DETAILED ACTION
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 .
This is the Non-Final Office Action in response to the Amendment filed on February 13, 2026 for Application No. 18/868,521 filed on November 22, 2024, title: “Credit Learning Device, Credit Learning Method, Credit Estimation Device, Credit Estimation Method And Medium”.
Status of the Claims
Claims 1-27 were restricted. By the 02/13/2026 Response, claims 13-24 (Group III) have been elected for prosecution with traverse, and claims 1-12 (Groups I and II) and 25-27 (Group IV) are not elected. In view of Applicant’s comments, claims 25-27 (Group IV) are examined together with claims 13-24 (Group III). Accordingly, claims 13-27 are pending in this application and have been examined.
Priority
This application was filed on 11/22/2024 and is a 371 of PCT/JP2023/020116 filed on 05/30/2023. This application claims the priority of foreign application JAPAN 2022-202883 filed on 12/20/2022
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed on 11/22/2024. For the purpose of examination, the date 10/04/2022 is considered to be the effective filing date.
Response to Election/Restrictions
In view of Applicant’s comments filed on 02/13/2026, Group IV (claims 25-27) and Group III (claims 23-24) are examined together. The rejection of Group I (claims 1-9) and Group II (claims 10-12) is MAINTAINED.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/22/2024 and 06/18/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS are being considered by the examiner. Copies of the PTO-1449 form with the examiner’s initials are enclosed to this Office Action.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 13-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Under the 2019 Revised PEG, Step 1 analysis, the claims are reviewed to determine whether they fall within the four statutory categories of patentable subject matter (i.e., process, machine, manufacture, or combination of matter).
Claims 13-22 recite a credit learning device comprising processors and memories, claim 23 recites a credit learning method comprising steps, and claim 24 recites a non-transitory computer-readable recording medium having recorded a program. Therefore, the claims recite a machine, process, and manufacture which fall within the four statutory categories of invention (Step 1-Yes, the claims are statutory).
Step 2A Prong 1:
Under the 2019 Revised PEG, Step 2A, Prong 1, the claims are reviewed to determine whether they recite a judicial exception by identifying if the claim limitations fall in one of the enumerated abstract idea groupings (i.e., organizing human activity, mathematical concepts, and mental processes) that amount to a judicial exception to patentability.
Claim 23, A credit learning method that causes a computer to perform:
training a first model using first training data that includes a combination of a first attribute data group related to a user belonging to a first segment among users who use a first service and a label corresponding to a score that varies according to a degree of risk borne by a provider of a second service when the user uses a second service different from the first service; and
training a second model, which has some of parameters of a configuration of the trained first model as fixed parameters, using second training data that includes a combination of a second attribute data group related to a user belonging to a second segment different from the first segment among the users who use the first service and a label corresponding to a score that varies according to a degree of risk borne by a provider of the second service when the user uses the second service.
The claim limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers a method of organizing human activity but for the recitation of generic computer components (e.g., a credit learning device comprising processor and memory with stored computer program – see claims 13 and 24). More specifically, the claim recites a method of using user’s inputted training data to train the credit learning models by: training a first model using first training data that includes a combination of a first attribute data group related to a user belonging to a first segment among users who use a first service and a label corresponding to a score that varies according to a degree of risk provided by a second service; and training a second model using second training data that includes a combination of a second attribute data group related to a user belonging to a second segment different from the first segment among the users who use the first service and a label corresponding to a score that varies according to a degree of risk provided by the second service.
The claim recites a method of organizing human activity with two essential steps for training the first/second credit learning models using the user’s inputted first/second training data to enhance the models in order to estimate the user’s creditworthiness. The claim recites the abstract idea of a method of organizing human activity because it relates to fundamental economic principles or practices (e.g. hedging, insurance, mitigating risk) and/or commercial or legal interactions (e.g. agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). See MPEP 2106.04(a)(2)III.C.2.
If a claim limitation, under its broadest reasonable interpretation, covers performance of a method for monitoring a user’s data, then, it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Claim 13 recites a credit learning device and claim 24 recites a computer program with comparable elements and limitations as discussed in claim 23. Therefore, these claims also recite the abstract idea (Step 2A Prong 1-Yes, the claims recite an abstract idea).
Step 2A Prong 2:
Under the 2019 Revised PEG, Step 2A, Prong 2, the claims are reviewed to determine whether the judicial exception (i.e., abstract idea) is integrated into a practical application. In order to make this determination, the additional element(s), or combination of elements, are analyzed to determine if the claim as a whole integrates the recited judicial exception into a practical application of that exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.
