DETAILED ACTION
This action is responsive to the claims filed on 05/18/2026. Claims 1-5, 7-13, and 15-22 are pending for examination.
This action is Final.
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 Amendments
This office action has been issued in response to Applicant's response filed on 05/18/2026. Claims 1-5, 7-13, and 15-22 are not rejected over prior art.
Applicant argues that the Desjardins Memo “underscore[s] that improvements in computational performance, learning, storage, data sets and structures” may constitute patent-eligible technological advancements. (Remarks, p. 11). The Examiner respectfully disagrees that the presently amended claims recite such an eligible technological advancement. The claims continue to recite determining an available quantity of training vectors, selecting a mathematical boost function based on a quantity range, generating a numerical selection quantity by applying the selected function, selecting exemplar vectors based on that quantity, and training a machine-learning model using the selected vectors. These limitations amount to mathematical concepts and/or mental processes implemented using generic computer components, and the claims do not recite a specific improvement to computer functionality, memory architecture, processor operation, or the internal operation of the machine-learning model itself.
Applicant further argues that “when the claimed system changes the architecture itself—e.g., how information flows, not just what it does—that may satisfy eligibility.” (Remarks, p. 12). The Examiner respectfully disagrees that the claims recite a change to architecture of the type contemplated by applicant. The claims do not recite a new computer architecture, a new machine-learning model architecture, a new memory structure, a new processor arrangement, or a particular technical mechanism for improving data flow within a computer system. Rather, the claims recite selecting a subset of training data based on a calculated quantity and then using that subset for training. Characterizing the abstract data-selection process as a change in “information flow” does not integrate the judicial exception into a practical application.
Applicant argues that the claims “do not merely evaluate training data in the abstract or apply a generic mathematical rule to data,” but instead modify “the architecture of a machine-learning training pipeline” by using a “quantity-range-dependent exemplar-selection mechanism.” (Remarks, p. 12). The Examiner respectfully disagrees. The claimed quantity-range-dependent mechanism is itself the abstract idea: determining a quantity, comparing that quantity to ranges or thresholds, selecting one of multiple mathematical functions, and using the resulting numerical output to select data. The claims do not provide an additional technological implementation beyond applying those mathematical and evaluative operations in the environment of machine-learning training.
Applicant argues that raw training vectors are “transformed, throttled, and structurally filtered” through a quantity-aware exemplar-generation stage before learning occurs. (Remarks, p. 14). The Examiner respectfully disagrees. The claims do not recite a transformation of the training vectors into a different technological state or thing. At most, the claims recite selecting a quantity of exemplar vectors from the training vectors based on a calculated numerical value. Merely reducing or filtering a data set for later use in model training is not, by itself, a technological transformation sufficient to integrate the abstract idea into a practical application.
Applicant argues that the different boost-function regimes are “not arbitrary” because they are keyed to ranges of training-vector quantity and dataset characteristics such as signal quantity, window quantity, and square-root or cube-root functions. (Remarks, p. 14). The Examiner respectfully disagrees that this distinction overcomes the § 101 rejection. Even if the functions are specifically selected and non-arbitrary, they remain mathematical rules for determining a numerical selection quantity. The claims do not recite an eligible technological improvement merely because the mathematical rules are particularized or because different equations are used in different ranges.
Applicant argues that the claims improve training by identifying an exemplar quantity that results in “least loss of accuracy” for a reduction in compute cost and permits training within compute-resource constraints while losing little to no accuracy. (Remarks, p. 14). The Examiner respectfully disagrees. These alleged benefits are stated as intended results of selecting fewer or different training vectors, not as claimed technical improvements to the computer or model itself. The claims do not require a particular accuracy threshold, a particular reduction in compute cost, a particular memory reduction, a specific processor-scheduling improvement, or any particular technical mechanism for achieving the asserted result. Any reduction in resource use flows from the abstract data-selection process itself.
Applicant argues that the claims are directed to improvements in “learning, data sets, and structures” because they restructure the training dataset that reaches the machine-learning model and change learning behavior by requiring training on selected exemplar vectors. (Remarks, p. 15). The Examiner respectfully disagrees. Selecting a subset of data for training, even when characterized as restructuring the dataset, does not by itself amount to an improvement in computer technology or machine-learning technology. The claims do not recite a new data structure, a new training algorithm for the model, or an improvement to how the model internally learns. Instead, the model is used as a generic tool that receives the selected exemplar vectors after the abstract selection process has been performed.
