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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 09/14/2026 has been entered.
Status of the Claims
Claims 1-2, 7-8, 10-11, 16-17, and 21 have been amended. Claims 1-23 are currently pending and have been considered by the Examiner.
Claim Objections
Claim 1 is objected to because of the following informalities: In claim 1, line 16, Examiner suggests deleting “and” from the end of the line.
In claim 1, line 17 improperly recites “execute the the”. Claims 10 and 16 recite the same minor informalities. 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 1-20 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.
In claim 1, the entire limitation in line 16 renders the claim indefinite. It is unclear what is meant by the term “cluster-conditioned”. It is unclear whether a cluster-conditioned time series is merely a time series comprising the normalized data values. It is unclear if a missing step of “conditioning” is required to generate the cluster-conditioned time series. For purpose of examination, Examiner treats line 16 to mean generating another time series comprising the normalized data values.
In claim 1, the limitations in lines 17-20 renders the claim indefinite for the following reasons. Line 3 recites a machine-learning model trained on sets of time series of data, and lines 18-19 recite the machine-learning model trained using time-series data normalized according to corresponding clusters. Since the claim uses different terms for the data, it is unclear if “sets of time series of data” in line 3 are different from “time-series data normalized” in line 18, it is unclear if the data in line 3 is normalized, and it is unclear if the machine-learning models recited in these two limitations are the same model. Examiner treats claim 1 to mean that in line 3, a machine-learning model is trained on normalized time-series data, that this is the same normalized time-series data as recited in lines 18-19, and that the machine-learning model recited in lines 3 and 18 are the same model.
Claim 1 is rendered indefinite because both lines 2 and 19 recite the limitation “a mixture model”, and line 8 recites “the mixture model”. It is unclear if these all refer to the same mixture model or different mixture models. If they are the same, it is unclear if the mixture model in line 19 was generated by the same time series of data recited in line 8. If they are different, it is unclear if the mixture model in line 19 was generated by different time series of data. Examiner treats claim 1 to mean the mixture model recited in line 19 is the same as the one in line 8, and that they were generated by the same time series data.
In claim 1, the limitation in line 21-22 renders the claim indefinite. Since lines 17-20 recite executing the machine learning model to predict a directionality of the time series of data, it is unclear if the execution and prediction recited in lines 17-20 are the same prediction and execution recited in lines 21-22. Lines 17-20 do not recite that the machine-learning model has been trained to predict a magnitude. Therefore, it is unclear if lines 21-22 should be treated as generating a second prediction relating to a magnitude based on a first prediction of a directionality. It is unclear if “a prediction relating to the directionality and/or magnitude” in lines 21-22 means a prediction of the directionality and/or magnitude. Examiner treats the limitations in lines 17-22 to mean executing the machine learning model with the cluster-conditioned time series to predict a directionality or a magnitude of the time series of data.
Claims 2-9 are rejected for failing to cure the deficiencies of claim 1.
Claim 10 recites similar indefinite limitation as claim 1 and is therefore rejected for at least the same reason. Claims 11-16 are rejected for failing to cure the deficiencies of claim 10.
Claim 16 recites similar indefinite limitation as claim 1 and is therefore rejected for at least the same reason. Claims 17-20 are rejected for failing to cure the deficiencies of claim 16.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-9 recite a system comprising a processor (a system), and claims 10-15 recite a method, claims 16-20 recite a non-transitory computer readable medium (a product). Each of a system and a method falls under one of the four statutory categories of patent eligible subject matter.
Claim 1
Step 2A Prong 1: Decompose the time series of data into a plurality of clusters to generate the mixture model, each cluster from among the plurality of clusters comprising a normal distribution of a respective subset of the plurality of data values from the time series of data is a judgement and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper. Specification paragraph [0033] and Figs. 3A-3B demonstrate how a human could decompose a non-normal time series of data into clusters comprising normal distributions.
For each data value in the time series of data: identify a corresponding cluster, from among the plurality of clusters of the mixture model, against which the data value is to be normalized is an observation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Determine a normalization value for the corresponding cluster is a mathematical calculation. In specification paragraph [0035], Equation 3 discloses a formula for normalizing a data value. Determining a normalization value includes calculating a mean and/or variance of the closest cluster.
Normalize the data value based on the normalization value is a mathematical calculation. In specification paragraph [0035], Equation 3 discloses a formula for normalizing a data value.
Generate a cluster-conditioned time series comprising the normalized data values is a judgment mental process which can reasonably be performed in the human mind with the aid of pencil and paper. A human could write a time series comprising the normalized data values on paper.