The judicial exception is not integrated into a practical application. In particular, the claims (13 and 23-24) recite the additional computer elements, such as the credit learning device comprising processors and memories with stored program to perform the training steps and functions. The recited additional elements in all steps are recited at a high level of generality and the limitations are done by the generically recited computer system (i.e., as a generic processor performing generic computer functions such as training the first model with the first training data and training the second mode with the second training data) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. This is substantiated by the Applicant’s Specification in paragraphs 26-37 and Figure 1 (see US Publication No. 2025/0335985). The claims recite a method with two essential steps for training the first/second credit learning models using the user’s inputted first/second training data to enhance the models in order to estimate the user’s creditworthiness. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea (Step 2A Prong 2-No, the claims are not integrated into a practical application).
Step 2B:
Under the 2019 Revised PEG, Step 2A, Prong 2, the claims are reviewed to determine whether the claims provide an inventive concept (i.e., whether the claim(s) include additional elements, or combinations of elements, that are sufficient to amount to significantly more than the judicial exception (i.e., abstract idea)).
The independent claims do not include additional elements, considered both individually and as an ordered combination, 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 using a computer to perform the training of the first/second models as claimed amounts to no more than mere instructions to apply the exception using a generic computer component. The generic computer functions are well-understood, routine, and conventional activities previously known to the industry similar to those referenced by MPEP 2106.05(d)II. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, the independent claims are not patent eligible.
Dependent claims 14-22 depend on claim 13, and therefore include all the limitations of claim 13. Therefore, the dependent claims also recite the same abstract idea as in their independent claim.
Claim 14 recites the additional elements “the processor further executes determining the user belonging to the first segment and the user belonging to the second segment by segmenting the users who use the first service.” (The elements are additional details for the processor to segment the users who use the first service). This claim individually or in combination with others do not integrate the abstract idea into a practical application or add an inventive concept to the abstract idea).
Claim 15 recites the additional elements “wherein the processor segments the users who use the first service on a basis of attribute data of the users who use the first service.” (The elements are additional details for the processor to segment the users who use the first service on a basis of attributed data of the users). This claim individually or in combination with others do not integrate the abstract idea into a practical application or add an inventive concept to the abstract idea).
Claim 16 recites the additional elements “wherein the processor segments the users who use the first service on a basis of a usage frequency of the first service within a prescribed period.” (The elements are additional details for the processor to segment the users who use the first service on a basis of a usage frequency of the first service). This claim individually or in combination with others do not integrate the abstract idea into a practical application or add an inventive concept to the abstract idea).
Claim 17 recites the additional elements “wherein the second service refers to a deferred payment service, and the risk borne by the provider of the second service refers to deferred payment risk, which is determined on a basis of a payment history in the deferred payment service and is caused when a deferred payment is not properly settled by the user while using the deferred payment.” (The elements are additional details for the second service and the risk borne by the provider of the second service). This claim individually or in combination with others do not integrate the abstract idea into a practical application or add an inventive concept to the abstract idea).
Claim 18 recites the additional elements “wherein the second service refers to a service where correlation between the risk borne by the provider of the first service when the user uses the first service and the risk borne by the provider of the second service when the user uses the second service is confirmed or inferred.” (The elements are additional details for the second service to a service where correlation is used). This claim individually or in combination with others do not integrate the abstract idea into a practical application or add an inventive concept to the abstract idea).
Claim 19 recites the additional elements “wherein the first model and the second model are gradient boosting decision trees, and the processor trains the second model, which has parameters of some of nodes in the first model as fixed parameters, using the second training data.” (The elements are additional details for the first mode and second model are gradient boosting decision trees and the processor trains the second model). This claim individually or in combination with others do not integrate the abstract idea into a practical application or add an inventive concept to the abstract idea).
Claim 20 recites the additional elements “wherein the first model and the second model are neural networks, and the second processor trains the second model, which has parameters of some of intermediate layers of the trained first model as fixed parameters, using the second training data.” (The elements are additional details for the first mode and second model are neural networks and the processor trains the second model). This claim individually or in combination with others do not integrate the abstract idea into a practical application or add an inventive concept to the abstract idea).
Claim 21 recites the additional elements “wherein the processor trains the second model, which has a plurality of copies of the trained first models arranged in parallel and has the parameters of some of the intermediate layers included in the second model as fixed parameters, using the second training data.” (The elements are additional details for the processor trains the second model). This claim individually or in combination with others do not integrate the abstract idea into a practical application or add an inventive concept to the abstract idea).
