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 .
Status of Claims
This action is in reply to the application filed on 3/11/2026.
Claims 1 and 4 are currently amended. Claim 15 is currently amended.
Claims 2-3, 5-13, 14, and 16 are previously presented.
Claims 1-16 are currently pending and have been examined.
The instant application is now being examined by Examiner Michael Anderson.
Response to Applicant’s Arguments
Claim Objection
The objection to Claim 4 is withdrawn in view of Applicant’s amendment correcting the punctuation.
Claim Rejections - 35 USC § 112
The rejection of Claim 15 under 35 U.S.C. 112(b) is withdrawn in view of Applicant’s amendment correcting the dependency to Claim 14.
Claim Rejections - 35 USC § 101
Applicant’s arguments filed 3/11/2026 have been fully considered but they are not persuasive.
Applicant argues that the claims cannot be practically performed by the human mind because the claims recite a natural language processor, trained classification model, and trained model, and that these limitations encompass AI in a way that cannot practically be performed in the human mind, citing the Memo issued by Charles Kim on August 4, 2025.
The Examiner acknowledges the guidance set forth in the Kim Memo regarding the mental process grouping and AI-related claim limitations. However, Applicant’s arguments are not persuasive for the following reasons.
First, even assuming arguendo that the mental process grouping does not apply to the claims as drafted, the claims still recite abstract ideas falling within other enumerated groupings under MPEP 2106.04(a)(2). Specifically, the claims recite mathematical concepts. “Processing the unstructured AMLR text data using a natural language processor to output an unstructured AMLR text feature vector” is a mathematical calculation, namely converting text data into a numerical vector representation. “Predicting a typology of the AMLR using a trained classification model” is a mathematical calculation performed by applying a mathematical classification algorithm to input data. “Predicting a value/risk score of the AMLR from a trained model” is likewise a mathematical calculation producing a numerical output from numerical inputs. The specification at paragraph [0031] confirms these are known mathematical techniques by identifying the models as “a random forest classifier, an XGBoost classifier or other type of classification model,” which are established mathematical algorithms that operate on numerical inputs to produce numerical outputs through defined mathematical operations (e.g., decision trees, gradient boosting functions). The claims therefore recite mathematical concepts regardless of whether the mental process grouping applies.
Additionally, the claims recite certain methods of organizing human activity, specifically commercial or legal interactions in the form of regulatory compliance activities. Evaluating anti-money laundering reports to predict typology and value/risk scores is a fundamental activity in legal and regulatory compliance for financial institutions. This abstract idea grouping is independent of the mental process grouping and is not addressed by the Kim Memo.
Second, regarding the Kim Memo specifically, the Examiner notes that the Memo states that “claim limitations that encompass AI in a way that cannot be practically performed in the human mind do not fall within this grouping.” However, the claims here do not recite AI with any specificity that would distinguish the claimed operations from high-level mathematical operations. The claims recite “a natural language processor,” “a trained classification model,” and “a trained model” without any recitation of a specific model architecture, specific training methodology, specific algorithmic operations, or any technical detail that would distinguish these from generic invocations of mathematical tools. The claims recite what these tools do (output a vector, predict a typology, predict a score) but not how they do so in any technically specific manner. Accordingly, even under the Kim Memo guidance, the claims are more appropriately characterized as reciting mathematical concepts applied by generic tools rather than as encompassing AI in a specific, technically meaningful way.
Regarding Step 2A Prong Two, Applicant argues that the claimed invention provides an improvement in the technology or technical field of processes for enriching anti-money laundering investigation reports.
The Examiner is not persuaded. The claimed “improvement” amounts to automating a previously manual process using generic machine learning tools. Merely automating a known manual process by applying conventional machine learning techniques does not constitute an improvement to technology or the functioning of a computer. See MPEP 2106.05(a). An improvement to technology must be a technical improvement, such as an improvement to the functioning of the computer itself, improved processing speed through a novel algorithm, reduced memory usage, improved accuracy through a specific technical mechanism, or similar technical advancement. The claims do not recite any such technical improvement. The claims recite the use of known ML tools (random forest, XGBoost per paragraph [0031] of the specification) applied to data to produce predictions. The specification does not describe any improvement to NLP processing, any novel classification algorithm, or any improvement to how trained models function. Rather, the specification describes applying known techniques to a new data set (AMLRs).
