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 the Claims
The previous Office Action is replaced/superseded.
This Office Action is in response to the Application filed 4 August 2025.
This is a second or subsequent Non-Final.
A previous Preliminary Amendment canceled Claim 1 and added new Claims 2-21.
Claims 2-21 are pending and has been examined in this Office Action.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/30/2025 was filed after the mailing date of the non-final office action on 03/04/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 2-21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims the claims of U.S. Patent No. 11,810,204 B2 as shown in the table below. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 2-21 of the examined application are anticipated by and obvious over the claims of the ‘204 patent as shown in the table below.
“A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985). In determining whether a nonstatutory basis exists for a double patenting rejection, the first question to be asked is: Is any invention claimed in the application anticipated by, or an obvious variation of, an invention claimed in the patent? If the answer is yes, then a nonstatutory double patenting rejection may be appropriate” (MPEP 804(II)(B)).
Claims 2-21 of the reviewed application is recited below and the limitations disclosed entirely by the claims of the ‘204 patent have been identified with bold in the table below. For clarity of the record, claims 1 and 2 of the ‘651 patent was also provided below.
11,810,204 B2
18/490,020
1
A computer-implemented method for anti-money laundering (AML) analysis, comprising:(a) obtaining, by a computer, a dataset comprising a plurality of accounts, each of the plurality of accounts corresponding to an account holder among a plurality of account holders, wherein each account of the plurality of accounts is defined by a plurality of account variables, wherein the plurality of account variables comprises variables about financial transactions associated with the account; (b) extracting, by the computer, a set of features from the dataset, wherein the set of features are associated with at least one of the plurality of account holders and at least one of the plurality of account variables;(c) applying, by the computer, a trained machine learning algorithm to the set of features to determine (i) a money laundering risk score for each of the plurality of account holders and (ii) a feature importance value for each of the set of features associated with the determined money laundering risk score; (d) ranking, by the computer, at least a subset of the plurality of account holders for investigation for money laundering, based at least in part on money laundering risk scores of account holders of the subset; and (e) displaying, by the computer via a graphical user interface, (i) at least the subset of the plurality of account holders ranked in and (ii) for each of the subset of the plurality of account holders, at least a subset of the set of features that contribute most to the money laundering risk score of the plurality of account holders.
2, 12 & 20
A method comprising: applying, by one or more processors in real time, an algorithm to a dataset to produce a risk score and a feature importance value corresponding to an account associated with an account holder, wherein the risk score indicates a predicted likelihood the account is associated with money laundering activity and the feature importance value indicates one or more features of a defined set of features that contributed to the risk score, wherein the dataset comprises information associated with a plurality of accounts; outputting, by the one or more processors in real time, the risk score and the feature importance value; updating, by the one or more processors in real time, the algorithm in real time in response to changes in the dataset, the changes including additional information associated with the account; and outputting, by the one or more processors in real time, an updated risk score and an updated feature importance value corresponding to the account.
2
wherein obtaining the dataset comprises aggregating datasets from a plurality of disparate sources selected from the group consisting of: online and retail transactions, account and account holder characteristics in a pre-selected time window, trading surveillance platforms, PEP lists, sanction and regulatory catalogs, terror and criminal watch lists, currency exchange history, and cross-border transaction information.
3 & 13
The method of claim 2, further comprising: obtaining different portions of the dataset from a plurality of disparate sources; and aggregating the different portions to produce the dataset.
2
wherein obtaining the dataset comprises aggregating datasets from a plurality of disparate sources selected from the group consisting of: online and retail transactions, account and account holder characteristics in a pre-selected time window, trading surveillance platforms, PEP lists, sanction and regulatory catalogs, terror and criminal watch lists, currency exchange history, and cross-border transaction information.
4 & 14
The method of claim 3, wherein the plurality of disparate sources is selected from the group consisting of: online and retail transactions, account and account holder characteristics in a pre-selected time window, trading surveillance platforms, politically exposed person (PEP) lists, sanction and regulatory catalogs, terror and criminal watch lists, currency exchange history, and cross-border transaction information.
5
The method of claim 1, wherein the trained machine learning algorithm is selected from the group consisting of: a support vector machine (SVM), a naive Bayes classification, a linear regression, a quantile regression, a logistic regression, a random forest, a neural network, and a gradient-boosted classifier or regressor.
