DETAILED ACTION
This Final Office Action is in response to the application filed on 06/07/2024 and the Amendment & Remark filed on 06/10/2026.
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 .
Claim Rejections - 35 USC § 112
The previous rejection under 35 USC 112(b) is withdrawn in view of the Amendment filed on 06/10/2026.
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 1, 3-8 and 10-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
As an initial matter, the claims as a whole are to a plurality of processes, an apparatus and a manufacture, which falls within one or more statutory categories. (Step 1: YES) The recitation of the claimed invention is then further analyzed as follow, in which the abstract elements are boldfaced.
Claim 1 recite:
Method for operating a evaluation model performed by an evaluation server linked, the method comprising:
a step of receiving log data of a user and selecting basic variable items included in the log data, the basic variable items corresponding to event codes included in the log data, and the event codes being classified using predetermined categories;
a step of generating candidate variables by calculating a frequency of the basic variable items in the log data, the different time windows comprising a plurality of time windows having different periods;
a step of generating a plurality of first derived variables by applying different time windows or different calculation methods to the candidate variables;
a step of calculating a P-value and an IV value of the plurality of first derived variables;
a step of selecting important variables by comparing the P-value and the IV value of the plurality of first derived variables with a predetermined standard value;
a step of generating clustered important variables by clustering the important variables according to one or more information domains;
a step of deriving a first-step model by using the important variables as input variables and using information on the user's credit as a dependent variable;
a step of selecting a first final variable to be applied to the first-step model among the clustered important variables and calculating a first weighted value for the first final variable;
a step of generating a plurality of second derived variables by using the first final variable and the first weighted value;
a step of deriving a second-step model by using the plurality of second derived variables as input variables and using evaluation information of the user as a dependent variable; and
a step of selecting a second final variable to be applied to the second-step model from among the second derived variables and calculating a second weighted value for the second final variable;
a step of performing a user evaluation on a new user by using the second final variable and the second weighted value.
Claim 3 recites:
wherein the step of generating the candidate variables includes calculating a term frequency (TF) and a term frequency-inverse document frequency (TF-IDF) of the variable basic items and generating the candidate variables, and the term frequency (TF) is calculated by using a simple frequency, a Boolean frequency, an incremental frequency, or a log frequency, and the term frequency-inverse document frequency (TF-IDF) is calculated by multiplying the term frequency (TF) by the term frequency-inverse document frequency (TF-IDF).
Claim 4 recites:
the calculation methods include an average, a sum, a maximum value, and a minimum value.
Claim 5 recites:
wherein the step of selecting the important variables further comprises selecting, as the important variable, the first derived variable, of which P-value obtained by univariate logistic regression analysis is less than a predetermined reference value, among the plurality of first derived variables, or the first derived variable, of which IV value is greater than a predetermined reference value, among the plurality of first derived variables, and the IV value is derived by an equation.
Claim 6 recites:
a step of grouping variables belonging to a same information domain (F) for the selected important variables, and wherein the step of deriving the first-step model includes selecting the first final variable targeting the important variables included in a certain information domain (F).
Claim 7 recites:
wherein the first-step model and the second-step model consist of a logistic regression model.
Claim 8 recites:
wherein the first-step model selects the first final variable to be applied to the first-step model from among the important variables by using a step-wise selection method, and the second-step model selects the second final variable to be applied to the second-step model from among the second derived variables by using the step-wise selection method.
Claim 10 recites:
A model operating method performed by a evaluation server, The method comprising:
a step of receiving log data of a user and selecting a frequency of event codes included in the log data and important variables through at least one preprocessing process for the frequency;
a step of deriving a first-step logistic regression model by using the important variables as input variables and using information on the user's credit as a dependent variable;
a step of selecting a first final variable to be applied to the first-step model among the important variables and calculating a first weighted value for the first final variable;
a step of generating a derived variable by using the first final variable and the first weighted value;
a step of deriving second-step logistic regression model by using the derived variable as an input variable and using information on the user's credit as a dependent variable; and
a step of selecting a second final variable to be applied to the second-step model from among one or more second derived variables and calculating a second weighted value for the second final variable.
Claims 11 and 16 recite:
wherein the first-step model selects the first final variable to be applied to the first-step model from among the important variables by using a step-wise selection method, and the second-step model selects the second final variable to be applied to the second-step model from among the second derived variables by using the step-wise selection method.
