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 .
Status of Claims
This action is in reply to the amended claims filed on 7/30/2025, wherein:
Claims 12, 21, 26, 31 have been amended;
Claim 1-11, 16-20, 22, 23, 27, 28, 32, 33 have been canceled;
Claims 13-15, 24, 25, 29, 30, 34, and 35 remain as original or previously presented; and
Claims 12-15, 21, 24-26, 29-31, and 34-35 are currently pending and have been examined.
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 12-15, 21, 24-26, 29-31, and 34-35 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a device for analyzing financial transactions to identify anomalous behavior which is considered a judicial exception because it falls under Certain Methods of Organizing Human Activity such as fundamental economic principles or practices, including mitigating risk. This judicial exception is not integrated into a practical application as discussed below and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception as discussed below.
This rejection follows the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed Reg 4, January 7, 2019, pp. 50-57 (“2019 PEG”)(MPEP 2106).
Analysis
Step 1 (Statutory Categories) – 2019 PEG pg. 53 (See MPEP 2106.03)
Claims 12-15, 21, 24-26, 29-31, and 34-35 are directed to the statutory category of a process.
Step 2A, Prong 1 (Do the claims recite an abstract idea?) – 2019 PEG pg. 54 (See MPEP 2106.04(a)-(c))
For independent claims 12, 21, 26, and 31, the claims recite an abstract idea of: analyzing financial transactions to identify anomalous behavior. The steps of independent claim 12 recite the abstract idea (in bold below) of: A compute device comprising: circuitry configured to: obtain a data analysis model, constructed with a user interface provided by the compute device, to be applied to financial transaction data indicative of financial transactions pertaining to a set of financial accounts; apply the data analysis model to the financial transaction data to identify one or more insights indicative of anomalous behavior, wherein to apply the data analysis model to the financial transaction data comprises to identify anomalous behavior within clusters of the financial accounts that have been grouped based on similarity in one or more attributes associated with the financial accounts; wherein to apply the data analysis model to the financial transaction data comprises to perform cohort analysis comprising producing one or more summaries of key performance indicators based on the one or more attributes; and present the one or more insights in the user interface. Independent claims 21, 26, and 31 recite similar steps that recite the abstract idea. Independent claims 12, 21, 26, and 31, as drafted, are a process that, under the broadest reasonable interpretation, covers Certain Methods of Organizing Human Activity, since they recite fundamental economic principles or practices including mitigating risk. If the claim limitations, under the broadest reasonable interpretation, covers methods of organizing human activity but for the recitation of additional elements including generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Other than reciting the abstract idea, the independent claims recite additional elements including generic computer components such as “a compute device comprising circuitry, a data analysis model, and a user interface provided by the compute device”, and nothing in the claims precludes the steps from being performed as a method of organizing human activity. Accordingly, the independent claims recite an abstract idea.
Dependent claims 13-15, 24, 25, 29, 30, 34, and 35 recite similar limitations as independent claims 12, 21, 26, and 31; and when analyzed as a whole are held to be patent ineligible under 35 U.S.C 101 because the additional recited limitations only refine the abstract idea further. Other than reciting the abstract idea, the dependent claims recite similar additional elements including generic computer components as the independent claims, such as “the compute device, the circuitry, and a user interface”. If a claim limitation, under its broadest reasonable interpretation, covers commercial or legal interactions, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
Step 2A, Prong 2 (Does the claim recite additional elements that integrate the judicial exception into a practical application?) – 2019 PEG pg. 54 (See MPEP 2106.04(d)-(c))
This judicial exception is not integrated into a practical application. In particular, independent claims 12, 21, 26, and 31 only recite the additional elements of “a compute device comprising circuitry, a data analysis model, and a user interface provided by the compute device”. A plain reading of the Figures and associated descriptions in the specification reveals that generic processors may be used to execute the claimed steps. The additional elements are recited at a high level of generality (i.e., as a generic processor performing generic computer functions) such that it amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)) and limits the judicial exception to a particular environment (See MPEP 2106.05(h)). Mere instructions to apply an exception using a generic computer component and limiting the judicial exception to a particular environment doesn’t integrate the abstract idea into a practical application in Step 2A. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Hence, independent claims 12, 21, 26, and 31 are directed to an abstract idea.