Applicant argues that the claims are not “merely reciting a mathematical concept in the abstract” because they do not seek to monopolize square roots, cube roots, or linear functions as such. (Remarks, p. 15). The Examiner respectfully disagrees. The rejection does not require a finding that the claims monopolize every use of the recited mathematical functions. Rather, the claims recite mathematical concepts and mental processes because they require determining quantities, applying mathematical functions, comparing ranges or thresholds, and selecting data according to the resulting numerical quantity. Limiting those mathematical operations to the field of machine-learning anomaly detection does not, without more, make the claims patent eligible.
Accordingly, applicant’s arguments have been considered but are not persuasive. The 35 U.S.C. § 101 is maintained.
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.
Statutory Categories
Claims 1-9 are directed to a method.
Claims 10-14 is directed to an computer-readable medium.
Claims 15-22 are directed to a system.
Independent Claim 1, 10, and 15
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes. Independent claim 1, 10 and 15 recites limitations that are abstract ideas in the form of mental processes:
Claim 1 recites:
A computer-implemented method, comprising: determining an available quantity of training vectors that are available in a set of time series signals, wherein the training vectors are designated for use in training a machine learning model; (this limitation recites determining a value based on predetermined values, stated at a high level with no further indication as to how the determination should be performed, which can reasonably be performed as a mental process or with aid of pen and paper)
selecting a boost function from a plurality of different boost functions, wherein the selected boost function is selected based on the available quantity of the training vectors falling within a quantity range associated with the selected boost function, (this limitation recites selecting a function based on predetermined values, which can reasonably be performed as a mental process or with aid of pen and paper. Further addressing the automatic limitation it should be noted that ‘The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid… Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer.’, as cited from MPEP 2106.04(iii))
wherein … selecting a boost function further comprises: where the quantity range is less than a first threshold, selecting a first boost function that is a linear function of a signal quantity of the time series signals; (selecting a function in response to a threshold being satisfied is being considered a mental process of evaluation that would be reasonably performed in human mind or with aid of pen and paper)
where the quantity range is between the first threshold and a second threshold that is higher than the first threshold, selecting a second boost function that is a function of a window quantity of windows that subdivide the training vectors and the signal quantity of the time series signals; (selecting a function in response to a threshold being satisfied is being considered a mental process of evaluation that would be reasonably performed in human mind or with aid of pen and paper)
where the quantity range is between the second threshold and a third threshold that is higher than the second threshold, selecting a third boost function that is tapered by a square root function of the available quantity of the training vectors; (selecting a function in response to a threshold being satisfied is being considered a mental process of evaluation that would be reasonably performed in human mind or with aid of pen and paper)
and where the quantity range is more than the third threshold, selecting a fourth boost function that is tapered by a cube root function of the available quantity of the training vectors. (selecting a function in response to a threshold being satisfied is being considered a mental process of evaluation that would be reasonably performed in human mind or with aid of pen and paper)
generating a selection quantity of the exemplar vectors to select from the training vectors by applying the selected boost function to the training vectors; (this limitation recites generating a numerical value from a function based on predetermined values, which can reasonably be performed as a mental process or with aid of pen and paper.)
selecting a quantity of the exemplar vectors from the training vectors based on the selection quantity; (this limitation recites selecting a subset of vectors based on preselected vectors, which can reasonably be performed as a mental process or with aid of pen and paper.)
This claim further recites the following additional elements for the purposes of Step 2A Prong Two analysis:
automatically selecting…and wherein each boost function from the plurality of different boost functions is configured to determine a different selection quantity of exemplar vectors to be selected from the training vectors; (this limitation invokes boost functions merely as a tool to perform an existing process and is considered as mere instructions to apply an exception, see MPEP 2106.05(f))
and training the machine learning model to detect anomalies in the time series signals based on the exemplar vectors that were selected. (this limitation invokes machine learning models merely as a tool to perform an existing process and is considered as mere instructions to apply an exception, see MPEP 2106.05(f))
The additional limitations fail step 2A Prong 2 of the 101 analysis because they do not transform the claim into a practical application. These limitations are too abstract or lack technical improvement that would make the concept practically useful. Without clear utility or integration into a specific field, the claim does not relate to any particular application. It does not meet the requirements of Step 2A Prong 2, as it fails to make the concept meaningfully applicable in practice. Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea.