Predict a directionality of the time series data is a judgement mental and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Generate,
Step 2A Prong 2: A mixture model that approximates a non-normal distribution of sequential data amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
A machine-learning model trained on one or more sets of time series of data amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
A processor programmed to: access a time series of data having a plurality of data values that exhibit a non-normal distribution, each data value from among the plurality of data values corresponding to a point in time in the time series of data amounts to an insignificant extra-solution activity under MPEP 2106.05(g). A processor is a generic computer component for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Execute the machine learning model with the cluster-conditioned time series, the machine learning model trained using time-series data normalized according to corresponding clusters of a mixture model amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are mere insignificant extra solution activities as disclosed in combination with generic computer functions that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea.
Step 2B: A mixture model that approximates a non-normal distribution of sequential data amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
A machine-learning model trained on one or more sets of time series of data amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
A processor programmed to: access a time series of data having a plurality of data values that exhibit a non-normal distribution, each data value from among the plurality of data values corresponding to a point in time in the time series of data is analogous to retrieving information from memory, which the courts have recognized as a well-understood, routine, conventional activity under MPEP 2106.05(d)(II). A processor is a generic computer component for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Execute the machine learning model with the cluster-conditioned time series, the machine learning model trained using time-series data normalized according to corresponding clusters of a mixture model amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are well-understood, routine and conventional activities as disclosed in combination with generic computer functions that are implemented to perform the abstract ideas disclosed above. The claim is not patent eligible.
Claim 2 incorporates the rejection from claim 1.
Step 2A Prong 1: The abstract ideas from claim 1 are incorporated. Generate the prediction is a judgement mental and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Encode,
Compare the normalized data values to a reconstructed input generated from the compressed input is an observation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Predict the directionality based on the comparison is a judgement and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2 and Step 2B: The machine-learning model comprises an autoencoder amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
The processor amounts to a generic computer component for applying the abstract ideas on a computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 3 incorporates the rejection from claim 2.
Step 2A Prong 1: The abstract ideas from claim 2 are incorporated. Generate the compressed input based on the normalized data values is a mathematical calculation and a judgement mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2 and Step 2B: The autoencoder comprises a trained encoder amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 4 incorporates the rejection from claim 3.
Step 2A Prong 1: The abstract ideas from claim 3 are incorporated. Generate the reconstructed input from the compressed input generated by the encoder is a mathematical calculation and a judgement and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2 and Step 2B: The autoencoder comprises a trained decoder amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 5 incorporates the rejection from claim 1.
Step 2A Prong 1: The abstract ideas from claim 1 are incorporated. Identify the corresponding normal distribution is an observation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Determine a distance between the data value to each normal distribution from among the plurality of normal distributions is a mathematical calculation. In specification paragraph [0035], the sentence in lines 5-6 discloses the distance may be a difference between the particular data value and the mean of the k-cluster.
Select the corresponding normal distribution that is closest to the data value based on the determined distances is a judgement and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2 and Step 2B: The processor amounts to a generic computer component for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 6 incorporates the rejection from claim 1.
Step 2A Prong 1: The abstract ideas from claim 1 are incorporated. Identify a number of the plurality of clusters to be used is an observation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
The time series of data is approximated based on the plurality of clusters is a judgement and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2 and Step 2B: The processor amounts to a generic computer component for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 7 incorporates the rejection from claim 1.
Step 2A Prong 1: The abstract ideas from claim 1 are incorporated. Decompose the time series of data into a Gaussian mixture comprising overlapping clusters of normal distributions is a judgement and evaluation mental process based on a mathematical calculation which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2 and Step 2B: The processor amounts to a generic computer component for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 8 incorporates the rejection from claim 1.
Step 2A Prong 1: The abstract ideas from claim 1 are incorporated. Decompose the time series of data into a Gaussian mixture comprising non-overlapping clusters of normal distributions is a judgement and evaluation mental process based on a mathematical calculation which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2 and Step 2B: The processor amounts to a generic computer component for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 9 incorporates the rejection from claim 1.
Step 2A Prong 1: The abstract ideas from claim 1 are incorporated. Determine the normalization value by determine a mean or variance of the corresponding cluster is a mathematical calculation. In specification paragraph [0035], Equation 3 discloses a normalization value is a mean or variance of the closest cluster.
Step 2A Prong 2 and Step 2B: The processor amounts to a generic computer component for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claims 10-15 each recites a method which incorporates the same features as the system of claims 1-6, respectively, and are therefore rejected for at least the same reasons.