Dependent claims 13-15, 24, 25, 29, 30, 34, and 35, recite similar additional elements as the independent claims including generic computer components, such as “the compute device, the circuitry, and a user interface”. The judicial exception is not integrated into a practical application because the additional elements in the dependent claims are also recited at a high-level of generality such that it amounts to more no more than mere instructions to apply the exception using generic computer components. Therefore, the additional elements do not integrate the abstract idea into a practical application because they also do not impose any meaningful limits on practicing the abstract idea. Also, the claims do not affect an improvement to another technology or technical field; the claims do not amount to an improvement of the functioning of a computer system itself; the claims do not effect a transformation or reduction of a particular article to a different state or thing; and the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment.
Step 2B (Does the claim recite additional elements that amount to significantly more than the judicial exception?) – 2019 PEG pg. 56 (See MPEP 2106.05)
Independent claims 12, 21, 26, and 31 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the recited additional elements amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)) and limits the judicial exception to the particular environment of computers (See MPEP 2106.05(h)). The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the function of the elements when each is taken alone. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept in Step 2B.
In addition, the dependent claims 13-15, 24, 25, 29, 30, 34, and 35 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the dependent claims to perform the claimed limitations, amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)). Similar to the independent claims, mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Also, for the same reasoning as the independent claims, the additional elements of the limitations of the dependent claims, when considered individually and as an ordered combination, together do not offer significantly more than the sum of the functions of the elements when each is taken alone and the dependent claims as a whole, do not amount to significantly more than the abstract idea itself. For these reasons, the dependent claims also are not patent eligible.
Claim Rejections - 35 USC § 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 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, 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.
Claims 12-15, 21, 24-26, 29-31, 34, and 35 are rejected under 35 U.S.C. 103 as being unpatentable over US 2025/0086636 to Teoh et al (hereinafter referred to as Teoh), in view of US 2023/0281541 to Wellmann et al. (hereinafter referred to as Wellmann) and further in view of US 2023/0409460 to Kakade et al. (hereinafter referred to as Kakade).
In regards to claim 12, Teoh discloses a compute device comprising: circuitry (system for detecting anomalies in mobile payment transactions including a server computer including a processor and a memory coupled to the processor, para. 0002) configured to: obtain a data analysis model (system further includes a three-layer framework including a probabilistic model to generate the preliminary transaction anomaly score, para. 0006), to be applied to financial transaction data indicative of financial transactions pertaining to a set of financial accounts (customer data from an issuer is used to more accurately model behavior for regular transfers and anomalous transfers, para. 0008, fig. 2); apply the data analysis model to the financial transaction data to identify one or more insights (if a data point is far away from its neighbors or the cluster centroids, it will be considered an outlier, given a high anomaly score, and become associated with an anomalous transaction, para. 0071, firs. 7A-B) indicative of anomalous behavior (anomaly detection system that allows for effective detection of fraudulent and anomalous mobile payment fund transfers, para. 0001), wherein to apply the data analysis model to the financial transaction data (FIG. 5 is a flow diagram showing a typical data science flow for a generative probabilistic model on mobile payment transactions, according to at least one aspect of the present disclosure, para. 0069, fig. 5) comprises to identify anomalous behavior (solution further includes using unsupervised statistical techniques to cluster anomalous mobile payments behavior, para. 0057) within clusters of the financial accounts that have been grouped based on similarity in one or more attributes associated with the financial accounts (identifying anomalous behavior in a current mobile payment transaction by clustering the account related attributes or relationship related attributes by an unsupervised statistical algorithm, para. 0085, fig. 18); wherein to apply the data analysis model to the financial transaction data (FIG. 5 is a flow diagram showing a typical data science flow for a generative probabilistic model on mobile payment transactions, according to at least one aspect of the present disclosure, para. 0069, fig. 5) comprises to perform analysis (perform an artificial intelligence (AI)/machine learning (ML) clustering model to determine a mobile payment transaction anomaly score based on account behaviors, para. 0083); and present the one or more insights (generate a final transaction anomaly score, and recommending an action for the current mobile payment transaction based on the final transaction anomaly score, para. 0012). However, Teoh fails to disclose a data analysis model constructed with a user interface provided by the compute device; perform cohort analysis comprising producing one or more summaries of key performance indicators based on the one or more attributes; and present insights in the user interface.
Wellmann, in the related field of insight analysis and reporting, teaches a data analysis model (calculation attributes and rules for executing functional logic may be provided to the insights platform 904 via, input via a user interface, para. 0086) constructed with a user interface (user may enter commands and information through a user interface 811) provided by the compute device (user interface 811 may include a command line interface, a graphical user interface (GUI), natural user interface (NUI), voice command interface, and/or other user interface (UI) presentations, which may be presented as distinct options or may be integrated, para. 0072); and present insights (insights platform may receive user request data including additional input data, requests for reports, and requests for additional output not included in the generated reports, para. 0089) in the user interface (reporting and business intelligence module may cause a display via a user interface, wherein the display may include reports, dashboards, and visualization tools, para. 0097). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the device of Teoh with the ability to receive input from a user interface and present insights in the user interface as taught by the device of Wellmann. The motivation for doing so would have been to receive user input data via a user interface and provide insights based on the output using the model via the user interface (Wellman, paras. 0007-0017).
