DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the Application filed on 7/21/2023. Claims 1-20 are pending in the case. Claims 1, 10, and 19 are independent claims.
Claim Rejections - 35 U.S.C. § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2, 8-11, and 17-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claim 1:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “performing a data preprocessing and a data sampling on the training dataset to obtain a processed and sampled dataset” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “obtaining a plurality of features from the processed and sampled dataset using a feature engineering operation” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “selecting a subset of features from the plurality of features [], wherein [] select the subset of features based on an optimal combination of the plurality of features for the fraud detection ML task” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “iteratively selecting from the plurality of detection rules for a subset of detection rules” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “[selecting] based on a calculated false positive ratio and a calculated detection rate for each of the plurality of detection rules when processing the training dataset” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Yes, the limitation “evaluating a rule performance of each rule in the subset of detection rules” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform rule creation operations” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “accessing a training dataset for determining a plurality of detection rules for a fraud detection ML task of an ML engine” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
No, the limitation “[selecting] using simulated annealing operations for the automated feature selection” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “[selecting] using simulated annealing operations for the automated feature selection” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “generating the plurality of detection rules using the subset of features and at least one decision tree training ML operation” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “generating the plurality of detection rules using the subset of features and at least one decision tree training ML operation” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform rule creation operations” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
No, the limitation “accessing a training dataset for determining a plurality of detection rules for a fraud detection ML task of an ML engine” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).
No, the limitation “[selecting] using simulated annealing operations for the automated feature selection” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “[selecting] using simulated annealing operations for the automated feature selection” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “generating the plurality of detection rules using the subset of features and at least one decision tree training ML operation” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “generating the plurality of detection rules using the subset of features and at least one decision tree training ML operation” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 2:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
The analysis of the parent claim is incorporated.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “providing the subset of detection rules with the rule performance to a fraud detection ML system, wherein the fraud detection ML system implements the subset of detection rules for ML alert generation for fraud detection by the fraud detection ML system” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “providing the subset of detection rules with the rule performance to a fraud detection ML system, wherein the fraud detection ML system implements the subset of detection rules for ML alert generation for fraud detection by the fraud detection ML system” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “providing the subset of detection rules with the rule performance to a fraud detection ML system, wherein the fraud detection ML system implements the subset of detection rules for ML alert generation for fraud detection by the fraud detection ML system” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “providing the subset of detection rules with the rule performance to a fraud detection ML system, wherein the fraud detection ML system implements the subset of detection rules for ML alert generation for fraud detection by the fraud detection ML system” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 8:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “calculating, for each of the plurality of detection rules, a detection rate and a false positive ratio based on a portion of the training dataset corresponding to each of the plurality of detection rules” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Yes, the limitation “discarding a corresponding rule from the selecting when at least one of the detection rate and the false positive ratio fails to meet a corresponding threshold” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “measuring a total measured detection rate for selected detection rules from the plurality of detection rules during a first iteration of the iteratively selecting” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “based on the total measured detection rate, selecting the selected detection rules for the subset of detection rules if the total measured detection rate meets or exceeds a detection rate threshold” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “performing a second iteration of the iteratively selecting using undetected data samples from the training dataset with matching features from the plurality of features if the total measured detection rate does not meet or exceed the detection rate threshold” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 9:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “wherein each rule of the plurality of detection rules comprises at least one of the plurality of features and at least one of an operator, a threshold, or a condition that separates two or more of the plurality of features with corresponding operators and thresholds” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “wherein the subset of detection rules are selected to maximize a detection rate while minimizing a false positive ratio for alert detection using different subsets of the plurality of detection rules” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 10:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “performing a data preprocessing and a data sampling on the training dataset to obtain a processed and sampled dataset” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “obtaining a plurality of features from the processed and sampled dataset using a feature engineering operation” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “selecting a subset of features from the plurality of features [], wherein [] select the subset of features based on an optimal combination of the plurality of features for the fraud detection ML task” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “iteratively selecting from the plurality of detection rules for a subset of detection rules” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “[selecting] based on a calculated false positive ratio and a calculated detection rate for each of the plurality of detection rules when processing the training dataset” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Yes, the limitation “evaluating a rule performance of each rule in the subset of detection rules” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “accessing a training dataset for determining a plurality of detection rules for a fraud detection ML task of an ML engine” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
No, the limitation “[selecting] using simulated annealing operations for the automated feature selection” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “[selecting] using simulated annealing operations for the automated feature selection” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “generating the plurality of detection rules using the subset of features and at least one decision tree training ML operation” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “generating the plurality of detection rules using the subset of features and at least one decision tree training ML operation” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “accessing a training dataset for determining a plurality of detection rules for a fraud detection ML task of an ML engine” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).