Claims 12 and 17 recite:
wherein the step of selecting the important comprises determining P-values and IV values, wherein the IV values are derived by an equation.
Claims 13 and 18 recite:
a step of grouping the important variables belonging to a same information domain (F) for the selected important variables, and wherein the step of deriving the first-step model includes selecting the first final variable targeting the important variables included in a certain information domain (F).
Claims 14 and 19 recite:
a step of performing a credit rating of a new user based on log data of the new user by using the first-step model to which the first final variable is applied and the second-step model to which the second final variable is applied.
Claim 15 recites:
A credit evaluation server comprising: a processor; a memory configured to load a computer program executed by the processor; and an interface configured to exchange data generated during execution of the computer program with a user terminal, wherein the computer program includes:
a step of receiving log data of a user from the user terminal and selecting a frequency of event codes included in the log data and important variables through at least one preprocessing process for the frequency;
a step of deriving a first-step logistic regression mode by using the important variables as input variables and using information on the user's credit as a dependent variable;
a step of selecting a first final variable to be applied to the first-step model among the important variables and calculating a first weighted value for the first final variable;
a step of generating a derived variable by using the first final variable and the first weighted value;
a step of deriving second-step logistic regression model by using the derived variable as an input variable and using information on the user's credit as a dependent variable; and
a step of selecting a second final variable to be applied to the second-step model from one or more second derived variables and calculating a second weighted value for the second final variable.
Claim 20 recites:
A non-transitory computer-readable medium embodying a program executable in at least one computing device, wherein when executed the program causes the at least one computing device to at least:
receive log data of a user and selecting basic variable items included in the log data, the basic variable items corresponding to event codes included in the log data, and the event codes being classified using predetermined categories;
generate candidate variables by calculating a frequency of the basic variable items in the log data;
generate a plurality of first derived variables by applying different time windows or different calculation methods to the candidate variables, the different time windows comprising a plurality of time windows having different periods;
calculate a P-value and an IV value of the plurality of first derived variables;
select important variables by comparing the P-value and the IV value of the plurality of first derived variables with a predetermined standard value;
generate clustered important variables by clustering the important variables according to one or more information domains;
derive a first-step model by using the clustered important variables as input variables and using evaluation information of the user as a dependent variable;
select a first final variable to be applied to the first-step model among the clustered important variables and calculating a first weighted value for the first final variable;
generate a plurality of second derived variables by using the first final variable and the first weighted value;
derive a second-step model by using the plurality of second derived variables as input variables and using evaluation information of the user as a dependent variable;
select a second final variable to be applied to the second-step model from among the second derived variables and calculating a second weighted value for the second final variable; and
perform a user evaluation on a new user by using the second final variable and the second weighted value.
Based on the limitations above, the claims describe a process that covers user evaluation using mathematical model. User evaluation manages relationship with a user and is considered to be a commercial interaction, which falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Mathematical models are ineligible subject matter, which do not render eligibility when added other Judicial Exception. The examiner also noted that despite removal of terms such as “credit” and “financial”, the only evaluation supported by the disclosure of the instant application is a credit evaluation. As such, the claim(s) recite(s) a Judicial Exception. (Step 2A prong one: Yes)
This analysis then evaluates whether the claims as a whole integrates the recited Judicial Exception into a practical application of the exception. In particular, the claims recite the additional element(s) of “server” or “processor” as a mere tool to perform the steps of the Judicial Exception, which encompasses no more than Mere Instruction to Apply.