Kakade, in the related field of optimizing performance by constructing a KPI tree structure to model workflow of a process using data received from a client device, teaches perform cohort analysis (stage may comprise a cohort analyzer automating the identification of cohorts/micro-segments that drive KPI movements using ML techniques such as Decision Trees, para. 0042) comprising producing one or more summaries of key performance indicators based on the one or more attributes (changes in KPI at a node are detected and analyzed to drill-down individual cohorts responsible for change, para. 0045). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the device of Teoh with the ability to identify cohorts that drive KPI movements as taught by the device of Kakade. The motivation for doing so would have been to utilize various AI and ML models and techniques such as segmentation, clustering and random forests to identify behavioral drivers and cohorts e.g., specific groups of accounts, users or transactions that are responsible for the movement of the said KPI (Kakade, paras. 0048).
In regards to claim 13, modified Teoh discloses the compute device of claim 12, and further discloses wherein to identify anomalous behavior within the clusters (identifying anomalous behavior in a current mobile payment transaction by clustering the account related attributes or relationship related attributes by an unsupervised statistical algorithm, para. 0085, fig. 18) comprises to evaluate time series historical data pertaining to the financial accounts (comprehensive data set includes days since a first transaction, days since a last transaction, number of transactions in a previous month, a number of transactions in a previous three months, a number of transactions in a previous six months, a total transaction amount in the previous month, a total transaction amount in the previous three months, and a total transaction amount in the previous six months, para. 0004).
In regards to claim 14, modified Teoh discloses the compute device of claim 13, and further discloses wherein the circuitry (system for detecting anomalies in mobile payment transactions including a server computer including a processor and a memory coupled to the processor, para. 0002) is further configured to determine one or more thresholds in the time series data (transaction amount condition may be a threshold value, para. 0052; identifying transactions that are most likely to be anomalous by scoring each transaction in real-time via a layered framework and comparing to a predefined threshold, para. 0057).
In regards to claim 15, modified Teoh discloses the compute device of claim 13, and further discloses wherein the circuitry is further configured to identify one or more outliers in a set of time series data (if a data point is far away from its neighbors or the cluster centroids, it will be considered an outlier, given a high anomaly score, and become associated with an anomalous transaction, para. 0071, figs. 6A-7B) that has at least a predefined number of data points (On the other hand, if a data point requires a large number of splits to be isolated, then it is likely part of a large cluster and close by to several other transactions, meaning that it will be assigned a low anomaly score, para. 0070, figs. 6A-7B).
In regards to claim 21, Teoh discloses a compute device comprising: circuitry (system for detecting anomalies in mobile payment transactions including a server computer including a processor and a memory coupled to the processor, para. 0002) configured to: obtain a data analysis model (system further includes a three-layer framework including a probabilistic model to generate the preliminary transaction anomaly score, para. 0006), to be applied to financial transaction data indicative of financial transactions pertaining to a set of financial accounts (customer data from an issuer is used to more accurately model behavior for regular transfers and anomalous transfers, para. 0008, fig. 2); apply the data analysis model to the financial transaction data to identify one or more insights (if a data point is far away from its neighbors or the cluster centroids, it will be considered an outlier, given a high anomaly score, and become associated with an anomalous transaction, para. 0071, firs. 7A-B) indicative of anomalous behavior (anomaly detection system that allows for effective detection of fraudulent and anomalous mobile payment fund transfers, para. 0001), wherein to apply the data analysis model to the financial transaction data (FIG. 5 is a flow diagram showing a typical data science flow for a generative probabilistic model on mobile payment transactions, according to at least one aspect of the present disclosure, para. 0069, fig. 5) comprises to evaluate time series historical data of the financial accounts (comprehensive data set includes days since a first transaction, days since a last transaction, number of transactions in a previous month, a number of transactions in a previous three months, a number of transactions in a previous six months, a total transaction amount in the previous month, a total transaction amount in the previous three months, and a total transaction amount in the previous six months, para. 0004) that have been grouped (solution further includes using unsupervised statistical techniques to cluster anomalous mobile payments behavior, para. 0057) based on similarity (identifying anomalous behavior in a current mobile payment transaction by clustering the account related attributes or relationship related attributes by an unsupervised statistical algorithm, para. 0085, fig. 18); wherein to apply the data analysis model to the financial transaction data (FIG. 5 is a flow diagram showing a typical data science flow for a generative probabilistic model on mobile payment transactions, according to at least one aspect of the present disclosure, para. 0069, fig. 5) comprises to perform analysis (perform an artificial intelligence (AI)/machine learning (ML) clustering model to determine a mobile payment transaction anomaly score based on account behaviors, para. 0083) and present the one or more insights (generate a final transaction anomaly score, and recommending an action for the current mobile payment transaction based on the final transaction anomaly score, para. 0012). However, Teoh fails to disclose a data analysis model constructed with a user interface provided by the compute device; perform cohort analysis comprising producing one or more summaries of key performance indicators based on the one or more attributes; and present insights in the user interface.
