ETAILED ACTION
Status of Claims
1. This office action is in response to amendment filed 12/9/2025.
2. Claims 1-11, 13 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 .
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 1-11, 13
Claims 1-11, 13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: Claims 1-11, 13 are directed to a method of which is one of the statutory categories of inventions.
Step 2A: A claim is eligible at revised Step 2A unless it recites a judicial exception and the exception is not integrated into a practical application of the application.
Prong 1: Prong One of Step 2A evaluates whether the claim recites a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon).
Groupings of Abstract Ideas:
I. MATHEMATICAL CONCEPTS
A. Mathematical Relationships
B. Mathematical Formulas or Equations
C. Mathematical Calculations
II. CERTAIN METHODS OF ORGANIZING HUMAN ACTIVITY
A. Fundamental Economic Practices or Principles (including hedging, insurance, mitigating risk)
B. Commercial or Legal Interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)
C. Managing Personal Behavior or Relationships or Interactions between People (including social activities, teaching, and following rules or instructions)
III. MENTAL PROCESSES.
Concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
See MPEP 2106.04 (a) (2) Abstract Idea Groupings [R-10.2019]
Independent claim 1 is directed to –
A computer-implemented method for testing a financial computing system to determine robustness of the financial computing system under different kinds of attacks the method comprising:
generating plural synthetic financial time series (FTS), wherein an individual synthetic FTS of the plural synthetic FTS is generated by a first process of synthesizing the individual synthetic FTS according to a reference FTS, and wherein plural respective reference FTS used for generating the plural synthetic FTS are different; and
testing the financial computing system under testing conditions respectively defined by the plural synthetic FTS to classify the financial computing system as being robust or not robust under the different kinds of attacks;
wherein the first process of synthesizing the individual synthetic FTS according to the reference FTS comprises
learning, from the reference FTS, a set of scale-invariant patterns for modeling the individual synthetic FTS by performing a joint process of segmenting the reference FTS into a first sequence of reference-FTS segments with variable segment lengths and clustering a plurality of normalized segments derived from the first sequence into a plurality of clusters to yield a plurality of cluster centroids under a segmentation requirement that a segment length of an individual reference-FTS segment is selected to minimize a distance from a normalized segment corresponding to the individual reference-FTS segment to a nearest centroid in the plurality of cluster centroids, wherein the plurality of normalized segments is obtained via normalizing the individual reference-FTS segment in magnitude and in segment duration, and wherein the plurality of cluster centroids is used as the plurality of scale-invariant patterns; and
generating the individual synthetic FTS as a second sequence of synthetic-FTS segments with variable segment lengths, wherein an individual synthetic-FTS segment is generated as a sample of a conditional distribution conditioned on a tuple of parameters associated with the individual synthetic-FTS segment, and wherein the tuple of parameters consists of a scale-invariant pattern selected from the set of scale-invariant patterns for controlling a waveshape of the generated individual synthetic-FTS segment, a magnitude-scaling factor for amplitude-scaling the waveshape in generating the individual synthetic-FTS segment, and a duration-scaling factor for controlling compression and expansion of the waveshape in time in generating the individual synthetic-FTS segment
– that constitutes Mathematical Concepts.
