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 .
Response to Amendment
The amendment filed September 26, 2025 has been entered. Claims 1-20 remain pending in the application.
Claim Objections
Claims 19-20 are objected to because of the following informalities:
In claim 19, line 4, “KYC” should read --know your client (KYC)--.
Claim 20 is further objected due to its dependency.
Appropriate correction is required.
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 1-20 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.
Under the Step 1 of the Section 101 analysis, Claims 1-9 are drawn to a method which is within the four statutory categories (i.e., a process), Claims 10-18 are drawn to a system which is within the four statutory categories (i.e. a machine), and Claims 19-20 are drawn to a non-transitory computer-readable medium which is within the four statutory categories (i.e., a manufacture).
Since the claims are directed toward statutory categories, it must be determined if the claims are directed towards a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea). Based on consideration of all of the relevant factors with respect to the claim as a whole, claims 1-20 are determined to be directed to an abstract idea. The rationale for this determination is explained below:
Regarding Claims 1, 10, and 19:
Claims 1, 10, and 19 are drawn to an abstract idea without significantly more. The claims recite “receiving, by one or more processors, input data comprising historical KYC audit cases and associated error data; extracting, by the one or more processors, KYC audit attributes from the historical KYC audit cases and associated error data; receiving, by the one or more processors, analyst attributes associated with analysts that performed the historical KYC audit cases; constructing, by the one or more processors, a dataset from input variables comprising the KYC audit attributes, the analyst attributes, and the associated error data; generating additional features by computing one or more group-by features and one or more statistical columns from the dataset; creating, by the one or more processors, a plurality of artificial intelligence (AI) models based, at least in part, on the dataset and the generated additional features; selecting an AI model of the plurality of AI models based on discovery of one or more patterns across the historical KYC audit cases and the associated error data; receiving, by the one or more processors, a KYC request; and applying, by the one or more processors, the selected AI model to a plurality of attributes of the KYC request to process the KYC request.”
Under the Step 2A Prong One, the limitations, as underlined above, are processes that, under its broadest reasonable interpretation, cover Certain Methods Of Organizing Human Activity such as commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). For example, but for the “processors”, “computing”, and “artificial intelligence (AI) model” language, the underlined limitations in the context of this claim encompass the human activity. The series of steps including business relations belong to a typical business relations, because data or information of customers are processed and used for creating a model to predict error propensity for a customer due diligence process.
Under the Step 2A Prong Two, this judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – “A computer-implemented method for model-based processing of know your customer (KYC) requests, comprising:”, “A system for creating a model for predicting error propensity, comprising: memory; and at least one processor coupled to the memory and configured to implement a method for model-based processing of know your customer (KYC) requests, the method comprising:”, “A computer-program product comprising a non-transitory computer-usable medium having computer-readable program code embodied therein, the computer-readable program code adapted to be executed to implement a method comprising:”, “processors”, “computing”, and “artificial intelligence (AI) model”. The additional elements are recited at a high-level of generality (i.e., performing generic functions of an interaction) such that it amounts no more than mere instructions to apply the exception using a generic computer component, merely implementing an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea. Additionally, regarding the specification and claims, there is no improvement in the functioning of a computer or an improvement to other technology or technical field present, there is no applying or using the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition present, there is no implementing the judicial exception with or using the judicial exception in conjunction with a particular machine or manufacture that is integral to the claim present, there is no effecting a transformation or reduction of a particular article to a different state or thing present, and there is no applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment present such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Accordingly, these additional elements, individually or in combination, 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.
Under the Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements in the process 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. The claims are not patent eligible.
Regarding Claims 2-9, 11-18, and 20:
Dependent claims 5-8 and 14-17 only further elaborate the abstract idea and do not recite additional elements.
Dependent claims 2-4, 9, 11-13, 18, and 20 include additional limitations, for example, “AI model” (Claims 2, 11, and 20); “AI models” and “hyper parameter optimization” (Claims 3 and 12); “AI models” (Claims 4 and 13); and “AI models” and “Bayesian optimization” (Claims 9 and 18), but none of these limitations are deemed significantly more than the abstract idea because, as stated above, they require no more than generic computer structures or signals to be executed, and do not recite any Improvements to the functioning of a computer, or Improvements to any other technology or technical field.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation or implementing the judicial exception on a generic computer.
Therefore, whether taken individually or as an ordered combination, claims 2-9, 11-18, and 20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fang (US 20220067738 A1) in view of Conort (US 20240256920 A1).
