The present application, filed on or after 16 March 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This office action is in response to Applicant’s submission filed on 23 September 2025. THIS ACTION IS NON-FINAL.
Status of Claims
Claims 1-3, 5, 7-19 are pending.
Claims 13-19 are withdrawn.
Claims 4, 6 are cancelled.
Claims 1-3, 5, 7-12 are rejected under 35 U.S.C. 101 for being directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
There is no art rejection for claims 1-3, 5, 7-12.
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.
Judicial Exception
Claims 1-3, 5, 7-12 of the claimed invention are directed to a judicial exception, an abstract idea, without significantly more.
Regarding claims 1-3, 5,
(Independent Claims) With regards to claim 1, the claim recites a method, which falls into one of the statutory categories.
2A – Prong 1: Claim 1, in part, recites “… selecting a data subset of the disparate dataset, selecting a machine learning model from the plurality of machine learning models, corresponding to the data subset, setting a feature importance vector representing a relative importance of features in the data subset for the machine learning model, and generating a prediction for a data point of the data subset by the machine learning model using the feature importance vector[, to obtain a plurality of trained models, wherein respective trained models of the plurality of trained models correspond to respective data subsets of the disparate dataset]; for each respective trained model, generating a respective trust score that is based on a data sparseness metric of a corresponding respective data subset and the feature importance vector of the respective trained model; … classifying, by the trained ensemble machine learning system, incoming payroll requests in real-time to the payroll processing system, to generate a combined prediction generated in real-time, wherein the combined prediction comprises a risk analysis of employer insufficient funds” (mental process and/or math concept); “setting … dynamic credit limits for credit amounts based on the risk analysis; and performing one of halting processing the incoming payroll requests and processing the incoming payroll requests, based on the risk analysis obtained from the ensemble learning system” (certain methods of organizing human activity and/or mental process) .
The limitation “… selecting a data subset of the disparate dataset, selecting a machine learning model from the plurality of machine learning models, corresponding to the data subset, setting a feature importance vector representing a relative importance of features in the data subset for the machine learning model, and generating a prediction for a data point of the data subset by the machine learning model using the feature importance vector[, to obtain a plurality of trained models, wherein respective trained models of the plurality of trained models correspond to respective data subsets of the disparate dataset]; for each respective trained model, generating a respective trust score that is based on a data sparseness metric of a corresponding respective data subset and the feature importance vector of the respective trained model; … classifying, by the trained ensemble machine learning system, incoming payroll requests in real-time to the payroll processing system, to generate a combined prediction generated in real-time, wherein the combined prediction comprises a risk analysis of employer insufficient funds”, as drafted, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, but for the possibility of using generic computing device to implement, “selecting”, “generating” in the claimed in the limitation citied above can be performed by human mind and/or with the aid of paper / pen / calculator (e.g., a human accounting data analyst can collect & process data to adjust models for risk prediction). That is, other than the possibility of using generic computer components, nothing in the claim element precludes the step from practically being performed in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas.
The limitation “setting … dynamic credit limits for credit amounts based on the risk analysis; and performing one of halting processing the incoming payroll requests and processing the incoming payroll requests, based on the risk analysis obtained from the ensemble learning system”, as drafted, under the broadest reasonable interpretation, covers method of organizing activity for mitigating risk. This is also a process that human can perform, e.g., a financial transaction manager can analyze data and perform this type of process to mitigate risk. If a claim limitation, under its broadest reasonable interpretation, covers method of organizing human activity but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
Accordingly, the claim recites an abstract idea.