Independent claim 13 is directed to –
A computer-implemented method for testing a financial computing system to determine resilience of the financial computing system under different degrees of work loading, the method comprising:
generating plural synthetic financial time series (FTS), wherein an individual synthetic FTS of the plural synthetic FTS is generated by a first process of synthesizing the individual synthetic FTS according to a reference FTS and an initial tuple of parameters, wherein plural respective reference FTS used for generating the plural synthetic FTS are same, and wherein plural respective initial tuples of parameters are mutually different; and
testing the financial computing system under testing conditions respectively defined by the plural synthetic FTS to classify the financial computing system as being resilient or not resilient under the different degrees of work loading;
wherein the first process of synthesizing the individual synthetic FTS according to the reference FTS and the initial tuple of parameters comprises:
learning, from the reference FTS, a set of scale-invariant patterns for modeling the individual synthetic FTS by performing a joint process of segmenting the reference FTS into a first sequence of reference-FTS segments with variable segment lengths and clustering a plurality of normalized segments derived from the first sequence into a plurality of clusters to yield a plurality of cluster centroids under a segmentation requirement that a segment length of an individual reference-FTS segment is selected to minimize a distance from a normalized segment corresponding to the individual reference-FTS segment to a nearest centroid in the plurality of cluster centroids, wherein the plurality of normalized segments is obtained via normalizing the individual reference- FTS segment in magnitude and in segment duration, and wherein the plurality of cluster centroids is used as the plurality of scale-invariant patterns;
generating the individual synthetic FTS as a second sequence of synthetic- FTS segments with variable segment lengths, wherein an individual synthetic-FTS segment is generated as a sample of a conditional distribution conditioned on a tuple of parameters associated with the individual synthetic-FTS segment, and wherein the tuple of parameters consists of a scale-invariant pattern selected from the set of scale-invariant patterns for controlling a waveshape of the generated individual synthetic-FTS segment, a magnitude-scaling factor for amplitude-scaling the waveshape in generating the individual synthetic-FTS segment, and a duration-scaling factor for controlling compression and expansion of the waveshape in time in generating the individual synthetic-FTS segment;
before the individual synthetic FTS is generated, setting up a first machine-learning (ML) model for generating a second tuple of parameters used for modeling a second synthetic-FTS segment in the second sequence from a first tuple of parameters used for modeling a first synthetic-FTS segment that immediately precedes the second synthetic-FTS segment in the second sequence such that temporal dynamics of the individual synthetic FTS are learnt, wherein the first ML model is trained with the reference FTS; and
before the individual synthetic FTS is generated, setting up a second ML model for generating the individual synthetic-FTS segment in the second sequence according to an input tuple of parameters, wherein the second ML model is trained with the first sequence;
wherein the generating of the individual synthetic FTS as the second sequence of synthetic-FTS segments with variable segment lengths comprises recursively using the first and second ML models to generate consecutive synthetic-FTS segments for the second sequence; and
wherein an initial segment among the consecutive synthetic-FTS segment is generated by the second ML model with the initial tuple of parameters used as the input tuple of parameters.
– that constitutes Mathematical Concepts.
The dependent claims are directed to –
(claim 2) wherein a greedy algorithm is employed in the joint process to jointly determine corresponding segment lengths of respective reference-FTS segments in the first sequence, and the plurality of cluster centroids
(claim 3) wherein the greedy algorithm comprises the steps of: (a) initializing the plurality of cluster centroids; (b) repeating a first subprocess until the reference FTS is fully segmented to form a candidate sequence of reference-FTS segments, wherein the first subprocess is programmed to determine a segment length of a presently-considered reference-FTS segment to fulfill the segmentation requirement given that respective sequence lengths of previously-considered reference-FTS segments have been determined; (c) after the step (b) is completed, updating the plurality of cluster centroids according to the candidate sequence of reference-FTS segments; (d) repeating a second subprocess with the updated plurality of cluster centroids until the candidate sequence of reference-FTS segments as a whole converges or until the second subprocess has been executed for a predetermined number of times, wherein the second subprocess comprises the steps (b) and (c); and (e) setting the candidate sequence of reference-FTS segments as obtained at completion of the step (d) to be the first sequence of reference-FTS segments