Applicant argues that “the current process not only differs in how the processing is done, but also in the results of the processing” and that the claims provide “efficient and consistent predictions and evaluations.” However, efficiency and consistency are inherent benefits of computerizing any manual process and do not constitute a technical improvement to technology. See Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Canada, 687 F.3d 1266, 1278 (Fed. Cir. 2012) (finding that the fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter). The claims do not recite any specific technical mechanism by which improved accuracy or consistency is achieved beyond simply using trained models, which is the expected function of any trained model.
Additionally, as noted in the previous Office Action, Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 (Fed. Cir. Apr. 18, 2025), reinforces that applying generic machine learning techniques to known problems without technical innovation in the machine learning methods themselves is insufficient for patent eligibility. Here, the claims apply known ML techniques (NLP, classification models, prediction models) to the known problem of evaluating AMLRs without any claimed technical innovation in the ML methods themselves.
The Kim Memo further states that “the examiner is reminded to consult the specification to determine whether the disclosed invention improves technology or a technical field, and evaluate the claim to ensure it reflects the disclosed improvement.” Consulting the specification, the Examiner finds no description of any technical improvement to NLP techniques, classification algorithms, or prediction models. The specification describes using existing tools in a conventional manner to process a particular type of data. The claims similarly do not reflect any technical improvement but rather recite applying tools to data at a high level of generality.
Regarding Step 2B, Applicant argues that the claims provide an inventive concept and significantly more because the claims provide an improvement in the technical field. For the same reasons discussed above regarding Step 2A Prong Two, this argument is not persuasive. The additional elements (natural language processor, trained classification model, trained model, processor, memory, CRM) are generic computer components and well-known machine learning tools used in their conventional manner. As supported by Applicant’s specification at paragraphs [0031] and [0038]-[0042], and the prior art of record including Kumar Singh and Yeri, these elements were well-understood, routine, and conventional in the art at the time of filing.
Accordingly, the rejection of claims 1-16 under 35 U.S.C. § 101 is maintained.
Claim Rejections - 35 USC § 103
Applicant’s arguments filed [DATE] have been fully considered but they are not persuasive.
Applicant argues that Singh does not teach or suggest any evaluation of an anti-money laundering report and does not retrieve any anti-money laundering report that comprises structured data and unstructured text data. Applicant contends that Singh’s process 500 is for ranking documentation from unstructured data sources through leveraging insights provided by structured data, and that there is no disclosure of retrieving an anti-money laundering report.
The Examiner respectfully disagrees. Under the broadest reasonable interpretation, an “anti-money laundering report” is a report or document that relates to anti-money laundering activities. Singh at paragraph [0078] explicitly discloses that the system processes documents relating to “know your customer (KYC)” and “money laundering.” Singh’s system retrieves documents that contain both structured data ([0080], identify 502 structured data) and unstructured data ([0085], unstructured data 412 identified 520) relating to money laundering compliance. Applicant’s claims do not require the AMLR to be a single, unified document filed with a regulatory body. Rather, the claims recite “retrieving an anti-money laundering report (AMLR) comprising structured AMLR data and unstructured AMLR text data.” Under BRI, Singh’s retrieval of structured and unstructured documents relating to money laundering satisfies this limitation because these documents constitute reports used in anti-money laundering processes. The claims do not specify a particular format or regulatory filing status of the AMLR.
Applicant further argues that Singh fails to disclose outputting any feature vector that is then used to predict a typology and a value/risk score. The Examiner notes that Singh at paragraph [0086] discloses that unstructured data documents 442 are analyzed 524 through natural language processing. The output of NLP processing inherently produces numerical representations (i.e., feature vectors) of the text for further computational processing. Singh’s NLP analysis produces structured output from unstructured text that is then used in subsequent processing steps, which under BRI reads on outputting a feature vector. The claims do not recite any specific type of feature vector or specific NLP architecture.
