DETAILED CORRESPONDENCE
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.
Status of the Application
Applicant's submission filed on November 3, 2025 has been entered.
Response to Amendment
Claims 8, 14, 21, 27-28, and 33 were amended. Claims 8-14 and 21-33 remain pending and are provided to be examined upon their merits. Claims 8-14 and 21-33 have been examined in the application.
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 8-14 and 21-33 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 8-14 and 21-33 are directed to the abstract idea of: Claim 8 -: 8, a method, comprising: obtaining a first set of input data values corresponding to an input feature and associated with a first set of transactions, wherein the input feature is one of a plurality of input features associated with a learning model that is trained, using historic transaction data associated with a second set of transactions, to perform predictions for incoming transaction requests, wherein the learning model is implemented comprising a plurality with each other, and wherein each in the plurality is associated with one or more parameters based on the historic transaction data used to train the learning model; comparing a first distribution associated with the first set of input data values against a benchmark distribution; detecting a pattern shift based on a difference between the first distribution and the benchmark distribution; identifying at least one parameter associated with from the plurality in the learning model that corresponds to the input feature based on analyzing the learning model; and in response to detecting the pattern shift and without retraining the learning model, modifying the learning model, wherein the modifying comprises adjusting one or more parameter thresholds of at least one parameter associated with in the learning model, and wherein the modifying enables the learning model to process the pattern shift in the performing of the predictions; detecting a change in the pattern shift; and reverting, by and based on the change, the modified learning model back to the learning model, wherein the reverting comprises re-adjusting the one or more parameter thresholds of the at least one parameter associated with in the learning model. (fundamental economic principles or practices, commercial or legal interactions, managing personal behavior or relationships or interactions between people, concepts performed in the human mind, (including an observation, evaluation, judgment, opinion). ) Claim 9 -: 9, the method of claim 8, further comprising: receiving a transaction request; extracting data values corresponding to the plurality of input features from the transaction request; and determining a risk value for the transaction request using the modified learning model based on the data values; and processing the transaction request based on the risk value. Claim 10 -: 10, the method of claim 8, further comprising: determining that an event related to the pattern shift occurred within a time threshold prior to a first period of time, wherein the learning model is modified based on the determining that the event related to the pattern shift occurred within the time threshold. Claim 11 -: 11, the method of claim 8, wherein the first set of input data values are submitted to the learning model, and wherein the predictions for the first set of transactions are based on the first set of input data values. Claim 12 -: 12, the method of claim 8, wherein the input feature corresponds to a monetary amount or an address. Claim 13 -: 13, the method of claim 8, wherein the first distribution comprises at least one of a kurtosis value, a cardinality, or a percentage of null values. Claim 14 -: 14, the method of claim 10, wherein the detecting the change in the pattern shift is based on the first period of time associated with the event. (fundamental economic principles or practices, commercial or legal interactions, managing personal behavior or relationships or interactions between people, concepts performed in the human mind, (including an observation, evaluation, judgment, opinion). ) Claim 21 -: 21, comprising: coupled with and configured to read instructions to cause to perform operations comprising: obtaining a first set of input data values corresponding to an input feature and associated with a first set of transactions, wherein the input feature is one of a plurality of input features associated with a learning model that is trained, using historic transaction data, to perform predictions for transaction requests, wherein the learning model comprises a plurality with each other, and wherein each in the plurality are associated with one or more parameters based on the historic transaction data used to train the learning model; comparing a first... [id. at 8], detecting a transaction behavior shift based on a difference between the first distribution and the benchmark distribution; identifying at least one parameter associated with from the plurality of the learning model that corresponds to the input feature based on analyzing the learning model; in response to detecting the transaction behavior shift and without retraining the learning model, modifying the learning model, wherein the modifying comprises adjusting one or more parameter thresholds associated with the at least one parameter within programming code of the learning model, and wherein the modifying enables the learning model to process the pattern shift in the performing of the predictions; and in response to detecting a condition associated with the transaction behavior shift, reverting the modified learning model back to the learning model. (fundamental economic principles or practices, commercial or legal interactions, managing personal behavior or relationships or interactions between people, concepts performed in the human mind, (including an observation, evaluation, judgment, opinion). ) Claim 22 -: 22, claim 21, wherein the operations further comprise: subsequent to the modifying the learning model, receiving a transaction request; extracting data values... [id. at 9], determining a risk value for the transaction request using the modified learning model based on the data values. Claim 23 -: 23, claim 21, wherein the operations further comprise: determining that an event related to the transaction behavior shift occurred within a time period, wherein the learning model is modified based on the determining that the event related to the transaction behavior shift occurred within the time period. Claim 24 -: 24, claim 21, wherein the first set of input data values were used by the learning model for performing the predictions for the first set of transactions. Claim 25 -: 25, claim 21, wherein the input... [id. at 12], Claim 26 -: 26, claim 21, wherein the first... [id. at 13], Claim 27 -: 27, claim 23, wherein the condition is associated with a termination of the event. (fundamental economic principles or practices, commercial or legal interactions, managing personal behavior or relationships or interactions between people, concepts performed in the human mind, (including an observation, evaluation, judgment, opinion). ) Claim 28 -: 28, readable stored readable instructions executable to cause to perform operations comprising: obtaining a first set of input data values corresponding to an input feature and associated with a first set of transactions, wherein the input feature is one of a plurality of input features associated with a learning model that is trained, using transaction data associated with a second set of transactions, to perform predictions for transaction requests, wherein the learning model comprises a plurality and wherein each in the plurality are associated with one or more parameters based on the transaction data used to train the learning model; comparing a first... detecting a pattern... [id. at 8], identifying at least... [id. at 21], in response to detecting the pattern shift and without retraining the learning model, modifying the learning model, wherein the modifying comprises adjusting one or more parameter thresholds of the at least one parameter associated with in the learning model, and wherein the modifying enables the learning model to process the pattern shift in the performing of the predictions detecting a condition associated with the pattern shift; and reverting, based on the condition, the modified learning model back to the learning model (fundamental economic principles or practices, commercial or legal interactions, managing personal behavior or relationships or interactions between people, concepts performed in the human mind, (including an observation, evaluation, judgment, opinion). ) Claim 29 -: 29, readable of claim 28, wherein the operations further comprise: subsequent to the... [id. at 22], extracting data values... [id. at 9], determining a risk... [id. at 22], Claim 30 -: 30, readable of claim 28, wherein the operations... [id. at 29], determining that an event related to the pattern shift occurred within a time period, wherein the learning model is modified based on the determining that the event related to the pattern shift occurred within the time period. Claim 31 -: 31, readable of claim 28, wherein the first... [id. at 24], Claim 32 -: 32, readable of claim 28, wherein the input... [id. at 12], Claim 33 -: 33, readable of claim 30, wherein the condition... [id. at 27], (fundamental economic principles or practices, commercial or legal interactions, managing personal behavior or relationships or interactions between people, concepts performed in the human mind, (including an observation, evaluation, judgment, opinion). ) . The identified limitation(s) falls within the subject matter groupings of abstract ideas enumerated in Section I of the 2019 Revised Patent Subject Matter Eligibility Guidance: b) Certain methods of organizing human activity – fundamental economic principles or practices, commercial or legal interactions, managing personal behavior or relationships or interactions between people, c) Mental processes – concepts performed in the human mind, (including an observation, evaluation, judgment, opinion).
