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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 20 November 2025 has been entered.
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 1-20 is/are rejected under 35 U.S.C. 101 because they are directed to abstract ideas without significantly more.
Regarding claims 1-20,
Step 1: With respect to claims 1-7, the preamble of claims 1-7 claims a method which falls within the statutory category of a process. With respect to claims 8-14, the preamble of claims 8-14 claims a system which falls within the statutory category of an apparatus. With respect to claims 15-20, the preamble of claims 15-20 claims a non-transitory computer-readable storage medium which falls within the statutory category of a manufacture.
Regarding claim 1,
Step 2A – Prong One: Claim 1 recites:
A method of improving an accuracy of predictions generated by a machine learning system, comprising:
configuring the machine learning system that comprises a prediction model and a Fourier layer;
receiving input data by the prediction model, wherein the input data has a periodic pattern, the prediction model is configured to process the input data and generate a feature representation of each input data sample in the input data, and each input data sample corresponds to a point of time within a period;
generating first Fourier coefficients in real time in response to receiving the input data by applying a first mapping model of the Fourier layer to the feature representation, wherein the first Fourier coefficients are to be applied to cosine values in a Fourier expansion;
generating second Fourier coefficients in real time in response to receiving the input data by applying a second mapping model of the Fourier layer to the feature representation, wherein the second Fourier coefficients are to be applied to sine values in the Fourier expansion;
applying the first Fourier coefficients to the cosine values to generate first products and applying the second Fourier coefficients to the sine values to generate second products;
transforming the first products into a first intermediate expansion result having a dimension by applying a third mapping model of the Fourier layer to the first products;
transforming the second products into a second intermediate expansion result having the dimension by applying a fourth mapping model of the Fourier layer to the second products; and
generating a prediction result having the dimension based on aggregating the first intermediate expansion result and the second intermediate expansion result.
The broadest reasonable interpretation, in light of the specification, of the bolded limitations above are directed to a mathematical concept. Generating first and second Fourier coefficients by applying a first and second mapping models of a Fourier layer to a feature representation is a mathematical calculation, and thus falls within the mathematical concepts grouping of abstract ideas. Applying the first and second Fourier coefficients to cosine and sine values, respectively, to generate first and second products is a mathematical calculation, and thus falls within the mathematical concepts grouping of abstract ideas. Transforming the first and second products into first and second intermediate expansion results having a dimension by applying third and fourth mapping models to the first and second products, respectively, is a mathematical calculation, and thus falls within the mathematical concepts grouping of abstract ideas. Generating a result by aggregating the first and second intermediate expansion results is a mathematical calculation, and thus falls within the mathematical concepts grouping of abstract ideas. Step 2A – Prong One (Yes).
Step 2A – Prong Two:
The additional elements in this claim regarding “configuring the machine learning system that comprises a prediction model and a Fourier layer;” are mere instructions to apply the exception using a generic computer (See MPEP 2106.05(f)). The additional elements in this claim regarding “receiving input data by the prediction model, wherein the input data has a periodic pattern, the prediction model is configured to process the input data and generate a feature representation of each input data sample in the input data, and each input data sample corresponds to a point of time within a period;” is insignificant extra-solution activity that amounts to mere data gathering (See MPEP 2106.05(g)).
Even when viewed in combination the additional element does not integrate the recited judicial exception into a practical application. Step 2A – Prong Two (No).
Step 2B: The additional elements in this claim regarding “configuring the machine learning system that comprises a prediction model and a Fourier layer;” are mere instructions to apply the exception using a generic computer (See MPEP 2106.05(f)). The computer is recited at a high level of generality and imposes no meaningful limitations on the claim. The additional elements in this claim regarding “receiving input data by the prediction model, wherein the input data has a periodic pattern, the prediction model is configured to process the input data and generate a feature representation of each input data sample in the input data, and each input data sample corresponds to a point of time within a period;” is insignificant extra-solution activity that amounts to mere data gathering (See MPEP 2106.05(g)). Data gathering is well-understood, routine conventional activity as recognized by the courts (See MPEP 2106.05(d)(II)).
Even when viewed in combination the additional element does not amount to significantly more than the judicial exception. Step 2B (No).
Claim 1 is ineligible.
Regarding claims 8 and 15,
These claims are similar in scope to claim 1 and are rejected under similar rationale. The processors and memory recited in these claims are also generic computing components.
Claims 8 and 15 are ineligible.
Regarding dependent claims,
Claims 2-4, 9-11, and 16-18: These claims recite further abstract ideas (mathematical concepts) and thus are ineligible.