5 & 15
The method of claim 2, wherein the algorithm is selected from the group consisting of: a support vector machine (SVM), a naive Bayes classification, a linear regression, a quantile regression, a logistic regression, a random forest, a neural network, and a gradient-boosted classifier or regressor.
34
The method of claim 1, wherein (f) further comprises displaying, for each of the subset of the plurality of account holders, the subset of the set of features that contribute most to the money laundering risk score of the account holder grouped by risk typologies.
6-7 & 16
The method of claim 2, wherein the set of features comprises features associated with different risk typologies.
1
… wherein the plurality of account variables comprises variables about financial transactions associated with the account; …
8 & 17
The method of claim 2, wherein dataset comprises account variables associated with financial transactions.
1
(c) applying, by the computer, a trained machine learning algorithm to the set of features to determine (i) a money laundering risk score for each of the plurality of account holders and (ii) a feature importance value for each of the set of features associated with the determined money laundering risk score; (d) ranking, by the computer, at least a subset of the plurality of account holders for investigation for money laundering, based at least in part on money laundering risk scores of account holders of the subset;
9, 18 & 21
The method of claim 2, further comprising: applying the algorithm to the dataset to produce one or more additional risk scores and additional feature importance values corresponding to additional accounts of the plurality of accounts, the additional accounts corresponding to a plurality of account holders; and selecting at least a subset of the plurality of account holders for investigation for money laundering based on the additional risk scores.
9
The method of claim 1, further comprising generating a weighted priority score for each of the plurality of account holders based at least in part on the money laundering risk score of the account holder and a quantitative measure of the account holder or of a transaction of the account holder.
10 & 19
The method of claim 2, further comprising generating a weighted priority score for each of the plurality of account holders based at least in part on the additional risk scores.
11
The method of claim 9, further comprising sorting the plurality of account holders based at least in part on the weighted priority scores for each of the plurality of account holders.
11
The method of claim 10, further comprising sorting the plurality of account holders based at least in part on the weighted priority scores for each of the plurality of account holders, wherein an account holder of the subset of the plurality of account holders is selected for investigation when the weighted priority score of the account holder of the subset meets a pre-determined criterion.
12
The method of claim 9, further comprising ranking an account holder of the subset of the plurality of account holders for investigation for money laundering when the weighted priority score of the account holder of the subset meets a pre-determined criterion.
11
The method of claim 10, further comprising sorting the plurality of account holders based at least in part on the weighted priority scores for each of the plurality of account holders, wherein an account holder of the subset of the plurality of account holders is selected for investigation when the weighted priority score of the account holder of the subset meets a pre-determined criterion.
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.
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites at least one step or act, including applying an algorithm to a dataset. Thus, the claim is to a process, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
The broadest reasonable interpretation of steps of applying an algorithm to a dataset to produce, in real time, a risk score and a feature importance value corresponding to an account associated with an account holder, wherein the risk score indicates a predicted likelihood the account is associated with money laundering activity and the feature importance value indicates one or more features of a defined set of features that contributed to the risk score, wherein the dataset comprises information associated with a plurality of accounts and updating, in real time, the algorithm in response to changes in the dataset, the changes including additional information associated with the account is that those steps fall within the mathematical concepts groupings of abstract ideas because they cover mathematical calculations (See MPEP 2106.04(a)(2), subsection I). Additionally, as claimed applying in real time an algorithm to a data set to determine a risk score that indicates a predicted likelihood the account is associated with money laundering activity and the feature importance value indicates one or more features of a defined set of features that contributed to the risk score also describe concepts relating to fundamental economic principles or practices (including mitigating risk) because the claims are directed to risk scoring to predict the likelihood the account is associated with money laundering activity as disclosed in the both the claims and the specification which cover concepts certain methods of organizing human activity (See MPEP 2106.04(a)(2), subsection II). Specifically, the claim recites applying an algorithm to a dataset to produce a risk score and a feature importance value corresponding to an account associated with an account holder, which Is a mathematical calculation but may also responsibly be interpreted to be the fundamental economic activity of mitigating risk.
Regarding steps of outputting in real time the risk score and the feature importance value and outputting an updated risk score and an updated feature importance value corresponding to the account. The claim does not impose any limits on how the data is output or require any particular components that are used to output the data. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). The claim recites the additional elements of “one or more processors”. The claims recite the steps are performed by the “one or more processors”.