These limitation excerpts, under their broadest reasonable interpretation, fall within the grouping(s) of abstract ideas of: Certain methods of organizing human activity – since: real-time modification of risk models based on feature stability as recited in the claim limitations, under their broadest reasonable interpretation, covers performance of the limitation(s) as fundamental economic principles or practices, (including hedging, insurance, mitigating risk); commercial or legal interactions, (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions). Mental processes – since: the above-underlined as recited in the claim limitations, under their broadest reasonable interpretation, covers performance of the limitation(s) as concepts performed in the human mind, (including an observation, evaluation, judgment, opinion). Therefore, the limitations fall within the above-identified grouping(s) of abstract ideas.
While independent claims 8, 21, and 28 do not explicitly recite verbatim this identified abstract idea, the concept of this identified abstract idea is described by the steps of independent claim 8 and is described by the steps of independent claim 21 and is described by the steps of independent claim 28.
Claim 8: Specifically regarding the analysis under Step 2A of the Office's § 101 Subject Matter Eligibility Test for Products and Processes, independent claim 8 further to the abstract idea includes additional elements of "one or more hardware processors", "machine learning", "neural network", "plurality of nodes", "connected", "each node", "a node", and "the node". However, independent claim 8 does not include additional elements that are sufficient to integrate the exception into a practical application because "one or more hardware processors", "machine learning", "neural network", "plurality of nodes", "connected", "each node", "a node", and "the node" of independent claim 8 recite generic computer and/or field of use components pertaining to the particular technological environment that are recited a high-level of generality that perform functions ("a method, comprising", "obtaining, by one or more … for incoming transaction requests", "wherein the machine learning model … the machine learning model", "comparing, by the one or … against a benchmark distribution", "detecting, by the one or … and the benchmark distribution", "identifying, by the one or … machine learning model; and", "in response to detecting the … performing of the predictions", "detecting, by the one or … the pattern shift; and" and "reverting, by the one or … the machine learning model") that merely perform, conduct, carry out, implement, and/or narrow the abstract idea itself [Step 2A Prong I] (e.g. all or portion(s) of the noted recited steps) and/or that recite generic computer and/or field of use functions that are recited at a high-level of generality and/or because the additional method steps comprise or include: evaluated additional elements individually and in combination for which the courts have identified examples in which a judicial exception has not been integrated into a practical application, [Step 2A Prong II] adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea -- see MPEP 2106.05(f) (all or portions of the noted step(s)), and generally linking the use of the judicial exception to a particular technological environment or field of use -- see MPEP 2106.05(h) (all or portions of the noted step(s)). Regarding Step 2B treatment of the evaluated additional elements individually and in combination, the additional elements do not amount to more than a recitation of the words "apply it" (or an equivalent) or are not more than mere instructions to implement an abstract idea or other exception on a computer, and the additional elements do not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use. None of the additional elements taken individually or when taken as an ordered combination amount to significantly more than the abstract idea. Accordingly, independent claim 8 is ineligible.
Claim 21: Materially regarding the analysis under Step 2A of the Office's § 101 Subject Matter Eligibility Test for Products and Processes, independent claim 21 further to the abstract idea includes additional elements of "system", "non-transitory memory", "one or more hardware processors", "machine learning", "plurality of nodes", "connected", "each node", and "a node". However, independent claim 21 does not include additional elements that are sufficient to integrate the exception into a practical application because "system", "non-transitory memory", "one or more hardware processors", "machine learning", "plurality of nodes", "connected", "each node", and "a node" of independent claim 21 recite generic computer and/or field of use components pertaining to the particular technological environment that are recited a high-level of generality that perform functions ("a system, comprising", "a non-transitory memory; and", "one or more hardware processors … to perform operations comprising", "obtaining a first set of … predictions for transaction requests", "wherein the machine learning model … the machine learning model", "comparing a first distribution associated … against a benchmark distribution", "detecting a transaction behavior shift … and the benchmark distribution", "identifying at least one parameter … the machine learning model", "in response to detecting the … of the predictions; and" and "in response to detecting a … the machine learning model") that merely perform, conduct, carry out, implement, and/or narrow the abstract idea itself (e.g. all or portion(s) of the noted recited steps) and/or that recite generic computer and/or field of use functions that are recited at a high-level of generality that include only steps narrowing the abstract idea [Step 2A Prong I] (e.g. all or portion(s) of the noted recited steps) and/or because the additional method steps comprise or include: evaluated additional elements individually and in combination for which the courts have identified examples in which a judicial exception has not been integrated into a practical application, [Step 2A Prong II] adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea -- see MPEP 2106.05(f) (all or portions of the noted step(s)), and adding insignificant extra-solution activity to the judicial exception -- see MPEP 2106.05(g) (all or portions of the "a non-transitory memory; and", "one or more hardware processors … to perform operations comprising" step(s)), and generally linking the use of the judicial exception to a particular technological environment or field of use -- see MPEP 2106.05(h) (all or portions of the noted step(s)). Regarding Step 2B treatment of the evaluated additional elements individually and in combination, the additional elements do not amount to more than a recitation of the words "apply it" (or an equivalent) or are not more than mere instructions to implement an abstract idea or other exception on a computer, and the additional elements do not add more than insignificant extra-solution activity to the judicial exception, and the additional elements do not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use. Furthermore, the additional method steps comprise or include: reciting additional elements in implementing the abstract idea that do not constitute significantly more than the abstract idea because they comprise or include well-understood, routine, and conventional activities previously known to the industry (e.g. all or portion(s) of the "a non-transitory memory; and", "one or more hardware processors … to perform operations comprising", (insignificant extra-solution activity) steps), see Alice Corp., 134 S. Ct. at 2360, and/or that are otherwise not significant toward constituting any inventive concept beyond the abstract idea. (E.g. The above-italicized grounds of rejection apply at least to all or portion(s) of the noted recited steps.) For example regarding well-understood, routine, and conventional activities, the cited rationale have recognized the following computer function as well-understood, routine, and conventional functions when it is claimed or as insignificant extra-solution activity: storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d at 1363, 115 USPQ2d at 1092-93 (Fed. Cir. 2015). None of the additional elements taken individually or when taken as an ordered combination amount to significantly more than the abstract idea. Accordingly, independent claim 21 is ineligible.