No, the limitation “[selecting] using simulated annealing operations for the automated feature selection” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “[selecting] using simulated annealing operations for the automated feature selection” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “generating the plurality of detection rules using the subset of features and at least one decision tree training ML operation” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “generating the plurality of detection rules using the subset of features and at least one decision tree training ML operation” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 11:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
The analysis of the parent claim is incorporated.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “providing the subset of detection rules with the rule performance to a fraud detection ML system, wherein the fraud detection ML system implements the subset of detection rules for ML alert generation for fraud detection by the fraud detection ML system” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “providing the subset of detection rules with the rule performance to a fraud detection ML system, wherein the fraud detection ML system implements the subset of detection rules for ML alert generation for fraud detection by the fraud detection ML system” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “providing the subset of detection rules with the rule performance to a fraud detection ML system, wherein the fraud detection ML system implements the subset of detection rules for ML alert generation for fraud detection by the fraud detection ML system” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “providing the subset of detection rules with the rule performance to a fraud detection ML system, wherein the fraud detection ML system implements the subset of detection rules for ML alert generation for fraud detection by the fraud detection ML system” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 17:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “calculating, for each of the plurality of detection rules, a detection rate and a false positive ratio based on a portion of the training dataset corresponding to each of the plurality of detection rules” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Yes, the limitation “discarding a corresponding rule from the selecting when at least one of the detection rate and the false positive ratio fails to meet a corresponding threshold” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “measuring a total measured detection rate for selected detection rules from the plurality of detection rules during a first iteration of the iteratively selecting” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “based on the total measured detection rate, selecting the selected detection rules for the subset of detection rules if the total measured detection rate meets or exceeds a detection rate threshold” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “performing a second iteration of the iteratively selecting using undetected data samples from the training dataset with matching features from the plurality of features if the total measured detection rate does not meet or exceed the detection rate threshold” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 18:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “wherein each rule of the plurality of detection rules comprises at least one of the plurality of features and at least one of an operator, a threshold, or a condition that separates two or more of the plurality of features with corresponding operators and thresholds” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “wherein the subset of detection rules are selected to maximize a detection rate while minimizing a false positive ratio for alert detection using different subsets of the plurality of detection rules” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 19:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a manufacture.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “performing a data preprocessing and a data sampling on the training dataset to obtain a processed and sampled dataset” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “obtaining a plurality of features from the processed and sampled dataset using a feature engineering operation” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “selecting a subset of features from the plurality of features [], wherein [] select the subset of features based on an optimal combination of the plurality of features for the fraud detection ML task” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “iteratively selecting from the plurality of detection rules for a subset of detection rules” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Yes, the limitation “[selecting] based on a calculated false positive ratio and a calculated detection rate for each of the plurality of detection rules when processing the training dataset” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Yes, the limitation “evaluating a rule performance of each rule in the subset of detection rules” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “non-transitory computer-readable medium having stored thereon computer-readable instructions” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “accessing a training dataset for determining a plurality of detection rules for a fraud detection ML task of an ML engine” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
No, the limitation “[selecting] using simulated annealing operations for the automated feature selection” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “[selecting] using simulated annealing operations for the automated feature selection” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “generating the plurality of detection rules using the subset of features and at least one decision tree training ML operation” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “generating the plurality of detection rules using the subset of features and at least one decision tree training ML operation” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “non-transitory computer-readable medium having stored thereon computer-readable instructions” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
No, the limitation “accessing a training dataset for determining a plurality of detection rules for a fraud detection ML task of an ML engine” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).
