DETAILED ACTION
1. 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
2. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. This communication is in response to the Applicant’s submission filed 09 June 2026 [hereinafter Response], where:
Claims 1, 4, 8, 11, 15, and 18 have been amended.
Claims 2, 5, 9, 12, 16, and 19 have been cancelled.
Claims 1, 3, 4, 6-8, 10, 11, 13-15, 17, 18, and 20 are pending.
Claims 1, 3, 4, 6-8, 10, 11, 13-15, 17, 18, and 20 are rejected.
Claim Rejections - 35 U.S.C. § 101
3. 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.
4. Claims 1, 3, 4, 6-8, 10, 11, 13-15, 17, 18, and 20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a system, which is a machine, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites the limitations of “[(b)] perform an encoding operation on the training dataset to provide an encoded dataset having a lower dimension space than a dimension space of the training dataset,” “[(d)] determine an output of the one or more prediction models in the lower dimension space based on an input provided to the one or more prediction models,” and “[(e)] perform a decoding operation on the output to project the output from the lower dimension space to the dimension space of the training dataset.” These limitations of “[(b)] perform an encoding operation,” “[(d)] determine,” and “[(e)] perform a decoding operation” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, are mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas.
More details or specifics are recited for the abstract idea of “[(b)] perform an encoding operation,” “[(b.1)] wherein, . . . the at least one processor is programmed or configured to: [(b.1.1)] perform the encoding operation on the training dataset based on a projection matrix, wherein . . . the at least one processor is programmed or configured to: [(b.1.1.1)] perform a factorization operation based on an optimization problem involving the projection matrix,” and accordingly, is merely more specific to the abstract idea. Still further, more details or specifics are recited for the abstract idea of “: [(b.1.1.1)] perform a factorization operation,” “[(b.1.1.1.1)] wherein, when performing the factorization operation, the at least one processor is programmed or configured to: [(b.1.1.1.1.1)] update the projection matrix using a least square optimization problem,” and “[(b.1.1.1.1.2)] update a transferred low-dimensional space using a graph-regularized alternating least squares (GRALS),” and accordingly, are merely more specific to the abstract idea.
Also, further details or specifics are recited for the abstract idea of “[(d)] determine,” “[(d.1)] wherein the input comprises a real-time event, wherein the event comprises a real-time electronic payment transaction,” and “[(d.2)] wherein the output of the one or more prediction models may include a predicted classification value for an event of a time series forecast of a plurality of events,” and accordingly, are merely more specific to the abstract idea.
Still further, the claim recites more details or specifics to the abstract idea of “[(e)] perform a decoding operation,” “[(e.1)] wherein . . . the at least one processor is programmed or configured to: [(e.1.1)] project the output from the lower dimension space to the dimension space of the training dataset using an inverse matrix corresponding to the projection matrix, wherein the inverse matrix is an inverse of the projection matrix,” and accordingly, is merely more specific to the abstract idea. Thus, claim 1 recites an abstract idea.
Under Step 2A Prong Two, the claim as a whole is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include a “system” and “at least one processor,” which are recited at a high-level of generality, and thus are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application. The claim also recites the element of “one or more prediction models,” which is recited at a high-level of generality, and thus, is a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application.
In this regard, the limitation of “[(c)] generate one or more prediction models based on the encoded dataset” is the use of the generic computer component (one or more prediction model) to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to implement the abstract idea into a practical application. The claim also recites more specifics or details to the additional element of “[(c)] generate one or more prediction models,” in that “[(c.1)] wherein the one or more prediction models are configured to provide an output in the lower dimension space,” “[(c.2)] wherein the one or more prediction models are configured to provide a predicted classification value for an event,” “[(c.3)] wherein . . . the at least one processor is programmed or configured to: [(c.3.1)] train the one or more prediction models in the lower dimension space based on the encoded dataset to provide one or more trained prediction models,” and accordingly, are merely more specific to the additional element.
The claim also recites the additional element of “[(a)] receive a training dataset of a plurality of data instances,” which is a pre-solution, insignificant extra-solution activity of mere data gathering, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application. The claim also recites more specifics or details to the additional element of “[(a)] receive,” where “[(a.1)] each data instance comprises a time series of data points,” [(a.2)] wherein each data point of the plurality of data instances represents an event,” and “[(a.3)] wherein the event comprises an electronic payment transaction,” and accordingly, are merely more specific to the additional element.