Claim 28: Materially with respect to the analysis under Step 2A of the Office's § 101 Subject Matter Eligibility Test for Products and Processes, independent claim 28 further to the abstract idea includes additional elements of "non-transitory machine-readable medium", "machine-readable", "machine", "machine learning", "plurality of nodes", "one or more layers", "each node", "a node", and "the node". However, independent claim 28 does not include additional elements that are sufficient to integrate the exception into a practical application because "non-transitory machine-readable medium", "machine-readable", "machine", "machine learning", "plurality of nodes", "one or more layers", "each node", "a node", and "the node" of independent claim 28 recite generic computer and/or field of use components pertaining to the particular technological environment that are recited a high-level of generality that perform functions ("a non-transitory machine-readable medium having … to perform operations comprising", "obtaining a first set of … predictions for transaction requests", "wherein the machine learning model … the machine learning model", "comparing a first distribution associated … against a benchmark distribution", "detecting a pattern shift based … and the benchmark distribution", "identifying at least one parameter … the machine learning model", "in response to detecting the … performing of the predictions", "detecting a condition associated with the pattern shift; and" and "reverting, based on the condition, … the machine learning model") that merely perform, conduct, carry out, implement, and/or narrow the abstract idea itself (e.g. all or portion(s) of the noted recited steps) and/or that recite generic computer and/or field of use functions that are recited at a high-level of generality that include only steps narrowing the abstract idea [Step 2A Prong I] (e.g. all or portion(s) of the noted recited steps) and/or because the additional method steps comprise or include: evaluated additional elements individually and in combination for which the courts have identified examples in which a judicial exception has not been integrated into a practical application, [Step 2A Prong II] adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea -- see MPEP 2106.05(f) (all or portions of the noted step(s)), and adding insignificant extra-solution activity to the judicial exception -- see MPEP 2106.05(g) (all or portions of the "a non-transitory machine-readable medium having … to perform operations comprising" step(s)), and generally linking the use of the judicial exception to a particular technological environment or field of use -- see MPEP 2106.05(h) (all or portions of the noted step(s)). Regarding Step 2B treatment of the evaluated additional elements individually and in combination, the same previously-stated legal authority and/or rationale supporting the grounds of rejection applied to the above Claim 21 also applies hereto. Additionally, the additional method steps comprise or include: reciting additional elements in implementing the abstract idea that do not constitute significantly more than the abstract idea because they comprise or include well-understood, routine, and conventional activities previously known to the industry (e.g. all or portion(s) of the "a non-transitory machine-readable medium having … to perform operations comprising", (insignificant extra-solution activity) steps), see Alice Corp., 134 S. Ct. at 2360, and/or that are otherwise not significant toward constituting any inventive concept beyond the abstract idea. (E.g. These previously-stated grounds of rejection that were italicized when applied to the referenced previous Claim(s) apply at least to all or portion(s) of the noted recited steps.) See discussion above regarding Claim 21 for pertinent previously cited rationale finding well-understood, routine, and conventional activities. None of the additional elements taken individually or when taken as an ordered combination amount to significantly more than the abstract idea. Accordingly, independent claim 28 is ineligible.
Independent Claims: Nothing in independent claims 8, 21, and 28 improves another technology or technical field, improves the functioning of any claimed computer device itself, applies the abstract idea with any particular machine, solves any computer problem with a computer solution, or includes any element that may otherwise be considered to amount to significantly more than the abstract idea.
None of the dependent claims 9-14, 22-27, and 29-33 when separately considered with each dependent claim's corresponding parent claim overcomes the above analysis because none presents any method step not directed to the abstract idea that amounts to significantly more than the judicial exception or any physical structure that amounts to significantly more than the judicial exception.
Claim 9: Dependent claim 9 adds an additional method step of "receiving a transaction request", "extracting data values corresponding to the plurality of input features from the transaction request; and", "determining a risk value for the transaction request using the modified machine learning model based on the data values; and", "processing the transaction request based on the risk value". However, the additional method step of dependent claims 9 is directed to the abstract idea noted above and does not otherwise alter the analysis presented above, and do not integrate the exception into a practical application, because the additional method step merely perform, conduct, carry out, and/or implement the abstract idea itself and/or only narrows the abstract idea (e.g. all or portion(s) of the noted recited steps) and/or because the additional method step comprises or includes: evaluated additional elements individually and in combination for which the courts have identified examples in which a judicial exception has not been integrated into a practical application, as previously discussed regarding Claim 8 above. Regarding Step 2B treatment of the evaluated additional elements individually and in combination, the same previously-stated legal authority and/or rationale supporting the grounds of rejection applied to the above Claim 8 also applies hereto. (E.g. These previously-stated grounds of rejection that were italicized when applied to the referenced previous Claim(s) apply at least to all or portion(s) of the noted recited steps.) Dependent claim 9 further does not specify any particular machine element(s) for the "receiving a transaction request", "extracting data values corresponding to the plurality of input features from the transaction request; and", "determining a risk value for the transaction request using the modified machine learning model based on the data values; and", "processing the transaction request based on the risk value" step and under the broadest reasonable interpretation, this step may be manually performed by a human only which also does not add significantly more than the abstract idea. No additional step introduced in this claim taken individually or when taken as an ordered combination amounts to significantly more than the abstract idea. Accordingly, dependent claim 9 is ineligible.
Claim 10: Dependent claim 10 adds an additional method step of "determining that an event related to the pattern shift occurred within a time threshold prior to a … the determining that the event related to the pattern shift occurred within the time threshold". However, the additional method step of dependent claims 10 is directed to the abstract idea noted above and does not otherwise alter the analysis presented above, and do not integrate the exception into a practical application, because the additional method step merely perform, conduct, carry out, and/or implement the abstract idea itself and/or only narrows the abstract idea (e.g. all or portion(s) of the noted recited step) and/or because the additional method step comprises or includes: evaluated additional elements individually and in combination for which the courts have identified examples in which a judicial exception has not been integrated into a practical application, as previously discussed regarding Claim 8 above. Regarding Step 2B treatment of the evaluated additional elements individually and in combination, the same previously-stated legal authority and/or rationale supporting the grounds of rejection applied to the above Claim 8 also applies hereto. (E.g. These previously-stated grounds of rejection that were italicized when applied to the referenced previous Claim(s) apply at least to all or portion(s) of the noted recited step.) Dependent claim 10 further does not specify any particular machine element(s) for the "determining that an event related to the pattern shift occurred within a time threshold prior to a … the determining that the event related to the pattern shift occurred within the time threshold" step and under the broadest reasonable interpretation, this step may be manually performed by a human only which also does not add significantly more than the abstract idea. No additional step introduced in this claim taken individually or when taken as an ordered combination amounts to significantly more than the abstract idea. Accordingly, dependent claim 10 is ineligible.
