Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Introduction
1. The following is a NON-FINAL Office Action in response to the communication received on 02/03/26. Claims 1-20 are now pending in this application.
2. A request for continued examination (RCE) under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application AFTER FINAL rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the FINALITY of the previous Office Action has been WITHDRAWN pursuant to 37 CFR 1.114. Applicant's submission filed on 12/30/25 has been entered.
Response to Amendments
3. Applicants Amendment has been acknowledged in that: Claims 1, 5, 9-11, 16 have been amended; hence such, claims 1-20 are now pending in this application.
RESPONSE TO ARGUMENTS
Applicant argues#1
For example, representative claim 1 as a whole integrates into a practical application because it is directed to improvements in the technical field of evaluating and recommending alternative data sources using machine learning techniques. Paragraph 13 of the specification states that "[s]ome implementations described herein relate to a decisioning system that may use one or more alternative data sources to estimate a creditworthiness for a loan or credit applicant and use one or more machine learning techniques to evaluate and recommend alternative data sources that are most likely to be effective in generating a credit decision for a loan or credit applicant with an insufficient credit history and/or a low credit score based on one or more profile attributes associated with the loan or credit applicant (e.g., depending on whether the applicant is a student, an immigrant, or associated with other suitable profile attributes)" and "[i]n this way, when subsequent users access the credit application, the available data sources may be filtered or customized based on the available data sources that are most likely to be effective in informing the credit decision. In this way, by recommending alternative data sources that are most likely to provide information relevant to the credit decision, the decisioning system may conserve resources that would otherwise have been wasted communicating with alternative data sources providing data that is likely to be ineffective in informing the credit decision and/or processing data obtained from such alternative data sources."
Thus, the specification explicitly discusses a technical solution that improves upon the inefficient and wasteful processes of evaluating and recommending alternative data sources using machine learning techniques.
Examiner Response
Examiner respectfully disagrees.
Examiner reproduces para 13 of the spec along with other spec paras:
[0013]Some implementations described herein relate to a decisioning system that may use one or more alternative data sources to estimate a creditworthiness for a loan or credit applicant and use one or more machine learning techniques to evaluate and recommend alternative data sources that are most likely to be effective in generating a credit decision for a loan or credit applicant with an insufficient credit history and/or a low credit score based on one or more profile attributes associated with the loan or credit applicant (e.g., depending on whether the applicant is a student, an immigrant, or associated with other suitable profile attributes). For example, in cases where a credit applicant has an insufficient credit history or a low credit score, the decisioning system may allow one or more alternative data sources (e.g., other than credit bureau data) to be used to determine income status, a bill payment history, a rental payment history, or other information that may be relevant to the creditworthiness of the applicant. However, the effectiveness or relevance of certain alternative data sources may vary depending on the type of applicant. For example, utility or telecommunication bill payments may have little or no relevant data for a student who has spent their whole life on their parents’ accounts. In another example, an immigrant may have no history of making rent payments on time. Accordingly, the decisioning system may support techniques to allow a credit applicant to select one or more alternative data sources to be relied upon when there is insufficient credit bureau data or the credit bureau indicates a low credit score. The decisioning system may then use machine learning techniques to evaluate the effectiveness of the alternative data source(s) used to render the credit decision. In this way, when subsequent users access the credit application, the available data sources may be filtered or customized based on the available data sources that are most likely to be effective in informing the credit decision. In this way, by recommending alternative data sources that are most likely to provide information relevant to the credit decision, the decisioning system may conserve resources that would otherwise have been wasted communicating with alternative data sources providing data that is likely to be ineffective in informing the credit decision and/or processing data obtained from such alternative data sources.
[0015] As shown in Fig. 1A, and by reference number 105, the decisioning system may present, to a first client device associated with a first user (e.g., shown as “User A”), an interface that allows the first user to submit an application for a credit product. For example, in some implementations, the first client device may access the decisioning system via a website, a mobile application, or another suitable channel that allows the first user to submit an application for a credit product, such as a credit card, a mortgage, a personal loan, a vehicle loan, or the like, to submit an application for pre-qualification and/or pre-approval for a credit product, and/or to submit an application to increase an amount of available credit or change existing credit terms, among other examples. In some implementations, as shown by reference number 110, the interface may include one or more fields or user interface elements to allow the first user to enter personal information (e.g., a name, birth date, and/or social security number). In addition, the interface may include one or more fields or other user interface elements for the first user to indicate contact information (e.g., a mailing address, a phone number, and/or an email address) and financial information (e.g., income, residential status, employment status, and/or an approximate amount that the first user spends on recurring debt payments and/or credit cards).
