DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
2. The Applicant filed Amendments on 01/23/2026. Claims 1-20 are pending and are rejected for the reasons set forth below.
Claim Rejections - 35 USC § 101
3. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
4. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
5. Analysis:
Step 1: Statutory Category?: (is the claim(s) directed to a process, machine, manufacture or composition of matter?) - YES: In the instant case, claims 1-10 are directed to a method (i.e., process), claims 11-15 are directed to a system (i.e., machine), claims 16-20 are directed to a platform (i.e., machine).
Regarding independent claim 1:
Step 2A - Prong 1: Judicial Exception Recited?: (is the claim(s) recited a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon) – YES: Independent claim 1 recites the at least following limitations of “receiving an application associated with a decision …, the application includes a plurality of factors associated with a user; determining … historical applications and decision data and configured to operate without reliance on a user … a subpopulation of applications from the historical applications and decision data most similar to the application based on applicable characteristics and factors of the application …; retrieving a positive outcome ratio for the subpopulation most similar to the application ,the positive outcome ratio being derived from the historical decisions without application of risk scoring or underwriter grading; determining whether the decision is consistent with positive outcomes for the subpopulation of applications; identifying, …, inconsistencies between the decision and historical decisions associated with the subpopulation; automatically generating a report of the inconsistencies associated with the decision utilizing the data platform in response to determining the decision is not consistent with the positive outcomes for the subpopulation of applications; and communicating a report … to one or more authorized parties indicating the inconsistencies associated with the decision.” These recited limitations of the claim, as drafted, under its broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they cover performance of the limitations in commercial interactions (including business relations/financial lending and borrowing) for providing guidance as it relates to fair lending process of making consistent loan decisions based on someone’s credit worthiness – Specification [0002]. Accordingly, the claim recites an abstract idea.
Step 2A - Prong 2: Integrated into a Practical Application?: (is the claim(s) recited additional elements that integrate the exception into a practical application of the exception) - NO: This judicial exception is not integrated into a practical application. In particular, independent claim 1 further to the abstract idea includes additional elements of “a data platform”, “a machine learning model”, “a memory of the data platform”, and “logic of the data platform”. However, the additional elements recite generic computer components such as a computer, computing devices, a server, and/or software programing that are recited a high-level of generality that merely perform, conduct, carry out, implement, and/or narrow the abstract idea itself. Accordingly, the additional elements evaluated individually and in combination do not integrate the abstract idea into a practical application because they comprise or include limitations that are not indicative of integration into a practical application such as adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea -- See MPEP 2106.05(f). The claim is directed to an abstract idea.
Step 2B: Claim provides an Inventive Concept?: (is the claim(s) recited additional elements that amount to an inventive concept (aka “significantly more”) than the recited judicial exception) - NO: The claim does 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 “a data platform”, “a machine learning model”, “a memory”, and “logic of the data platform” evaluated individually and in combination do not amount to more than a recitation of the words "apply it" (or an equivalent) or are not more than mere instructions to implement an abstract idea or other exception on a computer, or are not more than merely using a computer as a tool to perform an abstract idea. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more - See MPEP 2106.05(f)(2). None of the additional elements taken individually or when taken as an ordered combination amount to significantly more than the abstract idea. Accordingly, the claim is patent-ineligible.
Regarding independent claim 11:
Step 2A - Prong 1: Judicial Exception Recited?: (is the claim(s) recited a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon) – YES: Independent claim 11 recites the at least following limitations of “… receive an application associated with a consumer and a decision associated with the application; …, … receives the application associated with a decision for the consumer, the application includes a plurality of factors associated with a user, determines a subpopulation of applications from historical applications and decision data most similar to the application utilizing, retrieves a positive outcome ratio for the subpopulation of applications most similar to the application, the positive outcome ratio being derived from historical decisions associated with the historical applications and decision data without application of risk scoring or underwriter grading, determines whether the decision is consistent with positive outcomes for the subpopulation of applications, automatically generates a report of inconsistencies associated with the decision in response to determining the decision is not consistent with the positive outcomes for the subpopulation, and communicates a report … to receive inconsistencies associated with the decision.” These recited limitations of the claim, as drafted, under its broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they cover performance of the limitations in commercial interactions (including business relations/financial lending and borrowing) for providing guidance as it relates to fair lending process of making consistent loan decisions based on someone’s credit worthiness – Specification [0002]. Accordingly, the claim recites an abstract idea.
Step 2A - Prong 2: Integrated into a Practical Application?: (is the claim(s) recited additional elements that integrate the exception into a practical application of the exception) - NO: This judicial exception is not integrated into a practical application. In particular, independent claim 11 further to the abstract idea includes additional elements of “a plurality of electronic devices”, “a data application”, “a data platform accessible by the plurality of electronic devices”, “a machine learning model”, and “a memory of the platform”. However, the additional elements recite generic computer components such as a computer, computing devices, a server, and/or software programing that are recited a high-level of generality that merely perform, conduct, carry out, implement, and/or narrow the abstract idea itself. Accordingly, the additional elements evaluated individually and in combination do not integrate the abstract idea into a practical application because they comprise or include limitations that are not indicative of integration into a practical application such as adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea -- See MPEP 2106.05(f). The claim is directed to an abstract idea.