Further, the claim recites “[(f)] perform an action based on the output,” which is a post-processing insignificant extra-solution activity of outputting a result of the abstract idea, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application. The claim also recites more details or specifics to the additional element of “[(f)] perform an action,” where “[(f.1)] determine an action to take associated with the real-time electronic payment transaction,” which is merely more specific to the additional element. Therefore, claim 1 is directed to the abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. The claim includes a “system” and “at least one processor,” which are recited at a high-level of generality, and thus are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea. The claim also recites the element of “one or more prediction models,” which is recited at a high-level of generality, and thus, is a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea. In this regard, the limitation of “[(c)] generate one or more prediction models based on the encoded dataset” is the use of the generic computer component (one or more prediction model) to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea. The claim also recites more specifics or details to the additional element of “[(c)] generate one or more prediction models,” in that “[(c.1)] wherein the one or more prediction models are configured to provide an output in the lower dimension space,” “[(c.2)] wherein the one or more prediction models are configured to provide a predicted classification value for an event,” “[(c.3)] wherein . . . the at least one processor is programmed or configured to: [(c.3.1)] train the one or more prediction models in the lower dimension space based on the encoded dataset to provide one or more trained prediction models,” and accordingly, are merely more specific to the additional element.
The claim also recites the additional element of “[(a)] receive a training dataset of a plurality of data instances,” which is a well-understood, routine, and conventional activity of receiving or transmitting data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea. The claim also recites more specifics or details to the additional element of “[(a)] receive,” where “[(a.1)] each data instance comprises a time series of data points,” and “[(a.2)] wherein each data point of the plurality of data instances represents an event,” and “[(a.3)] wherein the event comprises an electronic payment transaction,” and accordingly, are merely more specific to the additional element.
Further, the claim recites “[(f)] perform an action based on the output,” which is a post-processing insignificant extra-solution activity of outputting a result of the abstract idea, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application. The claim also recites more details or specifics to the additional element of “[(f)] perform an action,” where “[(f.1)] determine an action to take associated with the real-time electronic payment transaction,” which is merely more specific to the additional element. Therefore, claim 1 is subject-matter ineligible.
Claim 8 recites a method, which is a process, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites the limitations of “[(b)] performing . . . an encoding operation on the training dataset to provide an encoded dataset having a lower dimension space than a dimension space of the training dataset,” “[(d)] determining . . . an output of the one or more trained prediction models in the lower dimension space based on an input provided to the one or more trained prediction models,” and “[(e)] performing . . . a decoding operation on the output to project the output from the lower dimension space to the dimension space of the training dataset.” These limitations of “[(b)] performing . . . an encoding operation,” “[(d)] determining,” and “[(e)] performing . . . a decoding operation” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, are mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas.
More details or specifics are recited for the abstract idea of “[(b)] performing . . . an encoding operation,” “[(b.1)] wherein, . . . comprises: [(b.1.1)] performing the encoding operation on the training dataset based on a projection matrix, wherein . . . comprising: [(b.1.1.1)] performing a factorization operation based on an optimization problem involving the projection matrix,” and accordingly, is merely more specific to the abstract idea. Still further, more details or specifics are recited for the abstract idea of “: [(b.1.1.1)] perform a factorization operation,” “[(b.1.1.1.1)] wherein, when performing the factorization operation, the at least one processor is programmed or configured to: [(b.1.1.1.1.1)] update the projection matrix using a least square optimization problem,” and “[(b.1.1.1.1.2)] update a transferred low-dimensional space using a graph-regularized alternating least squares (GRALS),” and accordingly, are merely more specific to the abstract idea.
Also, further details or specifics are recited for the abstract idea of “[(d)] determining,” “[(d.1)] wherein the input comprises a real-time event, wherein the event comprises a real-time electronic payment transaction,” and “[(d.2)] wherein the output of the one or more prediction models may include a predicted classification value for an event of a time series forecast of a plurality of events,” and accordingly, are merely more specific to the abstract idea.