For example, the limitation “a step of receiving log data of a user and selecting basic variable items included in the log data, the basic variable items corresponding to event codes included in the log data, and the event codes being classified using predetermined categories” encompasses no more than generically invoking a processor / server to apply the Judicial Exception step of receiving the log data of the user and selecting basic variable items included in the log data;
the limitation “a step of generating candidate variables by calculating a frequency of the basic variable items in the log data, the different time windows comprising a plurality of time windows having different periods” encompasses no more than generically invoking a processor / server to apply the Judicial Exception step of generating candidate variables by calculating frequency of the basic variable items;
the limitation “a step of generating a plurality of first derived variables by applying different time windows or different calculation methods to the candidate variables” encompasses no more than generically invoking a processor / server to apply the Judicial Exception step of generating the plurality of first derived variables by applying different time windows or calculation methods to the candidate variables;
the limitation “a step of calculating a P-value and an IV value of the plurality of first derived variables” encompasses no more than generically invoking a processor / server to apply the Judicial Exception step of calculating the P-value and the IV value of the plurality of first derived variables;
the limitation “a step of selecting important variables by comparing the P-value and the IV value of the plurality of first derived variables with a predetermined standard value” encompasses no more than generically invoking a processor / server to apply the Judicial Exception step of selecting important variables by comparing the P-value and IV value of the plurality of first derived variables with a predetermined standard value;
the limitation “a step of generating clustered important variables by clustering the important variables according to one or more information domains” encompasses no more than generically invoking a processor / server to apply the Judicial Exception step of generating the clustered important variables by clustering the important variables according to one or more information domains;
the limitation “a step of deriving a first-step model by using the important variables as input variables and using information on the user's credit as a dependent variable” encompasses no more than generically invoking a processor / server to apply the Judicial Exception step of deriving the first-step model by using the important variables as input variables and using user credit information as dependent variable;
the limitation “a step of selecting a first final variable to be applied to the first-step model among the clustered important variables and calculating a first weighted value for the first final variable” encompasses no more than generically invoking a processor / server to apply the Judicial Exception step of selecting the first final variable to be applied to the first-step model and calculating the first weighted value for first final variable;
the limitation “a step of generating a second derived variable by using the first final variable and the first weighted value” encompasses no more than generically invoking a processor / server to apply the Judicial Exception step of generating the second derived variable using the first final variable and the first weighted value;
the limitation “a step of deriving a second-step model by using the plurality of second derived variables as input variables and using evaluation information of the user as a dependent variable” encompasses no more than generically invoking a processor / server to apply the Judicial Exception step of deriving the second-step model by using the second derived variable as an input variable and using information on the user's credit as a dependent variable;
the limitation “a step of performing a user evaluation on a new user by using the second final variable and the second weighted value” encompasses no more than generically invoking a processor / server to apply the Judicial Exception step of performing a user evaluation on a new user by using the second final variable and the second weighted value;
the limitation “wherein the step of generating the candidate variables includes calculating a term frequency (TF) and a term frequency-inverse document frequency (TF-IDF) of the variable basic items and generating the candidate variables, and the term frequency (TF) is calculated by using a simple frequency, a Boolean frequency, an incremental frequency, or a log frequency, and the term frequency-inverse document frequency (TF-IDF) is calculated by multiplying the term frequency (TF) by the term frequency-inverse document frequency (TF-IDF)” encompasses no more than generically invoking a processor / server to apply the Judicial Exception step of calculating the TF and TF-IDF;
the limitation “wherein the calculation methods include an average, a sum, a maximum value, and a minimum value” encompasses no more than generically invoking a processor / server to apply the Judicial Exception step of performing calculation includes an average, a sum, a maximum value, and a minimum value ;
the limitation “wherein the step of selecting the important variables selecting, as the important variable, the first derived variable, of which P-value obtained by univariate logistic regression analysis is less than a predetermined reference value, among the plurality of first derived variables, or the first derived variable, of which IV value is greater than a predetermined reference value, among the plurality of first derived variables, and the IV value is derived by an equation” encompasses no more than generically invoking a processor / server to apply the Judicial Exception step of selecting the first derived variable with p-value less than predetermined reference value and IV value greater than predetermined reference value;
the limitation “wherein the step of deriving the first-step model includes selecting the first final variable targeting the important variables included in a certain information domain (F)” encompasses no more than generically invoking a processor / server to apply the Judicial Exception step of selecting the first final variable targeting the important variables;
the limitation “wherein the first-step model and the second-step model consist of a logistic regression model” encompasses no more than generically invoking a processor / server to apply the Judicial Exception step of using logistic regression model to generating final variables;