Step 2B: Claim provides an Inventive Concept?: (is the claim(s) recited additional elements that amount to an inventive concept (aka “significantly more”) than the recited judicial exception) - NO: The claim does 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 ““a plurality of electronic devices”, “a data application”, “a data platform accessible by the plurality of electronic devices”, “a machine learning model”, and “a memory of the platform” evaluated individually and in combination do not amount to more than a recitation of the words "apply it" (or an equivalent) or are not more than mere instructions to implement an abstract idea or other exception on a computer, or are not more than merely using a computer as a tool to perform an abstract idea. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more - See MPEP 2106.05(f)(2). None of the additional elements taken individually or when taken as an ordered combination amount to significantly more than the abstract idea. Accordingly, the claim is patent-ineligible.
Regarding independent claim 16:
Step 2A - Prong 1: Judicial Exception Recited?: (is the claim(s) recited a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon) – YES: Independent claim 16 recites the at least following limitations of “receive an application associated with a decision, the application includes a plurality of factors associated with a user, determine … historical applications and decision data and configured to operate without reliance on a user …, a subpopulation of applications from the historical applications and decision data most similar to the application a subpopulation of applications most similar to the application, retrieves a positive outcome ratio for the subpopulation most similar to the application, the positive outcome ratio being derived from historical decisions without application of risk scoring or underwriter grading, determine whether the decision is consistent with positive outcomes for the subpopulation of applications, identify, … , inconsistencies between the decision and historical decisions associated with the subpopulation, automatically generate a report of inconsistencies associated with the decision in response to determining the decision is not consistent with the positive outcomes for the subpopulation, and communicates a report … authorized to receive inconsistencies associated with the decision.” These recited limitations of the claim, as drafted, under its broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they cover performance of the limitations in commercial interactions (including business relations/financial lending and borrowing) for providing guidance as it relates to fair lending process of making consistent loan decisions based on someone’s credit worthiness – Specification [0002]. Accordingly, the claim recites an abstract idea.
Step 2A - Prong 2: Integrated into a Practical Application?: (is the claim(s) recited additional elements that integrate the exception into a practical application of the exception) - NO: This judicial exception is not integrated into a practical application. In particular, independent claim 16 further to the abstract idea includes additional elements of “a platform”, “a processor”, “a memory”, and “a machine learning model”. However, the additional elements recite generic computer components such as a computer, computing devices, a server, and/or software programing that are recited a high-level of generality that merely perform, conduct, carry out, implement, and/or narrow the abstract idea itself. Accordingly, the additional elements evaluated individually and in combination do not integrate the abstract idea into a practical application because they comprise or include limitations that are not indicative of integration into a practical application such as adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea -- See MPEP 2106.05(f). The claim is directed to an abstract idea.
Step 2B: Claim provides an Inventive Concept?: (is the claim(s) recited additional elements that amount to an inventive concept (aka “significantly more”) than the recited judicial exception) - NO: The claim does 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 “a platform”, “a processor”, “a memory”, and “a machine learning model” evaluated individually and in combination do not amount to more than a recitation of the words "apply it" (or an equivalent) or are not more than mere instructions to implement an abstract idea or other exception on a computer, or are not more than merely using a computer as a tool to perform an abstract idea. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more - See MPEP 2106.05(f)(2). None of the additional elements taken individually or when taken as an ordered combination amount to significantly more than the abstract idea. Accordingly, the claim is patent-ineligible.
Dependent claims 2-10, 12-15, and 17-20 have been given the full two-part analysis, analyzing the additional limitations both individually and in combination. The dependent claims, when analyzed individually and in combination, are also held to be patent-ineligible under 35 U.S.C. 101.