2A – Prong 2: This judicial exception is not integrated into a practical application. In particular, claim 1 recites additional elements: (a) using generic computer elements (like processor) (merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))); (b) “obtaining a disparate dataset comprising sparse data, compiled from a plurality of disparate data sources” (insignificant extra-solution activity (MPEP 2106.05(g) and/or WURC (MPEP 2106.05(d)(II))); (c) “training a plurality of machine learning models … to obtain a plurality of trained models, wherein respective trained models of the plurality of trained models correspond to respective data subsets of the disparate dataset”, “training a meta-model on the respective trust score and a respective model prediction generated by each respective trained model of the plurality of trained models, to generate a combined prediction that accounts for data sparsity of data points input into the plurality of trained models” (mere instructions to apply an exception (MPEP 2106.05(f))); (d) “deploying the plurality of trained models to generate a plurality of disparate model predictions for a data point, wherein the plurality of disparate model predictions is generated based on a plurality of disparate algorithms corresponding to the plurality of trained models”, “deploying the trained ensemble machine learning system to a payroll processing system” (mere instruction to apply an exception (MPEP 2106.05(f)) and/or field of use (MPEP 2106.05(h)) and/or WURC (MPEP 2106.05(d)(II))). For (a), these computer components are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) which is mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). For (b), these steps are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. data input / output for the claimed process as described in MPEP.2106.05(g). The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). For (c), according to the specification, it appeared that this limitation is using computer to implement a classifier model. These limitations are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component, as discussed in MPEP 2106.05(f). For (d), it appears that the limitation is mere application of judiciary exception (MPEP 2106.05(f)). 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. The claim is directed to an abstract idea.
2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional element of using generic computer elements is merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The additional element of “ obtaining a disparate dataset comprising sparse data, compiled from a plurality of disparate data sources “ is insignificant extra-solution activity (MPEP 2106.05(g)). The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). The additional element “training a plurality of machine learning models … to obtain a plurality of trained models, wherein respective trained models of the plurality of trained models correspond to respective data subsets of the disparate dataset”, “training a meta-model on the respective trust score and a respective model prediction generated by each respective trained model of the plurality of trained models, to generate a combined prediction that accounts for data sparsity of data points input into the plurality of trained models” is mere instructions to apply an exception (MPEP 2106.05(f)). The additional element of “deploying the plurality of trained models to generate a plurality of disparate model predictions for a data point, wherein the plurality of disparate model predictions is generated based on a plurality of disparate algorithms corresponding to the plurality of trained models”, “deploying the trained ensemble machine learning system to a payroll processing system” is mere instruction to apply an exception (MPEP 2106.05(f)) and/or filed of use (MPEP 2106.05(h))The claim is not patent eligible.
(Dependent claims)
Claims 2-3, 5-6 are dependent on claim 1 and include all the limitations of claim 1. Therefore, claims 2-3, 5-6 recite the same abstract ideas.
With regards to claim 2, the claim recites “wherein: each data subset is a respective view of the disparate dataset compiled comprises features and labels of the disparate dataset compiled from the plurality of disparate data sources; and the plurality of disparate data sources comprises historical payroll processing data”, which is further limitation on data & prediction models processing, without adding anything significantly more to the abstract idea. The claims are not patent eligible.
With regards to claim 3, the claim recites “wherein the data sparseness metric is a matrix that represents whether a certain feature is missing for the data point in a respective data subset”, which is further limitation on data & prediction models processing, without adding anything significantly more to the abstract idea. The claims are not patent eligible.
With regards to claim 5, the claims recite further limitation “wherein the respective trust score is a dot product of the data sparseness metric and the feature importance vector” (mathematical concept), as drafted, is a process that, under its broadest reasonable interpretation, covers mathematical concepts but for the recitation of generic computer components. That is, other than reciting server computer, processor, computer-readable medium coupled with the processors, the steps of factorizing / initializing / optimizing matrixes, based on their broadest reasonable interpretation, describe mathematical relationships and algorithms. Mathematical relationship and algorithms have been found by the courts to be abstract ideas, e.g., see MPEP 2106.04(a)(2) A. Mathematical Relationships, iv. organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula. 758 F.3d at 1350, 111 USPQ2d at 1721. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. The claim does not provide any element adding anything significantly more to the abstract idea. The claims are not patent eligible.
Regarding claims 7-12,
(Independent Claims) With regards to claim 7, the claim recites a method, which falls into one of the statutory categories.