Wellmann, in the related field of insight analysis and reporting, teaches a data analysis model (calculation attributes and rules for executing functional logic may be provided to the insights platform 904 via, input via a user interface, para. 0086) constructed with a user interface (user may enter commands and information through a user interface 811) provided by the compute device (user interface 811 may include a command line interface, a graphical user interface (GUI), natural user interface (NUI), voice command interface, and/or other user interface (UI) presentations, which may be presented as distinct options or may be integrated, para. 0072); and present insights (insights platform may receive user request data including additional input data, requests for reports, and requests for additional output not included in the generated reports, para. 0089) in the user interface (reporting and business intelligence module may cause a display via a user interface, wherein the display may include reports, dashboards, and visualization tools, para. 0097). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the device of Teoh with the ability to receive input from a user interface and present insights in the user interface as taught by the device of Wellmann. The motivation for doing so would have been to receive user input data via a user interface and provide insights based on the output using the model via the user interface (Wellman, paras. 0007-0017).
Kakade, in the related field of optimizing performance by constructing a KPI tree structure to model workflow of a process using data received from a client device, teaches perform cohort analysis (stage may comprise a cohort analyzer automating the identification of cohorts/micro-segments that drive KPI movements using ML techniques such as Decision Trees, para. 0042) comprising producing one or more summaries of key performance indicators based on the one or more attributes (changes in KPI at a node are detected and analyzed to drill-down individual cohorts responsible for change, para. 0045). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the device of Teoh with the ability to identify cohorts that drive KPI movements as taught by the device of Kakade. The motivation for doing so would have been to utilize various AI and ML models and techniques such as segmentation, clustering and random forests to identify behavioral drivers and cohorts e.g., specific groups of accounts, users or transactions that are responsible for the movement of the said KPI (Kakade, paras. 0048).
In regards to claim 24, modified Teoh discloses the compute device of claim 21, but fails to disclose wherein to present one or more insights in a user interface comprises to present one or more drivers of one or more key performance indicators
Wellmann, in the related field of insight analysis and reporting, teaches wherein to present one or more insights (calculation attributes and rules for executing functional logic may be provided to the insights platform 904 via, input via a user interface, para. 0086) in a user interface (user interface 811 may include a command line interface, a graphical user interface (GUI), natural user interface (NUI), voice command interface, and/or other user interface (UI) presentations, which may be presented as distinct options or may be integrated, para. 0072) comprises to present one or more drivers of one or more key performance indicators (natural language narratives component 324 may generate summaries in coherent text explaining key drivers or factors that underly a given output metric, based on data output by the continuous monitoring and insights component 321, para. 0053). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the device of Teoh with the ability to drivers of performance indicators in the user interface as taught by the device of Wellmann. The motivation for doing so would have been to provide classifications enabling transparency of data and explainability of key drivers and factors related to risk and loss for reporting (Wellman, paras. 00627).
In regards to claim 25, modified Teoh discloses the compute device of claim 21, but fails to disclose wherein to present one or more insights in a user interface comprises to enable drill down into financial data underlying an insight.
Wellmann, in the related field of insight analysis and reporting, teaches wherein to present one or more insights (calculation attributes and rules for executing functional logic may be provided to the insights platform 904 via, input via a user interface, para. 0086) in a user interface (user interface 811 may include a command line interface, a graphical user interface (GUI), natural user interface (NUI), voice command interface, and/or other user interface (UI) presentations, which may be presented as distinct options or may be integrated, para. 0072); comprises to enable (insights platform may receive user request data including additional input data, requests for reports, and requests for additional output not included in the generated reports, para. 0089) drill down into financial data underlying an insight (explainability engine component 323 may be configured to provide an understanding of how a machine learning model makes decisions, which may include, e.g., composition of data and intelligent drill-down capabilities, para. 0052). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the device of Teoh with the ability to drill down into financial data underlying an insight in the user interface as taught by the device of Wellmann. The motivation for doing so would have been to provide insights based on the output using the model via the user interface (Wellman, paras. 0007-0017).