Further, the claim recites more details or specifics to the abstract idea of “[(e)] performing a decoding operation,” “[(e.1)] wherein . . . the at least one processor is programmed or configured to: [(e.1.1)] projecting the output from the lower dimension space to the dimension space of the training dataset using an inverse matrix corresponding to the projection matrix, wherein the inverse matrix is an inverse of the projection matrix,” and “[(a.2)] wherein each data point of the plurality of data instances represents an event,” and “[(a.3)] wherein the event comprises an electronic payment transaction,” and accordingly, are merely more specific to the additional element.
Further, the claim recites “[(f)] perform an action based on the output,” which is a post-processing insignificant extra-solution activity of outputting a result of the abstract idea, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application. The claim also recites more details or specifics to the additional element of “[(f)] perform an action,” where “[(f.1)] determine an action to take associated with the real-time electronic payment transaction,” which is merely more specific to the additional element. Thus, claim 8 recites an abstract idea.
Under Step 2A Prong Two, the claim as a whole is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include a “at least one processor,” which is recited at a high-level of generality, and thus is a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application. The claim also recites the element of “one or more prediction models,” which is recited at a high-level of generality, and thus, is a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application.
In this regard, the limitation of “[(c)] generating . . . one or more prediction models based on the encoded dataset” is the use of the generic computer component (at least one processor, one or more prediction model) to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to implement the abstract idea into a practical application. The claim also recites more specifics or details to the additional element of “[(c)] generating . . . one or more prediction models,” in that “[(c.1)] wherein the one or more prediction models are configured to provide an output in the lower dimension space,” “[(c.2)] wherein the one or more prediction models are configured to provide a predicted classification value for an event,” “[(c.3)] wherein generating . . . comprises: [(c.3.1)] training the one or more prediction models in the lower dimension space based on the encoded dataset to provide one or more trained prediction models,” and accordingly, are merely more specific to the additional element.
The claim also recites the additional element of “[(a)] receiving a training dataset of a plurality of data instances,” which is a pre-solution, insignificant extra-solution activity of mere data gathering, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application. The claim also recites more specifics or details to the additional element of “[(a)] receiving,” where “[(a.1)] each data instance comprises a time series of data points,” and accordingly, is merely more specific to the additional element. Therefore, claim 8 is directed to the abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. The claim includes a “system” and “at least one processor,” which are recited at a high-level of generality, and thus are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea. The claim also recites the element of “one or more prediction models,” which is recited at a high-level of generality, and thus, is a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea. In this regard, the limitation of “[(c)] generate one or more prediction models based on the encoded dataset” is the use of the generic computer component (at least one processor, one or more prediction model) to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea. The claim also recites more specifics or details to the additional element of “[(c)] generating one or more prediction models,” in that “[(c.1)] wherein the one or more prediction models are configured to provide an output in the lower dimension space,” “[(c.2)] wherein the one or more prediction models are configured to provide a predicted classification value for an event,” “[(c.3)] wherein generating . . . comprises: [(c.3.1)] training the one or more prediction models in the lower dimension space based on the encoded dataset to provide one or more trained prediction models,” and accordingly, are merely more specific to the additional element. The claim also recites the additional element of “[(a)] receiving . . . a training dataset of a plurality of data instances,” which is a well-understood, routine, and conventional activity of receiving or transmitting data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea. The claim also recites more specifics or details to the additional element of “[(a)] receiving,” where “[(a.1)] each data instance comprises a time series of data points,” and accordingly, is merely more specific to the additional element. “[(a.2)] wherein each data point of the plurality of data instances represents an event,” and “[(a.3)] wherein the event comprises an electronic payment transaction,” and accordingly, are merely more specific to the additional element.
Further, the claim recites “[(f)] perform an action based on the output,” which is a post-processing insignificant extra-solution activity of outputting a result of the abstract idea, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application. The claim also recites more details or specifics to the additional element of “[(f)] perform an action,” where “[(f.1)] determine an action to take associated with the real-time electronic payment transaction,” which is merely more specific to the additional element. Therefore, claim 8 is subject-matter ineligible.