This claim recites the following additional elements for the purposes of Step 2B analysis:
automatically selecting…and wherein each boost function from the plurality of different boost functions is configured to determine a different selection quantity of exemplar vectors to be selected from the training vectors; (this limitation invokes boost functions merely as a tool to perform an existing process and is considered as mere instructions to apply an exception, see MPEP 2106.05(f))
and training the machine learning model to detect anomalies in the time series signals based on the exemplar vectors that were selected. (this limitation invokes machine learning models merely as a tool to perform an existing process and is considered as mere instructions to apply an exception, see MPEP 2106.05(f))
The claim also fails Step 2B of the analysis because the additional limitations do not amount to significantly more than the abstract idea itself. The additional limitations do not enhance the claim in a way that would move it beyond its abstract ideas as they minimally elaborate on the core concept without adding any inventive or technical substance. Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claims 10 and 15 recite additional limitations for consideration:
A non-transitory computer-readable medium that includes stored thereon computer-executable instructions that when executed by at least a processor of a computer system cause the computer system to: (Under step 2A prong II and step 2B, this limitation invokes computers and machinery merely as a tool to perform an existing process and is considered as mere instructions to apply an exception using generic computer, see MPEP 2106.05(f))
A computing system, comprising: at least one processor; at least one memory connected to the at least one processor; a non-transitory computer readable medium including instructions stored thereon that when executed by at least the processor cause the computing system to: (Under step 2A prong II and step 2B, this limitation invokes computers and machinery merely as a tool to perform an existing process and is considered as mere instructions to apply an exception using generic computer, see MPEP 2106.05(f))
Dependents of Claims 1, 10, and 15
The remaining dependent claims corresponding to independent claims 1, 10, and 15 do not recite additional elements, whether considered individually or in combination, that are sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. The analysis of which is shown below:
The claims below recite additional limitations which fail step 2A Prong 2 of the 101 analysis because they do not transform the claim into a practical application. These limitations are too abstract or lack technical improvement that would make the concept practically useful. Without clear utility or integration into a specific field, the claim does not relate to any particular application. It does not meet the requirements of Step 2A Prong 2, as it fails to make the concept meaningfully applicable in practice.
The claims also fails Step 2B of the analysis because the additional limitations do not amount to significantly more than the abstract idea itself. The additional limitations do not enhance the claim in a way that would move it beyond its abstract ideas as they minimally elaborate on the core concept without adding any inventive or technical substance. The claims are unpatentable.
Claim 2 recites the further limitation of:
The computer-implemented method of claim 1, wherein the selected boost function adjusts the selection quantity of the exemplar vectors by a different coefficient for each of a set of quantity ranges, wherein the set of quantity ranges includes the quantity range. (this limitation merely recites mathematics in the form of mathematical algorithms, functions, or calculation, see specification paragraphs [0085-0087] for the related mathematical disclosure)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 3 recites the further limitation of:
The computer-implemented method of claim 1, further comprising applying a taper coefficient in the boost function, wherein the taper coefficient reduces the selection quantity by an extent that is based on the available quantity of the training vectors. (this limitation merely recites mathematics in the form of mathematical algorithms, functions, or calculation, see specification paragraphs [0087-0088] for the related mathematical disclosure)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 4 recites the further limitation of:
The computer-implemented method of claim 1, further comprising, in response to the quantity range satisfying a threshold for being a memory- specific range, applying a square root taper coefficient in the boost function, wherein the square root taper coefficient is a square root function of the available quantity of the training vectors. (this limitation merely recites mathematics in the form of mathematical algorithms, functions, or calculation, see specification paragraphs [0087-0088] for the related mathematical disclosure)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 5 recites the further limitation of:
The computer-implemented method of claim 1, further comprising, in response to the quantity range satisfying a threshold for being a processor- specific range, applying a cube root taper coefficient in the boost function, wherein the cube root taper coefficient is a cube root function of the available quantity of the training vectors. (this limitation merely recites mathematics in the form of mathematical algorithms, functions, or calculation, see specification paragraphs [0088] for the related mathematical disclosure)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 7 recites the further limitation of:
The computer-implemented method of claim 1, further comprising subdividing the training vectors into a predetermined number of windows, wherein the quantity of the exemplar vectors are selected from the training vectors within more than one of the windows. (further dividing predetermined vectors into predetermined windows and selecting exemplars from more than one window is being considered a mental process of evaluation that would be reasonably performed in human mind or with aid of pen and paper)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 8 recites the further limitation of:
The computer-implemented method of claim 1, wherein the method further comprises, prior to selecting the exemplar vectors from the training vectors, constraining the selection quantity of the exemplar vectors to not exceed the available quantity of the training vectors. (further limiting the number of exemplars to be less than the amount of training vectors is being considered a mental process of evaluation that would be reasonably performed in human mind or with aid of pen and paper)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 9 recites the further limitation of:
The computer-implemented method of claim 1, further comprising: monitoring the time series signals with the trained machine learning model to detect an anomaly; (this limitation invokes machine learning models merely as a tool to perform an existing process and is considered as mere instructions to apply an exception, see MPEP 2106.05(f))
and in response to detecting a particular anomaly in the time series signals, generating an electronic alert that the particular anomaly has occurred. (Step 2A Prong II/Step 2B: this limitation merely recites receiving or transmitting data in the form of an alert when an anomaly has occurred and is being considered as well-understood, routine, and conventional insignificant extra-solution activity. It should be known that the courts have recognized receiving/transmitting data as well-understood, routine, and conventional activity, see MPEP 2106.05(d)(ii), Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information);)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 11 recite limitations substantially similar to claim 2, as such a similar analysis applies.