No, the limitation “[selecting] using simulated annealing operations for the automated feature selection” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “[selecting] using simulated annealing operations for the automated feature selection” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “generating the plurality of detection rules using the subset of features and at least one decision tree training ML operation” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “generating the plurality of detection rules using the subset of features and at least one decision tree training ML operation” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 20:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a manufacture.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
The analysis of the parent claim is incorporated.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “providing the subset of detection rules with the rule performance to a fraud detection ML system, wherein the fraud detection ML system implements the subset of detection rules for ML alert generation for fraud detection by the fraud detection ML system” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “providing the subset of detection rules with the rule performance to a fraud detection ML system, wherein the fraud detection ML system implements the subset of detection rules for ML alert generation for fraud detection by the fraud detection ML system” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “providing the subset of detection rules with the rule performance to a fraud detection ML system, wherein the fraud detection ML system implements the subset of detection rules for ML alert generation for fraud detection by the fraud detection ML system” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “providing the subset of detection rules with the rule performance to a fraud detection ML system, wherein the fraud detection ML system implements the subset of detection rules for ML alert generation for fraud detection by the fraud detection ML system” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
Claim Rejections - 35 U.S.C. § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. §§ 102 and 103 (or as subject to pre-AIA 35 U.S.C. §§ 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Claims 1-2, 9-11, and 18-20 are rejected under 35 U.S.C. § 103 as being unpatentable over He et al. (US 2025/0021837 A1, hereinafter He) in view of Ma et al. (US 2022/0036200 A1, hereinafter Ma) and Sharma (WO 2019/126585 A1).
As to independent claim 1, He teaches a rule creation system configured to generate machine learning (ML) rules for fraud detection based on an automated feature selection, the rule creation system comprising:
a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform (“The interconnection via system bus 75 allows the central processor 73 to communicate with each subsystem and to control the execution of a plurality of instructions from system memory 72 or the storage device(s) 79 (e.g., a fixed disk, such as a hard drive, or optical disk),” paragraph 0138 lines 12-17) rule creation operations which comprise:
accessing a training dataset (“the model can be trained on a training platform to create the model and to test various aspects of the model using training data and validation data,” paragraph 0002 lines 2-4) for determining a plurality of detection rules for a fraud detection ML task of an ML engine (“The resource security system 1000 may implement access rules to identify fraudulent access requests based on parameters of the access request. Such parameter may correspond to fields (nodes) of a data structure that is used to distinguish fraudulent access requests from authentic access requests,” paragraph 0127 lines 1-6);
selecting a subset of features from the plurality of features using simulated annealing operations for the automated feature selection, wherein the simulated annealing operations select the subset of features based on an optimal combination of the plurality of features for the fraud detection ML task (“a Simulated Annealing Heuristic Searching algorithm can be executed to simulate feature combinations of the model and identify an optimal combination of settings of the model for increased model performance,” paragraph 0035 lines 9-13); and
generating the plurality of detection rules using the subset of features [] (“The resource security system 1000 may implement access rules to identify fraudulent access requests based on parameters of the access request. Such parameter may correspond to fields (nodes) of a data structure that is used to distinguish fraudulent access requests from authentic access requests,” paragraph 0127 lines 1-6).
He does not appear to expressly teach a system comprising operations which comprise:
generating the plurality of detection rules using [] at least one decision tree training ML operation;
iteratively selecting from the plurality of detection rules for a subset of detection rules based on a calculated false positive ratio and a calculated detection rate for each of the plurality of detection rules when processing the training dataset; and
evaluating a rule performance of each rule in the subset of detection rules.
Ma teaches a system comprising operations which comprise:
generating the plurality of detection rules using [] at least one decision tree training ML operation (“These inner circle problems can then be fed into a deterministic decision tree to generate rules that are combined with domain-specific rules to provide understandable supporting evidence,” paragraph 0012 lines 11-14);
iteratively (“If there are more sets of data to process, then decision 480 branches to the ‘yes’ branch which loops back to step 410 to select and process the next set of data as described above,” paragraph 0041 lines 2-5) selecting from the plurality of detection rules for a subset of detection rules based on a calculated false positive ratio and a calculated detection rate for each of the plurality of detection rules when processing the training dataset (“During model training and building phase, the approach starts with machine learning algorithms from a trained system to find initial confidence scores. Data resulting in higher confidence scores as well as those that match behavior patterns are identified as inner circle candidates,” paragraph 0012 lines 6-11); and
evaluating a rule performance of each rule in the subset of detection rules (“During the scoring phase, each entity, such as customers, is processed by both the AI/ML process as well as the rule-based (decision tree) model. A different ensemble score can be generated using either a sequential process approach or a weighted approach,” paragraph 0012 lines 14-18).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the rule creation of He to comprise the rule selection of Ma. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely selecting rules (“During model training and building phase, the approach starts with machine learning algorithms from a trained system to find initial confidence scores. Data resulting in higher confidence scores as well as those that match behavior patterns are identified as inner circle candidates,” Ma paragraph 0012 lines 6-11). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
He/Ma does not appear to expressly teach a system comprising operations which comprise:
performing a data preprocessing and a data sampling on the training dataset to obtain a processed and sampled dataset; and
obtaining a plurality of features from the processed and sampled dataset using a feature engineering operation.