(claim 4) wherein the step (a) comprises: initializing a set of available reference-FTS segments, wherein respective segments in the initialized set of available reference-FTS segments are disjoint segments of the reference FTS and are of equal length; initializing a set of cluster centroids to be an empty set; when the set of cluster centroids is empty: randomly selecting a segment in the set of available reference-FTS segments to serve as a first cluster centroid in the set of cluster centroids; updating the set of cluster centroids with the first cluster centroid; and updating the set of available reference-FTS segments by removing the selected segment that serves the first cluster centroid; repeating a third subprocess with the updated set of cluster centroids and the updated set of available reference-FTS segments until the set of cluster centroids is filled with a preselected number of cluster centroids, wherein the third subprocess comprises: selecting a segment in the set of available reference-FTS segments to serve as a new cluster centroid in the set of cluster centroids, wherein a weight of an individual segment in the set of available reference-FTS segments to be selected as the new cluster centroid is proportional to a distance from said individual segment to a closest centroid in the set of cluster centroids, and wherein the segment to serve the new cluster centroid is selected according to respective weights computed for the set of available reference-FTS segments; updating the set of cluster centroids with the new cluster centroid; and updating the set of available reference-FTS segments by removing the selected segment that serves the new cluster centroid; and after the set of cluster centroids is filled with the preselected number of cluster centroids, setting the set of cluster centroids as the initialized plurality of cluster centroids
(claim 5) wherein dynamic time wrapping (DTW) is used as a distance matric in computing the distance from the normalized segment corresponding to the individual reference-FTS segment to the nearest centroid in the plurality of cluster centroids
(claim 6) wherein the conditional distribution is a multivariate Gaussian distribution
(claim 7) before the individual synthetic FTS is generated, setting up a first machine-learning (ML) model for generating a second tuple of parameters used for modeling a second synthetic-FTS segment in the second sequence from a first tuple of parameters used for modeling a first synthetic-FTS segment that immediately precedes the second synthetic-FTS segment in the second sequence such that temporal dynamics of the synthetic FTS are learnt, wherein the first ML model is trained with the reference FTS; and before the individual synthetic FTS is generated, setting up a second ML model for generating the individual synthetic-FTS segment in the second sequence according to an input tuple of parameters, wherein the second ML model is trained with the first sequence; wherein the generating of the individual synthetic FTS as the second sequence of synthetic-FTS segments with variable segment lengths comprises recursively using the first and second ML models to generate consecutive synthetic-FTS segments for the second sequence
(claim 8) wherein an initial segment among the consecutive synthetic-FTS segment is generated by the second ML model with an initial tuple of parameters used as the input tuple of parameters, the initial tuple of parameters being generated from the first ML model
(claim 9) wherein an initial segment among the consecutive synthetic-FTS segment is generated by the second ML model with an initial tuple of parameters used as the input tuple of parameters, the initial tuple of parameters being externally received from outside the first and second ML models
(claim 10) wherein the second ML model comprises: a scaling autoencoder (AE) comprising an encoder for transforming a first variable-length segment into a first fixed-length segment, and a decoder for transforming a second fixed-length segment from a second variable-length segment, wherein the first variable-length segment is computed according to the input tuple of parameters, wherein the second variable-length segment is used as one synthetic-FTS segment in the second sequence, and wherein the scaling AE is trained according to the reference FTS and the set of scale-invariant patterns; and a pattern-conditioned diffusion network for generating the second fixed-length segment from the first fixed-length segment
(claim 11) wherein the pattern-conditioned diffusion network is realized according to a denoising diffusion probabilistic model (DDPM)
– that also constitute Mathematical Concepts.
Hence under Prong One of Step 2A, claims 1-11, 13 recite a judicial exception.
Prong 2: Prong Two of Step 2A evaluates whether the claim recites additional elements that integrate the judicial exception into a practical application of the exception.
Limitations that are indicative of integration into a practical application include:
Improvements to the functioning of a computer or to any other technology or technical field – see MPEP 2106.05(a)
Applying the judicial exception with, or by use of, a particular machine – see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing – see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception – see MPEP 2106.05(e)
Limitations that are not indicative of integration into a practical application include:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f)
Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g)
Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
Additional elements recited by the claims, beyond the abstract idea, include: a financial computing system; a first machine learning model; a second machine learning model; learning. Examiner finds that any additional element(s), beyond the judicial exception, has been recited at a high level of generality such that the claim limitations amount to no more than mere instructions to apply the exception using generic components (see MPEP 2106.05(f)) or insignificant data gathering activities (see MPEP 2106.05(g)).