Applicant argues that Singh does not teach predicting a typology using a trained classification model using an unstructured text feature vector and structured data, nor predicting a value/risk score using a predicted typology, feature vector, and structured data. As acknowledged in the previous Office Action, Singh does not specifically disclose classification modeling. However, the combination of Singh with Yeri was relied upon for this limitation. Yeri at paragraph [0037] discloses classification modeling, and at paragraph [0073] discloses training the model. Singh at paragraph [0087] discloses mapping unstructured data documents 442 and structured data 434 to determine relationships, and at paragraph [0075] discloses classification of the data. The combination of Singh’s processing of structured and unstructured compliance data with Yeri’s trained classification modeling provides the claimed prediction of a typology using both feature vectors from unstructured text and structured data.
Regarding predicting a value/risk score using the predicted typology, feature vector, and structured data, Singh at paragraph [0091] discloses scoring based on the analyzed unstructured data documents 442, relevant structured data 434, and the established mapped relationships therebetween. The “established mapped relationships” determined through the classification/mapping step of [0087] correspond to the predicted typology that feeds into the scoring step. In other words, Singh’s scoring at [0091] relies upon the results of the classification/mapping of [0087] (which corresponds to the typology prediction), the NLP-processed unstructured data, and the structured data, which collectively read on the claimed limitation of predicting a value/risk score using the predicted typology, unstructured AMLR text feature vector, and structured AMLR data.
Applicant argues that “neither Singh nor Yeri teach or suggest all of the features of claim 1.” However, the rejection is based on the combination of Singh and Yeri, not on either reference individually. One cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). The combination of Singh’s AML-related document processing pipeline with Yeri’s trained classification modeling provides the complete claimed method, and the motivation to combine is proper as previously stated, namely to facilitate detection of non-compliant behavior as discussed in Yeri [0003] and Kumar Singh [0098].
Accordingly, the rejection of claims 1-3, 14, and 16 under 35 U.S.C. § 103 is maintained.
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 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The method of claim 1 is directed to a process. The system of claim 14 is directed to a machine. The non-transitory computer readable medium of claim 16 is directed to a manufacture. Accordingly, claims 1, 14, and 16 fall within at least one of the four statutory categories of invention.
Step 2A, Prong One
Claims 1, 14 and 16 recite a judicial exception. Using the limitations of independent claim 1 as representative, the claims recite:
A method of evaluating anti-money laundering reports comprising: retrieving an anti-money laundering report (AMLR) comprising structured AMLR data and unstructured AMLR text data; processing the unstructured AMLR text data using a natural language processor to output an unstructured AMLR text feature vector; predicting a typology of the AMLR using a trained classification model using both the unstructured AMLR text feature vector and the structured AMLR data as input; and predicting a value/risk score of the AMLR from a trained model using the predicted typology, unstructured AMLR text feature vector, and structured AMLR data as input.
The claim limitations, under their broadest reasonable interpretation, recite an abstract idea that falls within the mental processes grouping enumerated in MPEP 2106.04(a)(2). Specifically, the claims recite the mental process of evaluating anti-money laundering reports by retrieving report data, processing textual data to extract features, classifying the report into a typology, and predicting a value/risk score based on the classification and extracted features. These are observations, evaluations, judgments, and opinions that can practically be performed in the human mind or with pen and paper, as acknowledged by Applicant’s own specification at paragraph [0026], which states: “the assignment of a numerical score to an AMLR addresses the significant manual effort and time required to gain this insight. Current methods require manual assessment of each multi-page report by reading the individual documents to form conclusions on the risk and perceived value of the report.” This admission confirms that the claimed evaluation was previously performed as a mental process by human analysts.
Additionally, the claims recite mathematical concepts as enumerated in MPEP 2106.04(a)(2). Specifically, “processing the unstructured AMLR text data using a natural language processor to output an unstructured AMLR text feature vector” is a mathematical calculation (converting text to a numerical vector representation). “Predicting a typology of the AMLR using a trained classification model” and “predicting a value/risk score of the AMLR from a trained model” are mathematical calculations performed by applying mathematical models to input data to produce numerical outputs. The specification at paragraph [0031] confirms these are conventional mathematical techniques, identifying the models as “a random forest classifier, an XGBoost classifier or other type of classification model,” which are known mathematical algorithms.