The limitations of outputting the risk score and the feature importance value and outputting an updated risk score and an updated feature importance value corresponding to the account are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
Further, the limitations are recited as being performed by “one or more processors”. The “one or more processors” are recited at a high level of generality. In limitation (a), the computer is used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). The computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. As explained with respect to Step 2A, Prong Two, the additional elements are the “one or more processors”. The additional elements were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering and outputting. However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). As discussed in Step 2A, Prong Two above, the recitations of outputting the risk score and the feature importance value and outputting an updated risk score and an updated feature importance value corresponding to the account are recited at a high level of generality. These elements amount to transmitting data and are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. 10 As discussed in Step 2A, Prong Two above, the recitation of a processor to perform limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
Dependent claims 2-21 are not directed to any additional claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims. In this case, the claims are rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Thus, the claim is not patent eligible.
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.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 2-21 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Han et al., US Patent Application Publication US 2018/0365773 A1 (“Han”).
Claims 2, 12 and 20:
Han discloses the following limitation:
applying, by one or more processors in real time, an algorithm to a dataset to produce a risk score and a feature importance value corresponding to an account associated with an account holder, (see at least [0012] disclosing “the AML, platform may generate a data model, and may use the data model to determine scores and predict classes (e.g., sentiment scores) for entities identified in the graph data structure. The scores may be used to determine whether the target entity and/or the one or more related entities are engaging in money laundering. The AML platform may generate a recommendation indicating whether the target entity and/or the one or more related entities are engaging in money laundering based on the scores.” Also see [0028-32, 0094-97].)
wherein the risk score indicates a predicted likelihood the account is associated with money laundering activity and the feature importance value indicates one or more features of a defined set of features that contributed to the risk score, (see at least figures 1C-E and related text and [0028-32] disclosing “the AML platform may use information included in the graph data structure as input for the data model. For example, the AML platform may use the one or more nodes, the one or more edges, and/or the metadata associated with the one or more nodes as input for the data model. …. “As shown by reference number 145, the AML platform may generate a data model. For example, the AML platform may generate a data model using the training information and a training dictionary. In this case, the training dictionary may include a collection of words and phrases that associates the words and phrases with particular scores. In this way, the AML platform may generate the data model by using the training dictionary to score the training information”. Also see [0095-99].)
wherein the dataset comprises information associated with a plurality of accounts; (see at least figures 1B-D and 4 related text disclosing a dataset comprising information associated with a plurality of accounts.)
outputting, by the one or more processors in real time, the risk score and the feature importance value; (see at least figure 1E and related text including [0032] disclosing “As shown by reference number 170, the AML platform may generate a recommendation indicating whether the target entity is engaging in money laundering. For example, if a score is positive or very positive, the AML platform may generate a recommendation indicating that the entity is engaging in money laundering, and if a score is negative or very negative, the AML platform may generate a recommendation indicating that the entity is not engaging in money laundering. In some cases, rather than make a definitive determination, the recommendation may include a confidence score indicating a likelihood of the entity engaging in money laundering. As shown by reference number 175, the AML platform may provide the recommendation to an interested party (e.g., a financial institution, such as a bank).” Also see figure 5 and related text.)
updating, by the one or more processors in real time, the algorithm in response to changes in the dataset, the changes including additional information associated with the account; and (see at least figure 1B-D and 4 and related text including [0026-27] disclosing “the AML platform may update the graph data structure. For example, the AML platform may update the graph data structure by adding nodes, adding edges, removing nodes, removing edges, adding additional metadata for existing nodes, removing metadata for existing nodes, and/or the like. As shown as an example in FIG. 1C, assume the AML platform analyzes the additional information to determine that officer B is a wife of officer A, and that target entity A has a multi-million dollar contract with related entity B. In this case, the AML platform may update the data structure by adding two additional edges that identify the new relationships. … In this way, the AML platform may update the graph data structure with additional information that may be used to determine whether the target entity and/or the one or more related entities are engaging in money laundering.” Also see [0095-99].)
outputting, by the one or more processors in real time, an updated risk score and an updated feature importance value corresponding to the account. (see at least figure 1E and 4-5 and related text disclosing outputting an updated .)
memory; and one or more processors communicatively coupled to the memory, the one or more processors (see at least figure 3 and related text.)
A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, (see at least figure 3 and related text.)