Dependent claims 2, 12, and 17: simply provide further definition to “the method, the system, the platform” recited in independent claims 1, 11, and 16. Simply stating that further comprising: automatically accessing public data or private data utilizing the logic to find the subpopulation most similar to the application utilizing the plurality of factors amounts to no more than merely applying generic computer components and/or software programing to implement the abstract idea on a computer (i.e., the logic).Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claims 3, 13, and 18: simply provide further definition to “the logic, the platform” recited in independent claims 1, 11, 16. Simply stating that wherein the logic includes at least artificial intelligence configured to implement one or more models associated with the application; wherein the platform further: automatically accesses public data or private data utilizing the logic to find the subpopulations most similar to the application utilizing the plurality of factors; and automatically generates one or more narratives associated with the inconsistencies; wherein the platform includes artificial intelligence that implements models to generate the subpopulation, determination of whether the decision is consistent with the positive outcomes for the subpopulation, the inconsistencies, and the report amounts to no more than merely applying generic computer components and/or software programing to implement the abstract idea on a computer (i.e., the logic, the platform, one or more models, artificial intelligence).Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claims 4 and 14: simply provide further definition to “the process, the platform” recited in independent claims 1 and 11. Simply stating that wherein the process of determining inconsistencies is performed completely autonomously utilizing at least an artificial intelligence engine; wherein the platform includes artificial intelligence that implements models to generate the subpopulation, determination of whether the decision is consistent with the positive outcomes for the subpopulation, the inconsistencies, and the report amounts to no more than merely applying generic computer components and/or software programing to implement the abstract idea on a computer (i.e., the logic, the platform, one or more models, artificial intelligence engine).Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claim 5: simply provides further definition to “the report” recited in independent claim 1. Simply stating that wherein the report indicates whether anomalies exist in past decisions when considering disparities of the decision does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claims 6 and 15: simply provide further definition to “the method, the inconsistencies” recited in independent claims 1 and 11. Simply stating that further comprising: determining disparities between the decision and the positive outcomes for the subpopulation; wherein the inconsistencies indicate one or more discrepancies associated with the factors or non-relevant factors that were not considered in the decision do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claim 7: simply provides further definition to “the decision” recited in independent claim 1. Simply stating that wherein the decision is a current decision, proposed decision, or one or more past decisions does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claim 8: simply provides further definition to “the method” recited in independent claim 1. Simply stating that further comprising: automatically generating one or more narratives associated with the inconsistencies does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claim 9: simply provides further definition to “the method” recited in independent claim 1. Simply stating that further comprising: presenting a plurality of user interfaces allowing the user to filter data associated with the application, the factors, the subpopulation, the positive outcome ratio, and the inconsistencies amounts to no more than merely applying generic computer components and/or software programing to implement the abstract idea on a computer (i.e., a plurality of user interfaces).Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claim 19: simply provides further definition to “the report” recited in independent claim 16. Simply stating that wherein the inconsistencies indicate one or more discrepancies associated with the factors or non-relevant factors that were not considered in the decision does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claims 10 and 20: simply provide further definition to “the report” recited in independent claims 1 and 16. Simply stating that wherein the report includes at least suggestions to remove the inconsistencies from the application process do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Claim Rejections - 35 USC § 103
6. 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.
7. 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.
8. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
9. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over anticipated by Ross et al. (U.S. Patent No. 11,854,088), hereinafter, “Ross”, in view of Wellmann et al. (U.S. Pub. No. 2023/0281541), hereinafter, “Wellmann”.
Claim 1 –
Ross discloses:
a method for determining inconsistencies, the method comprising (Ross [Abstract], “systems and methods that improve the traditional underwriting process within a financial institution”, and Figure 1):
receiving an application associated with a decision utilizing a server, the application includes a plurality of factors associated with a user (Ross [Column 5, Lines 3-5], “internal database 106 receives and stores outcome data and provides said outcome data to underwriting platform 108”, and Figure 1);
determining a subpopulation most similar to the application utilizing logic of the server (Ross [Column 5, Lines 5-9], “Examples of internal data include insurance type requested, insurance amount requested, electronic copy of the insurance application, and note from interviews with insurance company representatives, underwriter profiles, among others”, and Figure 1);
retrieving a positive outcome ratio for the subpopulation most similar to the application, the positive outcome ratio being derived from the historical decisions without application of risk scoring or underwriter grading (Ross [Column 5, Lines 20-24], [Column 8, Lines 21-27], “underwriting platform 108 within system 100 retrieves internal data from internal database 106, outcome data from external database 104, and processes the received internal and outcome data to determine and otherwise select the best underwriters … the resolution provided by underwriting model 412 is a risk score calculation, a risk of loss assessment, or a risk classification, among others. In another embodiment, underwriting model 412 emulates the resolution patterns of top performing underwriters by using artificial intelligence tools such as expert systems and fuzzy logic”, and Figures 1, 4);
determining whether the decision is consistent with positive outcomes for the subpopulation (Ross [Column 6, Lines 36-39], “Underwriting platform 108 further includes analytical engine 306, ranking module 308, underwriting heuristics module 310, and decision tools 312”, and Figure 3);