The claims further recite certain methods of organizing human activity as enumerated in MPEP 2106.04(a)(2), specifically commercial or legal interactions including regulatory compliance activities. Evaluating anti-money laundering reports for typology, value, and risk is a fundamental activity in legal and regulatory compliance for financial institutions. See MPEP 2106.04(a)(2)(II).
Please see MPEP 2106.04(a)(2)(III)(A) which notes that claims recite a mental process when they contain limitations that can practically be performed in the human mind, including observations, evaluations, judgments, and opinions. See Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016) (a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind). The recitation of generic machine learning models and NLP tools does not negate the mental process nature of the underlying evaluation because the claims do not recite any particular technical improvement to the models themselves but rather use them as tools to perform the mental evaluation. See MPEP 2106.04(a)(2)(III)(c) discussing that a claim recites a mental process when it encompasses performance in the mind but merely uses a computer as a tool to perform the mental process.
Accordingly, claims 1, 14, and 16 recite an abstract idea.
Step 2A, Prong Two
Claims 1, 14, and 16 do not integrate the judicial exception into a practical application. The claims recite the following additional elements beyond the abstract idea: a processor, a memory, a computing system, a non-transitory computer readable medium, a natural language processor, a trained classification model, and a trained model. These additional elements are evaluated individually and in combination to determine whether they integrate the exception into a practical application.
The processor, memory, computing system, and non-transitory computer readable medium are recited at a high level of generality and merely serve as generic computing components used to implement the abstract idea. See MPEP 2106.05(f). Applicant’s specification at paragraphs [0038]-[0042] describes these components in generic terms, including references to CPUs, RAM, ROM, hard disk drives, Ethernet cards, and other standard computing hardware, confirming that no particular or specialized hardware is required.
The natural language processor, trained classification model, and trained model are recited at a high level of generality as tools to perform the abstract idea of evaluating AMLRs. The claims do not recite any specific technical architecture, novel algorithm, or improvement to the functioning of these tools themselves. Rather, they are invoked as black-box tools to perform the data analysis steps. The specification at paragraph [0031] identifies only conventional, off-the-shelf classifiers such as random forest and XGBoost without describing any improvement to these techniques. Please see MPEP 2106.05(f)(2) discussing that when the claim invokes computers or other machinery merely as a tool to perform an existing process, this does not integrate the judicial exception into a practical application.
The “retrieving” step constitutes insignificant extra-solution activity, specifically mere data gathering, which does not integrate the abstract idea into a practical application. See MPEP 2106.05(g).
The claims do not recite an improvement to the functioning of a computer or to any other technology or technical field. See MPEP 2106.05(a). The claims do not apply the judicial exception with or by use of a particular machine. See MPEP 2106.05(b). The claims do not effect a transformation or reduction of a particular article to a different state or thing. See MPEP 2106.05©. The claims do not apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(e).
Please also see MPEP 2106.05(f)(1) discussing that when a claim recites only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished, this does not show integration into a practical application. The claims here recite the goal of predicting typologies and risk/value scores without claiming any specific technical mechanism that constitutes an improvement to technology.
With respect to the trained classification model and trained model, please also see Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 (Fed. Cir. Apr. 18, 2025), which affirmed that applying generic machine learning techniques to known problems without technical innovation in the machine learning methods themselves is insufficient for patent eligibility under 35 U.S.C. § 101.
Accordingly, the additional elements do not integrate the abstract idea into a practical application.
Step 2B
Claims 1, 14, and 16 do not recite additional elements that amount to significantly more than the judicial exception. As discussed above with respect to Step 2A Prong Two, the additional elements of a processor, memory, computing system, non-transitory computer readable medium, natural language processor, trained classification model, and trained model amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. See MPEP 2106.05(f).
The generic nature of the computing components is supported by Applicant’s specification at paragraphs [0038]-[0042], which describes processors, memories, communication interfaces, GPUs, and other standard computing hardware in entirely generic terms. The use of NLP to process text and generate feature vectors, the use of classification models such as random forest or XGBoost to classify data, and the use of trained models to predict scores from input features are well-understood, routine, and conventional activities in the machine learning and data analytics arts. See Applicant’s specification at paragraph [0031] identifying random forest classifiers and XGBoost classifiers as the types of models used, which are well-known in the art. See also the prior art of record including Kumar Singh (US 2022/0148048) and Yeri (US 2021/0097605) which demonstrate that processing structured and unstructured data using NLP and trained models to evaluate compliance-related documents was known in the art.