Claim 15 recites a computer program product, which is an article of manufacture, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites the limitations of “[(b)] perform an encoding operation on the training dataset to provide an encoded dataset having a lower dimension space than a dimension space of the training dataset,” “[(d)] determine an output of the one or more prediction models in the lower dimension space based on an input provided to the one or more prediction models,” and “[(e)] perform a decoding operation on the output to project the output from the lower dimension space to the dimension space of the training dataset.” These limitations of “[(b)] perform an encoding operation,” “[(d)] determine,” and “[(e)] perform a decoding operation” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, are mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas.
More details or specifics are recited for the abstract idea of “[(b)] perform an encoding operation,” “[(b.1)] wherein, . . . cause the at least one processor to: [(b.1.1)] perform the encoding operation on the training dataset based on a projection matrix, wherein . . . cause the at least one processor to: [(b.1.1.1)] perform a factorization operation based on an optimization problem involving the projection matrix,” and accordingly, is merely more specific to the abstract idea. Still further, more details or specifics are recited for the abstract idea of “: [(b.1.1.1)] perform a factorization operation,” “[(b.1.1.1.1)] wherein, when performing the factorization operation, the at least one processor is programmed or configured to: [(b.1.1.1.1.1)] update the projection matrix using a least square optimization problem,” and “[(b.1.1.1.1.2)] update a transferred low-dimensional space using a graph-regularized alternating least squares (GRALS),” and accordingly, are merely more specific to the abstract idea.
Also, further details or specifics are recited for the abstract idea of “[(d)] determine,” “[(d.1)] wherein the input comprises a real-time event, wherein the event comprises a real-time electronic payment transaction,” and “[(d.2)] wherein the output of the one or more trained prediction models may include a predicted classification value for an event of a time series forecast of a plurality of events,” and accordingly, are merely more specific to the abstract idea.
Still further, the claim recites more details or specifics to the abstract idea of “[(e)] perform a decoding operation,” “[(e.1)] wherein . . . cause the at least one processor: [(e.1.1)] project the output from the lower dimension space to the dimension space of the training dataset using an inverse matrix corresponding to the projection matrix, wherein the inverse matrix is an inverse of the projection matrix,” and accordingly, is merely more specific to the abstract idea. Thus, claim 15 recites an abstract idea.
Under Step 2A Prong Two, the claim as a whole is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include “at least one non-transitory computer-readable medium including one or more instructions that, when executed by at least one processor,” which is recited at a high-level of generality, and thus is a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application. The claim also recites the element of “one or more prediction models,” which is recited at a high-level of generality, and thus, is a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application. In this regard, the limitation of “[(c)] generate one or more prediction models based on the encoded dataset” is the use of the generic computer component (at least one non-transitory computer-readable medium, at least one processor, one or more prediction model) to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to implement the abstract idea into a practical application. The claim also recites more specifics or details to the additional element of “[(c)] generate one or more prediction models,” in that “[(c.1)] wherein the one or more prediction models are configured to provide an output in the lower dimension space,” “[(c.2)] wherein the one or more prediction models are configured to provide a predicted classification value for an event,” “[(c.3)] wherein . . . the at least one processor is programmed or configured to: [(c.3.1)] train the one or more prediction models in the lower dimension space based on the encoded dataset to provide one or more trained prediction models,” and accordingly, are merely more specific to the additional element. The claim also recites the additional element of “[(a)] receive a training dataset of a plurality of data instances,” which is a pre-solution, insignificant extra-solution activity of mere data gathering, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application. The claim also recites more specifics or details to the additional element of “[(a)] receive,” where “[(a.1)] each data instance comprises a time series of data points,” and “[(a.2)] wherein each data point of the plurality of data instances represents an event,” and “[(a.3)] wherein the event comprises an electronic payment transaction,” and accordingly, are merely more specific to the additional element.