the limitation “wherein the first-step model selects the first final variable to be applied to the first-step model from among the important variables by using a step-wise selection method, and the second-step model selects the second final variable to be applied to the second-step model from among the second derived variables by using the step-wise selection method” encompasses no more than generically invoking a processor / server to apply the Judicial Exception step of using step-wise method to select final variables;
the limitation “a step of receiving log data of a user and selecting a frequency of event codes included in the log data and important variables through at least one preprocessing process for the frequency” encompasses no more than generically invoking a processor / server to apply the Judicial Exception step of receiving the log data and selecting a frequency of event codes;
the limitation “a step of deriving a first-step logistic regression model by using the important variables as input variables and using information on the user's credit as a dependent variable” encompasses no more than generically invoking a processor / server to apply the Judicial Exception step of deriving the first step logistic regression model;
the limitation “a step of selecting a first final variable to be applied to the first-step model among the important variables and calculating a first weighted value for the first final variable” encompasses no more than generically invoking a processor / server to apply the Judicial Exception step of selecting a first final variable to applied to the first-step model and calculating a first weighted value for the first final variable;
the limitation “a step of generating a derived variable by using the first final variable and the first weighted value” encompasses no more than generically invoking a processor / server to apply the Judicial Exception step of selecting a first final variable to be applied to the first step model and calculating a first weighted value for the first final variable;
the limitation “a step of deriving second-step logistic regression model by using the derived variable as an input variable and using information on the user's credit as a dependent variable” encompasses no more than generically invoking a processor / server to apply the Judicial Exception step of deriving second-step logistic regression model;
the limitation “a step of selecting a second final variable to be applied to the second-step model from among the derived variables and calculating a second weighted value for the second final variable” encompasses no more than generically invoking a processor / server to apply the Judicial Exception step of selecting a second final variable to be applied to the second step model and calculating a first weighted value for the second final variable.
Other than being generally linked to the steps of the Judicial Exception, the processor and server in the above step(s) is/are recited at a high-level of generality, without technological detail of how the particular steps are performed technologically.
The additional element(s) of “memory” and/or “non-transitory storage medium” are generically recited to store data and/or instructions of the Judicial Exception.
The additional element(s) of “an interface configured to exchange data generated during execution of the computer program with a user terminal” are generically recited to perform communication steps such as receiving and transmitting.
The examiner further noted generic computer affixes such as “…server” or “terminal” are appended to abstract elements such as “credit evaluation”, “financial” and “user”, but found that to be mere instructions to implement the Judicial Exception idea on a computer.
Indeed, the instant claims (1) attempted to cover a solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result; (2) used of a computer or other machinery in its ordinary capacity for economic or other tasks or simply added a general purpose computer or computer components after the fact to the Judicial Exception and (3) generally applied the Judicial Exception to a generic computing environment without limitation indicative of practical application (See MPEP 2106.04(d)I). Thus, the claims are no more than Mere Instruction to Apply the Judicial Exception (See MPEP 2106.05(f)) or adding insignificant extra-solution activity to the judicial exception (See MPEP 2106.05(g)), which do not integrate the cited Judicial Exception into practical application (Step 2A prong two: No) The claims are directed to a Judicial Exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to evaluate credit amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. No additional element currently recited in the claims amount the claims to be significantly more than the cited abstract idea. (Step 2B: No)
Therefore, claims 1, 3-8 and 10-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed on 06/10/2026 have been fully considered but they are not persuasive.
Regarding the applicant’s argument the amended claims do not recite a Judicial Exception, the examiner respectfully disagrees. The examiner noted that despite removal of terms such as “credit” and “financial”, the only evaluation supported by the disclosure of the instant application is a credit evaluation. Even if the applicant’s drafting effort is taken at its face value, the evaluation of a user using mathematical model would nonetheless be considered as a commercial interaction as evaluation of a person manages a relationship with that person. As such, the argument is not persuasive.
Regarding the applicant’s argument the amended claims integrate the Judicial Exception into practical application, the examiner respectfully disagrees. The applicant contended that the claimed invention improves upon conventional logistic-regression credit-evaluation models. However, it should be noted that improvement to credit evaluation or logistic regression models is neither "Improvements to another technology or technical field” nor "Improvements to the functioning of a computer itself”. As such, the argument is not persuasive.
Regarding the applicant’s argument the amended claims amount to significantly more, the examiner respectfully disagrees. As explained above, an improvement to the Judicial Exception itself, such as improvement to a logistical-regression credit evaluation models does not render the Judicial Exception eligible. As such, the argument is not persuasive.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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 CHO KWONG whose telephone number is (571)270-7955. The examiner can normally be reached 9am - 5pm EST M-F.
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, MICHAEL W ANDERSON can be reached at 571-270-0508. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHO YIU KWONG/Primary Examiner, Art Unit 3693
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