2A – Prong 1: Claim 7, in part, recites “generating a model prediction by each of the plurality of trained models to obtain a plurality of disparate model predictions, wherein the plurality of disparate model predictions is generated based on a plurality of disparate algorithms corresponding to the plurality of trained models; for each respective trained model, generating a respective trust score that is based on a data sparseness metric of the respective data subset and a feature importance vector of the respective trained model to obtain a plurality of trust scores; [receiving the plurality of model predictions and the plurality of trust scores as input to a trained meta-model of the ensemble machine learning system;] and combining by the trained meta-model, the plurality of model predictions and the plurality of trust scores to generate the combined prediction comprising a risk analysis of employer insufficient funds, wherein the combined prediction accounts for data sparsity of a data point input into the plurality of trained models” (mental process and/or math concept); “setting … dynamic credit limits for credit amounts based on the risk analysis; and performing one of halting processing the incoming payroll requests and processing the incoming payroll requests, based on the risk analysis obtained from the ensemble learning system” (methods of organizing human activity and/or mental process).
The limitation “generating a model prediction by each of the plurality of trained models to obtain disparate model predictions, wherein the plurality of disparate model predictions is generated based on a plurality of disparate algorithms corresponding to the plurality of trained models; for each respective trained model, generating a respective trust score that is based on a data sparseness metric of the respective data subset and a feature importance vector of the respective trained model to obtain a plurality of trust scores; [receiving the plurality of model predictions and the plurality of trust scores as input to a trained meta-model of the ensemble machine learning system;] and combining by the trained meta-model, the plurality of model predictions and the plurality of trust scores to generate the combined prediction comprising a risk analysis of employer insufficient funds, wherein the combined prediction accounts for data sparsity of a data point input into the plurality of trained models”, as drafted, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, but for the possibility of using generic computing device to implement, “generating”, “combining” in the claimed in the limitation citied above can be performed by a human preparing documents for certain purpose (e.g., a human accounting data analyst can collect & process data to adjust models for risk prediction). That is, other than the possibility of using generic computer components, nothing in the claim element precludes the step from practically being performed in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas.
The limitation “setting … dynamic credit limits for credit amounts based on the risk analysis; and performing one of halting processing the incoming payroll requests and processing the incoming payroll requests, based on the risk analysis obtained from the ensemble learning system”, as drafted, under the broadest reasonable interpretation, covers method of organizing activity for mitigating risk. This is also a process that human can perform, e.g., a financial transaction manager can analyze data and perform this type of process to mitigate risk. If a claim limitation, under its broadest reasonable interpretation, covers method of organizing human activity but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
Accordingly, the claim recites an abstract idea.
2A – Prong 2: This judicial exception is not integrated into a practical application. In particular, claim 1 recites additional elements: (a) “receiving a data point as input to a plurality of trained models of an ensemble machine learning system deployed to a payroll processing system …”, “receiving the plurality of model predictions and the plurality of trust scores as input to a trained meta-model …”, “receiving the plurality of model predictions and the plurality of trust scores as input to a trained meta-model of the ensemble machine learning system” (insignificant extra solution activity (MPEP.2106.05(g)) and/or WURC (MPEP 2106.05(d)(II))); (b) “wherein each trained model is trained from a respective data subset of a disparate dataset” (mere instructions to apply an exception (MPEP 2106.05(f))). For (a), these steps are recited at a high level of generality and amounts to extra-solution activity of data input / output as described in MPEP.2106.05(g). The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). For (b), it appeared that this limitation is using computer to implement prediction models. These limitations are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component, as discussed in MPEP 2106.05(f). 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.
2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional element “receiving a data point as input to a plurality of trained models”, “receiving the plurality of model predictions and the plurality of trust scores as input to a trained meta-model …”, which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). The additional element “wherein each model is trained from a respective data subset of a disparate data” is mere instructions to apply an exception (MPEP 2106.05(f)). The claim is not patent eligible.
(Dependent claims)
Claims 8-12 are dependent on claim 7 and include all the limitations of claim 7. Therefore, claims 8-12 recite the same abstract ideas.