Claims 3 and 13:
Han discloses the following limitation:
obtaining different portions of the dataset from a plurality of disparate sources; and aggregating the different portions to produce the dataset. (see at least figures 7, 11A-B, 14 and 16-17 and related text disclosing obtaining different portions of the dataset from a plurality of disparate sources and aggregating the different portions to produce the dataset.)
Claims 4 and 14:
Han discloses the following limitation:
wherein the plurality of disparate sources is selected from the group consisting of: online and retail transactions, account and account holder characteristics in a pre-selected time window, trading surveillance platforms, politically exposed person (PEP) lists, sanction and regulatory catalogs, terror and criminal watch lists, currency exchange history, and cross-border transaction information. (see at least [0034] disclosing “the AML platform may determine whether an individual is engaging in money laundering, such as a member of a criminal syndicate, a gang member, and/or the like. In some implementations, the AML platform may determine whether an entity or an individual is engaging in an illegal act that is related to money laundering, such as fraud, insider trading, market manipulation, and/or the like.” Also see figure 4 and related text and [0014-15, 0036, 0066, 0085-86].)
Claims 5 and 15:
Han discloses the following limitation:
wherein the algorithm is selected from the group consisting of: a support vector machine (SVM), a naive Bayes classification, a linear regression, a quantile regression, a logistic regression, a random forest, a neural network, and a gradient-boosted classifier or regressor. (see at least [0104] disclosing “In some implementations, AML platform 220 may use machine learning techniques to analyze the training information to generate a model. The machine learning techniques may include, for example, supervised and/or unsupervised techniques, such as artificial networks, Bayesian statistics, learning automata, Hidden Markov Modeling, linear classifiers, quadratic classifiers, decision trees, association rule learning, or the like. In some implementations, AML platform 220 may use another kind of computer-implemented technique, such as artificial intelligence, machine perception, computer vision, or the like, to analyze the training information and generate a model.”)
Claims 6 and 16:
Han discloses the following limitation:
wherein the set of features comprises features associated with different risk typologies. (see at least [0076-80] disclosing “AML platform 220 may determine one or more similarity-based relationships. For example, AML platform 220 may analyze information included in the target entity information and/or information included in the related entity information to determine a degree of similarity between the target entity and a related entity. The degree of similarity between the target entity and a related entity may, for example, be based on whether the target entity and related entity share a common field of business, whether the geographic location of the target entity is in close proximity to the related entity geographic location, whether employees of the target entity live in a geographic location that is similar to a geographic location in which employees of the related entity live, whether employees of the target entity and employees of the related entity are within a threshold social distance, and/or the like. Additionally, AML platform 220 may assign weight values to the target entity information and/or the related entity information, and may use the weighted values to determine a degree of similarity between the target entity and the one or more related entities”. Examiner notes that the risk typologies are the similarity based relationships. Also see [0085-95].)
Claim 7 and 16:
Han discloses the following limitation:
further comprising adapting the algorithm to emerging risk topologies. (see at least [0085] disclosing that the “AML platform 220 may determine that a relationship not previously identified between the target entity and a related entity satisfies a threshold level of similarity and, thus, should be included in the graph data structure.” Also see figure 4 and related text and [0089. 0094-95].)
Claims 8 and 17:
Han discloses the following limitation:
wherein dataset comprises account variables associated with financial transactions. (see at least [0014-15] disclosing that the dataset comprises account variables associated with financial transactions.)
Claims 9, 18 and 21:
Han discloses the following limitation:
applying the algorithm to the dataset to produce one or more additional risk scores and additional feature importance values corresponding to additional accounts of the plurality of accounts, the additional accounts corresponding to a plurality of account holders; and (see at least [0017-18] disclosing that “the AML platform may analyze the target entity information and/or the related entity information by determining a distance between the target entity and each related entity of the set of related entities. In this case, the AML platform may assign weight values to related entities based on the distance between the related entities and the target entity. Additionally, the AML platform may determine whether the weight values satisfy a distance threshold. If a related entity weight value satisfies the distance threshold (e.g., is close enough to the target entity), the AML platform may identify the related entity as a money laundering candidate”. Also see [0069-80].)
selecting at least a subset of the plurality of account holders for investigation for money laundering based on the additional risk scores. (see at least [0116-123) disclosing that “AML platform 220 may generate a recommendation indicating whether the target entity and/or the one or more related entities are engaging in money laundering. For example, if a score indicates a high or very high probability of money laundering, AML platform 220 may generate a recommendation indicating that the target entity and/or the one or more related entities are engaging in money laundering. If a score indicates a low or very low probability of money laundering, AML platform 220 may generate a recommendation indicating that the target entity and/or the one or more related entities are not engaging in money laundering. In some implementations, AML platform 220 may generate a recommendation indicating a likelihood of the target entity and/or the one or more related entities engaging in money laundering, and may use the score as a confidence level value …AML platform 220 may provide a recommendation that includes a statement identifying the entity as engaging in money laundering as well as additional information that may be useful in preventing additional money laundering and/or in convicting the entity of money laundering.”)