The “retrieving” step is recognized by the courts as well-understood, routine, and conventional. See MPEP 2106.05(d)(II) (receiving or transmitting data over a network is conventional).
Considering the additional elements individually and in ordered combination does not change this conclusion. The combination of generic computing components performing conventional machine learning operations does not amount to significantly more than the abstract idea.
Accordingly, claims 1, 14, and 16 are not patent eligible under 35 U.S.C. § 101.
Dependent Claims
Dependent claims 2-13 and 15 do not cure the deficiencies of their respective independent claims and are also not patent eligible.
Claim 2 recites predicting the typology and value/risk score for a plurality of AMLRs and generating a graphical user interface displaying a representation of the predicted typology, value and risk score. Predicting scores for a plurality of AMLRs merely repeats the abstract idea for multiple inputs and does not add a meaningful limitation. Generating a graphical user interface displaying results constitutes insignificant extra-solution activity in the form of mere outputting or displaying of data. See MPEP 2106.05(g). Displaying collected data is also recognized as well-understood, routine, and conventional. See MPEP 2106.05(d)(II) and Electric Power Group, 830 F.3d at 1354.
Claim 3 recites that the trained classification model provides an indication of one or more of a plurality of predefined classes. This limitation further defines the abstract mathematical classification operation and does not add any additional element that integrates the exception into a practical application or provides significantly more.
Claim 4 recites that predicting the value/risk score comprises predicting a value score using a trained value model and predicting a risk value using a trained risk model. This limitation further narrows the abstract idea by specifying that separate models are used for value and risk predictions, which remains a mathematical concept and mental evaluation. The trained value model and trained risk model are recited at a high level of generality as tools to perform the abstract evaluation.
Claims 5-6 further define the value model as providing predictions to predefined entities including a value to a bank, a value to society, and an overall value. These limitations merely narrow the field of use and further define the abstract evaluation criteria without adding any technical improvement.
Claims 7-8 further define the risk model as providing predictions to predefined risk entities including regulatory, legal, financial, and reputational. These limitations similarly narrow the field of use and further define the abstract evaluation criteria without adding any technical improvement.
Claim 9 recites training each of the classification model, the value model, and the risk model. Training machine learning models is a mathematical process and further narrows the abstract idea without integrating it into a practical application. See MPEP 2106.05(f).
Claim 10 recites that training comprises retrieving a plurality of AMLRs, presenting each to an evaluator, and for each AMLR receiving an indication of a classification, a value score, and a risk score. These steps recite data gathering (retrieving AMLRs and receiving evaluator inputs) and further define the training of models, which remains a mathematical concept. Presenting data to a human evaluator and receiving their input is a method of organizing human activity.
Claims 11-13 recite that indications of classification, value, and risk are used to train the respective models. These limitations further define the mathematical training process without adding any additional element that integrates the exception into a practical application or provides significantly more.
Claim 15 recites a processing device with a second processor and second memory configured to retrieve AMLRs, present them to an evaluator, and receive classifications, value scores, and risk scores. The second processor and second memory are generic computing components recited at a high level of generality. The functional steps merely recite data gathering and organizing human activity for the purpose of training models. See MPEP 2106.05(f)(2).
Accordingly, claims 1-16 are not patent eligible under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
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.
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.
Claims 1-3, 14 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar Singh (US 2022/0148048) in view of Yeri (US 2021/0097605).