Claim 11: Dependent claim 11 adds additional method steps of "wherein the first set of input data values are submitted to the machine learning model, and wherein … the first set of transactions are based on the first set of input data values". However, the additional method steps of dependent claims 11 are directed to the abstract idea noted above and do not otherwise alter the analysis presented above, and do not integrate the exception into a practical application, because the additional method steps merely perform, conduct, carry out, and/or implement the abstract idea itself and/or only narrow the abstract idea (e.g. all or portion(s) of the noted recited steps) and/or because the additional method steps comprise or include: evaluated additional elements individually and in combination for which the courts have identified examples in which a judicial exception has not been integrated into a practical application, as previously discussed regarding Claim 8 above. Regarding Step 2B treatment of the evaluated additional elements individually and in combination, the same previously-stated legal authority and/or rationale supporting the grounds of rejection applied to the above Claim 8 also applies hereto. (E.g. These previously-stated grounds of rejection that were italicized when applied to the referenced previous Claim(s) apply at least to all or portion(s) of the noted recited steps.) Dependent claim 11 further does not specify any particular machine element(s) for the "wherein the first set of input data values are submitted to the machine learning model, and wherein … the first set of transactions are based on the first set of input data values" steps and under the broadest reasonable interpretation, these steps may be manually performed by a human only which also does not add significantly more than the abstract idea. No additional step introduced in this claim taken individually or when taken as an ordered combination amounts to significantly more than the abstract idea. Accordingly, dependent claim 11 is ineligible.
Claims 12, 25, and 32: Dependent claims 12, 25, and 32 add an additional method step of "wherein the input feature corresponds to a monetary amount or an address". However, the additional method step of dependent claim 12, 25, and 32 is directed to the abstract idea noted above and does not otherwise alter the analysis presented above, and do not integrate the exception into a practical application, because the additional method step merely perform, conduct, carry out, and/or implement the abstract idea itself and/or only narrows the abstract idea (e.g. all or portion(s) of the noted recited step) and/or because the additional method step comprises or includes: evaluated additional elements individually and in combination for which the courts have identified examples in which a judicial exception has not been integrated into a practical application, as previously discussed regarding Claim 8 above. Regarding Step 2B treatment of the evaluated additional elements individually and in combination, the same previously-stated legal authority and/or rationale supporting the grounds of rejection applied to the above Claim 8 also applies hereto. (E.g. These previously-stated grounds of rejection that were italicized when applied to the referenced previous Claim(s) apply at least to all or portion(s) of the noted recited step.) Dependent claims 12, 25, and 32 further do not specify any particular machine element(s) for the "wherein the input feature corresponds to a monetary amount or an address" step and under the broadest reasonable interpretation, this step may be manually performed by a human only which also does not add significantly more than the abstract idea. No additional step introduced in this claim taken individually or when taken as an ordered combination amounts to significantly more than the abstract idea. Accordingly, dependent claims 12, 25, and 32 are ineligible.
Claims 13 and 26: Dependent claims 13 and 26 add an additional method step of "wherein the first distribution comprises at least one of a kurtosis value, a cardinality, or a percentage of null values". However, the additional method step of dependent claim 13 and 26 is directed to the abstract idea noted above and does not otherwise alter the analysis presented above, and do not integrate the exception into a practical application, because the additional method step merely perform, conduct, carry out, and/or implement the abstract idea itself and/or only narrows the abstract idea (e.g. all or portion(s) of the noted recited steps) and/or because the additional method step comprises or includes: evaluated additional elements individually and in combination for which the courts have identified examples in which a judicial exception has not been integrated into a practical application, as previously discussed regarding Claim 8 above. Regarding Step 2B treatment of the evaluated additional elements individually and in combination, the same previously-stated legal authority and/or rationale supporting the grounds of rejection applied to the above Claim 8 also applies hereto. (E.g. These previously-stated grounds of rejection that were italicized when applied to the referenced previous Claim(s) apply at least to all or portion(s) of the noted recited steps.) Dependent claims 13 and 26 further do not specify any particular machine element(s) for the "wherein the first distribution comprises at least one of a kurtosis value, a cardinality, or a percentage of null values" (claim 13) step and under the broadest reasonable interpretation, this step may be manually performed by a human only which also does not add significantly more than the abstract idea. No additional step introduced in this claim taken individually or when taken as an ordered combination amounts to significantly more than the abstract idea. Accordingly, dependent claims 13 and 26 are ineligible.
Claim 14: Dependent claim 14 adds an additional method step of "wherein the detecting the change in the pattern shift is based on the first period of time associated with the event". However, the additional method step of dependent claims 14 is directed to the abstract idea noted above and does not otherwise alter the analysis presented above, and do not integrate the exception into a practical application, because the additional method step merely perform, conduct, carry out, and/or implement the abstract idea itself and/or only narrows the abstract idea (e.g. all or portion(s) of the noted recited step) and/or because the additional method step comprises or includes: evaluated additional elements individually and in combination for which the courts have identified examples in which a judicial exception has not been integrated into a practical application, as previously discussed regarding Claim 8 above. Regarding Step 2B treatment of the evaluated additional elements individually and in combination, the same previously-stated legal authority and/or rationale supporting the grounds of rejection applied to the above Claim 8 also applies hereto. (E.g. These previously-stated grounds of rejection that were italicized when applied to the referenced previous Claim(s) apply at least to all or portion(s) of the noted recited step.) Dependent claim 14 further does not specify any particular machine element(s) for the "wherein the detecting the change in the pattern shift is based on the first period of time associated with the event" step and under the broadest reasonable interpretation, this step may be manually performed by a human only which also does not add significantly more than the abstract idea. No additional step introduced in this claim taken individually or when taken as an ordered combination amounts to significantly more than the abstract idea. Accordingly, dependent claim 14 is ineligible.
Claim 22: Dependent claim 22 adds an additional method step of "subsequent to the modifying the machine learning model, receiving a transaction request", "extracting data values corresponding to the plurality of input features from the transaction request; and", "determining a risk value for the transaction request using the modified machine learning model based on the data values". However, the additional method step of dependent claims 22 is directed to the abstract idea noted above and does not otherwise alter the analysis presented above, and do not integrate the exception into a practical application, because the additional method step merely perform, conduct, carry out, and/or implement the abstract idea itself and/or only narrows the abstract idea (e.g. all or portion(s) of the noted recited steps) and/or because the additional method step comprises or includes: evaluated additional elements individually and in combination for which the courts have identified examples in which a judicial exception has not been integrated into a practical application, as previously discussed regarding Claim 8 above. Regarding Step 2B treatment of the evaluated additional elements individually and in combination, the same previously-stated legal authority and/or rationale supporting the grounds of rejection applied to the above Claim 8 also applies hereto. (E.g. These previously-stated grounds of rejection that were italicized when applied to the referenced previous Claim(s) apply at least to all or portion(s) of the noted recited steps.) No additional step introduced in this claim taken individually or when taken as an ordered combination amounts to significantly more than the abstract idea. Accordingly, dependent claim 22 is ineligible.