Further, the claim recites “[(f)] perform an action based on the output,” which is a post-processing insignificant extra-solution activity of outputting a result of the abstract idea, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application. The claim also recites more details or specifics to the additional element of “[(f)] perform an action,” where “[(f.1)] determine an action to take associated with the real-time electronic payment transaction,” which is merely more specific to the additional element. Therefore, claim 15 is directed to the abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. The claim includes a “at least one non-transitory computer-readable medium including one or more instructions that, when executed by at least one processor,” which are recited at a high-level of generality, and thus are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea. The claim also recites the element of “one or more prediction models,” which is recited at a high-level of generality, and thus, is a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea. In this regard, the limitation of “[(c)] generate one or more prediction models based on the encoded dataset” is the use of the generic computer component (at least one non-transitory computer-readable medium, at least one processor, one or more prediction model) to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea. The claim also recites more specifics or details to the additional element of “[(c)] generate one or more prediction models,” in that “[(c.1)] wherein the one or more prediction models are configured to provide an output in the lower dimension space,” “[(c.2)] wherein the one or more prediction models are configured to provide a predicted classification value for an event,” “[(c.3)] wherein . . . the at least one processor is programmed or configured to: [(c.3.1)] train the one or more prediction models in the lower dimension space based on the encoded dataset to provide one or more trained prediction models,” and accordingly, is merely more specific to the additional element. The claim also recites the additional element of “[(a)] receive a training dataset of a plurality of data instances,” which is a well-understood, routine, and conventional activity of receiving or transmitting data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea. The claim also recites more specifics or details to the additional element of “[(a)] receive,” where “[(a.1)] each data instance comprises a time series of data points,” and [(a.2)] wherein each data point of the plurality of data instances represents an event,” and “[(a.3)] wherein the event comprises an electronic payment transaction,” and accordingly, are merely more specific to the additional element.
Further, the claim recites “[(f)] perform an action based on the output,” which is a post-processing insignificant extra-solution activity of outputting a result of the abstract idea, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application. The claim also recites more details or specifics to the additional element of “[(f)] perform an action,” where “[(f.1)] determine an action to take associated with the real-time electronic payment transaction,” which is merely more specific to the additional element. Therefore, claim 15 is subject-matter ineligible.
Claim 3 depends from claim 1. Claim 10 depends from claim 8. Claim 17 depends from claim 15. The claims recite more details or specifics to the abstract idea of “[(b.1)] perform a factorization operation” “[(b.1.1)] wherein the optimization problem is the following:
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wherein Yit is the training dataset, i is an i-th observation target, t is a time stamp of each data point of the time series of data points, F is a projection matrix,
f
i
T
is a translation to an i-th row of the projection matrix F, X is the encoded dataset having a lower dimension space, xt is a data point of the time series of data points, k is a first dimension of the encoded dataset X, ℝf(F) is a squared Frobenius norm of the projection matrix F, λf is a weight of the projection matrix F, W is an autoregression model, ℝw(W) is a squared Frobenius norm of the auto-regression model W, λw is a weight of the auto-regression model W, τAR is a score of the auto-regression model W, λX is a weight of the score of the auto-regression model τAR, ℒ is a second dimension of the encoded dataset X, w is a prediction model, η is a weight to a vector norm, l is an individual component of the second dimension ℒ, T is a total time of the of the time series of data points, and ϵt is an error value associated with xt,” and accordingly, is merely more specific to the abstract idea. The abstract idea of these claims are not integrated into a practical application, (see MPEP § 2106.05(d)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05(d)), because the claims recite no more than the abstract idea. Therefore, claims 3, 10, and 17 are subject-matter ineligible.
Claim 4 depends directly or indirectly from claim 1. Claim 11 depends directly or indirectly from claim 8. Claim 18 depends directly or indirectly from claim 15. The claims recite more details or specifics to the abstract idea of “[(b.1)] performing the factorization operation,” to “[(b.1.1)] update the auto-regression model W by solving the following:
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and accordingly, are merely more specific to the abstract idea. The abstract idea of these claims are not integrated into a practical application, (see MPEP § 2106.05(d)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05(d)), because the claims recite no more than the abstract idea. Therefore, claims 4, 11, and 18 are subject-matter ineligible.