Claim 1 recites:
A method of evaluating anti-money laundering reports comprising: (Kumar Singh, Fig. 4, [0078], KYC, money laundering; Fig. 5, [0080], process 500 for ranking documentation)
retrieving an anti-money laundering report (AMLR) comprising structured AMLR data and unstructured AMLR text data; (Kumar Singh, Figs. 4 and 5, [0080], identify 502 structured data; Kumar Singh, Figs. 4 and 5, [0085], unstructured data 412 identified 520)
processing the unstructured AMLR text data using a natural language processor to output an unstructured AMLR text feature vector; (Kumar Singh, Figs. 4 and 5, [0086], unstructured data documents 442 analyzed 524 through natural language processing looking for evidence of money laundering)
predicting a typology of the AMLR using a trained classification model using both the unstructured AMLR text feature vector and the structured AMLR data as input; and (Kumar Singh, Figs. 4 and 5, [0087], mapping of unstructured data documents 442 and structured data 434 is executed 528 to determine relationships; [0075], classification of structured, unstructured data. Kumar Singh does not discuss classification modeling. Yeri, [0037], discusses classification modeling, and Yeri, [0073], discusses training the model. It would have been obvious to a person of ordinary skill in the art before the time of effective filing to modify the classification of Kumar Singh to include the classification modeling and training as in Yeri to facilitate detection of non-compliant behavior as discussed in the Yeri, [0003], and Kumar Singh, [0098]. Further, it would have been obvious to one of ordinary skill in the art before the time of effective filing to include the features as taught in Yeri in Kumar Singh since the claimed invention is merely a combination of old elements, and in 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. Additionally, both are in the field of data analytics and one of ordinary skill in the art would recognize the combination to be predictable.)
predicting a value/risk score of the AMLR from a trained model using the predicted typology, unstructured AMLR text feature vector, and structured AMLR data as input. (Kumar Singh, Figs. 4 and 5, [0091], scoring based on analyzed unstructured data documents 442, relevant structured data 434 and the established mapped relationships therebetween)
Claim 2 recites:
The method of claim 1, further comprising: predicting the typology, and value/risk score for a plurality of AMLRs; and (Kumar Singh, Figs. 4 and 5, [0091], scoring based on analyzed unstructured data documents 442, relevant structured data 434 and the established mapped relationships therebetween. See also MPEP 2144.04 regarding obvious duplication of parts)
generating a graphical user interface displaying a representation of the predicted typology, value and risk score for the plurality of AMLRs. (Kumar Singh, Figs. 4 and 5, [0091], scoring based on analyzed unstructured data documents 442, relevant structured data 434 and the established mapped relationships therebetween. Kumar Singh does not specifically disclose generating a graphical user interface displaying a representation. Yeri, Fig. 6, [0073], shows outputting results. It would have been obvious to a person of ordinary skill in the art before the time of effective filing to modify the scoring of Kumar Singh to include the outputting of results as in Yeri to facilitate detection of non-compliant behavior as discussed in the Yeri, [0003], and Kumar Singh, [0098].)
Claim 3 recites:
The method of claim 1, wherein the trained classification model provides an indication of one or more of a plurality of predefined classes that apply to the AMLR. (Kumar Singh, [0075], classification. Kumar Singh does not specifically disclose predefined classes. Yeri, [0060], discusses multiple, particular classes. It would have been obvious to a person of ordinary skill in the art before the time of effective filing to modify the classification of Kumar Singh to include particular classes as in Yeri to facilitate detection of non-compliant behavior as discussed in the Yeri, [0003], and Kumar Singh, [0098].)
Claim 14 recites:
A system for use in evaluating anti-money laundering reports, comprising: (Kumar Singh, Fig. 4, [0077], system 400)
a processor for executing instructions; and (Kumar Singh, Fig. 4, [0077], processing devices 404)
a memory storing instructions which when executed by the processor configure the system to provide a method according to claim 1. (Kumar Singh, Fig. 4, [0077], memory devices 406)
Claim 16 recites:
A non-transitory computer readable medium having instructions stored thereon which when executed by a processor of a computing system configure the system to provide a method according to claim 1. (Kumar Singh, Fig. 1, [0054], CRM 120)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure includes: US 20240220511; US 20240086815; US 20240086816; US 11893632; US 20230325852; US 20230153830; US 20220405261; US 20220358508; US 20220292309; US 20220237934; US 20220059234; US 20210373721; US 20210377310; US 20210224922; US 20210133207; US 20210073693; US 20200372369; WO 2020167558; US 20190349321; US 20190347282; US 20190325528; US 20190311367; US 20190259033; US 20190213498; and US 20190171985.
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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Mike Anderson
Supervisor Patent Examiner
Art Unit 3693
/Mike Anderson/Supervisory Patent Examiner, Art Unit 3693
Prosecution Insights