In regards to claim 26, Teoh discloses a compute device comprising: circuitry (system for detecting anomalies in mobile payment transactions including a server computer including a processor and a memory coupled to the processor, para. 0002) configured to: obtain a data analysis model (system further includes a three-layer framework including a probabilistic model to generate the preliminary transaction anomaly score, para. 0006), to be applied to financial transaction data indicative of financial transactions pertaining to a set of financial accounts (customer data from an issuer is used to more accurately model behavior for regular transfers and anomalous transfers, para. 0008, fig. 2); apply the data analysis model to the financial transaction data to identify one or more insights (if a data point is far away from its neighbors or the cluster centroids, it will be considered an outlier, given a high anomaly score, and become associated with an anomalous transaction, para. 0071, firs. 7A-B) indicative of anomalous behavior (anomaly detection system that allows for effective detection of fraudulent and anomalous mobile payment fund transfers, para. 0001), wherein to apply the data analysis model to the financial transaction data (FIG. 5 is a flow diagram showing a typical data science flow for a generative probabilistic model on mobile payment transactions, according to at least one aspect of the present disclosure, para. 0069, fig. 5) comprises to identify anomalous behavior (solution further includes using unsupervised statistical techniques to cluster anomalous mobile payments behavior, para. 0057) within clusters of the financial accounts that have been grouped based on similarity in one or more attributes (identifying anomalous behavior in a current mobile payment transaction by clustering the account related attributes or relationship related attributes by an unsupervised statistical algorithm, para. 0085, fig. 18) comprises to evaluate time series historical data pertaining to the financial accounts (comprehensive data set includes days since a first transaction, days since a last transaction, number of transactions in a previous month, a number of transactions in a previous three months, a number of transactions in a previous six months, a total transaction amount in the previous month, a total transaction amount in the previous three months, and a total transaction amount in the previous six months, para. 0004); determine one or more thresholds in the time series data (transaction amount condition may be a threshold value, para. 0052; identifying transactions that are most likely to be anomalous by scoring each transaction in real-time via a layered framework and comparing to a predefined threshold, para. 0057); wherein to apply the data analysis model to the financial transaction data (FIG. 5 is a flow diagram showing a typical data science flow for a generative probabilistic model on mobile payment transactions, according to at least one aspect of the present disclosure, para. 0069, fig. 5) comprises to perform analysis (perform an artificial intelligence (AI)/machine learning (ML) clustering model to determine a mobile payment transaction anomaly score based on account behaviors, para. 0083) and present the one or more insights (generate a final transaction anomaly score, and recommending an action for the current mobile payment transaction based on the final transaction anomaly score, para. 0012). However, Teoh fails to disclose a data analysis model constructed with a user interface provided by the compute device; perform cohort analysis comprising producing one or more summaries of key performance indicators based on the one or more attributes; and present insights in the user interface.
Wellmann, in the related field of insight analysis and reporting, teaches a data analysis model (calculation attributes and rules for executing functional logic may be provided to the insights platform 904 via, input via a user interface, para. 0086) constructed with a user interface (user may enter commands and information through a user interface 811) provided by the compute device (user interface 811 may include a command line interface, a graphical user interface (GUI), natural user interface (NUI), voice command interface, and/or other user interface (UI) presentations, which may be presented as distinct options or may be integrated, para. 0072); and present insights (insights platform may receive user request data including additional input data, requests for reports, and requests for additional output not included in the generated reports, para. 0089) in the user interface (reporting and business intelligence module may cause a display via a user interface, wherein the display may include reports, dashboards, and visualization tools, para. 0097). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the device of Teoh with the ability to receive input from a user interface and present insights in the user interface as taught by the device of Wellmann. The motivation for doing so would have been to receive user input data via a user interface and provide insights based on the output using the model via the user interface (Wellman, paras. 0007-0017).
Kakade, in the related field of optimizing performance by constructing a KPI tree structure to model workflow of a process using data received from a client device, teaches perform cohort analysis (stage may comprise a cohort analyzer automating the identification of cohorts/micro-segments that drive KPI movements using ML techniques such as Decision Trees, para. 0042) comprising producing one or more summaries of key performance indicators based on the one or more attributes (changes in KPI at a node are detected and analyzed to drill-down individual cohorts responsible for change, para. 0045). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the device of Teoh with the ability to identify cohorts that drive KPI movements as taught by the device of Kakade. The motivation for doing so would have been to utilize various AI and ML models and techniques such as segmentation, clustering and random forests to identify behavioral drivers and cohorts e.g., specific groups of accounts, users or transactions that are responsible for the movement of the said KPI (Kakade, paras. 0048).