Claim 23: Dependent claim 23 adds an additional method step of "determining that an event related to the transaction behavior shift occurred within a time period, wherein the … determining that the event related to the transaction behavior shift occurred within the time period". However, the additional method step of dependent claims 23 is directed to the abstract idea noted above and does not otherwise alter the analysis presented above, and do not integrate the exception into a practical application, because the additional method step merely perform, conduct, carry out, and/or implement the abstract idea itself and/or only narrows the abstract idea (e.g. all or portion(s) of the noted recited steps) and/or because the additional method step comprises or includes: evaluated additional elements individually and in combination for which the courts have identified examples in which a judicial exception has not been integrated into a practical application, as previously discussed regarding Claim 8 above. Regarding Step 2B treatment of the evaluated additional elements individually and in combination, the same previously-stated legal authority and/or rationale supporting the grounds of rejection applied to the above Claim 8 also applies hereto. (E.g. These previously-stated grounds of rejection that were italicized when applied to the referenced previous Claim(s) apply at least to all or portion(s) of the noted recited steps.) No additional step introduced in this claim taken individually or when taken as an ordered combination amounts to significantly more than the abstract idea. Accordingly, dependent claim 23 is ineligible.
Claims 24 and 31: Dependent claims 24 and 31 add an additional method step of "wherein the first set of input data values were used by the machine learning model for performing the predictions for the first set of transactions". However, the additional method step of dependent claim 24 and 31 is directed to the abstract idea noted above and does not otherwise alter the analysis presented above, and do not integrate the exception into a practical application, because the additional method step merely perform, conduct, carry out, and/or implement the abstract idea itself and/or only narrows the abstract idea (e.g. all or portion(s) of the noted recited step) and/or because the additional method step comprises or includes: evaluated additional elements individually and in combination for which the courts have identified examples in which a judicial exception has not been integrated into a practical application, as previously discussed regarding Claim 8 above. Regarding Step 2B treatment of the evaluated additional elements individually and in combination, the same previously-stated legal authority and/or rationale supporting the grounds of rejection applied to the above Claim 8 also applies hereto. (E.g. These previously-stated grounds of rejection that were italicized when applied to the referenced previous Claim(s) apply at least to all or portion(s) of the noted recited step.) No additional step introduced in this claim taken individually or when taken as an ordered combination amounts to significantly more than the abstract idea. Accordingly, dependent claims 24 and 31 are ineligible.
Claims 27 and 33: Dependent claims 27 and 33 add an additional method step of "wherein the condition is associated with a termination of the event". However, the additional method step of dependent claim 27 and 33 is directed to the abstract idea noted above and does not otherwise alter the analysis presented above, and do not integrate the exception into a practical application, because the additional method step merely perform, conduct, carry out, and/or implement the abstract idea itself and/or only narrows the abstract idea (e.g. all or portion(s) of the noted recited step) and/or because the additional method step comprises or includes: evaluated additional elements individually and in combination for which the courts have identified examples in which a judicial exception has not been integrated into a practical application, as previously discussed regarding Claim 8 above. Regarding Step 2B treatment of the evaluated additional elements individually and in combination, the same previously-stated legal authority and/or rationale supporting the grounds of rejection applied to the above Claim 8 also applies hereto. (E.g. These previously-stated grounds of rejection that were italicized when applied to the referenced previous Claim(s) apply at least to all or portion(s) of the noted recited step.) No additional step introduced in this claim taken individually or when taken as an ordered combination amounts to significantly more than the abstract idea. Accordingly, dependent claims 27 and 33 are ineligible.
Claim 29: Dependent claim 29 adds an additional method step of "wherein the operations further comprise", "subsequent to the modifying the machine learning model, receiving a transaction request", "extracting data values corresponding to the plurality of input features from the transaction request; and", "determining a risk value for the transaction request using the modified machine learning model based on the data values". However, the additional method step of dependent claims 29 is directed to the abstract idea noted above and does not otherwise alter the analysis presented above, and do not integrate the exception into a practical application, because the additional method step merely perform, conduct, carry out, and/or implement the abstract idea itself and/or only narrows the abstract idea (e.g. all or portion(s) of the noted recited steps) and/or because the additional method step comprises or includes: evaluated additional elements individually and in combination for which the courts have identified examples in which a judicial exception has not been integrated into a practical application, as previously discussed regarding Claim 8 above. Regarding Step 2B treatment of the evaluated additional elements individually and in combination, the same previously-stated legal authority and/or rationale supporting the grounds of rejection applied to the above Claim 8 also applies hereto. (E.g. These previously-stated grounds of rejection that were italicized when applied to the referenced previous Claim(s) apply at least to all or portion(s) of the noted recited steps.) No additional step introduced in this claim taken individually or when taken as an ordered combination amounts to significantly more than the abstract idea. Accordingly, dependent claim 29 is ineligible.
Claim 30: Dependent claim 30 adds an additional method step of "wherein the operations further comprise", "determining that an event related to the pattern shift occurred within a time period, wherein the machine … the determining that the event related to the pattern shift occurred within the time period". However, the additional method step of dependent claims 30 is directed to the abstract idea noted above and does not otherwise alter the analysis presented above, and do not integrate the exception into a practical application, because the additional method step merely perform, conduct, carry out, and/or implement the abstract idea itself and/or only narrows the abstract idea (e.g. all or portion(s) of the noted recited steps) and/or because the additional method step comprises or includes: evaluated additional elements individually and in combination for which the courts have identified examples in which a judicial exception has not been integrated into a practical application, as previously discussed regarding Claim 8 above. Regarding Step 2B treatment of the evaluated additional elements individually and in combination, the same previously-stated legal authority and/or rationale supporting the grounds of rejection applied to the above Claim 8 also applies hereto. (E.g. These previously-stated grounds of rejection that were italicized when applied to the referenced previous Claim(s) apply at least to all or portion(s) of the noted recited steps.) No additional step introduced in this claim taken individually or when taken as an ordered combination amounts to significantly more than the abstract idea. Accordingly, dependent claim 30 is ineligible.
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§101 Subject Matter Eligibility Test for Products and Processes
Response to Arguments
Regarding eligibility rejections under 35 U.S.C. § 101, the Applicant's arguments submitted November 3, 2025 (hereinafter "REMARKS") in response to the Official Correspondence mailed August 28, 2025 (hereinafter "Final Correspondence") have been fully considered but are not persuasive. Further to the August 28, 2025 Final Correspondence, the reiterated grounds of rejection are fully set forth above under the 35 U.S.C. § 101 heading as applied to the herein examined current claims.
• Specifically, the Applicant argued:
"[] The rejections are respectfully traversed in light of the claim amendments [].
"[] Applicant respectfully submits the rejection cannot be maintained over the claims as currently amended [].
"Applicant respectfully asserts that the amended claimed subject matter is not directed to an abstract idea. []
"[E]ven if it is determined that amended claim 8 recites an abstract idea, Applicant respectfully submits that when viewed as a whole, amended claim 8 integrates the abstract idea into a practical application under Step 2A, Prong Two of the Alice/Mayo Test. [A]mended claim 8 recites, at least in part, [claim 8 language as amended.]
