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 .
Response to Amendment
In response to the Non-final Office Action from 9/25/2025, Applicant has filed an amendment on 1/17/2026. In this reply, Applicant has significantly overhauled independent claim 1 to more narrowly define processing at a temporal relation-effect layer module (TREL), temporal decomposition gate module (TDG) with specific learning equations and an equation for generating temporal effect analysis data using a hyperbolic-tangent function, and combining the data yielded by such modules/layer to generate a second time-point time-series data. Each of the dependent claims were also amended while claims 12-17 were cancelled.
Applicant has also argued that the prior art of record fails to teach the limitations added via the instant amendment (Remarks, Pages 12-13). These arguments have been fully considered and Claims 1-11 have been found to be directed towards potentially allowable subject matter for the reasons noted in the below Potentially Allowable Subject Matter section.
In response to the replacement drawings filed for Figs. 4 and 8 that resolve readability issues (Remarks, Pages 9-10), the objections to the drawings are now moot and have been withdrawn.
Applicant argues that the 35 U.S.C. 112(b) is moot due to the cancellation of claim 12 (Remarks, Page 10).
In response to the cancellation of claim 12, the 35 U.S.C. 112(b) rejection is now moot and has been withdrawn.
In regards to the patent subject matter eligibility rejection of claims 1-17 under 35 U.S.C. 101, Applicant argues that as amended claim 1 now recites more than a generic training step because it instead recites "specific runtime processing performed by the claimed multi-layer TREL to generate second time-point data and to update/reuse hidden state matrix data." Applicant also argues that the recited processing proposes a neural network architecture and a memory-based hidden-state update/reuse mechanism that modify how the computing system processes time-series data so as to integrate these operations into a practical application/technical improvement in machine-learning based time-series prediction processing over conventional approaches (Remarks, Pages 10-11).
In response, as a first matter it is noted that the 35 U.S.C. 101 of claims 12-17 is now moot and has been withdrawn due to the cancellation of these claims. Next, claim 1 was completely overhauled to incorporate a specifically structured neural network model used for time series prediction having a particularly structured TREL, TEG and TDB modules. Due to this specifically structured machine learning model leading to an improvement in machine-learning approaches for time series prediction in step 2A prong 2, purely human/mental processes under the broadest reasonable interpretation (BRI) are avoided and while certain steps involve mathematical calculations, the recited process is not purely mathematical and contains and improvement to machine-learning specific time series prediction. Accordingly, claims 1-11 are no longer directed towards a judicial exception without significantly more under the BRI and the 35 U.S.C. 101 patent subject matter eligibility rejection has been withdrawn.
Claim Objections
Claims 1-11 are objected to because of the following informalities:
In claim 1, equation 1, the meaning of the variables wi and bi have not been explained similar to the other portions of equation 1. The remaining dependent claim inherit and fail to resolve these minor informalities, and thus, have also been objected to due to minor informalities by virtue of their dependency.
Also, with the presumed filing of future amendments overcoming these minor informalities, Applicant is advised to provide clearer copies of the equations instead of the instant greyscale that is difficult to read. Doing so may preclude the need for refiling claims as such unclear claims would be likely to be flagged by publications should this application be found to be allowable in the future.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The instant amendment to independent claim 1 and its dependents has resulted in a number of antecedent basis issues under 35 U.S.C. 112(b):
In Claim 1, Line 39 "a parameter WIH" was already introduced in equation 2 so it is unclear if the earlier instance is being referenced or if a new instance of the parameter is being introduced. For claim interpretation, in the interest of compact prosecution, "a parameter WIH" will be construed as --the parameter WIH--.
In Claim 1, Line 40, "a parameter WH" is discussed when it is unclear what parameter in equation 3 is being explained. For claim interpretation, in the interest of compact prosecution, "a parameter WH" will be construed as --the parameter WHH--.
In Claim 1, Line 46, "the hidden state matrix data" lacks antecedent basis and it is unclear what limitation is being referenced by this term. For claim interpretation purposes in the interest of compact prosecution, this limitation will be construed as --hidden state matrix data--.