Sharma teaches a system comprising operations which comprise:
performing a data preprocessing and a data sampling on the training dataset to obtain a processed and sampled dataset (“Each feature selection algorithm may rank (or score) the candidate features according to a set of criteria associated with the feature selection algorithm. As such, the candidate features may be ranked (or scored) differently according to the different feature selection algorithms. The set of dominative features may then be determined by analyzing the different rankings (or scores) of the potential features. The set of dominative features may include only a portion, but not all, of the candidate features that are related to or associated with an electronic transaction,” paragraph 00016 lines 2-8); and
obtaining a plurality of features from the processed and sampled dataset using a feature engineering operation (“The set of dominative features may then be compressed (or reduced) into a number of representations, wherein the number of representations is fewer than the set of dominative features,” paragraph 00017 lines 7-9).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the rule creation of He/Ma to comprise the feature engineering of Sharma. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely “compress[ing] the input variables into fewer numbers of representations” (Sharma paragraph 0020 lines 1-2). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
As to dependent claim 2, the rejection of claim 1 is incorporated. He/Ma/Sharma further teaches a system wherein the rule creation operations further comprise providing the subset of detection rules with the rule performance to a fraud detection ML system, wherein the fraud detection ML system implements the subset of detection rules for ML alert generation for fraud detection by the fraud detection ML system (“the training platform 302 and the inference platform 304 can perform model onboarding 310A-B by performing an onboarding process 312. The onboarding process 312 can include a series of steps to migrate the model from the training platform 302 to the inference platform 304,” He paragraph 59 lines 1-5).
As to dependent claim 9, the rejection of claim 1 is incorporated. He/Ma/Sharma further teaches a system wherein each rule of the plurality of detection rules comprises at least one of the plurality of features and at least one of an operator, a threshold, or a condition that separates two or more of the plurality of features with corresponding operators and thresholds (“The resource security system 1000 may implement access rules to identify fraudulent access requests based on parameters of the access request. Such parameter may correspond to fields (nodes) of a data structure that is used to distinguish fraudulent access requests from authentic access requests,” He paragraph 0127 lines 1-6), and wherein the subset of detection rules are selected to maximize a detection rate while minimizing a false positive ratio for alert detection using different subsets of the plurality of detection rules (“During the scoring phase, each entity, such as customers, is processed by both the AI/ML process as well as the rule-based (decision tree) model. A different ensemble score can be generated using either a sequential process approach or a weighted approach,” Ma paragraph 0012 lines 14-18).
As to independent claim 10, He teaches a method to generate machine learning (ML) rules for fraud detection based on an automated feature selection using a rule creation system, which method comprises:
accessing a training dataset (“the model can be trained on a training platform to create the model and to test various aspects of the model using training data and validation data,” paragraph 0002 lines 2-4) for determining a plurality of detection rules for a fraud detection ML task of an ML engine (“The resource security system 1000 may implement access rules to identify fraudulent access requests based on parameters of the access request. Such parameter may correspond to fields (nodes) of a data structure that is used to distinguish fraudulent access requests from authentic access requests,” paragraph 0127 lines 1-6);
selecting a subset of features from the plurality of features using simulated annealing operations for the automated feature selection, wherein the simulated annealing operations select the subset of features based on an optimal combination of the plurality of features for the fraud detection ML task (“a Simulated Annealing Heuristic Searching algorithm can be executed to simulate feature combinations of the model and identify an optimal combination of settings of the model for increased model performance,” paragraph 0035 lines 9-13); and
generating the plurality of detection rules using the subset of features [] (“The resource security system 1000 may implement access rules to identify fraudulent access requests based on parameters of the access request. Such parameter may correspond to fields (nodes) of a data structure that is used to distinguish fraudulent access requests from authentic access requests,” paragraph 0127 lines 1-6).