Claim 16 recites a product which incorporates the same features as the system of claim 1 and is therefore rejected for at least the same reasons.
In Step 2A Prong 2 and Step 2B, a non-transitory computer-readable storage medium storing instructions that, when executed by a processor, programs the processor amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claims 17-20 each recites a product which incorporates the same features as the system of claims 2-5, respectively, and are therefore rejected for at least the same reasons.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim 21 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Xu et al. (“A Parallel GRU Recurrent Network Model and Its Application to Multi-Channel Time-Varying Signal Classification”).
Regarding claim 21, Xu teaches: A system, comprising: a plurality of recursive neural networks (RNNs) configured to operate in parallel to collectively form a parallel neural network architecture, each neural network from among the plurality of RNNs comprising: an input layer that receives a time series of data, one or more RNN layers, and one or more dense layers; (A “parallel neural network architecture” corresponds to the parallel GRU in Fig. 3. Each box labelled “D-RNN” in Fig. 3 represents a GRU deep recurrent network. See page 4, col. 1, § A, lines 1-7, from the line starting with step “(3) The GRU recurrent network…” to § B, line 12; page 5, col. 1, § C, lines 1-11; and page 7, col. 2, § B, lines 1-7, 10-12. Although not shown in Figs. 2-3, the layer that outputs a feature is a fully-connected layer as disclosed by equations (6) and (7) on page 4.)
a processor programmed to: (Page 7, col. 2, § C (1), lines 6-7)
provide each RNN, from among the plurality of RNNs, with a respective time series of data, each respective time series of data comprising sequential data values that vary independently of one another over time; (Page 3, col. 1, entire second paragraph; page 6, col. 1, lines 1-9, and page 7, col. 2, final 2 lines and page 8, col. 1, lines 1-6)
obtain an output from a last one of the one or more dense layers of each RNN; (Page 4, col. 1, final 3 lines to col. 2, line 3.)
merge the output from each of the plurality of RNNs; and (Page 5, § C, lines 5-9)
generate a prediction based on the merged output. (Page 5, § C, lines 9-10)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Xu et al. (“A Parallel GRU Recurrent Network Model and Its Application to Multi-Channel Time-Varying Signal Classification”) in view of Abbaszadeh et al. (US 20200067969 A1, cited in the PTO-892 issued 02/12/2026).
Regarding claim 22, Xu teaches: The system of claim 21, wherein the processor is further programmed to:
However, Xu does not explicitly teach: generate a mixture model for each of the respective time series of data, the mixture model comprising a plurality of clusters of normal distributions that together approximates the respective time series of data.
But Abbaszadeh teaches: generate a mixture model for each of the respective time series of data, the mixture model comprising a plurality of clusters of normal distributions that together approximates the respective time series of data. ([0070], lines 6-10, [0096] and [0097], lines 1-10)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have clustered Xu’s time series data based on Abbaszadeh’s GMM. A motivation for the combination is that a GMM may allow for the building of a complex probability distribution from a linear superposition of simpler components. Gaussian distributions may be the most common choice as mixture components because of the mathematical simplicity of parameter estimation as well as their ability to perform well in many situations. (Abbaszadeh, [0096])
Regarding claim 23, the combination of Xu and Abbaszadeh teaches: The system of claim 22, wherein the processor is further programmed to:
Xu teaches: normalize values of each of the respective time series of data based on [minimum and maximum values of]
However, Xu does not explicitly teach: normalize values of each of the respective time series of data based on the mixture model generated for the respective time series of data.
Abbaszadeh teaches a mixture model generated for each respective time series of data at [0096] and [0097], lines 1-10. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have applied Xu’s normalization the time series data belonging to each of Abbaszadeh’s clusters. A motivation for the combination is the same as the motivation given for claim 22.
Examiner’s Note
No prior art rejection has been issued for claims 1-20. The features of a mixture model that approximates a non-normal distribution of sequential data; a machine-learning model trained on one or more sets of time series of data; and a processor programmed to: access a time series of data having a plurality of data values that exhibit a non-normal distribution, each data value from among the plurality of data values corresponding to a point in time in the time series of data; decompose the time series of data into a plurality of clusters to generate the mixture model, each cluster from among the plurality of clusters comprising a normal distribution of a respective subset of the plurality of data values from the time series of data; for each data value in the time series of data: identify a corresponding cluster, from among the plurality of clusters of the mixture model, against which the data value is to be normalized, determine a normalization value for the corresponding cluster, and normalize the data value based on the normalization value; generate a cluster-conditioned time series comprising the normalized data values; and execute the machine-learning model with the cluster-conditioned time series, the machine-learning model trained using time-series data normalized according to corresponding clusters of a mixture model to predict a directionality of the time series of data; and generate, based on execution of the machine-learning model, a prediction relating to the directionality and/or a magnitude of the time series of data, when considered in the context of the claim as a whole, were not uncovered in the prior art.