The combination of additional elements does not purport to improve the functioning of a computer or effect an improvement in any other technology or technical field. Instead, the additional elements do no more than “use the computer as a tool” and/or “link the use of the judicial exception to a particular technological environment or field of use.” The focus of the claims is not on improvement in computers, but on certain independently abstract ideas – synthesizing a synthetic financial time series (FTS) – that merely uses generic computers as tools. Steps that do no more than spell out what it means to “apply it on a computer” cannot confer patent eligibility. Indeed, nothing in claim 1 improves the functioning of the computer, makes it operate more efficiently, or solves any technological problem. See Trading Techs. Int’l, Inc. v. IBG LLC, 921 F.3d 1378, 1384-85 (Fed. Cir. 2019).
Hence, the additional elements, when considered individually or in combination, do not integrate the judicial exception into a practical application.
Hence, the claims are ineligible under Step 2A.
Step 2B: In Step 2B, the evaluation consists of whether the claim recites additional elements that amount to an inventive concept (aka “significantly more”) than the recited judicial exception.
As discussed in Prong Two, the additional elements in the claims amount to no more than mere instructions to apply the exception using generic components, which is insufficient to provide an inventive concept. When considered individually or as an ordered combination, the additional elements fail to transform the abstract idea of – synthesizing a synthetic financial time series (FTS) – into significantly more.
See MPEP 2106.05(f) Mere Instructions To Apply An Exception [R-10.2019].
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.
Hence, the claims are ineligible under Step 2B.
Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to a judicial exception without significantly more.
Response to Arguments
Applicant's arguments filed 12/9/2025 have been fully considered but they are not persuasive.
101
Applicant argues that the claim 1 limitation “for testing a financial computing system to determine robustness of the financial computing system under different kinds of attacks” is an additional element that effects the transformation or reduction of a particular article to a different state or thing in that the financial system is reduced to a first or second specific state of robust or non robust. Applicant also argues that claim 13 limitation “testing a financial computing system to determine resilience of the financial computing system under different degrees of work loading” reduces the state of the financial system from more specific state of resilient or non-resilient.
Examiner respectfully disagrees.
First, the phrase “to determine” in the limitations “testing a financial computing system to determine robustness of the financial computing system” or “testing a financial computing system to determine resilience of the financial computing system under different degrees of work loading” indicates intentional use or aspirational determination that does not actively determine robustness or resilience.
Second, it is entirely unclear from the specification what is meant by the terms “robustness” or “resilience” of a financial system. For example, as per para [0152], rich variety of synthetic FTS is generated that may be used to test, e.g., robustness of the financial computing system under different kinds of attacks. However, the specification does not explain why and how the generation of plural synthetic FTS is able to test the robustness of a financial system, let alone what is meant by “robust under the different types of attacks” since no examples of any type of attacks have been provided in the specification. As per para [0153], the plural synthetic FTS are likely to have similar (normalized) waveshapes. In case each synthetic FTS represents a certain amount of loading to the financial computing system, the generated plural synthetic FTS may be used to test, e.g., resilience of the financial computing system under different degrees of work loading. It is not clear how and why synthetic FTS with similar waveshapes may be used to test the resilience of the financial system. Nowhere in the specification is there any disclosure of a state of financial system either in robust state or non robust state. At most, the claimed invention generates synthetic FTS using mathematical formulas or equations but this has nothing to do with improvement in computers or technology.
The combination of limitations does not bring about (i) an improvement to the functionality of a computer or other technology or technical field; (ii) a “particular machine” to apply or use the judicial exception; (iii) a particular transformation of an article to a different thing or state; or (iv) any other meaningful limitation. See MPEP 2106.05(a)-(c), (e)-(h). Hence, the additional elements fail to integrate the abstract idea into a practical application or provide significantly more. See MPEP 2106.05(f).
For the above reasons, Applicant’s arguments are not persuasive.
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.
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/ARUNAVA CHAKRAVARTI/Primary Examiner, Art Unit 3692