He does not appear to expressly teach a method comprising:
generating the plurality of detection rules using [] at least one decision tree training ML operation;
iteratively selecting from the plurality of detection rules for a subset of detection rules based on a calculated false positive ratio and a calculated detection rate for each of the plurality of detection rules when processing the training dataset; and
evaluating a rule performance of each rule in the subset of detection rules.
Ma teaches a method comprising:
generating the plurality of detection rules using [] at least one decision tree training ML operation (“These inner circle problems can then be fed into a deterministic decision tree to generate rules that are combined with domain-specific rules to provide understandable supporting evidence,” paragraph 0012 lines 11-14);
iteratively (“If there are more sets of data to process, then decision 480 branches to the ‘yes’ branch which loops back to step 410 to select and process the next set of data as described above,” paragraph 0041 lines 2-5) selecting from the plurality of detection rules for a subset of detection rules based on a calculated false positive ratio and a calculated detection rate for each of the plurality of detection rules when processing the training dataset (“During model training and building phase, the approach starts with machine learning algorithms from a trained system to find initial confidence scores. Data resulting in higher confidence scores as well as those that match behavior patterns are identified as inner circle candidates,” paragraph 0012 lines 6-11); and
evaluating a rule performance of each rule in the subset of detection rules (“During the scoring phase, each entity, such as customers, is processed by both the AI/ML process as well as the rule-based (decision tree) model. A different ensemble score can be generated using either a sequential process approach or a weighted approach,” paragraph 0012 lines 14-18).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the rule creation of He to comprise the rule selection of Ma. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely selecting rules (“During model training and building phase, the approach starts with machine learning algorithms from a trained system to find initial confidence scores. Data resulting in higher confidence scores as well as those that match behavior patterns are identified as inner circle candidates,” Ma paragraph 0012 lines 6-11). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
He/Ma does not appear to expressly teach a method comprising:
performing a data preprocessing and a data sampling on the training dataset to obtain a processed and sampled dataset; and
obtaining a plurality of features from the processed and sampled dataset using a feature engineering operation.
Sharma teaches a method comprising:
performing a data preprocessing and a data sampling on the training dataset to obtain a processed and sampled dataset (“Each feature selection algorithm may rank (or score) the candidate features according to a set of criteria associated with the feature selection algorithm. As such, the candidate features may be ranked (or scored) differently according to the different feature selection algorithms. The set of dominative features may then be determined by analyzing the different rankings (or scores) of the potential features. The set of dominative features may include only a portion, but not all, of the candidate features that are related to or associated with an electronic transaction,” paragraph 00016 lines 2-8); and
obtaining a plurality of features from the processed and sampled dataset using a feature engineering operation (“The set of dominative features may then be compressed (or reduced) into a number of representations, wherein the number of representations is fewer than the set of dominative features,” paragraph 00017 lines 7-9).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the rule creation of He/Ma to comprise the feature engineering of Sharma. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely “compress[ing] the input variables into fewer numbers of representations” (Sharma paragraph 0020 lines 1-2). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
As to dependent claim 11, the rejection of claim 10 is incorporated. He/Ma/Sharma further teaches a method comprising providing the subset of detection rules with the rule performance to a fraud detection ML system, wherein the fraud detection ML system implements the subset of detection rules for ML alert generation for fraud detection by the fraud detection ML system (“the training platform 302 and the inference platform 304 can perform model onboarding 310A-B by performing an onboarding process 312. The onboarding process 312 can include a series of steps to migrate the model from the training platform 302 to the inference platform 304,” He paragraph 59 lines 1-5).
As to dependent claim 18, the rejection of claim 10 is incorporated. He/Ma/Sharma further teaches a method wherein each rule of the plurality of detection rules comprises at least one of the plurality of features and at least one of an operator, a threshold, or a condition that separates two or more of the plurality of features with corresponding operators and thresholds (“The resource security system 1000 may implement access rules to identify fraudulent access requests based on parameters of the access request. Such parameter may correspond to fields (nodes) of a data structure that is used to distinguish fraudulent access requests from authentic access requests,” He paragraph 0127 lines 1-6), and wherein the subset of detection rules are selected to maximize a detection rate while minimizing a false positive ratio for alert detection using different subsets of the plurality of detection rules (“During the scoring phase, each entity, such as customers, is processed by both the AI/ML process as well as the rule-based (decision tree) model. A different ensemble score can be generated using either a sequential process approach or a weighted approach,” Ma paragraph 0012 lines 14-18).