Claim 6 depends from claim 1. Claim 13 depends from claim 8. Claim 20 depends from claim 15. The claims recite more details or specifics to the abstract idea of “[(b)] perform an encoding operation,” wherein “[(b.1)] the lower dimension space has a first dimension and a second dimension,” “[(b.2)] the dimension space of the training dataset has a first dimension and a second dimension,” “[(b.3)] the first dimension of the lower dimension space is less than the first dimension of the training dataset,” and “[(b.4)] the second dimension of the lower dimension space is equal to the second dimension of the training dataset,” and accordingly, are merely more specific to the abstract idea. The abstract idea of these claims are not integrated into a practical application, (see MPEP § 2106.05(d)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05(d)), because the claims recite no more than the abstract idea. Therefore, claims 6, 13, and 20 are subject-matter ineligible.
Claim 7 depends directly or indirectly from claim 1. Claim 14 depends directly or indirectly from claim 8. The claims recite more details or specifics of the additional element of “[(c)] generate one or more prediction models,” “[(c.2)] wherein the one or more prediction models comprise a number of prediction models,” and “[(c.2)] wherein the number of prediction models is equal to the first dimension of the lower dimension space,” and accordingly, are merely more specific to the additional element. Therefore, claims 7 and 14 are subject-matter ineligible.
Response to Arguments
5. Examiner has fully considered Applicant’s arguments, and responds below accordingly.
35 U.S.C. § 101
6. Applicant submits that the “limitations of claim 1 demonstrate that claim 1 is directed to an unconventional system that improves generation and execution of machine learning models by generating a prediction model through training a machine learning model using encoded training datasets at a reduced dimension in an encoded space and generating a prediction in the encoded space that may be decoded into a final prediction.
As amended, claim 1 recites [a] system . . .
at least one processor programmed or configured to:
[(a)] receive a training dataset of a plurality of data instances,
[(a.1)] wherein each data instance comprises a time series of data points;
[(a.2)] wherein each data point of the plurality of data instances represents an event, and wherein the event comprises an electronic payment transaction;
[(b)] perform an encoding operation on the training dataset to provide an encoded dataset having a lower dimension space than a dimension space of the training dataset,
[(b.1)] wherein, when performing the encoding operation on the training dataset to provide the encoded dataset, the at least one processor is programmed or configured to:
[(b.1.1)] perform the encoding operation on the training dataset based on a projection matrix, wherein, when performing the encoding operation on the training dataset based on the projection matrix, the at least one processor is programmed or configured to:
[(b.1.1.1)] perform a factorization operation based on an optimization problem involving the projection matrix;
[(b.1.1.1.1)] wherein, when performing the factorization operation, the at least one processor is programmed or configured to:
[(b.1.1.1.1.1)] update the projection matrix using a least square optimization problem; and
[(b.1.1.1.1.2)] update a transferred low-dimensional space using a graph-regularized alternating least squares (GRALS);
[(c)] generate one or more prediction models based on the encoded dataset,
[(c.1)] wherein the one or more prediction models are configured to provide an output in the lower dimension space,
[(c.2)] wherein the one or more prediction models are configured to provide a predicted classification value for an event, and
[(c.3)] wherein, when generating the one or more prediction models based on the encoded dataset, the at least one processor is programmed or configured to:
[(c.3.1)] train the one or more prediction models in the lower dimension space based on the encoded dataset to provide one or more trained prediction models;
[(d)] determine an output of the one or more trained prediction models in the lower dimension space based on an input provided to the one or more trained prediction models,
[(d.1)] wherein the output of the one or more prediction models may include a predicted classification value for an event of a time series forecast of a plurality of events; and
[(e)] perform a decoding operation on the output to project the output from the lower dimension space to the dimension space of the training dataset,
[(e.1)] wherein, when performing the decoding operation on the output to project the output from the lower dimension space to the dimension space of the training dataset, the at least one processor is programmed or configured to:
[(e.1.1)] project the output from the lower dimension space to the dimension space of the training dataset using an inverse matrix corresponding to the projection matrix, wherein the inverse matrix is an inverse of the projection matrix;
[(f)] perform an action based on the output, wherein, when performing the action, the at least one processor is configured to:
[(f.1)] determine an action to take associated with the real-time electronic payment transaction.
[(Response at pp. 13-14] (Applicant’s amendments shown in underline)).