Claim 22 recites the additional elements “the processor further determining a factual attribute that is confirmable as a fact about the user, on a basis of user-provided data provided by the user himself/herself or history data of the user; and
determining an inferred attribute about the user, on a basis of at least the factual attribute related to the use.” (The elements are additional details for determining a factual attribute and an inferred attribute). This claim individually or in combination with others do not integrate the abstract idea into a practical application or add an inventive concept to the abstract idea).
Claims 24-27 recite the additional elements “an estimation step of estimating a second score set for a target user by inputting an attribute data group related to the target user into the second model trained by the training method according to claim 23.” (The elements are additional step for estimating a second score set for a target user). These claims individually or in combination with others do not integrate the abstract idea into a practical application or add an inventive concept to the abstract idea).
The dependent claims do no more than providing additional instructions and administrative requirements for the functional steps already recited in the independent claims. The additional recited limitations further narrow the scope of the abstract idea and are merely insignificant solution activities which only refine the abstract idea further and do not include additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea. The claims further describe the business relations of the certain method of organizing human activity (abstract idea) and do not include additional elements other than those of claims 13 and 23-24 to provide a practical application or significantly more than the judicial exception. Each and every recited combination between the recited computing hardware and the recited computing functions has been considered. No non-generic or non-conventional arrangement is found. There is no inventive concept found in the claims. Therefore, the dependent claims also are not patent eligible.
The focus of the claims is on a method implemented on a generic computer system (i.e., a credit learning device) for training the first/second credit learning models using the user’s inputted first/second training data to enhance the models in order to estimate the user’s creditworthiness. The claims are not directed to a new type of processor, network, system memory, or user interface, nor do they provide a method of processing data that improves existing technological processes. The focus of the claims is not on improving computer-related technology, but on an independently abstract idea that uses computers as tools. The claims do not add a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field. Accordingly, when viewed as a whole, the claims do no more than generally linking the use of the judicial exception to a particular technological environment or field of use. No inventive concept is found in the claims. Therefore, the claims do not add significantly more (i.e., an inventive concept) to the abstract idea (Step 2B-No, the claims are not significantly more than the abstract idea).
Therefore, claims 13-27 are not patent eligible under the 35 USC § 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 13-15 and 17-27 are rejected under 35 U.S.C. 103 as being unpatentable over Gao (US Patent No. 11,961,137) (hereinafter “Gao”) and further in view of Li (US Publication No. 2022/0005117) (hereinafter “Li”).
As per claim 13, Gao teaches a credit learning device comprising:
a memory; and a processor coupled to the memory, the processor being configured to execute (see Gao, column 5, line 50-45, column 6; Figure 1):
Gao in view of Li teaches the following limitations:
training a first model using first training data that includes a combination of a first attribute data group related to a user belonging to a first segment among users who use a first service (see Gao, at least column 15, lines 27-8, column 16, claim 1) and a label corresponding to a score that varies according to a degree of risk borne by a provider of a second service when the user uses a second service different from the first service (see Li, at least para. 78 “…the credit record may further include a default label of a user. The default label is used to indicate whether the user is a default user. For example, when the default label is 0, it indicates that the user is not a default user; and when the default label is 1, it indicates that the user is a default user. When the quantity of default times of the user exceeds a preset quantity of times, the default label is 1. The preset quantity of times is greater than or equal to 1, and may be set by the application platform according to different service requirements. This is not limited in this embodiment.”, para. 89, claim 1 “a default label of the user”); and
training a second model, which has some of parameters of a configuration of the trained first model as fixed parameters, using second training data that includes a combination of a second attribute data group related to a user belonging to a second segment different from the first segment among the users who use the first service (see Gao, at least column 16, lines 2-38, claim 1) and a label corresponding to a score that varies according to a degree of risk borne by a provider of the second service when the user uses the second service (see Li, at least para. 78, 89, claim 1 “a default label of the user”).
It would have been obvious to one of ordinary skill in the art at the time of the invention was filed to incorporate the “label user” features, as taught by Li, in the system/method of Gao since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. One of ordinary skill in the art would have been motivated to incorporate the “label user” features for the purpose of providing a creditworthiness estimation method.
As per claim 14, Gao in view of Li teaches the credit learning device as in claim 13. Gao further teaches the processor further execute:
determining the user belonging to the first segment and the user belonging to the second segment by segmenting the users who use the first service (see Gao, at least column 15, lines 27-8, column 16, claim 1).
As per claim 15, Gao in view of Li teaches the credit learning device as in claim 13. Gao further teaches wherein
the processor segments the users who use the first service on a basis of attribute data of the users who use the first service (see Gao, at least column 15, lines 27-8, column 16, claim 1).