In Claim 4, Line 7, "hidden state matrix data" was previously introduced in claim 1 so it is unclear whether a new instance of the term is being introduced or if the term should refer back to the claim 1 instance. For claim interpretation, this limitation will be construed as --the hidden state matrix data--. Also in Line 8, "temporal relation analysis data" was previously introduced in claim 1 so it is unclear whether a new instance of the term is being introduced or if the term should refer back to the claim 1 instance. For claim interpretation, this limitation will be construed as --the temporal relation analysis data--.
In Claim 6, Line 8, "temporal effect analysis data" was previously introduced in claim 1 so it is unclear whether a new instance of the term is being introduced or if the term should refer back to the claim 1 instance. For claim interpretation, this limitation will be construed as --the temporal effect analysis data--.
Dependent claims 2-11 also inherit and fail to resolve the indefinite subject matter of their respective parent claims, and thus, are also rejected under 35 U.S.C. 112(b) by virtue of their dependency.
Potentially Allowable Subject Matter
Claims 1-11 would be allowable if rewritten to overcome the previous rejections under 35 U.S.C. 112(b).
The following is a statement of reasons for the indication of potentially allowable subject matter:
With respect to independent Claim 1, the prior art of record fails to explicitly teach or fairly suggest taken individually or in a combination a method for providing a time-series prediction deep learning neural network using a processor of a computer system wherein the received first time-point time-series data is processed by a temporal relation-effect layer module (TREL) that is implemented as a plurality of interconnected layers to generate second time-point time-series data where the TREL is structured as particularly set forth in independent claim 1 and the hidden state matrix data store in the memory based upon the temporal relation analysis data and the temporal effect analysis data is updated and reused as an input for processing a subsequent time-point time-series data.
Most Pertinent Prior Art:
Bae, et al. (U.S. PG Publication: 2023/0267304 A1) discloses the use of a deep neural network having multiple layers with "input time-series data" to generate second time series data in the form of predictions (Paragraphs 0028, 0074-0075, 0083-0085, 0097, and 0101-0111). Note that the deep neural network is structured with a STL cell gate (Paragraph 0039-0046), an autocorrelation/temporal relation gate (Paragraphs 0037 and 0047-0050) and a temporal gate/correlation gate that measures temporal effects such as trends (Paragraphs 0037, 0039, and 0052-0054). Bae, however, uses the STL cell for time series decomposition not variational mode decomposition by performing learning based upon equation 1, does not teach the use of a softmax function according to equation 2 in the autocorrelation gate, and although Bae discloses updating of hidden states based upon trend information updated at the autocorrelation gate (Paragraphs 0013 and 0049-0050), the updating does not occur to a hidden state matrix in the manner claimed.
While other prior art such as Li, et al. (U.S. PG Publication: 2025/0067564 A1) evidences that VMD of time series data is known in the art (see Paragraphs 0025 and 0054), these references fail to overcome the deficiencies of Bae. Han, et al. ("A Temporal Window Attention-Based Window-Dependent Long Short-Term Memory Network for Multivariate Time Series Prediction," 2022) adds the re-use of a previous hidden state matrix in multivariate time series prediction (Section 3.2, Page 5; Section 3.2.2, Pages 6-7; Fig. 3), but fails to resolve all deficiencies of Bae. Accordingly, the prior art of record fails to explicitly teach or fairly suggest the invention set forth in independent claim 1.
The remaining dependent claims inherit the potentially allowable subject matter of parent claim 1, and thus, are also directed towards potentially allowable subject matter by virtue of their dependency.
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Xu, et al. ("Tensorized LSTM with Adaptive Shared Memory for Learning Trends in Multivariate Time Series," 2020)- discloses an LSTM to model the temporal patterns of long-term trend sequences (Abstract).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES S WOZNIAK whose telephone number is (571)272-7632. The examiner can normally be reached 7-3, off alternate Fridays.
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JAMES S. WOZNIAK
Primary Examiner
Art Unit 2655
/JAMES S WOZNIAK/Primary Examiner, Art Unit 2655