'Independent claims 21 and 28 recite similar limitations as independent claim 8. Applicant respectfully submits that at least [] additional elements recited in the claims integrate the abstract idea into a practical application of dynamically modifying a machine learning model to accommodate an anticipated shift of behavior, such that the accuracy performance of the machine learning model can be maintained after the shift. [] "[A] machine learning model can be trained to learn patterns of real-world behavior and may use the learned pattern to perform predictions (e.g., risk predictions, etc.)... however, certain real-world events may cause the behavior of legitimate transactions to abruptly shift" (Application, paragraphs [0002]-[0003]). Re-training a model using updated training data is a typical solution for accommodating such a change in pattern. [T]here are deficiencies in re-training a machine learning model.[] "[S]ince training the machine learning model to recognize new patterns can take a substantial amount of time, data, and processing (e.g., collecting and correctly labeling training data, etc.), the machine learning model may not be updated in time to recognize the new behavior, which may result in incorrect or inaccurate predictions" (Application, paragraph [0003]). [S]ince the pattern shift may be associated with a temporary condition (e.g., associated with an event that happens within a particular time period, etc.), "by the time the machine learning model is re-trained... the behavior pattern... may have shifted back to normal" (Application, paragraph [0046]).
'The claimed solution provides a framework for "dynamically modifying a computer-based risk model based on detected shifts of one or more features (e.g., one or more input variables)" (Application, paragraph [0010]). [W]hen a shift associated with a particular input feature is detected (e.g., the average transaction amount has substantially increased, a sudden increase of transactions from a particular location, etc.), a computer system would determine whether the shift is related to an event. The computer system then analyzes the machine learning model to determine one or more parameters that are related to the input feature associated with the shift, and adjusts the parameter thresholds of the one or more parameters without re-training the machine learning model. By adjusting the parameter thresholds (instead of retraining the machine learning model), the machine learning model is modified to process the shift quickly, such that the accuracy performance of the machine learning model is maintained. As recited in the claims, the computer system also reverts the modified machine learning model back to the original machine learning model when it detects that the event is over, which enables the machine learning model to accurately perform predictions for subsequent transactions after the pattern shifts back to normal.
"Using the techniques recited in the claims, the internal structures of machine learning models can be dynamically modified to accommodate abrupt and temporary shifts in patterns, which cannot be accomplished using any conventional training and/or re-training techniques. The techniques recited in the claims are also unique to computer- based artificial neural networks, and do not have applications outside of the computer field. [T]he claims provide an improvement to the technical field of machine learning (see MPEP[]2106.05(a), Finjan Inc. v. Blue Coat Systems and Data Engine Techs., LLC v. Google LLC).
'[] Applicant respectfully submits that amended claims 8, 21, and 28 can be closely analogized to claim 3 in Example 47 of the Subject Matter Eligibility Examples[]. [] Similar to claim 3 in Example 47, the limitations recited in claims 8, 21, and 28 provide a specific solution to solve the problem illustrated in the background section of the Specification, which is to enable machine learning models to accommodate (e.g., maintain a prediction accuracy above a threshold, etc.) abrupt and temporary shifts in pattern. By "identifying... at least one parameter associated with a node from the plurality of nodes in the machine learning model that corresponds to the input feature" and "adjusting one or more parameter thresholds of the at least one parameter associated with the node in the machine learning model," the machine learning model is modified to accommodate an abrupt shift in the data pattern. [B]y "detecting... a reversion of the pattern shift" and "reverting... the modified machine learning model back to the machine learning model," the modification to the machine learning model can be immediately reverted, such that the machine learning model can perform the same way prior to the pattern shift. [A]mended claims 8, 21, and 28 (and other claims) as recited herein, should be found to be patent eligible based on the same rationale as provided for claim 3 in Example 47.
"[] Applicant respectfully asserts that the pending claims are not directed to an abstract idea as the claims integrate the abstract idea into a practical application [].
"[I]t is submitted that the application is now in condition for allowance. []"
(REMARKS, pp. 8-12).
However, the above-quoted arguments submitted November 3, 2025 at REMARKS pp. 8-12 regarding rejections under 35 U.S.C. § 101 have been fully considered, but are not persuasive. Considerably, the Office respectfully disagrees with the Applicant's above-quoted factual allegations and legal conclusion. '[T]he "invention" is what is claimed'. Zoltek Corp. v. United States, 672 F.3d 1309, 1318, 102 USPQ2d 1001, 1008 (Fed. Cir. 2012). The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. Contrary to the Applicant's above-quoted assertions, the Applicant's alleged invention as delineated by the currently pending claims appears to be deeply rooted in the abstract idea. The Applicant's claims do not purport to improve the functioning of the computer itself, or to improve any other technology or technical field, rather "the focus of the claims is not on [] an improvement in computers as tools, but on certain independently abstract ideas that use computers as tools." Electric Power Group, LLC, v. Alstom, 830 F.3d 1350, 1354, 119 U.S.P.Q.2d 1739, 1742 (Fed. Cir. 2016).
In response to Applicant's argument that the claimed subject matter provides any improvement to any technology or technical field, the alleged improvement(s) in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. Example(s) that the courts have indicated may not be sufficient to show an improvement in computer-functionality: ii. Accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016); iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential); vii. Providing historical usage information to users while they are inputting data, in order to improve the quality and organization of information added to a database, because "an improvement to the information stored by a database is not equivalent to an improvement in the database's functionality," BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1287-88, 127 USPQ2d 1688, 1693-94 (Fed. Cir. 2018); Examples that the courts have indicated may not be sufficient to show an improvement to technology include: i. A commonplace business method being applied on a general purpose computer, Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); ii. Using well-known standard laboratory techniques to detect enzyme levels in a bodily sample such as blood or plasma, Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1355, 1362, 123 USPQ2d 1081, 1082-83, 1088 (Fed. Cir. 2017); iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48; See Alice Corp., 134 S. Ct. at 2358: 'Stating an abstract idea "while adding the words 'apply it'" is not enough for patent eligibility. Mayo, supra, at ___, 132 S. Ct. 1289, 182 L. Ed. 2d 321, 325. Nor is limiting the use of an abstract idea "'to a particular technological environment.'" Bilski, supra, at 610-611, 130 S. Ct. 3218, 177 L. Ed. 2d 792.' Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011). Regarding the Applicant's reference(s) to one or more Subject Matter Eligibility Examples: Abstract Ideas, the Office notes that the examples are presented as hypothetical and only intended to be interpreted based on the fact patterns set forth therein as other fact patterns may have different eligibility outcomes. In response to applicant's argument that the claim requires an additional element or a combination of additional elements to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, it is noted that the features upon which applicant relies are not recited in the rejected claim(s) (i.e., are not required to present by the broadest reasonable interpretation of the rejected claim(s)). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). For example, none of the claims recite "the internal structures of machine learning models can be dynamically modified to accommodate abrupt and temporary shifts in patterns" or "internal structures" or "to accurately perform predictions for subsequent transactions", etc.. With respect to the Finjan v. Blue Coat Systems court decision cited by the Applicant, the Office determines that the legal holdings of Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299 (Fed. Cir. 2018), when applied to the facts pertaining to the Applicant's claims, do not support the eligibility of Applicant's claims under Step 2A or 2B of the above-depicted § 101 Subject Matter Eligibility Test for Products and Processes flowchart. In taking into consideration the specific facts and the particular holdings of Finjan v. Blue Coat Systems in the Applicant's pending matter, the Office notes that in Finjan, the U.S. Court of Appeals for the Federal Circuit (Federal Circuit) issued a precedential decision finding claims to a software-related invention patent eligible under 35 U.S.C. § 101 because they are not directed to an abstract idea and be deemed patent-eligible subject matter at the first step of the Alice/Mayo analysis (Step 2A in the Office's Subject Matter Eligibility Analysis framework depicted in the above-illustrated § 101 Subject Matter Eligibility Test for Products and Processes). In Finjan, claims were found to be eligible in Step 2A for method, system, and computer readable medium regarding a virus scan that generates a security profile identifying both hostile and potentially hostile operations by means of a method of attaching a downloadable security profile to a downloadable application program. In Finjan, the claimed invention involved a method of virus scanning that scans an application program, generates a security profile identifying any potentially suspicious code in the program, and links the security profile to the application program. Nevertheless, the precise facts present in the Applicant's pending matter are markedly different from the material facts in Finjan v. Blue Coat Systems regarding Finjan's holdings. In Finjan, the claims were held patent eligible because the court concluded that the claimed method recites specific steps that accomplish a result that realizes an improvement in computer functionality. In particular, in Finjan the method generates a security profile that identifies both hostile and potentially hostile operations, and can protect the user against both previously unknown viruses and "obfuscated code." This is an improvement in Finjan over traditional virus scanning, which only recognized the presence of previously-identified viruses. The method in Finjan also enables more flexible virus filtering and greater user customization. Contrasted thereto, the Applicant's alleged invention involves real-time modification of risk models based on feature stability, and the claims presented in the Applicant's currently pending application do not make any "non-abstract improvement to computer technology", do not recite specific steps that accomplish a result that realizes an improvement in computer functionality, are not an improvement over traditional technology, but rather recite concepts similar to previously identified abstract ideas as fully analyzed and presented above under the 35 U.S.C. § 101 heading, contrary to the Applicant's above-argued assertions, the Office maintains that the relevant substantive facts in the instant pending Application are materially dissimilar to the facts in Finjan v. Blue Coat Systems. Consequently, the Office concludes that the legal holdings of Finjan v. Blue Coat Systems can not properly be applied to the Applicant's pending matter to support any finding of eligibility under Step 2A or 2B. The Applicant is encouraged to please see and refer to the current rejection based upon the currently pending claims under the 35 U.S.C. § 101 heading above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
USPGPub No. US 20150278405 A1 by ANDERSEN; Kim Emil et al. discloses METHOD FOR EVALUATING A PERFORMANCE PREDICTION FOR A WIND FARM.
USPAT No. US 10984423 B2 to Adjaoute; Akli discloses Method of operating artificial intelligence machines to improve predictive model training and performance.
USPGPub No. US 20180350006 A1 by Agrawal; Shubham et al. discloses System, Method, and Apparatus for Self-Adaptive Scoring to Detect Misuse or Abuse of Commercial Cards.
USPGPub No. US 20190102718 A1 by Agrawal; Vikas et al. discloses TECHNIQUES FOR AUTOMATED SIGNAL AND ANOMALY DETECTION.
USPAT No. US 10902428 B1 to Amram; Shay et al. discloses Maintaining a risk model using feedback directed to other risk models.
USPGPub No. US 20210264318 A1 by BUTVINIK; Danny et al. discloses COMPUTERIZED-SYSTEM AND METHOD FOR GENERATING A REDUCED SIZE SUPERIOR LABELED TRAINING DATASET FOR A HIGH-ACCURACY MACHINE LEARNING CLASSIFICATION MODEL FOR EXTREME CLASS IMBALANCE OF INSTANCES.
USPGPub No. US 20190362245 A1 by Buda; Teodora et al. discloses ANOMALY DETECTION.
USPGPub No. US 20080183638 A1 by CHIGIRINSKIY; Michael et al. discloses METHOD AND SYSTEM FOR MULTIPLE PORTFOLIO OPTIMIZATION.
USPGPub No. US 20160344762 A1 by CYZE M J et al. discloses Method for aggregating and ranking security event and alert data, involves applying function to subset of set of alerts to compute aggregate risk score, where function based factor, and prioritizing aggregate risk score in risk score list.
USPGPub No. US 20200005172 A1 by Cai; Fan et al. discloses SYSTEM AND METHOD FOR GENERATING MULTI-FACTOR FEATURE EXTRACTION FOR MODELING AND REASONING.
USPAT No. US 10460320 B1 to Cao; Bokai et al. discloses Fraud detection in heterogeneous information networks.
USPGPub No. US 20210158193 A1 by Davis; Sashka T. et al. discloses Interpretable Supervised Anomaly Detection for Determining Reasons for Unsupervised Anomaly Decision.
USPGPub No. US 20210065191 A1 by De Shetler; Natalie et al. discloses MACHINE LEARNING-BASED DETERMINATION OF LIMITS ON MERCHANT USE OF A THIRD PARTY PAYMENTS SYSTEM.
USPGPub No. US 20190128686 A1 by EPPERLEIN; Jonathan et al. discloses ASSESSING PERSONALIZED RISK FOR A USER ON A JOURNEY.
USPGPub No. US 20200184488 A1 by Ebel; Lior et al. discloses FRAMEWORK FOR GENERATING RISK EVALUATION MODELS.
USPGPub No. US 20150317337 A1 by Edgar; Marc Thomas discloses Systems and Methods for Identifying and Driving Actionable Insights from Data.
USPGPub No. US 20190325524 A1 by Gebara; Fadi et al. discloses TECHNIQUES FOR ACCURATE EVALUATION OF A FINANCIAL PORTFOLIO.
USPAT No. US 11373189 B2 to Gorelik; Boris et al. discloses Self-learning online multi-layer method for unsupervised risk assessment.
USPGPub No. US 20150127595 A1 by Hawkins, II; Jeffrey C. et al. discloses MODELING AND DETECTION OF ANOMALY BASED ON PREDICTION.
USPGPub No. US 20160253672 A1 by Hunter; Sean et al. discloses SYSTEM AND METHODS FOR DETECTING FRAUDULENT TRANSACTIONS.
USPGPub No. US 20070005479 A1 by Ikeda; Yuichi et al. discloses Enterprise portfolio simulation system.
USPGPub No. US 20200134628 A1 by JIA; Yuting et al. discloses MACHINE LEARNING SYSTEM FOR TAKING CONTROL ACTIONS.
USPGPub No. US 20190164015 A1 by Jones, JR.; Stuart et al. discloses MACHINE LEARNING TECHNIQUES FOR EVALUATING ENTITIES.
USPGPub No. US 20200234305 A1 by KNUTSSON; Albert et al. discloses IMPROVED DETECTION OF FRAUDULENT TRANSACTIONS.