[0026]As shown in Fig. 1B, and by reference number 140, the decisioning system may use one or more artificial intelligence or machine learning models to evaluate an effectiveness of the data sources that were used to generate the decision on the application of the first user. For example, in some implementations, the alternative data sources that are most helpful or effective to generate the decision on the application of the first user may vary depending on the applicant or consumer type. For example, a student with little to no credit history is unlikely to have a significant history of paying utility, phone, or other bills on time, but data available from academic reports or academic institutions may be more likely to be effective or relevant in predicting the creditworthiness of the student applicant. In another example, for an immigrant with no domestic credit history but an extensive credit history in a home country, alternative data sources such as banks, bill payment histories, and/or rental payment histories may be more relevant or effective than academic reports. Furthermore, whether alternative data sources were used or influenced the decision on the credit application may be evaluated even for users with credit scores or credit report data available from the primary data source (e.g., because the alternative data sources may indicate risk factors that are not reflected in traditional credit scores or credit report data and/or may bolster a good credit score or credit report data). Accordingly, in some implementations, the decisioning system may use the artificial intelligence or machine learning models to evaluate the data sources that were used to generate the decision on the credit application of the first user in combination with one or more attributes related to a profile of the first user (e.g., whether the first user is a credit invisible, a student, an immigrant, a new-to-credit consumer, a consumer with an extensive domestic credit history, or the like, a type of credit product that the first user is applying for, and/or whether the user is associated with a demographic profile that is more likely to have no credit history or insufficient current credit history to produce a credit score, among other examples). In this way, the decisioning system may predict and/or generate recommendations about which alternative data source combinations are most effective for future applicants associated with certain attributes or profile information. In this way, the predictions and/or recommendations about which alternative data source combinations are most effective for future applicants may be used to determine which alternative data sources are presented or recommended to future applicants applying for credit.
0034]The target variable may represent a value that a machine learning model is being trained to predict, and the feature set may represent the variables that are input to a trained machine learning model to predict a value for the target variable. The set of observations may include target variable values so that the machine learning model can be trained to recognize patterns in the feature set that lead to a target variable value. A machine learning model that is trained to predict a target variable value may be referred to as a supervised learning model.
[0035] In some implementations, the machine learning model may be trained on a set of observations that do not include a target variable. This may be referred to as an unsupervised learning model. In this case, the machine learning model may learn patterns from the set of observations without labeling or supervision, and may provide output that indicates such patterns, such as by using clustering and/or association to identify related groups of items within the set of observations.
[0036] As shown by reference number 220, the machine learning system may train a machine learning model using the set of observations and using one or more machine learning algorithms, such as a regression algorithm, a decision tree algorithm, a neural network algorithm, a k-nearest neighbor algorithm, a support vector machine algorithm, or the like. For example, in some implementations, the machine learning system may evaluate whether data obtained from a given alternative data source was effective in informing a credit decision for a given credit applicant based on a type or profile associated with the credit applicant, based on whether the credit decision was to approve or reject the credit application, and/or based on one or more terms (e.g., an interest rate) that were offered in cases where the credit decision was to approve the credit application, among other examples. After training, the machine learning system may store the machine learning model as a trained machine learning model 225 to be used to analyze new observations.
Applicant argued the claims present a technical improvement. Examiner does not find this argument persuasive. Applicant’s claims do not improve technology; the underlying technology remains unaffected by the claims. Applicant is merely using existing technology (for its intended purpose) to implement the steps of the identified abstract idea. Any improvements lie in the abstract idea itself, not in underlying technology
The additional limitations (outside of the abstract idea) in the claim and disclosed in the spec paras cited above (an interface associated with an application to a first client device, a second client device and the machine learning model) are recited at a high level of generality, are being used in their ordinary capacity, and are being used as tools to implement the steps of the identified abstract idea, see MPEP 2106.05(f).
Therefore, there are no additional elements that are indicative of integration into a practical application.
The rejection is maintained.
Applicant argues#2
In this regard, representative claim 1, as amended, recites, for example, one or more processors to, "electronically communicate with at least one of the one or more alternative data sources that are selected via the interface; generate a decision associated with the application for the first user based on electronically communicating with the at least one of the one or more alternative data sources; evaluate, using a machine learning model, an effectiveness of the one or more alternative data sources used to generate the decision associated with the application for the first user; and
present the interface associated with the application to a second client device
associated with a second user, wherein the interface presented to the second client device indicates, based on filtering available data sources using the machine learning model after the machine learning model is trained based on the effectiveness of the one or more alternative data sources used to generate the decision associated with the
application for the first user, a second set of alternative data sources that are likely
to be effective for providing information related to behavioral attributes of the
second user."
Therefore, the above features of representative claim 1 provide an improvement to a
technology or technical field. Thus, the claims integrate the alleged abstract idea into a practical application and are patent-eligible under 35 U.S.C. § 101.
Accordingly, Applicant respectfully requests that the Examiner reconsider and withdraw the rejection of claims 1-20 under 35 U.S.C. § 101.
Examiner Response
Examiner respectfully disagrees.
The limitations (communicate with at least one of the one or more alternative data sources that are selected; generate a decision associated with the application for the first user based on communicating with the at least one of the one or more alternative data sources; evaluate, an effectiveness of the one or more alternative data sources used to generate the decision associated with the application for the first user; and present the application to a second user, indicates, based on filtering available data source on the effectiveness of the one or more alternative data sources used to generate the decision associated with the application for the first user, a second set of alternative data sources that are likely to be effective for providing information related to behavioral attributes of the second user) are part of the identified abstract idea.
