set.” This limitation could encompass mentally determining that the data should be augmented based on the performance.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that certain steps are performed “via the processor”, which is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that certain steps are performed “via the processor”, which is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Claim 3
Step 1: A process, as above.
Step 2A Prong 1: The claim recites:
[A]ugmenting … the training data of a class with the class-specific recognition performance less than the second threshold: This limitation could encompass mentally augmenting the training data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that certain steps are performed “via the processor”, which is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that certain steps are performed “via the processor”, which is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Claim 4
Step 1: A process, as above.
Step 2A Prong 1: The claim recites that “the calculating of the second threshold comprises calculating the second threshold … by increasing a reflection ratio of the maximum value of the class-specific recognition performance as the scale factor increases and by increasing a reflection ratio of the overall recognition performance as the scale factor decreases.” This limitation recites the mathematical concept of calculating a threshold value based on a set of ratios.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that certain steps are performed “via the processor”, which is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that certain steps are performed “via the processor”, which is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Claim 5
Step 1: A process, as above.
Step 2A Prong 1: The claim recites:
[C]alculating an application probability … based on the second threshold and the class-specific recognition performance: This limitation recites the mathematical concept of calculating a probability based on a threshold and recognition performance.
[D]etermining whether to augment each piece of the training data … based on the application probability: This limitation could encompass mentally determining whether to augment the training data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that certain steps are performed “via the processor”, which is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that certain steps are performed “via the processor”, which is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Claim 6
Step 1: A process, as above.
Step 2A Prong 1: The claim recites “calculating the application probability for the each class … based on a value obtained by subtracting the class-specific recognition performance from the second threshold.” This limitation also recites a mathematical concept.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that certain steps are performed “via the processor”, which is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that certain steps are performed “via the processor”, which is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Claim 7
Step 1: A process, as above.
Step 2A Prong 1: The claim recites:
[U]pdating the overall recognition performance … and the class-specific recognition performance using validation data for evaluating performance of the deep learning model: This limitation could encompass mentally updating the recognition performances.
[D]etermining whether to terminate training of the deep learning model … based on the overall recognition performance that is updated and the overall recognition performance calculated in the previous epoch: This limitation could encompass mentally making the determination that the training should be terminated.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that certain steps are performed “via the processor”, which is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that certain steps are performed “via the processor”, which is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Claim 8
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the same judicial exceptions as in claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that “the training data comprise acoustic data labeled with an acoustic event corresponding to individual acoustic objects or acoustic data labeled with an acoustic scene corresponding to a combination of the individual acoustic objects, and the deep learning model is trained to predict the acoustic event or the acoustic scene, via the processor, by inputting the acoustic data.” However, these limitations merely restrict the judicial exception to the field of use of model training. MPEP § 2106.05(h).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that “the training data comprise[] acoustic data labeled with an acoustic event corresponding to individual acoustic objects or acoustic data labeled with an acoustic scene corresponding to a combination of the individual acoustic objects, and the deep learning model is trained to predict the acoustic event or the acoustic scene, via the processor, by inputting the acoustic data.” However, these limitations merely restrict the judicial exception to the field of use of model training. MPEP § 2106.05(h).
Claim 9
Step 1: The claim recites a method; therefore, it is directed to the statutory category of processes.
Step 2A Prong 1: The claim recites, inter alia:
[I]dentifying input data and a trained deep learning model: This limitation could encompass mentally identifying the data and the model.
[P]redicting a class of the identified input data: This limitation could encompass mentally predicting the class.
[I]dentifying the training data labeled for each class: This limitation could encompass mentally identifying the training data.
[D]etermining whether to augment the training data … based on overall recognition performance indicating prediction accuracy of the deep learning model calculated in a previous epoch: This limitation could encompass mentally determining whether to augment the data based on recognition performance of the model by visually observing the model’s outputs.
[A]ugmenting the training data based on class-specific recognition performance indicating class-specific prediction accuracy of the deep learning model calculated in the previous epoch according to a determination of whether to augment the training data: This limitation could encompass mentally augmenting the training data.
[P]redicting a class: This limitation could encompass mentally predicting a class.
[C]alculating a second threshold … using the overall recognition performance, a maximum value of the class-specific recognition performance, and a scale factor that determines a reflection ratio of the overall recognition performance and the maximum value of the class-specific recognition performance: This limitation is directed to the mathematical concept of calculating a threshold, a maximum value, and a scale factor.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites “inputting the identified input data to the deep learning model, wherein the deep learning model is trained by [performing the above]”. The claim further recites that “the training is based on a labeled class and the predicted class.” These limitations amount to a mere instruction to apply the judicial exception using a generic computer programmed with a generic class of computer equipment. MPEP § 2106.05(f).
The claim further recites “inputting the training data or the training data that are augmented to the deep learning model according to a determination of whether to augment the training data”. This limitation recites the insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g).