As to independent claim 19, He teaches a non-transitory computer-readable medium having stored thereon computer-readable instructions executable to generate machine learning (ML) rules for fraud detection based on an automated feature selection using a rule creation system, the computer-readable instructions executable to perform (“The interconnection via system bus 75 allows the central processor 73 to communicate with each subsystem and to control the execution of a plurality of instructions from system memory 72 or the storage device(s) 79 (e.g., a fixed disk, such as a hard drive, or optical disk),” paragraph 0138 lines 12-17) rule creation operations which comprise:
accessing a training dataset (“the model can be trained on a training platform to create the model and to test various aspects of the model using training data and validation data,” paragraph 0002 lines 2-4) for determining a plurality of detection rules for a fraud detection ML task of an ML engine (“The resource security system 1000 may implement access rules to identify fraudulent access requests based on parameters of the access request. Such parameter may correspond to fields (nodes) of a data structure that is used to distinguish fraudulent access requests from authentic access requests,” paragraph 0127 lines 1-6);
selecting a subset of features from the plurality of features using simulated annealing operations for the automated feature selection, wherein the simulated annealing operations select the subset of features based on an optimal combination of the plurality of features for the fraud detection ML task (“a Simulated Annealing Heuristic Searching algorithm can be executed to simulate feature combinations of the model and identify an optimal combination of settings of the model for increased model performance,” paragraph 0035 lines 9-13); and
generating the plurality of detection rules using the subset of features [] (“The resource security system 1000 may implement access rules to identify fraudulent access requests based on parameters of the access request. Such parameter may correspond to fields (nodes) of a data structure that is used to distinguish fraudulent access requests from authentic access requests,” paragraph 0127 lines 1-6).
He does not appear to expressly teach a medium comprising instructions which comprise:
generating the plurality of detection rules using [] at least one decision tree training ML operation;
iteratively selecting from the plurality of detection rules for a subset of detection rules based on a calculated false positive ratio and a calculated detection rate for each of the plurality of detection rules when processing the training dataset; and
evaluating a rule performance of each rule in the subset of detection rules.
Ma teaches a medium comprising instructions which comprise:
generating the plurality of detection rules using [] at least one decision tree training ML operation (“These inner circle problems can then be fed into a deterministic decision tree to generate rules that are combined with domain-specific rules to provide understandable supporting evidence,” paragraph 0012 lines 11-14);
iteratively (“If there are more sets of data to process, then decision 480 branches to the ‘yes’ branch which loops back to step 410 to select and process the next set of data as described above,” paragraph 0041 lines 2-5) selecting from the plurality of detection rules for a subset of detection rules based on a calculated false positive ratio and a calculated detection rate for each of the plurality of detection rules when processing the training dataset (“During model training and building phase, the approach starts with machine learning algorithms from a trained system to find initial confidence scores. Data resulting in higher confidence scores as well as those that match behavior patterns are identified as inner circle candidates,” paragraph 0012 lines 6-11); and
evaluating a rule performance of each rule in the subset of detection rules (“During the scoring phase, each entity, such as customers, is processed by both the AI/ML process as well as the rule-based (decision tree) model. A different ensemble score can be generated using either a sequential process approach or a weighted approach,” paragraph 0012 lines 14-18).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the rule creation of He to comprise the rule selection of Ma. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely selecting rules (“During model training and building phase, the approach starts with machine learning algorithms from a trained system to find initial confidence scores. Data resulting in higher confidence scores as well as those that match behavior patterns are identified as inner circle candidates,” Ma paragraph 0012 lines 6-11). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
He/Ma does not appear to expressly teach a medium comprising instructions which comprise:
performing a data preprocessing and a data sampling on the training dataset to obtain a processed and sampled dataset; and
obtaining a plurality of features from the processed and sampled dataset using a feature engineering operation.