Regarding Claims 1, 10, and 19, Fang teaches A computer-implemented method for model-based processing of know your customer (KYC) requests, comprising (Fang: Paragraph(s) 0128, 0135, 0222, 0065): A system for creating a model for predicting error propensity, comprising: memory; and at least one processor coupled to the memory and configured to implement a method for model-based processing of know your customer (KYC) requests, the method comprising (Fang: Paragraph(s) 0128, 0135, 0222, 0312, 0314, 0065): A computer-program product comprising a non-transitory computer-usable medium having computer-readable program code embodied therein, the computer-readable program code adapted to be executed to implement a method comprising (Fang: Paragraph(s) 0128, 0135, 0222, 0312, 0314):
receiving, by one or more processors, input data comprising historical KYC audit cases and associated error data (Fang: Paragraph(s) 0065-0066, 0128, 0135, 0222, 0005, 0312 teach(es) Most crypto currencies exchanges are need to have customer Know Your Customer (KYC) regulations, conceptually, by maintaining a database to map the crypto currency address to a real world identity; Auto-machine learning can reduce the errors and bias that may occur because of a human who is designing the machine learning models in the first place); extracting, by the one or more processors, KYC audit attributes from the historical KYC audit cases and associated error data (Fang: Paragraph(s) 0068, 0070 teach(es) to receive digital on blockchain information and digital off blockchain information, and extract digital data from the digital on blockchain information and the digital off blockchain information); receiving, by the one or more processors, analyst attributes associated with analysts that performed the historical KYC audit cases (Fang: Paragraph(s) 0131-0133, 0065-0066 teach(es) In machine learning and data science, experts are needed to tune the algorithms. To achieve the right goal, they tune several parameters. An expert's job is to fine-tune all the parameters regularly to find the desired results; Most crypto currencies exchanges are need to have customer Know Your Customer (KYC) regulations, conceptually, by maintaining a database to map the crypto currency address to a real world identity); constructing, by the one or more processors, a dataset from input variables comprising the KYC audit attributes, the analyst attributes, and the associated error data (Fang: Paragraph(s) 0206, 0042, 0051, 0071, 0074, 0065-0066, 0135, 0219, 0230-0235 teach(es) In built feature selection. Additional irrelevant features will be less used so that they can be removed on subsequent runs. The hierarchy of attributes in a decision tree reflects the importance of attributes; The training involves models to make guesses, finding the error and then on the basis of that, correcting their guesses for making more precise predictions); generating additional features by computing one or more group-by features and one or more [statistical columns] from the dataset (Fang: Paragraph(s) 0031, 0135, 0267 teach(es) Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group called cluster); creating, by the one or more processors, a plurality of artificial intelligence (AI) models based, at least in part, on the dataset and the generated additional features (Fang: Paragraph(s) 0130-0131, 0135 teach(es) Transfer Learning is the concept where a pre-trained model is used to transfer its knowledge to a new model with similar datasets. This results in less power and computation time and gives high accuracies. It is the best option for any machine learning model having similar datasets to the one used for pre-trained models); selecting an AI model of the plurality of AI models based on discovery of one or more patterns across the historical KYC audit cases and the associated error data (Fang: Paragraph(s) 0075, 0122, 0135, 0065-0066 teach(es) the method selects a best machine learning model using an automated selection process; For selecting a machine learning model, typically the three elements are provided to the Automated Machine Learning. These elements are the dataset, the optimization metrics, and the constraints; machine learning models are dumb and with time they learn and get trained with the right data to find the pattern; Most crypto currencies exchanges are need to have customer Know Your Customer (KYC) regulations, conceptually, by maintaining a database to map the crypto currency address to a real world identity); receiving, by the one or more processors, a KYC request (Fang: Paragraph(s) 0065-0066, 0279 teach(es) Most crypto currencies exchanges are need to have customer Know Your Customer (KYC) regulations, conceptually, by maintaining a database to map the crypto currency address to a real world identity; An alert may be generated requesting further investigation of suspicious blockchain addresses with un-transferred funds); and applying, by the one or more processors, the selected AI model to a plurality of attributes of the KYC request to process the KYC request (Fang: Paragraph(s) 0065-0067 teach(es) The risk classification engine comprises a machine learning model. The risk scoring regression engine comprises a machine learning algorithm. The entity knowledge base engine comprise a black list intelligence database, a device intelligence database, a computer network intelligence database).
However, Fang does not explicitly teach computing … and one or more statistical columns from the dataset.
Conort from same or similar field of endeavor teaches computing … and one or more statistical columns from the dataset (Conort: Abstract; Paragraph(s) 0070, 0102 teach(es) descriptive statistics characterizing values in columns of the table, and/or a semantic type assigned to a column of the table).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Fang to incorporate the teachings of Conort for computing … and one or more statistical columns from the dataset.
There is motivation to combine Conort into Fang because Conort’s teachings of descriptive statistics characterizing values in columns of the table would facilitate deriving and serving features suitable for training and operating artificial intelligence systems (Conort: Paragraph(s) 0002).
Regarding Claims 2, 11, and 20, the combination of Fang and Conort teaches all the limitations of claims 1, 10, and 19 above; and Fang further teaches wherein the applying the selected AI model comprises predicting an error propensity for the KYC request via the selected AI model (Fang: Paragraph(s) 0128, 0135, 0219, 0065-0066, 0047 teach(es) Auto-machine learning can reduce the errors and bias that may occur; evaluating metrics of the AutoML model, selecting a best machine learning model using an automated selection process, and serializing the best machine learning model).