Finally, the claim recites that certain steps of the method are “executed by a processor of an electronic device comprising a memory storing training data”. However, this is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of step 2A, prong 2, with the exception that the inputting limitation, in addition to being insignificant extra-solution activity, is also directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). As an ordered whole, the claim is directed to a mentally performable process of predicting a class using an augmented dataset. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 10
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia, making a “determination that the training data are to be augmented in response to the overall recognition performance being greater than a first threshold that is set.” This limitation could encompass mentally determining that the data should be augmented based on the performance.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that “the deep learning model is trained” by performing these steps, which is a mere limitation of the judicial exception to the field of use of model training. MPEP § 2106.05(h). The claim further recites that certain steps are performed “via the processor”, which is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that “the deep learning model is trained” by performing these steps, which is a mere limitation of the judicial exception to the field of use of model training. MPEP § 2106.05(h). The claim further recites that certain steps are performed “via the processor”, which is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Claim 11
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
[A]ugmenting … the training data of a class with the class-specific recognition performance less than the second threshold: This limitation could encompass mentally augmenting the training data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that “the deep learning model is trained” by performing these steps, which is a mere limitation of the judicial exception to the field of use of model training. MPEP § 2106.05(h). The claim further recites that certain steps are performed “via the processor”, which is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that “the deep learning model is trained” by performing these steps, which is a mere limitation of the judicial exception to the field of use of model training. MPEP § 2106.05(h). The claim further recites that certain steps are performed “via the processor”, which is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Claim 12
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia, “calculating the second threshold … by increasing a reflection ratio of the maximum value of the class-specific recognition performance as the scale factor increases and by increasing a reflection ratio of the overall recognition performance as the scale factor decreases.” This limitation recites the mathematical concept of calculating a threshold value based on a set of ratios.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that “the deep learning model is trained” by performing these steps, which is a mere limitation of the judicial exception to the field of use of model training. MPEP § 2106.05(h). The claim further recites that certain steps are performed “via the processor”, which is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that “the deep learning model is trained” by performing these steps, which is a mere limitation of the judicial exception to the field of use of model training. MPEP § 2106.05(h). The claim further recites that certain steps are performed “via the processor”, which is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Claim 13
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
[C]alculating an application probability … based on the second threshold and the class-specific recognition performance: This limitation recites the mathematical concept of calculating a probability based on a threshold and recognition performance.
[D]etermining whether to augment each piece of the training data … based on the application probability: This limitation could encompass mentally determining whether to augment the training data.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that “the deep learning model is trained” by performing these steps, which is a mere limitation of the judicial exception to the field of use of model training. MPEP § 2106.05(h). The claim further recites that certain steps are performed “via the processor”, which is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that “the deep learning model is trained” by performing these steps, which is a mere limitation of the judicial exception to the field of use of model training. MPEP § 2106.05(h). The claim further recites that certain steps are performed “via the processor”, which is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Claims 14-18
Step 1: The claims recite an electronic device comprising a processor; therefore, they are directed to the statutory category of machines.
Step 2A Prong 1: The claims recite the same judicial exceptions as in claims 9-13, respectively.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The analysis at this step mirrors that of claims 9-13, respectively, except insofar as these claims recite an “electronic device comprising: a processor; wherein the processor is configured to [perform the method]”. However, this is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis at this step mirrors that of claims 9-13, respectively, except insofar as these claims recite an “electronic device comprising: a processor; wherein the processor is configured to [perform the method]”. However, this is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Claim 19
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia, “calculating the application probability for the each class … based on a value obtained by subtracting the class-specific recognition performance from the second threshold.” This limitation is directed to the mathematical calculation of subtracting class-specific recognition performance from a threshold.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites that “the deep learning model is trained by” performing the calculating. This limitation merely restricts the judicial exception to the field of use of model training. MPEP § 2106.05(h). The claim further recites that certain steps are performed “via the processor”, which is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites that “the deep learning model is trained by” performing the calculating. This limitation merely restricts the judicial exception to the field of use of model training. MPEP § 2106.05(h). The claim further recites that certain steps are performed “via the processor”, which is a mere instruction to apply the judicial exception using a generic computer. MPEP § 2106.05(f).
Response to Arguments
Applicant's arguments filed November 13, 2025 (“Remarks”) have been fully considered but they are, except insofar as rendered moot by the withdrawal of a ground of rejection, not persuasive.
Applicant argues that the claims as amended are allegedly eligible under 35 USC § 101 because (a) they now recite that the relevant operations are performed by a processor, (b) the claims as amended allegedly provide significantly more than the judicial exception because they now recite adaptive augmentation of training data, which allegedly results in improved recognition performance and reduced class imbalance, and (c) the precedential decision of the Appeals Review Panel in Ex parte Desjardins allegedly states that equating machine learning with an unpatentable algorithm and the additional elements as generic computer components is improper. Remarks at 11-12.
However, argument (a) is in direct conflict with the holding of the U.S. Supreme Court in Alice Corp. v. CLS Bank Int’l, 573 U.S. 208 (2014), namely that implementing an abstract idea on a computer is not enough to transform the abstract idea into eligible subject matter. Here, other than a nominal recitation of training the model using augmented training data toward the end of the independent claims, which is itself a mere restriction of the judicial exception to a field of use, the claim as a whole is not directed to machine learning as such, but rather to the creation of a particular type of training dataset, which is, but for the recitation that such creation is performed on a computer, mentally performable.
Argument (b) is no more convincing because it amounts to an assertion that the abstract idea itself provides the inventive concept. The new wherein clause in the independent claims, culled directly from language previously found in claims 3, 11, and 16, recites a mathematical calculation of computing a threshold based on the claimed factors that is merely applied to the field of use of model training. The inventive concept cannot come from the abstract idea itself. MPEP § 2106.05(I).
Regarding argument (c), Examiner finds no evidence that Desjardins stands for the proposition that Applicant asserts. Rather, the decision in Desjardins was based on the conclusion that the claimed subject matter improves machine learning itself rather than an abstract idea. By contrast, in the instant case, any recitation of the use of the judicial exception in machine learning is nominal and perfunctory at best and merely restricts the judicial exception to a field of use.
The arguments with respect to the art rejections, Remarks at 12-13, are moot in light of the withdrawal of that ground of rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN C VAUGHN whose telephone number is (571)272-4849. The examiner can normally be reached M-R 7:00a-5:00p ET.
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/RYAN C VAUGHN/ Primary Examiner, Art Unit 2125