Sharma teaches a medium comprising instructions which comprise:
performing a data preprocessing and a data sampling on the training dataset to obtain a processed and sampled dataset (“Each feature selection algorithm may rank (or score) the candidate features according to a set of criteria associated with the feature selection algorithm. As such, the candidate features may be ranked (or scored) differently according to the different feature selection algorithms. The set of dominative features may then be determined by analyzing the different rankings (or scores) of the potential features. The set of dominative features may include only a portion, but not all, of the candidate features that are related to or associated with an electronic transaction,” paragraph 00016 lines 2-8); and
obtaining a plurality of features from the processed and sampled dataset using a feature engineering operation (“The set of dominative features may then be compressed (or reduced) into a number of representations, wherein the number of representations is fewer than the set of dominative features,” paragraph 00017 lines 7-9).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the rule creation of He/Ma to comprise the feature engineering of Sharma. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely “compress[ing] the input variables into fewer numbers of representations” (Sharma paragraph 0020 lines 1-2). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
As to dependent claim 20, the rejection of claim 19 is incorporated. He/Ma/Sharma further teaches a medium wherein the rule creation operations further comprise providing the subset of detection rules with the rule performance to a fraud detection ML system, wherein the fraud detection ML system implements the subset of detection rules for ML alert generation for fraud detection by the fraud detection ML system (“the training platform 302 and the inference platform 304 can perform model onboarding 310A-B by performing an onboarding process 312. The onboarding process 312 can include a series of steps to migrate the model from the training platform 302 to the inference platform 304,” He paragraph 59 lines 1-5).
Allowable Subject Matter
Claims 3-7 and 12-16 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: The prior art does not teach, suggest, or render obvious, alone or in combination, all of the limitations in the independent claims. The closest prior art found is He et al. (US 2025/0021837 A1), which discloses selecting a subset of features from the plurality of features using simulated annealing operations for the automated feature selection, wherein the simulated annealing operations select the subset of features based on an optimal combination of the plurality of features for the fraud detection ML task; and generating the plurality of detection rules using the subset of features; Ma et al. (US 2022/0036200 A1), which discloses generating the plurality of detection rules using [] at least one decision tree training ML operation; iteratively selecting from the plurality of detection rules for a subset of detection rules based on a calculated false positive ratio and a calculated detection rate for each of the plurality of detection rules when processing the training dataset; and evaluating a rule performance of each rule in the subset of detection rules; and Sharma (WO 2019/126585 A1), which discloses performing a data preprocessing and a data sampling on the training dataset to obtain a processed and sampled dataset; and obtaining a plurality of features from the processed and sampled dataset using a feature engineering operation. However, it is not obvious to combine these references to arrive at the claimed invention because they do not teach or suggest generating an initial random solution to the automated feature selection based on a random combination of the plurality of features and the training dataset; defining a neighbor function to the initial random solution that generates a new solution to the automated feature selection; defining an objective function that evaluates a quality of the initial random solution and the new solution based on a score associated with a harmonic mean of a precision and a recall for a corresponding solution; and defining an initial temperature and a cooling schedule for the initial temperature by a temperature cooling process of the simulated annealing operations. The combination of these features in combination with the rest of the limitations cannot be found in the prior art and no rationale exists to modify prior art in such a manner.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
Osegi et al., “Comparative analysis of credit card fraud detection in Simulated Annealing trained Artificial Neural Network and Hierarchical Temporal Memory,” 15 December 2021, https://doi.org/10.1016/j.mlwa.2021.100080 https://www.sciencedirect.com/science/article/pii/S2666827021000402) disclosing simulated annealing for fraud detection
Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
In the interests of compact prosecution, Applicant is invited to contact the examiner via electronic media pursuant to USPTO policy outlined MPEP § 502.03. All electronic communication must be authorized in writing. Applicant may wish to file an Internet Communications Authorization Form PTO/SB/439. Applicant may wish to request an interview using the Interview Practice website: http://www.uspto.gov/patent/laws-and-regulations/interview-practice.
Applicant is reminded Internet e-mail may not be used for communication for matters under 35 U.S.C. § 132 or which otherwise require a signature. A reply to an Office action may NOT be communicated by Applicant to the USPTO via Internet e-mail. If such a reply is submitted by Applicant via Internet e-mail, a paper copy will be placed in the appropriate patent application file with an indication that the reply is NOT ENTERED. See MPEP § 502.03(II).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ryan Barrett whose telephone number is 571 270 3311. The examiner can normally be reached 9:00am to 5:30pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, Applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Michelle Bechtold can be reached at 571 431 0762. The fax phone number for the organization where this application or proceeding is assigned is 571 273 8300.
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/Ryan Barrett/
Primary Examiner, Art Unit 2148
Prosecution Insights