The limitations of claim 1 demonstrate that claim 1 is directed to an unconventional system that improves generation and execution of machine learning models by generating a prediction model through training a machine learning model using encoded training datasets at a reduced dimension in an encoded space and generating a prediction in the encoded space that may be decoded into a final prediction. The encoded training datasets are encoded based on a projection matrix, and, when performing the encoding operation on the training dataset based on the projection matrix, a factorization operation is performed based on an optimization problem involving the projection matrix, and, when performing the factorization operation, the projection matrix is updated using a least square optimization problem and a transferred low-dimensional space is updated using a graph-regularized alternating least squares (GRALS). Furthermore, one or more prediction models are generated based on the encoded dataset.” (Response at p. 15).
Examiner Response:
Examiner respectfully disagrees because the rejections identify the abstract idea (i.e., judicial exception) by referring to what is recited (i.e., set forth or described) in the claim and explain why it is considered an abstract idea. (MPEP § 2106.07(a)), as set out above in detail.
Under Step 2A Prong Two, the rejection identifies any additional elements recited in the claim beyond the identified judicial exception (i.e., abstract idea); and evaluate the integration of the judicial exception into a practical application by explaining that the claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application using the considerations set forth in MPEP §§ 2106.04(d), 2106.05(a)-(c) and (e)-(h).
“Integration” may be based on the improvements in the functioning of a computer or an improvement to any other technology or technical field. (MPEP § 2106.04(d)(1)). The evaluation requires, [i]n sum, that (1) the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Next, (2) if the specification sets forth such an improvement, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement.
By way of example to Desjardins, the MPEP provides under Step 2A Prong Two that “the [Desjardins] specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of ‘catastrophic forgetting’ encountered in continual learning systems. Importantly, the [appeals review panel (ARP)] evaluated the claims as a whole in discerning at least the limitation ‘adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task’ reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were deemed to be outside any specific, enumerated judicial exception (Step 2A: NO).” (MPEP § 2106.04(d) sub III; see “Advance Notice of Change to the MPEP in light of Ex Parte Desjardins” (05 December 2025) at p. 2)).
In general, the problem addressed by the Applicant’s disclosure appears to be that training separate time-series models on portions of a large data pool can require a lot of data and computational resources. It also says separate training can add unnecessary preprocessing, make models sensitive to missing data and noise, and increase overfitting risk. As a result, time-series predictions may be inaccurate. The disclosure seeks to improve training efficiency, robustness, and prediction accuracy by reducing dimensionality before model training (see Specification ¶¶ 0005,0006, & 0049-54).
However, under the second leg of MPEP § 2106.05(d)(1), the claims are directed to reducing computational burdens while preserving predictive structure, where in contrast, the guidance of ex parte Desjardins emphasizes “training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of ‘catastrophic forgetting’ encountered in continual learning systems.” (see above regarding ex parted Desjardins).
That is, the training is directed to improve a machine vision job accuracy by the intended result “to eliminate an incorrect outcome of the analysis.” Generally, the claimed methods are not rendered patent eligible by the fact that using existing machine learning technology performs a task previously undertaken by humans with greater speed and efficiency than could previously be achieved. (Recentive Analytics, Inc. v. Fox Corp., 2025 USPQ2d 628 at p.*6 (Fed. Cir. 2025); see MPEP § 2106.05(d)(1)); Specification ¶ 0235 & Fig. 9)).