Claim 12 recites limitations substantially similar to claim 4, as such a similar analysis applies.
Claim 13 recites limitations substantially similar to claim 5, as such a similar analysis applies.
Claim 16 recites the further limitation of:
The computing system of claim 15, wherein the instructions to generate the selection quantity of exemplar vectors further cause the computing system to: in response to selection of the first boost function, adjust the selection quantity of the exemplar vectors by a first coefficient, (this limitation merely recites mathematics in the form of mathematical algorithms, functions, or calculation, see specification paragraphs [0087-0088] for the related mathematical disclosure)
and in response to selection of the second boost function, adjust the selection quantity of the exemplar vectors by a second coefficient. (this limitation merely recites mathematics in the form of mathematical algorithms, functions, or calculation, see specification paragraphs [0087-0088] for the related mathematical disclosure)
Claim 17 recites the further limitation of:
The computing system of claim 15, wherein the instructions to generate the selection quantity of exemplar vectors further cause the computing system to: in response to selection of the first boost function, lessen the selection quantity of the exemplar vectors by a square root taper coefficient that attenuates the selection quantity by a square root function, (this limitation merely recites mathematics in the form of mathematical algorithms, functions, or calculation, see specification paragraphs [0087-0088] for the related mathematical disclosure)
and in response to selection of the second boost function, lessen the selection quantity of the exemplar vectors by a cube root taper coefficient that attenuates the selection quantity by a cube root function. (this limitation merely recites mathematics in the form of mathematical algorithms, functions, or calculation, see specification paragraphs [0088] for the related mathematical disclosure)
Claim 18 recites the further limitation of:
The computing system of claim 15, wherein the instructions further cause the computing system to: subdivide the training vectors into a plurality of windows; (a mental process of evaluation which can reasonably be performed in human mind or with aid of pen and paper)
and increase the selection quantity of the exemplar vectors to accommodate selections of the training vectors from within the plurality of windows. (a mental process of evaluation which can reasonably be performed in human mind or with aid of pen and paper)
Claim 19 recites the further limitation of:
The computing system of claim 15, wherein the instructions further cause the computing system to reduce the selection quantity of the exemplar vectors to the available quantity of the training vectors in response to the selection quantity exceeding the available quantity. (a mental process of evaluation which can reasonably be performed in human mind or with aid of pen and paper)
Claim 20 recites the further limitation of:
The computing system of claim 15, wherein the instructions further cause the computing system to detect an anomaly in the time series signals using the trained machine learning model. (this limitation invokes machine learning models merely as a tool to perform an existing process and is considered as mere instructions to apply an exception, see MPEP 2106.05(f)).
Claim 21 recites the further limitation of:
The computing system of claim 15, wherein in the detection of the anomalies, the machine learning model produces estimates of values of one variable based on inputs values of other variables, and wherein residuals between actual values and the estimates are used to detect the anomalies. (this limitation invokes machine learning models merely as a tool to perform an existing process and is considered as mere instructions to apply an exception, see MPEP 2106.05(f))
Claim 22 recites the further limitation of:
The computing system of claim 15, wherein the machine learning model is trained using the exemplar vectors to produce estimates that closely track actual values such that mean and variance of residuals produced by the machine learning model approach zero. (this limitation invokes machine learning models merely as a tool to perform an existing process and is considered as mere instructions to apply an exception, see MPEP 2106.05(f))
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2022/0351072 A1
US 2021/0224696 A1
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/H.B.Y./Examiner, Art Unit 2124
/DANIEL T PELLETT/Primary Examiner, Art Unit 2121
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