The closest prior art of record is Pallath et al. (US 20180150547 A1, cited in the PTO-892 issued 02/12/2026) and Lomnitz (US 20170153324 A1).
Pallath teaches obtaining non-normal time series data, clustering subsequences of the time series data, and using a model for time series predictions.
Lomnitz at [0075]-[0079] teaches clustering a data vector via a GMM and calculating a normalized intensity for the data vector.
Response to Arguments
Below are the Examiner’s responses to the Applicant’s remarks filed 09/14/2026.
Applicant’s Arguments Under 35 U.S.C. 101 – Step 2A Prong 1: On pages 10-11 of the remarks, the Applicant states that the independent claims require a computer-implemented machine-learning pipeline that decomposes a non-normal time series into multiple normal-distribution clusters, selects a corresponding cluster for each individual time-series value, produces a cluster-conditioned time series from the differently normalized values, and executes a machine-learning model that was trained using time-series data normalized according to corresponding clusters of a mixture model. The Applicant argues these recitations define the structure of the data supplied to the machine-learning model and the training compatibility required of that machine-learning model. They do not set forth a mathematical formula.
Applicant argue that a human cannot practically generate and preserve a cluster-conditioned sequence across a potentially extensive time series, execute a trained machine-learning model on that sequence, and generate the claimed prediction based on model execution. Applicant argue that the Office Action separates individual phrases from their claimed computational context and labels each phrase an observation, judgment, or calculation. That approach does not address the actual claimed operation or the interaction among the recitations.
Applicant argues that the Examiner's reference to Equation 3 in the Specification does not establish that the claims recite that equation, and cites the August 4, 2025 memorandum.
Applicant argues that claim 21 likewise recites multiple RNNs operating in parallel, each having an input layer, one or more RNN layers, and one or more dense layers, with the processor obtaining outputs from the last dense layers and merging those machine-generated outputs. Applicant argues that actual execution of multiple RNNs and extraction and merger of outputs produced by their dense layers cannot practically occur in the human mind, and that describing those operations as a judgment or evaluation disregards the expressly recited neural-network architecture.
Examiner’s Response: Applicant's arguments have been fully considered but they are not persuasive. In Step 2A, Prong 1, the step of decomposing a non-normal time series into multiple normal-distribution clusters is a judgement and evaluation mental process. Specification paragraph [0033] and Figs. 3A-3B demonstrate how a human could decompose a non-normal time series of data into clusters comprising normal distributions.
Selecting or identifying a corresponding cluster for each individual time-series value is a judgement and observation mental process.
The step of “determining a normalization value for a corresponding cluster” includes calculating some normalization value (such as mean and/or variance) which is a mathematical calculation. The step “normalize a data value based on the normalization value” is a mathematical calculation such as equation 3 from specification paragraph [0035]. Examiner respectfully disagrees with the Applicant’s arguments that the claims do not recite a mathematical concept, and that the claimed system merely involves mathematical processing internally. Equation 3 in paragraph [0035] provides evidence that normalization is a mathematical calculation, even though the claim does not explicitly reproduce the equation. The claimed system explicitly performs a mathematical calculation when it executes the step “normalize the data value based on the normalization value”. MPEP 2106.04(a)(2) subsection I. C. states, “A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number… For example, a step of ‘determining’ a variable or number using mathematical methods or ‘performing’ a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.”
Preserving (which is claimed as “generating”) a cluster-conditioned time series from the differently normalized values is a judgment mental process which can reasonably be performed in the human mind with the aid of pencil and paper. A human could write down a time series comprising the normalized data values on paper. Additionally, defining the structure of the data is a judgement mental process for the same reasons.
Predicting a directionality of the time series of data, and generating a prediction relating to directionality and/or magnitude of the time series of data are judgement mental processes. A human could predict a directionality or magnitude of the time series of data.
The limitations of “executing a machine-learning model that was trained using time-series data normalized according to corresponding clusters of a mixture model” is not a judicial exception.