automatically generating a report of the inconsistencies associated with the decision utilizing the server in response to determining the decision is not consistent with the positive outcomes for the subpopulation (Ross [Column 1, Lines 39-45], [Column 10, Lines 7-9], “the underwriting process may be biased by the judgment of the underwriter. Variation in factors such as underwriter training, experience, and quality of previous assessments may cause underwriters to make different decisions and judgments. As a result, there can be a large amount of variability and inconsistencies in the insurance underwriting process … the previously built and tested underwriting model is deployed into a set of decision tools within the underwriting platform”, and Figure 5); and
communicating a report from the server to one or more authorized parties indicating the inconsistencies associated with the decision (Ross [Column 10, Lines 9-13], “decision tools help to validate and identify when an actual decision, within the underwriting process, varies significantly from the heuristic decisions made by the best underwriters and sends an alert to the user before proceeding with that decision”, and Figure 5)
Ross does not explicitly disclose:
[[determining, using a machine learning model trained on historical applications and decision data and configured to operate without reliance on a user and stored in at least a memory of the data platform, a subpopulation of applications from the historical applications and decision data most similar to the application based on applicable characteristics and factors of the application; identifying, by the machine learning model, inconsistencies between the decision and historical decisions associated with the subpopulation]]
Wellmann disclose [[determining, using a machine learning model trained on historical applications and decision data and configured to operate without reliance on a user and stored in at least a memory of the data platform, a subpopulation of applications from the historical applications and decision data most similar to the application based on applicable characteristics and factors of the application; identifying, by the machine learning model, inconsistencies between the decision and historical decisions associated with the subpopulation]]] (See at least Wellmann [0052], [0060], “the explainability engine component 323 may identify the most important features that contributed to a particular machine learning model decision while also identifying any biases or inconsistencies in the model's decision-making process, thereby improving the trust and accountability of machine learning systems … a step 530, where anomaly detection may be performed based on, e.g., transformation rules and historical data trends. Anomalies may be detected, e.g., by identifying data points that are significantly different from the rest of the data set, or by detecting unusual patterns or trends”, see also Figures 3, 5). It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the method of Ross to include a machine learning model to identify inconsistencies between a decision and historical decisions associated with a subpopulation as taught by Wellmann, in order to computing loan option score of a customer (see Wellmann Paragraphs [0052], [0060], Figures 3, 5).
Claim 2 –
Ross/Wellmann disclose the method of claim 1, as shown above.
Ross further discloses:
further comprising: automatically accessing public data or private data utilizing the logic to find the subpopulation most similar to the application utilizing the plurality of factors (Ross [Column 8, Lines 21-27], “the resolution provided by underwriting model 412 is a risk score calculation, a risk of loss assessment, or a risk classification, among others. In another embodiment, underwriting model 412 emulates the resolution patterns of top performing underwriters by using artificial intelligence tools such as expert systems and fuzzy logic”, and Figure 4).
Claim 3 –
Ross/Wellmann disclose the method of claim 1, as shown above.
Ross further disclose:
wherein the logic includes at least artificial intelligence configured to implement one or more models associated with the application Ross [Column 8, Lines 21-27], “the resolution provided by underwriting model 412 is a risk score calculation, a risk of loss assessment, or a risk classification, among others. In another embodiment, underwriting model 412 emulates the resolution patterns of top performing underwriters by using artificial intelligence tools such as expert systems and fuzzy logic”, and Figure 4).
Claim 4 –
Ross/Wellmann disclose the method of claim 1, as shown above.
Ross further disclose:
wherein the process of determining inconsistencies is performed completely autonomously utilizing at least an artificial intelligence engine Ross [Column 8, Lines 21-27], “the resolution provided by underwriting model 412 is a risk score calculation, a risk of loss assessment, or a risk classification, among others. In another embodiment, underwriting model 412 emulates the resolution patterns of top performing underwriters by using artificial intelligence tools such as expert systems and fuzzy logic”, and Figure 4).
Claim 5 –
Ross/Wellmann disclose the method of claim 1, as shown above.
Ross further disclose:
wherein the report indicates whether anomalies exist in past decisions when considering disparities of the decision Ross [Column 8, Lines 21-27], “the resolution provided by underwriting model 412 is a risk score calculation, a risk of loss assessment, or a risk classification, among others. In another embodiment, underwriting model 412 emulates the resolution patterns of top performing underwriters by using artificial intelligence tools such as expert systems and fuzzy logic”, and Figure 4).
Claim 6 –
Ross/Wellmann disclose the method of claim 1, as shown above.
Ross further disclose:
further comprising: determining disparities between the decision and the positive outcomes for the subpopulation (Ross [Column 10, Lines 9-13], “decision tools help to validate and identify when an actual decision, within the underwriting process, varies significantly from the heuristic decisions made by the best underwriters and sends an alert to the user before proceeding with that decision”, and Figure 5).
Claim 7 –
Ross/Wellmann disclose the method of claim 1, as shown above.
Ross further disclose:
wherein the decision is a current decision, proposed decision, or one or more past decisions (Ross [Column 10, Lines 9-13], “decision tools help to validate and identify when an actual decision, within the underwriting process, varies significantly from the heuristic decisions made by the best underwriters and sends an alert to the user before proceeding with that decision”, and Figure 5).
Claim 8 –
Ross/Wellmann disclose the method of claim 1, as shown above.
Ross further disclose:
further comprising: automatically generating one or more narratives associated with the inconsistencies (Ross [Column 10, Lines 9-13], “decision tools help to validate and identify when an actual decision, within the underwriting process, varies significantly from the heuristic decisions made by the best underwriters and sends an alert to the user before proceeding with that decision”, and Figure 5).
Claim 9 –
Ross/Wellmann disclose the method of claim 1, as shown above.