In regards to claim 29, modified Teoh discloses the compute device of claim 26, but fails to disclose wherein to present one or more insights in a user interface comprises to present one or more drivers of one or more key performance indicators.
Wellmann, in the related field of insight analysis and reporting, teaches wherein to present one or more insights (calculation attributes and rules for executing functional logic may be provided to the insights platform 904 via, input via a user interface, para. 0086) in a user interface (user interface 811 may include a command line interface, a graphical user interface (GUI), natural user interface (NUI), voice command interface, and/or other user interface (UI) presentations, which may be presented as distinct options or may be integrated, para. 0072) comprises to present one or more drivers of one or more key performance indicators (natural language narratives component 324 may generate summaries in coherent text explaining key drivers or factors that underly a given output metric, based on data output by the continuous monitoring and insights component 321, para. 0053). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the device of Teoh with the ability to drivers of performance indicators in the user interface as taught by the device of Wellmann. The motivation for doing so would have been to provide classifications enabling transparency of data and explainability of key drivers and factors related to risk and loss for reporting (Wellman, paras. 00627).
In regards to claim 30, modified Teoh discloses the compute device of claim 26, but fails to disclose wherein to present one or more insights in a user interface comprises to enable drill down into financial data underlying an insight.
Wellmann, in the related field of insight analysis and reporting, teaches wherein to present one or more insights (calculation attributes and rules for executing functional logic may be provided to the insights platform 904 via, input via a user interface, para. 0086) in a user interface (user interface 811 may include a command line interface, a graphical user interface (GUI), natural user interface (NUI), voice command interface, and/or other user interface (UI) presentations, which may be presented as distinct options or may be integrated, para. 0072); comprises to enable (insights platform may receive user request data including additional input data, requests for reports, and requests for additional output not included in the generated reports, para. 0089) drill down into financial data underlying an insight (explainability engine component 323 may be configured to provide an understanding of how a machine learning model makes decisions, which may include, e.g., composition of data and intelligent drill-down capabilities, para. 0052). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the device of Teoh with the ability to drill down into financial data underlying an insight in the user interface as taught by the device of Wellmann. The motivation for doing so would have been to provide insights based on the output using the model via the user interface (Wellman, paras. 0007-0017).
In regards to claim 31, modified Teoh discloses a compute device comprising: circuitry (system for detecting anomalies in mobile payment transactions including a server computer including a processor and a memory coupled to the processor, para. 0002) configured to: obtain a data analysis model (system further includes a three-layer framework including a probabilistic model to generate the preliminary transaction anomaly score, para. 0006), to be applied to financial transaction data indicative of financial transactions pertaining to a set of financial accounts (customer data from an issuer is used to more accurately model behavior for regular transfers and anomalous transfers, para. 0008, fig. 2); apply the data analysis model to the financial transaction data to identify one or more insights (if a data point is far away from its neighbors or the cluster centroids, it will be considered an outlier, given a high anomaly score, and become associated with an anomalous transaction, para. 0071, firs. 7A-B) indicative of anomalous behavior (anomaly detection system that allows for effective detection of fraudulent and anomalous mobile payment fund transfers, para. 0001), wherein to apply the data analysis model to the financial transaction data (FIG. 5 is a flow diagram showing a typical data science flow for a generative probabilistic model on mobile payment transactions, according to at least one aspect of the present disclosure, para. 0069, fig. 5) comprises to identify anomalous behavior within clusters (identifying anomalous behavior in a current mobile payment transaction by clustering the account related attributes or relationship related attributes by an unsupervised statistical algorithm, para. 0085, fig. 18) of the financial accounts that have been grouped based on similarity in one or more attributes comprises to evaluate time series historical data pertaining to the financial accounts (comprehensive data set includes days since a first transaction, days since a last transaction, number of transactions in a previous month, a number of transactions in a previous three months, a number of transactions in a previous six months, a total transaction amount in the previous month, a total transaction amount in the previous three months, and a total transaction amount in the previous six months, para. 0004); identify one or more outliers (if a data point is far away from its neighbors or the cluster centroids, it will be considered an outlier, given a high anomaly score, and become associated with an anomalous transaction, para. 0071, figs. 6A-7B) in a set of time series data (comprehensive data set includes days since a first transaction, days since a last transaction, number of transactions in a previous month, a number of transactions in a previous three months, a number of transactions in a previous six months, a total transaction amount in the previous month, a total transaction amount in the previous three months, and a total transaction amount in the previous six months, para. 0004) that has at least a predefined number of data points (On the other hand, if a data point requires a large number of splits to be isolated, then it is likely part of a large cluster and close by to several other transactions, meaning that it will be assigned a low anomaly score, para. 0070, figs. 6A-7B); and present the one or more insights (generate a final transaction anomaly score, and recommending an action for the current mobile payment transaction based on the final transaction anomaly score, para. 0012). However, Teoh fails to disclose a data analysis model constructed with a user interface provided by the compute device; and present insights in the user interface.