Regarding Claims 3 and 12, the combination of Fang and Conort teaches all the limitations of claim 1 and 10 above; and Fang further teaches comprising optimizing the plurality of AI models using hyper parameter optimization (Fang: Paragraph(s) 0122, 0131-0132, 0085 teach(es) For selecting a machine learning model, typically the three elements are provided to the Automated Machine Learning. These elements are the dataset, the optimization metrics, and the constraints; Several libraries like Eclipse Arbiter, Google TensorFlow's Vizier or open-source Python library Spearmint allow automating hyperparameter optimization).
Regarding Claims 4 and 13, the combination of Fang and Conort teaches all the limitations of claim 3 and 12 above; and Fang further teaches comprising cleansing the dataset, wherein the plurality of AI models are created based on the cleansed dataset, and wherein the cleansing comprises: imputing missing values to respective columns in the dataset (Fang: Paragraph(s) 0075-0077, 0088 teach(es) Pre-processing the labeled data may involve cleaning the data, such removing null values, outliers and normalizing the data into the form for AutoML); encoding categorical variables of the dataset (Fang: Paragraph(s) 0049 teach(es) the machine learning classification model is a behavior based model and may involve recognizing behavioral characteristics in at least one feature category for an entity); and scaling numerical variables of the dataset (Fang: Paragraph(s) 0139, 0251, 0198 teach(es) H20AutoML is a distributed in-memory machine learning platform that is known for scalability; H2O.ai can also completely automate some of the most productive and challenging tasks of data science such as model ensembling, feature engineering, model tuning and model deployment; Ability to handle both numerical and categorical data compared to other techniques that are usually specialized in analyzing datasets that have one type of variable).
Regarding Claims 5 and 14, the combination of Fang and Conort teaches all the limitations of claim 1 and 10 above; and Fang further teaches wherein the applying comprises classifying a risk associated with the KYC request (Fang: Paragraph(s) 0065-0068, 0071, 0074 teach(es) The system comprises a digital asset intake engine, a risk classification engine, a risk scoring regression engine, a risk policy engine, a security control system, an entity knowledge base engine, and a blockchain ledger).
Regarding Claims 6 and 15, the combination of Fang and Conort teaches all the limitations of claim 1 and 10 above; and Fang further teaches wherein the one or more group-by features are computed using categorical variables based, at least in part, on exploratory data analysis (Fang: Paragraph(s) 0031, 0135, 0267, 0093-0099 teach(es) Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group called cluster; These categories include a statistics feature category, a topology feature category, a temporal feature category, a linkage feature category, a derived feature category, and a sequential feature category).
Regarding Claims 7 and 16, the combination of Fang and Conort teaches all the limitations of claim 6 and 15 above; and Fang further teaches wherein the one or more group-by features are each created by finding an association between two or more of the categorical variables (Fang: Paragraph(s) 0198, 0208-0209 teach(es) Ability to handle both numerical and categorical data compared to other techniques that are usually specialized in analyzing datasets that have one type of variable).
Regarding Claims 8 and 17, the combination of Fang and Conort teaches all the limitations of claim 1 and 10 above; and Fang further teaches wherein the applying comprises periodically verifying information related to a client associated with the KYC request (Fang: 0131 teach(es) To achieve the right goal, they tune several parameters. An expert's job is to fine-tune all the parameters regularly to find the desired results).
Regarding Claims 9 and 18, the combination of Fang and Conort teaches all the limitations of claim 1 and 10 above; and Fang further teaches comprising optimizing the plurality of AI models using Bayesian optimization (Fang: Paragraph(s) 0132, 0147, 0149, 0151).
Response to Arguments
Applicant's arguments filed September 26, 2025 have been fully considered but they are not persuasive.
Regarding applicant’s argument under Claim Rejections - 35 USC § 101 that the rejection is moot in view of the amended claim set presented herein, examiner respectfully argues that the amended limitations do not overcome the rejection, as stated above. The claims are still not patent eligible.
Regarding applicant’s argument under Claim Rejections - 35 USC § 102 & 103 that “Fang fails to disclose the above-noted features of independent claim 1. Fang fails to disclose, for example, KYC audit cases, analyst attributes, and generated additional features that include one or more group-by features and one or more statistical columns,” examiner respectfully argues that the combination of Fang and Conort teaches all the features, as recited, as stated above with respect to the 103 rejections (Fang: Paragraph(s) 0131-0133, 0065-0066; Conort: Abstract; Paragraph(s) 0070, 0102).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Neely (WO 2025073012 A1) teaches System, Method And Data Communications Network For Certifying Digital User Accounts, PROFILES AND IDENTITIES, including KYC, due diligence, and machine learning.
Robell (US 20240412220 A1) teaches Technologies For Creating Non-Fungible Tokens For Know Your Customer And Anti-Money Laundering, including artificial intelligence, due diligence, and KYC.
Ma (US 20230325852 A1) teaches Method And System For Automation Of Due Diligence, including KYC, due diligence, and machine learning.
THIS ACTION IS MADE FINAL. 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.
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/CLAY C LEE/Primary Examiner, Art Unit 3699