Ross further disclose:
further comprising: presenting a plurality of user interfaces allowing the user to filter data associated with the application, the factors, the subpopulation, the positive outcome ratio, and the inconsistencies (Ross [Column 7, Lines 40-45], “decision tools 312 is configured to aid one or more underwriters during the underwriting process. In these embodiment, decision tools 312, through the user interface (not shown in FIG. 3), is triggered when junior underwriters underwriting one or applicants match one or more key elements data”, and Figures 3).
Claim 10 –
Ross/Wellmann disclose the method of claim 1, as shown above.
Ross further disclose:
wherein the report includes at least suggestions to remove the inconsistencies from the application process (Ross [Column 10, Lines 9-13], “decision tools help to validate and identify when an actual decision, within the underwriting process, varies significantly from the heuristic decisions made by the best underwriters and sends an alert to the user before proceeding with that decision”, and Figure 5).
Claim 11 –
Ross disclose:
a system for determining inconsistencies associated with a business, comprising (Ross [Abstract], “systems and methods that improve the traditional underwriting process within a financial institution”, and Figure 1):
a plurality of electronic devices executing a data application, the data application is configured to receive an application associated with a consumer and a decision associated with the application (Ross [Column 5, Lines 3-5], “internal database 106 receives and stores outcome data and provides said outcome data to underwriting platform 108”, and Figure 1);
a platform accessible by the plurality of electronic devices, the platform receives the application associated with a decision for the consumer, the application includes a plurality of factors associated with a user (Ross [Column 1, Lines 55-58], [Column 5, Lines 3-5], “the system includes one or more client computing devices, one or more external sources, one or more internal databases, an underwriting platform, and one or more of the following software modules: analytical engine, ranking module, underwriting heuristics, and decision tools … internal database 106 receives and stores outcome data and provides said outcome data to underwriting platform 108”, and Figure 1), determines a subpopulation most similar to the application utilizing(Ross [Column 5, Lines 5-9], “Examples of internal data include insurance type requested, insurance amount requested, electronic copy of the insurance application, and note from interviews with insurance company representatives, underwriter profiles, among others”, and Figure 1) , retrieves a positive outcome ratio for the subpopulation most similar to the application, the positive outcome ratio being derived from historical decisions without application of risk scoring or underwriter grading (Ross [Column 5, Lines 20-24], [Column 8, Lines 21-27], “underwriting platform 108 within system 100 retrieves internal data from internal database 106, outcome data from external database 104, and processes the received internal and outcome data to determine and otherwise select the best underwriters … the resolution provided by underwriting model 412 is a risk score calculation, a risk of loss assessment, or a risk classification, among others. In another embodiment, underwriting model 412 emulates the resolution patterns of top performing underwriters by using artificial intelligence tools such as expert systems and fuzzy logic”, and Figures 1, 4), determines whether the decision is consistent with positive outcomes for the subpopulation (Ross [Column 6, Lines 36-39], “Underwriting platform 108 further includes analytical engine 306, ranking module 308, underwriting heuristics module 310, and decision tools 312”, and Figure 3), automatically generates a report of inconsistencies associated with the decision in response to determining the decision is not consistent with the positive outcomes for the subpopulation (Ross [Column 1, Lines 39-45], [Column 10, Lines 7-9], “the underwriting process may be biased by the judgment of the underwriter. Variation in factors such as underwriter training, experience, and quality of previous assessments may cause underwriters to make different decisions and judgments. As a result, there can be a large amount of variability and inconsistencies in the insurance underwriting process … the previously built and tested underwriting model is deployed into a set of decision tools within the underwriting platform”, and Figure 5), and communicates a report from the platform to the plurality of electronic devices authorized to receive inconsistencies associated with the decision (Ross [Column 10, Lines 9-13], “decision tools help to validate and identify when an actual decision, within the underwriting process, varies significantly from the heuristic decisions made by the best underwriters and sends an alert to the user before proceeding with that decision”, and Figure 5)
Ross does not explicitly disclose:
[[determines a subpopulation of applications from historical applications and decision data most similar to the application using a machine learning model configured to operate without reliance on a user and stored in a memory of the platform]]]
Brenner discloses [[determines a subpopulation of applications from historical applications and decision data most similar to the application using a machine learning model configured to operate without reliance on a user and stored in a memory of the platform]] (See at least Wellmann [0052], [0060], “the explainability engine component 323 may identify the most important features that contributed to a particular machine learning model decision while also identifying any biases or inconsistencies in the model's decision-making process, thereby improving the trust and accountability of machine learning systems … a step 530, where anomaly detection may be performed based on, e.g., transformation rules and historical data trends. Anomalies may be detected, e.g., by identifying data points that are significantly different from the rest of the data set, or by detecting unusual patterns or trends”, see also Figures 3, 5). It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the system of Ross to include a machine learning model to identify inconsistencies between a decision and historical decisions associated with a subpopulation as taught by Wellmann, in order to computing loan option score of a customer (see Wellmann Paragraphs [0052], [0060], Figures 3, 5).