Claims 5, 12, and 19: These claims recite a further mathematical concept (“determining the prediction result by aggregating…”) and also recite further insignificant extra-solution activities that amount to mere data gathering (“obtain a third intermediate prediction result generated from an output layer…”). Data gathering is a well-understood, routine conventional activity as recognized by the courts (See MPEP 2106.05(d)(II)). Thus, these claims are ineligible.
Claims 6, 13, and 20: These claims recite further insignificant extra-solution activities that amount to mere data gathering (See MPEP 2106.05(g)). Data gathering is a well-understood, routine conventional activity as recognized by the courts (See MPEP 2106.05(d)(II)). Thus, these claims are ineligible.
Claim 7 and 14: These claims recite further instructions to apply the abstract idea on a generic computer (See MPEP 2106.05(f)). The computer is recited at a high level of generality and imposes no meaningful limitations on the claim. Thus, these claims are ineligible.
No Prior Art Rejection
None of the prior art of record, alone or combined, fairly teaches or suggests claims 1, 8, and 15. These claims would be allowable if the rejections under 35 U.S.C. 101 are resolved.
Response to Arguments
Applicant's arguments filed 23 December 2025 have been fully considered but they are not persuasive. Applicant argues on pp. 1-2 of Remarks that “The claims contain additional elements that clearly integrate any alleged abstract idea into practical application.” Examiner respectfully disagrees. The additional elements of claim 1 are “configuring the machine learning system that comprises…” and “receiving input data by a prediction model, wherein the input data has a periodic pattern… and generate a feature representation of each input data sample…”. The additional element of claim 1 regarding “configuring the machine learning system that comprises…” are mere instructions to apply the exception on a generic computer (See MPEP 2106.05(f)). The computer is recited at a high level of generality and imposes no meaningful limitations on the claim. The additional element of claim 1 regarding “receiving input data by a prediction model, wherein the input data has a periodic pattern… and generate a feature representation of each input data sample…” is insignificant extra-solution activity that amounts to mere data gathering (See MPEP 2106.05(g)). Data gathering is a well-understood, routine conventional activity as recognized by the courts (See MPEP 2106.05(d)(II)). Even when viewed alone or in combination, the additional element does not integrate the recited judicial exception into a practical application.
Applicant argues on pp. 1-2 of Remarks that “Applicant’s claims are inextricably tied to an improved technique for improving an accuracy of predictions…”. Examiner respectfully disagrees. MPEP 2106.04(d)(1) states “if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification.” The claim fails to reflect the purported improvement of “improving an accuracy of predictions”. The claims broadly recite “generating first Fourier coefficients…”, “generating second Fourier coefficients…”, “applying the first Fourier coefficients to cosine values… applying second Fourier coefficients to sine values…”, “transforming the first products into a first intermediate expansion… by applying a third mapping model…”, “transforming the second products into a second intermediate expansion… by applying a fourth mapping model…”, and “generating a prediction result having the dimension based on aggregating…” without reciting the specific technical steps by which the alleged improved accuracy is achieved.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bouboulis et al. (NPL: Online Distributed Learning Over Networks in RKH Spaces Using Random Fourier Features, published April 2018) teaches a protocol for distributed online learning in reproducing kernel Hilbert space. Teaches receiving input data and generating Fourier features for the kernel. Teaches generating Fourier features in real-time in response to receiving input.
Yamakaji et al. (FOR: EP 4030346 A1, filed Sept. 2019) teaches an information processing device for processing an input signal with a neural network that comprises a Fourier transform layer for performing a Fourier transform on the input signal. Teaches generating amplitude signals in real time in response to the received input data. Batch learning or online learning may be utilized. Teaches transforming input from spatial domain to spatial frequency domain.
Nakadai et al. (US Pub. No. 2020/0293857, filed March 2020) teaches a CNN processing device with a storage unit for storing a Fourier base function. Teaches a convolution operation unit for modeling an element in coefficients of the kernels in a CNN using N-order Fourier series expansion and to perform convolution on processing target information. Teaches wherein the element has a periodicity and generates Fourier coefficients based on the element.
Tominaga et al. (US Pub. No. 2006/0242214, filed March 2006) teaches a periodicity judgement apparatus for judging the periodicity of time series data. Teaches a discrete Fourier transform unit for performing a Fourier transform on the time series data to obtain a Fourier coefficient vector. Teaches an inverse discrete Fourier transform unit for obtaining the inverse transformed data, thus mapping the intermediate result of the transform unit to the original dimension.
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/LOUIS CHRISTOPHER NYE/Examiner, Art Unit 2141
/TAN H TRAN/Primary Examiner, Art Unit 2141