Wellmann, in the related field of insight analysis and reporting, teaches a data analysis model (calculation attributes and rules for executing functional logic may be provided to the insights platform 904 via, input via a user interface, para. 0086) constructed with a user interface (user may enter commands and information through a user interface 811) provided by the compute device (user interface 811 may include a command line interface, a graphical user interface (GUI), natural user interface (NUI), voice command interface, and/or other user interface (UI) presentations, which may be presented as distinct options or may be integrated, para. 0072); and present insights (insights platform may receive user request data including additional input data, requests for reports, and requests for additional output not included in the generated reports, para. 0089) in the user interface (reporting and business intelligence module may cause a display via a user interface, wherein the display may include reports, dashboards, and visualization tools, para. 0097). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the device of Teoh with the ability to receive input from a user interface and present insights in the user interface as taught by the device of Wellmann. The motivation for doing so would have been to receive user input data via a user interface and provide insights based on the output using the model via the user interface (Wellman, paras. 0007-0017).
In regards to claim 34, modified Teoh discloses the compute device of claim 31, but fails to disclose wherein to present one or more insights in a user interface comprises to present one or more drivers of one or more key performance indicators.
Wellmann, in the related field of insight analysis and reporting, teaches wherein to present one or more insights (calculation attributes and rules for executing functional logic may be provided to the insights platform 904 via, input via a user interface, para. 0086) in a user interface (user interface 811 may include a command line interface, a graphical user interface (GUI), natural user interface (NUI), voice command interface, and/or other user interface (UI) presentations, which may be presented as distinct options or may be integrated, para. 0072) comprises to present one or more drivers of one or more key performance indicators (natural language narratives component 324 may generate summaries in coherent text explaining key drivers or factors that underly a given output metric, based on data output by the continuous monitoring and insights component 321, para. 0053). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the device of Teoh with the ability to drivers of performance indicators in the user interface as taught by the device of Wellmann. The motivation for doing so would have been to provide classifications enabling transparency of data and explainability of key drivers and factors related to risk and loss for reporting (Wellman, paras. 00627).
In regards to claim 35, modified Teoh discloses the compute device of claim 31, but fails to disclose wherein to present one or more insights in a user interface comprises to enable drill down into financial data underlying an insight.
Wellmann, in the related field of insight analysis and reporting, teaches wherein to present one or more insights (calculation attributes and rules for executing functional logic may be provided to the insights platform 904 via, input via a user interface, para. 0086) in a user interface (user interface 811 may include a command line interface, a graphical user interface (GUI), natural user interface (NUI), voice command interface, and/or other user interface (UI) presentations, which may be presented as distinct options or may be integrated, para. 0072); comprises to enable (insights platform may receive user request data including additional input data, requests for reports, and requests for additional output not included in the generated reports, para. 0089) drill down into financial data underlying an insight (explainability engine component 323 may be configured to provide an understanding of how a machine learning model makes decisions, which may include, e.g., composition of data and intelligent drill-down capabilities, para. 0052). It would have been obvious to one having ordinary skill in the art at the time the invention was filed to provide the device of Teoh with the ability to drill down into financial data underlying an insight in the user interface as taught by the device of Wellmann. The motivation for doing so would have been to provide insights based on the output using the model via the user interface (Wellman, paras. 0007-0017).
Response to Arguments
Applicant’s arguments with respect to claims 12-15, 21, 24-26, 29-31, and 34-35 have been fully considered by the Examiner. Applicant’s arguments and amended claims have been considered with respect to objection of claim 6 and the previous objection is withdrawn.
Applicant’s arguments with respect to the rejection of claims 12-15, 21, 24-26, 29-31, and 34-35 under 35 USC 101 have been fully considered by the Examiner. However, the Examiner does not find the Applicant’s arguments persuasive, and therefore the rejections of claims 12-15, 21, 24-26, 29-31, and 34-35 under 35 USC 101 are maintained.