Claim 12 –
Ross/Wellmann disclose the system of claim 11, as shown above.
Ross further disclose:
further comprising: one or more databases storing public information associated with the subpopulation including at least applications, factors, and decisions for each user that is part of the subpopulation (Ross [Column 8, Lines 21-27], “the resolution provided by underwriting model 412 is a risk score calculation, a risk of loss assessment, or a risk classification, among others. In another embodiment, underwriting model 412 emulates the resolution patterns of top performing underwriters by using artificial intelligence tools such as expert systems and fuzzy logic”, and Figure 4).
Claim 13 –
Ross/Wellmann disclose the system of claim 12, as shown above.
Ross further disclose:
wherein the platform further: automatically accesses public data or private data utilizing the logic to find the subpopulations most similar to the application utilizing the plurality of factors; and automatically generates one or more narratives associated with the inconsistencies (Ross [Column 8, Lines 21-27], “the resolution provided by underwriting model 412 is a risk score calculation, a risk of loss assessment, or a risk classification, among others. In another embodiment, underwriting model 412 emulates the resolution patterns of top performing underwriters by using artificial intelligence tools such as expert systems and fuzzy logic”, and Figure 4)..
Claim 14 –
Ross/Wellmann disclose the system of claim 11, as shown above.
Ross further disclose:
wherein the platform includes artificial intelligence that implements models to generate the subpopulation, determination of whether the decision is consistent with the positive outcomes for the subpopulation, the inconsistencies, and the report (Ross [Column 8, Lines 21-27], “the resolution provided by underwriting model 412 is a risk score calculation, a risk of loss assessment, or a risk classification, among others. In another embodiment, underwriting model 412 emulates the resolution patterns of top performing underwriters by using artificial intelligence tools such as expert systems and fuzzy logic”, and Figure 4).
Claim 15 –
Ross/Wellmann disclose the system of claim 11, as shown above.
Ross further disclose:
wherein the inconsistencies indicate one or more discrepancies associated with the factors or non-relevant factors that were not considered in the decision (Ross [Column 8, Lines 21-27], “the resolution provided by underwriting model 412 is a risk score calculation, a risk of loss assessment, or a risk classification, among others. In another embodiment, underwriting model 412 emulates the resolution patterns of top performing underwriters by using artificial intelligence tools such as expert systems and fuzzy logic”, and Figure 4).
Claim 16 –
Ross disclose:
a platform for determining inconsistencies associated with an application, comprising: a processor executing a set of instructions; a memory storing the set of instructions, wherein the instructions are executed to Ross [Column 1, Lines 55-58], [Column 5, Lines 3-5], “the system includes one or more client computing devices, one or more external sources, one or more internal databases, an underwriting platform, and one or more of the following software modules: analytical engine, ranking module, underwriting heuristics, and decision tools … internal database 106 receives and stores outcome data and provides said outcome data to underwriting platform 108”, and Figure 1): receive an application associated with a decision, the application includes a plurality of factors associated with a user (Ross [Column 5, Lines 3-5], “internal database 106 receives and stores outcome data and provides said outcome data to underwriting platform 108”, and Figure 1),
determine a subpopulation most similar to the application utilizing (Ross [Column 5, Lines 5-9], “Examples of internal data include insurance type requested, insurance amount requested, electronic copy of the insurance application, and note from interviews with insurance company representatives, underwriter profiles, among others”, and Figure 1), retrieves a positive outcome ratio for the subpopulation most similar to the application, the positive outcome ratio being derived from historical decisions without application of risk scoring or underwriter grading (Ross [Column 5, Lines 20-24], [Column 8, Lines 21-27], “underwriting platform 108 within system 100 retrieves internal data from internal database 106, outcome data from external database 104, and processes the received internal and outcome data to determine and otherwise select the best underwriters … the resolution provided by underwriting model 412 is a risk score calculation, a risk of loss assessment, or a risk classification, among others. In another embodiment, underwriting model 412 emulates the resolution patterns of top performing underwriters by using artificial intelligence tools such as expert systems and fuzzy logic”, and Figures 1, 4),
determine whether the decision is consistent with positive outcomes for the subpopulation (Ross [Column 6, Lines 36-39], “Underwriting platform 108 further includes analytical engine 306, ranking module 308, underwriting heuristics module 310, and decision tools 312”, and Figure 3),