As per claim 17, Gao in view of Li teaches the credit learning device as in claim 13. Gao further teaches wherein
the second service refers to a deferred payment service (see Gao, column 15, lines 27-38, column 16, claim 1; Figure 4/elements 408-414), and
the risk borne by the provider of the second service refers to deferred payment risk, which is determined on a basis of a payment history in the deferred payment service and is caused when a deferred payment is not properly settled by the user while using the deferred payment (see Gao, column 15, lines 27-38, column 16, claim 1; Figure 4/elements 408-414).
As per claim 18, Gao in view of Li teaches the credit learning device as in claim 13. Gao further teaches wherein
the second service refers to a service where correlation between the risk borne by the provider of the first service when the user uses the first service and the risk borne by the provider of the second service when the user uses the second service is confirmed or inferred (see Gao, column 15, lines 27-38, column 16, claim 1; Figure 4/elements 408-414).
As per claim 19, Gao in view of Li teaches the credit learning device as in claim 13. Gao further teaches wherein
the first model and the second model are gradient boosting decision trees (see Gao, at least column 13, lines 6-32; Figure 1/elements 230), and
the processor trains the second model, which has parameters of some of nodes in the first model as fixed parameters, using the second training data (see Gao, at least column 15, lines 27-38, column 16, claim 1; Figure 4/elements 408-414).
As per claim 20, Gao in view of Li teaches the credit learning device as in claim 13. Gao further teaches wherein
the first model and the second model are neural networks (see Gao, at least column 4, lines 18-23 “neural network algorithms”), and
the processor trains the second model, which has parameters of some of intermediate layers of the trained first model as fixed parameters, using the second training data (see Gao, at least column 15, lines 27-38, column 16, claim 1; Figure 4/elements 408-414).
As per claim 21, Gao in view of Li teaches the credit learning device as in claim 13. Gao further teaches wherein
the processor trains the second model, which has a plurality of copies of the trained first models arranged in parallel and has the parameters of some of the intermediate layers included in the second model as fixed parameters, using the second training data (see Gao, at least column 15, lines 27-38, column 16, claim 1; Figure 4/elements 408-414).
As per claim 22, Gao in view of Li teaches the credit learning device as in claim 13. Gao further teaches the processor further executes:
determining a factual attribute that is confirmable as a fact about the user, on a basis of user-provided data provided by the user himself/herself or history data of the user (see Gao, at least column 15, lines 27-38, column 16, claim 1; Figure 4/elements 408-414); and
determining an inferred attribute about the user, on a basis of at least the factual attribute related to the user (see Gao, at least column 15, lines 27-38, column 16, claim 1; Figure 4/elements 408-414).
As per claim 25, Gao teaches a credit estimation device comprising:
estimation means for estimating a second score set for a target user by inputting an attribute data group related to the target user into the second model trained by the training method according to claim 23 (see Gao, at least column 12, lines 32-55, column 13; Figure 2B/elements 224, 232-238).
As per claim 23, this claim written in method form corresponds to claim 13 and has the same elements and limitations. Hence, it is rejected under the rationale provided in claim 13.
As per claim 24, this claim written in computer program form corresponds to claim 13 and has the same elements and limitations. Hence, it is rejected under the rationale provided in claim 13.
As per claim 26, this claim written in method form corresponds to claim 25 and has the same elements and limitations. Hence, it is rejected under the rationale provided in claim 25.
As per claim 27, this claim written in computer program form corresponds to claim 25 and has the same elements and limitations. Hence, it is rejected under the rationale provided in claim 25.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Gao in view of Li and further in view of Sardari et al. (US Publication No. 2023/0316280, Application filed on 03/16/2022) (hereinafter “Sardari”).
As per claim 16, Gao in view of Li teaches the credit learning device as in claim 13. Gao in view Li does not disclose the following limitations; however, Sardari teaches further teaches wherein
the processor segments the users who use the first service on a basis of a usage frequency of the first service within a prescribed period (see Sardari, at least para. 24 “users may be offered loyalty incentive in exchange for the users achieving a certain level of usage of the payment service, such as keeping the payment application installed on an electronic device for a certain amount of time, completing an above-threshold number of transactions over a prescribed time period using the payment application, or the like, paras. 38, 55).
One of ordinary skill in the art at the time of the invention was filed would have been motivated to incorporate the “usage frequency” feature, as taught by Sardari, in the method/system of Gao and Li for the purpose of users segmentation.
Conclusion
Claims 13-24 are rejected.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAI TRAN whose telephone number is (571)272-7364. The examiner can normally be reached Monday-Friday, 9-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christine M. Behncke can be reached at 571-272-8103. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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HAI TRAN
Primary Examiner
Art Unit 3695
/HAI TRAN/Primary Examiner, Art Unit 3695
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