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 Final Office Action in response to the Amendment filed on June 30, 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 13-27 were pending. By the 06/30/2026 Response, claims 13, 14, 23, and 24 have been amended, claims 15-16 have been cancelled, and no claim has been added. Claims 1-12 are withdrawn from examination via the 02/13/2026 Response. 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 13-24) are examined together. Group I (claims 1-9) and Group II (claims 10-12) are withdrawn from examination via the 02/13/2026 Response.
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 13-27 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, claim 24 recites a non-transitory computer-readable recording medium having recorded a program, and claims 25-27 recite a credit estimation device, a credit estimation method, and a credit estimation 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 analysis, 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 first model using first training data - data processing over the network)
training a second model, which has some of parameters of intermediate layers, nodes, or weights 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, (training second model using second training data - data processing over the network)
wherein the computer further determines the user belonging to the first segment and the user belonging to the second segment by segmenting the users who use the first service on a basis of attribute data of the users who use the first service, and wherein the first segment comprises users having an amount or quality of attribute data obtained from the first service above a prescribed threshold and the second segment comprises users having an amount or quality of attribute data obtained from the first service below the prescribed threshold. (Additional detailed instructions for segmenting the users into first segment and second segment according to their quality of attribute data – data processing over the network)
The claim limitations (underlined), 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 processors and memories with stored computer program, first model, second model – 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 main steps for training the first and second credit learning models using an user’s inputted first and second training data 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 – estimating user’s creditworthiness (e.g. hedging, insurance, mitigating risk) and/or commercial or legal interactions – business interaction between user and credit leaning models (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.
While claim 24 is addressed above, the analysis above can be applied to claim 13 where the processors and memories also serve as mere instructions to apply an exception using generic computer components. Similarly, the non-transitory computer readable medium of claim 23 is an additional element that serves as mere instructions to apply an exception using a generic computer component and does not provide a practical application or significantly more than the judicial exception. Therefore, these claims also recite an abstract idea.
Claims 25-27 recite a credit estimation device, a credit estimation method, and a credit estimation program for estimating a second score set for a target user. These claims have a scope broader than the scope of claims 13 and 23-24, hence, the analysis above can also be applied to these claims. Therefore, these claims also recite an 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 analysis, 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, claims 13 and 23-24 recite the newly added additional elements:
wherein the computer further determines the user belonging to the first segment and the user belonging to the second segment by segmenting the users who use the first service on a basis of attribute data of the users who use the first service, and wherein the first segment comprises users having an amount or quality of attribute data obtained from the first service above a prescribed threshold and the second segment comprises users having an amount or quality of attribute data obtained from the first service below the prescribed threshold.
The newly added amendments are basically additional detailed instructions for the computer to segment the users into either the first segment or second segment based on the users that have quality of attribute data either above or below the prescribed threshold. The newly added amendments further narrow the scope of the claims, but do not change the analysis. As explained in the December 16, 2014, Interim Eligibility Guidance from the USPTO (in reference to the buySAFE, Inc. V. Google, Inc. decision), further narrowing the details of an abstract idea does not change the 101 analysis since a narrower abstract idea does not make it any less abstract. A specific abstract idea is still an abstract idea and is not eligible for patent protection without “significantly more” recited in the claim. A claim must be significant more to transform the abstract idea into patent eligible subject matter in order to make the claim patent-eligible per the Supreme Court in Alice. The present amended claims still recite an abstract idea, do not include additional elements that integrate the abstract idea into a practical application, and do not provide an inventive concept.
The claims (13 and 23-27) further recite the additional computer elements, such as the credit learning device comprising processors, memories with stored instructions, first model and second model 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 2B analysis, 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)).
Independent claims (13 and 23-27) 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, 17-22 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 on a basis of a usage frequency of the first service within a prescribed period.” (The elements are additional detailed instructions 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 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 detailed instructions 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 detailed instructions 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 detailed instructions 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 detailed instructions 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 detailed instructions 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 detailed instructions 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).
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 (13, 14, and 17-27) is on a method implemented on a generic computing device (i.e., a credit learning device) for training the first/second credit learning models using an user’s inputted first/second training data 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).
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 13-14 and 17-27 are rejected under 35 U.S.C. 103 as being unpatentable over Margolin (US Publication No. 2023/0076010-A1) (hereinafter “Gao”) and further in view of Li (US Publication No. 2022/0005117-A1) (hereinafter “Li”).