automatically generate a report of inconsistencies associated with the decision in response to determining the decision is not consistent with the positive outcomes for the subpopulation (Ross [Column 1, Lines 39-45], [Column 10, Lines 7-9], “the underwriting process may be biased by the judgment of the underwriter. Variation in factors such as underwriter training, experience, and quality of previous assessments may cause underwriters to make different decisions and judgments. As a result, there can be a large amount of variability and inconsistencies in the insurance underwriting process … the previously built and tested underwriting model is deployed into a set of decision tools within the underwriting platform”, and Figure 5), and
communicates a report from the platform to the plurality of electronic devices authorized to receive inconsistencies associated with the decision (Ross [Column 10, Lines 9-13], “decision tools help to validate and identify when an actual decision, within the underwriting process, varies significantly from the heuristic decisions made by the best underwriters and sends an alert to the user before proceeding with that decision”, and Figure 5)
Ross does not explicitly disclose:
[[determine, using a machine learning model trained on historical applications and decision data and configured to operate without reliance on a user and stored in the memory, a subpopulation of applications from the historical applications and decision data most similar to the application; identify, by the machine learning model, inconsistencies between the decision and historical decisions associated with the subpopulation]]
Brenner discloses [[determine, using a machine learning model trained on historical applications and decision data and configured to operate without reliance on a user and stored in the memory, a subpopulation of applications from the historical applications and decision data most similar to the application; identify, by the machine learning model, inconsistencies between the decision and historical decisions associated with the subpopulation]] (See at least Wellmann [0052], [0060], “the explainability engine component 323 may identify the most important features that contributed to a particular machine learning model decision while also identifying any biases or inconsistencies in the model's decision-making process, thereby improving the trust and accountability of machine learning systems … a step 530, where anomaly detection may be performed based on, e.g., transformation rules and historical data trends. Anomalies may be detected, e.g., by identifying data points that are significantly different from the rest of the data set, or by detecting unusual patterns or trends”, see also Figures 3, 5). It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the platform of Ross to include a machine learning model to identify inconsistencies between a decision and historical decisions associated with a subpopulation as taught by Wellmann, in order to computing loan option score of a customer (see Wellmann Paragraphs [0052], [0060], Figures 3, 5).
Claim 17 –
Ross/Wellmann disclose the platform of claim 16, as shown above.
Ross further disclose:
wherein the platform communicates with one or more databases storing public information associated with the subpopulation including at least applications, factors, and decisions for each user that is part of the subpopulation (Ross [Column 8, Lines 21-27], “the resolution provided by underwriting model 412 is a risk score calculation, a risk of loss assessment, or a risk classification, among others. In another embodiment, underwriting model 412 emulates the resolution patterns of top performing underwriters by using artificial intelligence tools such as expert systems and fuzzy logic”, and Figure 4).
Claim 18 –
Ross/Wellmann disclose the platform of claim 16, as shown above.
Ross further disclose:
wherein the platform includes artificial intelligence that implements models to generate the subpopulation, determination of whether the decision is consistent with the positive outcomes for the subpopulation, the inconsistencies, and the report (Ross [Column 8, Lines 21-27], “the resolution provided by underwriting model 412 is a risk score calculation, a risk of loss assessment, or a risk classification, among others. In another embodiment, underwriting model 412 emulates the resolution patterns of top performing underwriters by using artificial intelligence tools such as expert systems and fuzzy logic”, and Figure 4).
Claim 19 –
Ross/Wellmann disclose the platform of claim 16, as shown above.
Ross further disclose:
wherein the inconsistencies indicate one or more discrepancies associated with the factors or non-relevant factors that were not considered in the decision (Ross [Column 8, Lines 21-27], “the resolution provided by underwriting model 412 is a risk score calculation, a risk of loss assessment, or a risk classification, among others. In another embodiment, underwriting model 412 emulates the resolution patterns of top performing underwriters by using artificial intelligence tools such as expert systems and fuzzy logic”, and Figure 4).
Claim 20 –
Ross/Wellmann disclose the platform of claim 16, as shown above.
Ross further disclose:
wherein the report includes at least suggestions to remove the inconsistencies from the application process (Ross [Column 10, Lines 9-13], “decision tools help to validate and identify when an actual decision, within the underwriting process, varies significantly from the heuristic decisions made by the best underwriters and sends an alert to the user before proceeding with that decision”, and Figure 5).