USPGPub No. US 20180107944 A1 by Lin; Ying et al. discloses Processing Machine Learning Attributes.
USPGPub No. US 20140279384 A1 by Loevenich; Reinhold discloses MONITORING FINANCIAL RISKS USING A QUANTITY LEDGER.
USPGPub No. US 20190139144 A1 by MISHRA; BHUBANESWAR et al. discloses SYSTEM, METHOD AND COMPUTER-ACCESSIBLE MEDIUM FOR EFFICIENT SIMULATION OF FINANCIAL STRESS TESTING SCENARIOS WITH SUPPES-BAYES CAUSAL NETWORKS.
USPGPub No. US 20120066125 A1 by Ma; Jianjie et al. discloses COMPUTER-BASED COLLECTIVE INTELLIGENCE RECOMMENDATIONS FOR TRANSACTION REVIEW.
USPGPub No. US 20210133357 A1 by Machani; Salah E. et al. discloses Privacy Preserving Centralized Evaluation of Sensitive User Features for Anomaly Detection.
USPGPub No. US 20200143376 A1 by Manoharan; Srinivasan et al. discloses SYSTEMS AND METHODS FOR PROVIDING CONCURRENT DATA LOADING AND RULES EXECUTION IN RISK EVALUATIONS.
USPGPub No. US 20160189041 A1 by Moghtaderi; Azadeh et al. discloses ANOMALY DETECTION FOR NON-STATIONARY DATA.
USPGPub No. US 20180060839 A1 by Murali; Ashwath discloses SYSTEMS AND METHODS FOR PREDICTING CHARGEBACK STAGES.
USPAT No. US 11468383 B1 to Nair; Vijayan Narayana et al. discloses Model validation of credit risk.
USPGPub No. US 20210383407 A1 by PATI; Debabrata et al. discloses PROBABILISTIC FEATURE ENGINEERING TECHNIQUE FOR ANOMALY DETECTION.
USPGPub No. US 20110289017 A1 by RENSHAW A discloses Computer-implemented method for computing and reporting constituent asset holdings of asynchronous risk model return portfolio for constructing exchange traded/mutual fund in e.g. server, involves outputting asset holdings.
USPAT No. US 8533089 B1 to RENSHAW A discloses Computer based method for constructing factor index of portfolio weights, involves determining security weights for factor index, and outputting factor index weights as electronic output by programmed computer.
USPGPub No. US 20130332391 A1 by RENSHAW A A discloses Computer based method for constructing factor index of portfolio weights for security, involves determining weights of each security for factor index, so that tracking error is less than prescribed amount.
USPGPub No. US 20180260904 A1 by RENSHAW A A discloses Method for constructing factor index of portfolio weights that replicate returns associated with factor, involves performing optimization by determining weights, and outputting factor index weights as electronic output displayed on display.
USPAT No. US 11037236 B1 to Ram; Siddharth et al. discloses Algorithm and models for creditworthiness based on user entered data within financial management application.
USPGPub No. US 20110289017 A1 by Renshaw; Anthony discloses Systems and Methods for Asynchronous Risk Model Return Portfolios.
USPGPub No. US 20140108295 A1 by Renshaw; Anthony A. discloses Methods and Apparatus for Generating Purified Minimum Risk Portfolios.
USPGPub No. US 20210342847 A1 by SHACHAR; Amir et al. discloses ARTIFICIAL INTELLIGENCE SYSTEM FOR ANOMALY DETECTION IN TRANSACTION DATA SETS.
USPGPub No. US 20170006135 A1 by SIEBEL; THOMAS M. et al. discloses SYSTEMS, METHODS, AND DEVICES FOR AN ENTERPRISE INTERNET-OF-THINGS APPLICATION DEVELOPMENT PLATFORM.
USPGPub No. US 20200089558 A1 by STANKEVICHUS; Aleksey Alekseevich discloses METHOD OF DETERMINING POTENTIAL ANOMALY OF MEMORY DEVICE.
USPGPub No. US 20190197549 A1 by Sharma; Nitin Satyanarayan discloses ROBUST FEATURES GENERATION ARCHITECTURE FOR FRAUD MODELING.
USPGPub No. US 20190197550 A1 by Sharma; Nitin Satyanarayan discloses GENERIC LEARNING ARCHITECTURE FOR ROBUST TEMPORAL AND DOMAIN-BASED TRANSFER LEARNING.
USPGPub No. US 20190066110 A1 by Shen; Shiwen et al. discloses CONVOLUTIONAL NEURAL NETWORKS FOR VARIABLE PREDICTION USING RAW DATA.
USPGPub No. US 20190066130 A1 by Shen; Shiwen et al. discloses UNIFIED ARTIFICIAL INTELLIGENCE MODEL FOR MULTIPLE CUSTOMER VALUE VARIABLE PREDICTION.
USPGPub No. US 20180276541 A1 by Studnitzer; Ari et al. discloses DEEP LEARNING FOR CREDIT CONTROLS.
USPGPub No. US 20190259095 A1 by Templeton; Andrew discloses DETERMINING PRESENT AND FUTURE VIRTUAL BALANCES FOR A CLIENT COMPUTING DEVICE.
USPGPub No. US 20200349169 A1 by VENKATESAN; Mahesh et al. discloses ARTIFICIAL INTELLIGENCE (AI) BASED AUTOMATIC DATA REMEDIATION.
USPGPub No. US 20190294786 A1 by Villavicencio; Francisco et al. discloses Intelligent Security Risk Assessment.
USPGPub No. US 20180268506 A1 by Wodetzki; Jamie et al. discloses MACHINE EVALUATION OF CONTRACT TERMS.
USPGPub No. US 20200007564 A1 by Xie; Zhen et al. discloses FRAUD DETECTION BASED ON ANALYSIS OF FREQUENCY-DOMAIN DATA.
USPAT No. US 11593773 B1 to Yip; Timothy et al. discloses Payment transaction authentication system and method.
USPGPub No. US 20200402058 A1 by ZHOU; Yan et al. discloses SYSTEMS AND METHODS FOR REAL-TIME PROCESSING OF DATA STREAMS.
USPGPub No. US 20200118136 A1 by Zhang; Xiaoying et al. discloses SYSTEMS AND METHODS FOR MONITORING MACHINE LEARNING SYSTEMS.
USPGPub No. US 20200175421 A1 by Zhou; Keguo discloses MACHINE LEARNING METHODS FOR DETECTION OF FRAUD-RELATED EVENTS.
USPGPub No. US 20200118135 A1 by Zhou; Xianzhe et al. discloses SYSTEMS AND METHODS FOR MONITORING MACHINE LEARNING SYSTEMS.
USPGPub No. US 20200327549 A1 by Zhou; Yanzan et al. discloses Robust and Adaptive Artificial Intelligence Modeling.
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Sincerely,
/SLADE E SMITH/Primary Examiner, Art Unit 3696 01/20/2026