The additional limitations outside of the abstract idea (the interface, using the machine learning model after the machine learning model is trained based on the effectiveness of the one or more alternative data sources and the second client device) are recited at a high level of generality (operating in their ordinary capacity) and are being used as tool to implement the steps of the identified abstract idea, see MPEP 2106.05(f).
The rejection is maintained.
Claim Rejections- 35 U.S.C § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
1. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims are either directed to a method, system and computer readable medium which are one of the statutory categories of invention. (Step 1: YES).
Representative Claim 1 recites the limitations of:
A system for evaluating alternative data sources, the system
comprising: one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to:
present an interface associated with an application to a first client device
associated with a first user,
wherein the interface indicates a first set of alternative data sources
available for providing information related to behavioral attributes of the first
user;
receive, from the first client device via the interface, a request indicating one or more alternative data sources that are selected, from the first set of alternative data sources, for providing information related to the behavioral attributes of the first user;
electronically communicate with at least one of the one or more alternative data sources that are selected via the interface;
generate a decision associated with the application for the first user based on electronically communicating with the at least one of the one or more alternative data sources;
evaluate, using a machine learning model, an effectiveness of the one or more alternative data sources used to generate the decision associated with the application for the first user; and
present the interface associated with the application to a second client device
associated with a second user,
wherein:
the interface presented to the second client device indicates, based on
filtering available data sources using the machine learning model after the
machine learning model is trained based on the effectiveness of the one or more
alternative data sources used to generate the decision associated with the
application for the first user, a second set of alternative data sources that are likely to be effective for providing information related to behavioral attributes of the second user.
The claim recites elements that are in bold above, (e.g., present an application to a
first user, indicates a first set of alternative data sources available for providing information related to behavioral attributes of the first user; receive, a request indicating one or more alternative data sources that are selected, from the first set of alternative data sources, for providing information related to the behavioral attributes of the first user; communicate with at least one of the one or more alternative data sources that are selected; generate a decision associated with the application for the first user based on communicating with the at least one of the one or more alternative data sources; evaluate, an effectiveness of the one or more alternative data sources used to generate the decision associated with the application for the first user; and present the application to a second user,
indicates, based on filtering available data source on the effectiveness of the one or more alternative data sources used to generate the decision associated with the application for the first user, a second set of alternative data sources that are likely to be effective for providing information related to behavioral attributes of the second user), under its broadest reasonable interpretation, covers performance of the limitation(s) as a mental process, more specifically a concept performed mentally by a human with a pen and paper, (steps for generating a recommendation of alternative data sources based on a set observations related to an effectiveness of alternative data sources)
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a certain method of a concept performed in the human mind, then it falls within the “mental process” grouping of abstract ideas. Accordingly, claim 1 recites an abstract idea. Claims 10,16 recite substantially the same subject matter as claim 1 and are abstract for similar reasons.
(Step 2A-Prong 1: YES. The claims are abstract)
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) 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 (MPEP 2106.05.f), (2) Adding insignificant extra solution activity to the judicial exception (MPEP 2106.05.g), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05.h).
Claims 1, ,10, 16 includes the following additional elements:
-One or more memories
-One or more processors
-Using the machine learning model after the machine learning model is trained based on the effectiveness of the one or more alternative data sources
-An interface associated with an application to a first client device
-A first client device
-A second client device
-A decisioning system
-A non-transitory computer readable medium
The one or more memories, one or more processors, using the machine learning model after the machine learning model is trained based on the effectiveness of the one or more alternative data sources, interface associated with an application to a first client device, second client device, a decisioning system and a non-transitory computer readable medium are recited at a high level of generality and being used in its ordinary capacity and are being used as a tool for implementing the steps of the identified abstract idea, see MPEP 2106.05(f), where applying a computer or using a computer as a tool to perform the abstract idea is not indicative of a practical application.
Therefore, there are no additional elements in the claim that amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use.
Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 1, 10,16 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using computer hardware amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use. Generally linking the use of the judicial exception to a particular technological environment or field of use, with the use of generic computer components, cannot provide an inventive concept - rendering the claim patent ineligible. Thus claims 1,10, 16 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 2-9, 11-15, 17-20 which further define the abstract idea that is present in their respective independent claims 1, 10, 16 and thus correspond to a Mental process and hence are abstract for the reasons presented above.
Therefore, the dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination.
Therefore, the dependent claims 2-9, 11-15, 17-20 are directed to an abstract idea. Thus, claims 1-20 are not patent-eligible.
CONCLUSION
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD Z SHAIKH whose telephone number is (571)270-3444. The examiner can normally be reached M-T, 9-600; Fri, 8-11, 3-5.
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/MOHAMMAD Z SHAIKH/Primary Examiner, Art Unit 3694
4/2/2026
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