Accordingly, as set out above in detail, the instant claims are subject-matter ineligible
35 U.S.C. § 103
7. Applicant submits that “[w]ith regard to claim 1, as amended, claim 1 recites "[a] system for generating a machine learning model based on encoded time series data using model reduction techniques, the system comprising: at least one processor programmed or configured to: receive a training dataset of a plurality of data instances, wherein each data instance comprises a time series of data points, wherein each data point of the plurality of data instances represents an event, and wherein the event comprises an electronic payment transaction; perform an encoding operation on the training dataset to provide an encoded dataset having a lower dimension space than a dimension space of the training dataset, wherein, when performing the encoding operation on the training dataset to provide the encoded dataset, the at least one processor is programmed or configured to: perform the encoding operation on the training dataset based on a projection matrix, wherein, when performing the encoding operation on the training dataset based on the projection matrix, the at least one processor is programmed or configured to: perform a factorization operation based on an optimization problem involving the projection matrix, wherein, when performing the factorization operation, the at least one processor is programmed or configured to: update the projection matrix using a least square optimization problem; and update a transferred low-dimensional space using a graph-regularized alternating least squares (GRALS); generate one or more prediction models based on the encoded dataset, wherein the one or more prediction models are configured to provide an output in the lower dimension space, wherein the one or more prediction models are configured to provide a predicted classification value for an event that is provided as an input, and wherein, when generating the one or more prediction models based on the encoded dataset, the at least one processor is programmed or configured to: train the one or more prediction models in the lower dimension space based on the encoded dataset to provide one or more trained prediction models; determine an output of the one or more trained prediction models in the lower dimension space based on a real-time input provided to the one or more trained prediction models, wherein the input comprises a real-time event, wherein the event comprises a real-time electronic payment transaction, and wherein the output of the one or more trained prediction models may include a predicted classification value for the real-time event of a time series forecast of a plurality of events; and perform a decoding operation on the output to project the output from the lower dimension space to the dimension space of the training dataset, wherein, when performing the decoding operation on the output to project the output from the lower dimension space to the dimension space of the training dataset, the at least one processor is programmed or configured to: project the output from the lower dimension space to the dimension space of the training dataset using an inverse matrix corresponding to the projection matrix, wherein the inverse matrix is an inverse of the projection matrix; and perform an action based on the output, wherein, when performing the action, the at least one processor is configured to: determine an action to take associated with the real-time electronic payment transaction." [Response at pp. 17-22](amendments shown in underline).
“Further, none of Dirac, Shi, or Yu, alone or in combination, teaches or suggests at least one processor programmed or configured to determine an output of the one or more trained prediction models in the lower dimension space based on a real-time input provided to the one or more trained prediction models, wherein the input comprises a real-time event, wherein the event comprises a real-time electronic payment transaction, and wherein the output of the one or more trained prediction models may include a predicted classification value for the real-time event of a time series forecast of a plurality of events; and perform a decoding operation on the output to project the output from the lower dimension space to the dimension space of the training dataset, wherein, when performing the decoding operation on the output to project the output from the lower dimension space to the dimension space of the training dataset, the at least one processor is programmed or configured to: project the output from the lower dimension space to the dimension space of the training dataset using an inverse matrix corresponding to the projection matrix, wherein the inverse matrix is an inverse of the projection matrix; and perform an action based on the output, wherein, when performing the action, the at least one processor is configured to: determine an action to take associated with the real-time electronic payment transaction, as recited in claim 1.
Examiner’s Response:
Examiner finds Applicant’s arguments and amendments thereto persuasive, and accordingly, WITHDRAWS the rejection under Section 103.
Conclusion
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
(US Published Application 20210326888 to Adjaoute) teaches educing financial fraud by operating artificial intelligence machines organized into parallel sets of predictive models with each set specially trained with supervised and unsupervised training data filtered for a particular financial channel. Each set integrates several artificial intelligence classifiers like neural networks, case based reasoning, decision trees, genetic algorithms, fuzzy logic, business rules and constraints, smart agents and associated real-time profiling, recursive profiles, and long-term profiles. Suspicious and abnormal activities in any channel communicate across predictive models for all the financial channels through real-time memory storage updates to the smart agent profiles they all share.
(Qin, “Latent Vector Autoregressive Modeling for Reduced Dimensional Dynamic Feature Extraction and Prediction” IEEE (2021)) teaches a novel latent vector autoregressive (LaVAR) modeling algorithm with a canonical correlation analysis (CCA) objective to estimate a fully-interacting reduced dimensional dynamic model. This algorithm is an advancement of the dynamic inner canonical correlation analysis (DiCCA) algorithm, which builds univariate latent autoregressive models that are non-interacting. The dynamic latent variable scores of the proposed algorithm are enforced to be orthogonal or contemporaneously independent, similar to those of DiCCA.
9. Any inquiry concerning this communication or earlier communications from the Examiner should be directed to KEVIN L. SMITH whose telephone number is (571) 272-5964. Normally, the Examiner is available on Monday-Thursday 0730-1730.
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If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, KAKALI CHAKI can be reached on 571-272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/K.L.S./
Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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