As per claim 13, Margolin teaches a credit learning device comprising:
a memory; and a processor coupled to the memory, the processor being configured to execute (see Margolin, at least para. 91-97; Figure 8):
Margolin 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 Margolin, at least Abstract “… using a firs and a second model …”, para. 34 “… a first model …”) 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 intermediate layers, nodes, or weights 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 Margolin, at least para. 101-104 and Figure 9) 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”),
wherein the processor further determines the user belonging to the first segment and the user belonging to the second segment by segmenting the users who use the first service on a basis of attribute data of the users who use the first service, and wherein the first segment comprises users having an amount or quality of attribute data obtained from the first service above a prescribed threshold and the second segment comprises users having an amount or quality of attribute data obtained from the first service below the prescribed threshold (see Margolin, at least Abstract “… Each of a first subset of the entities has a respective attribute value below a threshold. Each of a second subset of the entities has a respective attribute value above the threshold …”, para. 19 “… The schema 20 allows the service provider to segment or classify their users or other entities operating on, or otherwise associated with, their electronic system…”, 20-23, 55-56 “… where the entities are segmented into different segments or classes …”). Note: the Examiner notes that segmenting entities and/or users are not different because an user can be an entity, and an entity can be an 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 Margolin 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” feature for the purpose of providing a creditworthiness estimation method.
As per claim 14, Margolin in view of Li teaches the credit learning device as in claim 13. Margolin 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 Margolin, at least Abstract, para. 19-23 and 55-56).
As per claim 17, Margolin in view of Li teaches the credit learning device as in claim 13. Margolin further teaches wherein
the second service refers to a deferred payment service (see Margolin, at least Abstract, para. 19-23 and 55-56), 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 Margolin, at least Abstract, para. 19-23 and 55-56).
As per claim 18, Margolin in view of Li teaches the credit learning device as in claim 13. Margolin 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 Margolin, at least Abstract, para. 19-23 and 55-56).
As per claim 19, Margolin in view of Li teaches the credit learning device as in claim 13. Margolin further teaches wherein
the first model and the second model are gradient boosting decision trees (see Margolin, at least Abstract, para. 19-23 and 55-56), 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 Margolin, at least Abstract, para. 19-23 and 55-56).
As per claim 20, Margolin in view of Li teaches the credit learning device as in claim 13. Margolin further teaches wherein
the first model and the second model are neural networks (see Margolin, at least para. 101-105 and Figure 9), 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 Margolin, at least para. 101-105 and Figure 9).
As per claim 21, Margolin in view of Li teaches the credit learning device as in claim 13. Margolin 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 Margolin, at least Abstract, para. 19-23 and 55-56).
As per claim 22, Margolin in view of Li teaches the credit learning device as in claim 13. Margolin 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 Margolin, at least Abstract, para. 19-23 and 55-56); and
determining an inferred attribute about the user, on a basis of at least the factual attribute related to the user (see Margolin, at least Abstract, para. 19-23 and 55-56).
As per claim 25, Margolin 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 Margolin, at least Abstract, para. 19-23 and 55-56).
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.
Response to Arguments
Claim Rejections - 35 USC § 101
Applicant's arguments filed 06/30/2026 have been fully considered but they are not persuasive.
Per page 13, Applicant argues that the currently amended claims are patent-eligible subject matter and the rejection should be withdraw.
Response:
The Examiner respectfully disagrees. As explained in the 101 rejection above, the amended claims still recite an abstract idea. Therefore, Applicant’s arguments are not persuasive.
Per pages 13-15, Applicant argues that the amended claims are analogous to the Desjardins case because the claims recite a specific improvement to machine learning model training technology and are integrated into a practical application.
Response:
The Examiner respectfully disagrees. Although both the Desjardins case and the amended claims are directed to improving machine learning model training, they do not recite the same or analogous training approach.
Desjardins is directed to improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of "catastrophic forgetting" encountered in continual learning systems. The claims when evaluated as a whole in discerning at least the limitation "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task" reflected the improvement disclosed in the Specification. Accordingly, the claims as a whole integrated the abstract into a practical application.
This is different from the amended claims. As explained in the 101 rejection above, the amended claims is a method implemented on a generic computing device (i.e., a credit learning device) for training the first/second credit learning models using an user’s inputted first/second training data in order to estimate the user’s creditworthiness. The focus of the amended claims is on estimating an individual’s creditworthiness, this is “a business solution” and supported in paragraphs 6-7 of the Specification (see Publication No. 2025/0335985-A1).
