DETAILED ACTION
This action is in response to claims filed 01 October 2025 for application 17940126 filed 08 September 2022. Currently claims 1-17 and 20-22 are pending.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02 March 2026 has been entered.
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 1-17 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to the abstract idea of concatenating data for use in a model without significantly more.
In step 1, claims 1, 8 and 15 are directed to the statutory category of a system, a method, and an article of manufacture.
In step 2a prong 1, claims 1, 8 and 15 recite, in part, accessing synthetic time series data that has been concatenated from discontinuous data, determining a characteristic with the concatenation point, generating a sample weight for the point and characteristic, applying the sample weight in a machine learning model, executing the machine learning model with a loss function that uses the sample weight, and generating a prediction based on the executed model. Claims 8 and 15 are directed to largely the same limitations. The limitations of accessing, determining, generating, applying and executing are processes that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “processor” and “non-transitory storage medium” in the context of the claims, the limitations encompass removing discontinuities in data by concatenating the data together and executing a model by minimizing the errors caused by concatenation in the mind or with aid. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
In step 2a prong 2, this judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of “processor” and “non-transitory storage medium”. The computer components in the claim are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Please see MPEP §2106.04.(a)(2).III.C. The claim also recites the additional element of transmitting the prediction for display. This limitation amounts to mere insignificant extra-solution activity of transmitting information. Please see MPEP §2106.05(g).
In step 2b, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, either alone or in combination. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “processor” and “non-transitory storage medium” to perform the steps of the claims amount to no more than mere instructions to apply the exception using a generic computer component. The step of transmitting the prediction for display was considered to be an insignificant extra solution activity in step 2A, and thus it is re-evaluated in step 2B to determine if it is more than well-understood, routine and conventional activity in the field. This step amounts to insignificant extra-solution activity of transmitting information and is well-understood, routine and conventional activity in view of MPEP 2106.05(d)(II)(i), (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Claims 2-7, 9-14 and 16-20 recite further limitations of the characteristic being a frequency or a magnitude, assigning different weights to different concatenation points, generating a first weight for a first point and a second weight for a second point, generating an array of weights as input to the model, and weights of concatenation points are higher than other portions. These limitations amount to the same abstract idea identified above. No further additional elements are recited and thus the claims do not amount to a practical application in step 2 a prong 2 or significantly more in step 2b.
Response to Arguments
Applicant's arguments filed 02 March 2026 have been fully considered but they are not persuasive. Applicant argues that the claims are not directed to a mental process and are directed to a practical application and significantly more. Examiner respectfully disagrees.
Applicant argues that the custom loss function is a practical application in step 2a prong 2 and improvement to machine learning in view of the Desjardins decision. Applicant also argues that the machine learning is not a well-understood, routine and conventional in step 2b. Examiner respectfully disagrees. The improvement of the loss function merely improves the abstract idea of applying the machine learning to the mental process and does not improve the training itself. Desjardins also recites the eligible “training the machine learning model…” that as a whole makes the claims patent eligible. Similar subject matter is now recited in instant claims 21-22 that are not rejected under §101. The machine learning not including the training or specific hardware is merely a mental process and generic computer component and is not significantly more than the abstract idea itself.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC NILSSON whose telephone number is (571)272-5246. The examiner can normally be reached M-F: 7-3.
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/ERIC NILSSON/Primary Examiner, Art Unit 2151
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