The Examiner respectfully disagrees with the Applicant’s argument that separating individual phrases from their claimed computational context does not address the actual claimed operation or the interaction among the recitations. The claimed execution, by the processor, of a trained machine-learning model is an additional element. In Step 2A Prong 2 and in Step 2B, the Office Action has evaluated whether the combination of additional elements integrates the abstract ideas into a practical application, and the Office Action has evaluated whether any additional element, in combination with the abstract ideas, amounts to significantly more than the abstract ideas.
Applicant’s arguments regarding claim 21 have been fully considered and are persuasive. The rejection of claim 21 has been withdrawn.
Applicant’s First Arguments Under 35 U.S.C. 101 – Step 2A Prong 2: On page 11-12, the Applicant argues the claims recite an improvement to machine-learning data processing, which is a practical application of the abstract ideas. For example , the system decomposes the non-normal distribution into clusters representing respective normal distributions. For each time-series value, the system identifies the corresponding cluster and normalizes that value using the normalization value for that corresponding cluster. The system then preserves the resulting normalized values as a cluster-conditioned time series and executes a machine-learning model trained using the same form of cluster-conditioned time-series data. Thus, each data value is processed relative to the locally corresponding region of the non-normal distribution, rather than relative to a single global approximation that may obscure the local distributional structure.
On page 13, the Applicant the claimed methodology is simar to Desjardins because it addresses a technical defect in machine-learning operation through a particular data-conditioning architecture reflected in the claims, which are directed to generating a specialized machine-learning input representation and are coupled to execution of a model trained to operate on that representation. The Applicant cites the USTPO August 4, 2025 memorandum.
Examiner’s Response: Applicant's arguments have been fully considered but they are not persuasive. The Examiner respectfully disagrees that the claims recite an improvement to machine-learning data processing. Steps of a system that decomposes the non-normal distribution into clusters representing respective normal distributions; for each time-series value, the system identifies the corresponding cluster and normalizes that value using the normalization value for that corresponding cluster; and preserves (claimed as “generating”) the resulting normalized values as a cluster-conditioned time series are abstract ideas. It is noted that the judicial exception alone cannot provide the improvement, and that an improvement in the abstract idea itself is not an improvement in technology. See MPEP 2106.05(a) and (a)(II).
In Step 2A Prong 2, the limitation of executing a machine-learning model trained using the same form of cluster-conditioned time-series data amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
The current Office Action has evaluated the limitations of claim 1 as a whole. The additional elements as disclosed in claim 1, alone or in combination, do not integrate the abstract ideas into a practical application as they are mere insignificant extra solution activities as disclosed in combination with generic computer functions that are implemented to perform the abstract ideas disclosed in claim 1.
Applicant has argued that the claimed improvement is that “each data value is processed relative to the locally corresponding region of the non-normal distribution, rather than relative to a single global approximation that may obscure the local distributional structure”. Examiner respectfully disagrees. MPEP 2106.05(f) subsection (2) states, “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.”
The claim invention amounts to normalizing each data value relative to the locally corresponding region of the non-normal distribution, generating a cluster-conditioned time series comprising the normalized data values, and executing a machine-learning model with the cluster-conditioned time series. The technical improvement is in generating the cluster-conditioned time series, which itself cannot provide a technical improvement. Executing, by a processor, the cluster-conditioned time series through a machine-learning model amounts to simply adding a general purpose computer or computer components after the fact to an abstract idea. This does not incorporate the abstract ideas into a practical application.
In response to the Applicant’s arguments regarding Desjardins and the USTPO memorandum, the Examiner’s response above has shown how the technical improvements are to the abstract ideas themselves rather than to executing the machine-learning model.
Applicant’s Second Arguments Under 35 U.S.C. 101 – Step 2A Prong 2: On pages 13-14, the Applicant argues that claim 21 also recites a separate practical application.
Examiner’s Response: Applicant’s arguments have been fully considered and are persuasive. The rejection of claim 21 has been withdrawn.
Applicant’s Arguments Under 35 U.S.C. 103: On pages 15-16, the Applicant argues that pending claims 1, 10, and 16 are not taught by Pallath, Abbaszadeh, and Ouyang. On pages 16-17, the Applicant argues that pending claim 21 is not taught by Ranjan and Shamir.
Examiner’s Response: Applicant’s arguments with respect to the rejections of claims 1, 10, and 16 have been fully considered and are persuasive. The rejections of claims 1-20 have been withdrawn.
Applicant’s arguments with respect to the rejection of claim 21 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of the Xu reference.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Asher H. Jablon whose telephone number is (571)270-7648. The examiner can normally be reached Monday - Friday, 9:00 am - 6:00 pm.
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/A.H.J./Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