[0006] Conventionally, various technologies have been proposed to assess users' creditworthiness regarding prescribed services. The conventional technologies produce certain effects in accurately assessing individual creditworthiness but have room for improvements in flexibly enhancing the accuracy of creditworthiness estimation in specific domains.
[0007] In view of the above problem, the present disclosure has an object of flexibly enhancing the accuracy of individual creditworthiness estimation in specific domains.
With respect to the newly added amendments, such as “intermediate layers, nodes, or weights” and “wherein the processor further determines … below the prescribed threshold.” are basically additional detailed instructions for the computer to segment the users into either the first segment or the second segment based on the users that have quality of attribute data either above or below the prescribed threshold.
As explained in the 101 rejection above, the added amendments further narrow the scope of the claims, but do not change the analysis. Further narrowing the details of an abstract idea does not change the 101 analysis since a narrower abstract idea does not make it any less abstract. A specific abstract idea is still an abstract idea and is not eligible for patent protection without significantly more recited in the claims. A claim must be significant more to transform the abstract idea into patent eligible subject matter in order to make the claim patent-eligible per the Supreme Court in Alice. In conclusion, the amended claims still do not include additional elements that integrate the abstract idea into a practical application. Therefore, Applicant’s amendments and arguments are not persuasive.
Per pages 15-16, Applicant cites the McRO case and argues that the Office Action has oversimplified the claims to merely “training credit learning models to estimate creditworthiness.” Applicant argues that the claims describe a particular solution to solve the technical problem of training effective ML models with limited data.
Response:
The Examiner respectfully disagrees. When determining whether a claim amounts to an abstract idea (Step 2A), the courts always break down the claim into the essential scope of the claim. In doing so, the courts do not recite specific details of what is recited in the claim(s) but focus on determining the overall abstract idea encompassed by the claim(s) as a whole. The courts also compare the overall abstract idea to previous court decisions. All of these court decisions do not label the abstract idea in a way that all of the claim elements are identified.
In the present case, as explained in the 101 rejection above, the claims, under the broadest reasonable interpretation, cover a method for training the first/second credit learning models using an user’s inputted training data to train the credit learning models in order to estimate the user’s creditworthiness. Enhancing estimation of an user’s credit worthiness is not a technical improvement, but is an improvement over “a business method” which is “a business solution”. The claims do not recite (i) an improvement to the functionality of a computer or other technology or technical field (MPEP § 2106.05(a)); (ii) a particular machine to apply or use the judicial exception (MPEP § 2106.05(b)); (iii) a particular transformation of an article to a different thing or state (MPEP § 2106.05(c)); or (iv) any other meaningful limitation (MPEP § 2106.05(d)).
The amended claims are different from the McRO case. McRO is directed to improving computer animation through the use of specific rules, rather than human artists, to set morph weights and transition parameters between phonemes. The amended claims do not lay out a particular set of rules for verifying a restriction associated with the model based on a lexical item corresponding to a property of an instrument. The amended claims do not include improvement to the functioning of computer itself over existing computer technologies. There is no analogy between the McRO case and the amended claims. The only similarity between the McRO case and the amended claims is that they all use computer technology. Also, as discussed in the 101 rejection above, there is no hardware improvement recited in the claims or described in Applicant's Specification, Applicant's computer system is no more than a general purpose computer (see at least paragraphs 26-37 and Figure 1 of US Publication No. 2025/0335985-A1). Therefore, Applicant’s amendments and arguments are not persuasive.
Per pages 15-16, Applicant argues that the claims are not directed to a judicial except at Step 2A, therefore, the analysis for Step 2B is not needed.
The Examiner respectfully disagrees. As explained in the 101 rejection above, the amended independent and dependent claims are not patent eligible.
In conclusion, Applicant’s amendments and arguments are not persuasive and the rejection of the claims is MAINTAINED.
Claim Rejections - 35 USC § 103
Applicant’s arguments with respect to the rejections of claims 13-14 and 17-27 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of Margolin and Li references.
Conclusion
Claims 13-14 and 17-27 are rejected.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
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.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
HAI TRAN
Primary Examiner
Art Unit 3695
/HAI TRAN/Primary Examiner, Art Unit 3695
Prosecution Insights