Response to Applicant’s Arguments
10. 35 U.S.C. §101 Rejections: Applicant’s arguments with respect to amended claims 1-20 that are rejected under 35 U.S.C. 101 have been considered but they are not persuasive because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Applicant’s Argument: From Applicant Arguments/Remarks, Applicants respectfully submit that the amended independent claim recites receiving an application associated with a decision utilizing a data platform, followed by determining, using a machine learning model trained on historical applications and decision data and configured to operate without reliance on a user, a subpopulation of applications from the historical applications and decision data most similar to the application based on applicable characteristics and factors … Importantly, the claim further specifies that the machine learning model is trained on historical applications and decision data and operates without reliance on a user, and that the positive outcome ratio is derived from historical decisions without application of risk scoring or underwriter grading. This language defines a non-conventional machine learning architecture that improves decision-analysis technology by eliminating subjective human inputs and predictive scoring mechanisms that introduce bias and model risk. This constitutes an improvement to the functioning of the data platform itself, consistent with the type of technological improvements found patent-eligible in Enfish and McRO … they define a specific analytical workflow performed by the machine learning model that detects deviations between current decisions and historical decision outcomes within a data-derived peer group. This process produces new technical insight that did not previously exist in the data platform and directly improves automated decision-evaluation systems … The claim further recites automatically generating a report of the inconsistencies..., utilizing the data platform..., and communicating a report... to one or more authorized parties. These steps integrate the machine learning analysis into a real-world operational pipeline, ensuring the claim is applied in a practical manner and produces a tangible, actionable output. The report is not a mere display of information but is generated in response to machine-identified inconsistencies and enables corrective action, auditability, or system-level feedback. As such, the claim integrates any alleged abstract idea into a practical application, satisfying USPTO Step 2A, Prong Two (MPEP §2106.05(a)). Even assuming, arguendo, that the claim is viewed as involving an abstract idea, the claim nonetheless recites significantly more under Step 2B … as reflected in Example 42, claims are eligible where artificial intelligence is used to improve the reliability, accuracy, or performance of a system, rather than simply producing information for human review. Here, the claim requires that the machine learning model be configured to operate without reliance on a user, and that outcome ratios be derived without application of risk scoring or underwriter grading. These limitations define a concrete technical improvement over conventional decision systems that rely on human-defined weights, predictive scores, or subjective grading. By eliminating such reliance and instead using historical decision outcomes to identify inconsistencies, the claimed invention improves the technological process of automated decision consistency analysis itself, consistent with USPTO guidance under MPEP §2106.05(a). Accordingly, when evaluated as a whole, the amended claim is directed to a patent-eligible technological improvement to data platforms and machine learning-based decision analysis systems, not to an abstract idea. Applicant therefore respectfully requests withdrawal of the §101 rejection (See Applicant Arguments/Remarks Pages 2-5).
In response to Applicant’s arguments, Examiner respectfully disagrees and submits that unlike the claims in Enfish and McRO, independent claims 1, 11, and 16 further to the abstract idea include additional elements of “a data platform”, “a machine learning model”, “a memory of the data platform”, and “logic of the data platform”. However, the additional elements recite generic computer components such as a computer, computing devices, a server, and/or software programing that are recited a high-level of generality that merely perform, conduct, carry out, implement, and/or narrow the abstract idea itself. Accordingly, the additional elements evaluated individually and in combination do not integrate the abstract idea into a practical application because they comprise or include limitations that are not indicative of integration into a practical application such as adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea -- See MPEP 2106.05(f). None of the additional elements taken individually or when taken as an ordered combination amount to significantly more than the abstract idea.
Examiner also submits that the features recited in amended independent claims 1, 11, and 16 allow for fair lending process of making consistent loan decisions based on someone’s credit worthiness, which is a solution of a business but not an improvement of a technology/technical field. See details of Claim Rejections - 35 USC § 101 in the section above.
11. 35 U.S.C. §103 Rejections: Applicant’s arguments with respect to amended 1-20 that are rejected under 35 U.S.C. 103 as being unpatentable over anticipated by Ross et al. (U.S. Patent No. 11,854,088), hereinafter, “Ross”, in view of Brenner (U.S. Pub. No. 2011/0055065), hereinafter, “Brenner” have been considered but are moot in view of the new ground(s) of rejection (See Applicant Arguments/Remarks Pages 5-8).
Examiner notes that the new prior art Wellmann teach the amended claim limitations of independent claims 1, 11, and 16 “determining, using a machine learning model trained on historical applications and decision data and configured to operate without reliance on a user and stored in at least a memory of the data platform, a subpopulation of applications from the historical applications and decision data most similar to the application based on applicable characteristics and factors of the application; identifying, by the machine learning model, inconsistencies between the decision and historical decisions associated with the subpopulation” as in (See at least Wellmann [0052], [0060], “the explainability engine component 323 may identify the most important features that contributed to a particular machine learning model decision while also identifying any biases or inconsistencies in the model's decision-making process, thereby improving the trust and accountability of machine learning systems … a step 530, where anomaly detection may be performed based on, e.g., transformation rules and historical data trends. Anomalies may be detected, e.g., by identifying data points that are significantly different from the rest of the data set, or by detecting unusual patterns or trends”, see also Figures 3, 5). See details of Claim Rejections - 35 USC § 103 of amended claims 1-20 in the section above.
Relevant Prior Art
12. The prior art made of record and not relied upon are considered pertinent to applicant's disclosure:
Cochran et al. (U.S. Pub. No. 2005/0033710) teach modeling decision making process.
Conclusion
13. 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 date of this final action.
14. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Liz Nguyen whose telephone number is (571) 272-5414. The examiner can normally be reached on Monday to Friday 8:00 A.M to 5:00 P.M.
15. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Gart, can be reached on (571) 272-3955. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
16. Information regarding the status of an application may be obtained from the Patent Center system (visit: https://patentcenter.uspto.gov). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call (800) 786-9199 (USA or CANADA) or (571) 272-1000.
/LIZ P NGUYEN/
Examiner, Art Unit 3696
/MATTHEW S GART/Supervisory Patent Examiner, Art Unit 3696
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