With regards to claim 8, the claim recites “wherein; each data subset is a respective view of the disparate dataset compiled comprises features and labels of the disparate dataset, the disparate dataset is compiled from a plurality of disparate data sources”, and the plurality of disparate data sources comprises historical payroll processing data”, which is further limitation on data & prediction models processing, without adding anything significantly more to the abstract idea. The claims are not patent eligible.
With regards to claim 9, the claim recites “wherein the data sparseness metric is a matrix that represents whether a certain feature is missing for the data point in the respective data subset”, and the plurality of disparate data sources comprises historical payroll processing data”, which is further limitation on data & prediction models processing, without adding anything significantly more to the abstract idea. The claims are not patent eligible.
With regards to claim 10, the claim recites “wherein the feature importance vector represents a relative importance of features in the respective data subset used by a respective trained model to generate a model result”, and the plurality of disparate data sources comprises historical payroll processing data”, which is further limitation on data & prediction models processing, without adding anything significantly more to the abstract idea. The claims are not patent eligible.
With regards to claim 11, the claims recite further limitation “wherein the respective trust score is a dot product of the data sparseness metric and the feature importance vector” (mathematical concept), as drafted, is a process that, under its broadest reasonable interpretation, covers mathematical concepts but for the recitation of generic computer components. That is, other than reciting server computer, processor, computer-readable medium coupled with the processors, the steps of factorizing / initializing / optimizing matrixes, based on their broadest reasonable interpretation, describe mathematical relationships and algorithms. Mathematical relationship and algorithms have been found by the courts to be abstract ideas, e.g., see MPEP 2106.04(a)(2) A. Mathematical Relationships, iv. organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula. 758 F.3d at 1350, 111 USPQ2d at 1721. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. The claim does not provide any element adding anything significantly more to the abstract idea. The claims are not patent eligible.
With regards to claim 12, the claims recite further limitation “receiving the data point as request from a client device via an interface; and returning the combined prediction as a response to the client device via the interface” (insignificant extra solution activity MPEP 2106(g) or WURC, MPEP 2106(d) II). These steps are electronically transmitting and/or storing information, which is Well Understood, Routine, Conventional (WURC) activities, as stated in MPEP.2106(d) II, “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
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ii. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.");
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iii. Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log);
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iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
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v. Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014) (optical character recognition); and
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vi. A Web browser’s back and forward button functionality, Internet Patent Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015). “
Accordingly, this additional element does not integrate the abstract idea into a practical application, nor does it add anything significantly more to the abstract idea. The claim is not patent eligible.
Response to Argument
Applicant’s arguments filed 23 September 2025 has been fully considered but they are not fully persuasive.
Regarding 101 rejections,
1)Applicant argued that (p.9-10) …
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Examiner replies: As stated before, human can handle multiple prediction models, e.g., statisticians can generate multiple statistical models to enable human agents to make decision right away upon occurrence of events (e.g., request for financial transactions). Training with computer is mere instructions to apply an exception (MPEP 2106.05(f)). There is no elements identified adding anything significantly more to the abstract idea. Hence, the claims are not patent eligible.
2) Applicant argued that (p.12-13) …
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Examiner replies: The invention claimed is directed to generating and using abstract data processing models to make decision, which is an abstract idea. Improving an abstract idea is still an abstract idea. Other than citing generic computer elements to implement the abstract idea, there is no additional element identified showing integration into a practical application. 101 rejection is maintained.
3) Applicant argued that (p.14-15) …
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Examiner replies: As stated earlier, model processing as claimed is directed to an abstract idea and training as claimed is mere instruction to apply judiciary exception. Except citing generic computer element to implement the abstract idea, there is no additional elements identified adding something significantly more to the abstract idea. 101 rejection is maintained.
4) To overcome the issues, suggest Applicant to include additional inventive concept elements into claims: (1) to show integration into a practical application; and/or (2) to show a specific physical implementation that is not WURC; (3) that is not practical for human mind to process and not WURC.
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 extension fee 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TSU-CHANG LEE whose telephone number is 571-272-3567. The fax number is 571-273-3567.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas, can be reached 571-272-2589.
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/TSU-CHANG LEE/
Primary Examiner, Art Unit 2128