The Applicant argues that under Prong 1 of Step 2A, the claims do not recite an abstract idea because although the claims recite financial transaction data, the claims are not directed to fundamental economic principles or mitigating risk. Applicant further argues on page 6 of their Remarks that the claims are directed to a solving a technical deficiency in computer systems and a specific architecture to identify anomalous behavior. Applicant a method for analyzing coin-mixing service and do not belong to Certain Methods of Organizing Human activity such as fundamental economic principles and practices. The Applicant further states on page 7 of their remarks, that the limitations of the independent claims under Prong 2 of Step 2A are indicative of integration into a practical application because they solve a technical problem in analyzing large data sets that previously required specialized teams and custom software to present summaries of the data. Applicant further argues on pages 7 and 8 of their Remarks that the claims are indicative of an inventive concept under Step 2B because the claimed limitations recite a specific ordered combination of computing components and data processing structure that provide an improvement to a technical field and amount to significantly more than well-known, routine and customary instructions executed in generic computers.
Examiner respectfully disagrees with Applicant’s argument that the claimed limitations do not recite any of the groupings of abstract ideas. Under Prong 1 of Step 2A, the claims do fall under the abstract idea of Certain Method of Organizing Human Activity. If a claim limitation, under its broadest reasonable interpretation, covers fundamental economic principles or practices, including mitigating risk, but for the recitation of additional elements including generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Under the broadest reasonable interpretation, the claims recite fundamental economic principles or practices including mitigating risk. Analyzing financial transactions to identify anomalous behavior is mitigating risk.
Examiner respectfully disagrees with Applicant’s argument that the claimed limitations are indicative of integration into a practical application under Prong 2 of Step 2A of the PEG. Using a computer to: obtain a data analysis model and apply it to financial transaction data to identify anomalous behavior by using cohort analysis to identify key performance indicators; is nothing more than executing instructions to apply the exception to a computer. This is interpreted by the Examiner as using a computer as a tool to perform an abstract idea (See MPEP 2106.05(f)). The additional elements of “a compute device comprising circuitry, a data analysis model, and a user interface provided by the compute device” are recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). There is no improvement to the claimed computer elements, or to any other technology or technical field. The only improvements identified in the specification are generic speed and efficiency improvements inherent in applying the use of a computer to any task. Therefore, the claimed limitations do not meet the criteria or considerations as indicative of integration into a practical application.
Examiner respectfully disagrees with Applicant’s further argument under Step 2B of the PEG, that the amended claim limitations recite a specific ordered combination of computing components and data processing structures that amount to an inventive concept that renders the claims patent eligible because the claims provide for improvements to the technical field. As stated previously, using a computer to: obtain a data analysis model and apply it to financial transaction data to identify anomalous behavior by using cohort analysis to identify key performance indicators; is nothing more than executing instructions to apply the exception to a computer. The additional elements of “a compute device comprising circuitry, a data analysis model, and a user interface provided by the compute device” amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. In addition, as indicated further in the final rejection above, the claimed limitations under MPEP 2106.05(d)(ii) amount to well-understood routine and conventional activities of: receiving and transmitting data over a network, the performance of repetitive calculations, electronic recordkeeping, storing and retrieving information in memory, and presenting results of an analysis. Therefore, the rejections of the claims pursuant to 35 USC 101 are maintained.
With respect to the Applicant’s arguments regarding the previous rejection of independent claims 12, 21, 26, and 31 under 35 USC 103, the Applicant argues that the prior art of Teoh, Wellman, and Kakade fails to disclose the limitations of the amended claims. Applicant argues that Kakade fails to disclose “producing summaries of KPIs”, Examiner respectfully disagrees with Applicant’s assertion. As stated in para. 0042 Kakade discloses “stage may comprise a cohort analyzer automating the identification of cohorts/micro-segments that drive KPI movements using ML techniques such as Decision Trees”. Applicant’s specification in para. 0017 states that the summarized KPI attributes of interest are presented in time series charts on a user interface including historical actual values, identified thresholds so that user may review and analyze the reports to determine outliers and/or understand drivers of KPI. Similarly, Kakade in para. 0045 discloses determining changes in KPI and analyzing cohorts responsible for change. As referenced above in the examiner’s art rejections pursuant to 35 USC § 103, the combination of Teoh, Wellman, and Kakade teach all of the limitations of amended claims 12, 21, 26, and 31. Therefore, the rejections of the independent claims are maintained.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/PAUL S SCHWARZENBERG/Primary Examiner, Art Unit 3695 4/29/2026
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