Claims 10 and 19:
Han discloses the following limitation:
generating a weighted priority score for each of the plurality of account holders based at least in part on the additional risk scores. (see at least [0017-18] disclosing that “the AML platform may analyze features such as distance (e.g., geographic distance, social distance, etc.), address information, working sector information, social media information, and/or the like. In this case, the AML platform may assign weight values to the one or more related entities based on the degree of similarity between the one or more features associated with the target entity and the one or more features associated with the one or more related entities. Additionally, the AML platform may determine whether the weight values satisfy a threshold, and the AML platform may identify a related entity as a money laundering candidate if the one or more features for the related entity satisfy the threshold.” Also see [0072-74, 0116-123].)
Claim 11:
Han discloses the following limitation:
sorting the plurality of account holders based at least in part on the weighted priority scores for each of the plurality of account holders, wherein an account holder of the subset of the plurality of account holders is selected for investigation when the weighted priority score of the account holder of the subset meets a pre-determined criterion. (see at least [0116-123) disclosing that “AML platform 220 may generate a recommendation indicating whether the target entity and/or the one or more related entities are engaging in money laundering. For example, if a score indicates a high or very high probability of money laundering, AML platform 220 may generate a recommendation indicating that the target entity and/or the one or more related entities are engaging in money laundering. If a score indicates a low or very low probability of money laundering, AML platform 220 may generate a recommendation indicating that the target entity and/or the one or more related entities are not engaging in money laundering. In some implementations, AML platform 220 may generate a recommendation indicating a likelihood of the target entity and/or the one or more related entities engaging in money laundering, and may use the score as a confidence level value.)
Response to Arguments
Applicant’s arguments with respect to the rejection under 35 USC 101 that the addition of the phrase “in real time” precludes the claims from practically being performed mentally or manually, Examiner notes that the rejection under 35 USC 101 never asserts that the claims are a mental process or that they can be done manually. Please refer to the rejection above.
With regard to Applicant’s argument relating to the rejection of the claims under 35 USC 102 that the rejection is traversed because Han (e.g., in paragraph 0012) describes determining scores, but does not describe producing a risk score and a feature importance value, Examiner disagrees. First, as noted in the rejection above, Examiner not only pointed to [0012] be added the additional clarifying citations figures 1B-1E and 4 and related text and [0028-32] and [0094-99]. In at least [0032] Han discloses “As shown in FIG. 1E, and by reference number 160, the AML platform may use information included in the graph data structure as input for the data model. For example, the AML platform may use the one or more nodes, the one or more edges, and/or the metadata associated with the one or more nodes as input for the data model. As shown by reference number 165, the AML platform may determine scores (e.g., sentiment scores) using the data model. In this case, a higher score may indicate a higher likelihood of an entity engaging in money laundering (and vice versa). Shown as an example, the scores may range from 0 to 4, where 0 indicates a very low probability, 1 indicates a low probability, 2 indicates a neutral probability, 3 indicates a high probability, and 4 indicates a very high probability. In this example, the data model may receive information associated with officer A as input, may analyze the information, and may output a score of 4, indicating a very high probability that officer A is engaging in money laundering.” As shown in figure 1B and then 1C and related paragraphs [0025-26], Han discloses As shown in FIG. 1C, and by reference number 125, the AML platform may obtain additional information from a second data source. The additional information may include employee demographic information, information associated with a particular business transaction, and/or the like. As shown by reference number 130, the AML platform may analyze the additional information. For example, the AML platform may analyze the additional information to identify additional nodes and/or edges for the graph data structure, to verify existing nodes and edges of the graph data structure, to determine additional metadata for existing nodes of the graph data structure, to extend the existing graph with additional nodes and edges from other sources and/or the like. …As shown by reference number 135, the AML platform may update the graph data structure. For example, the AML platform may update the graph data structure by adding nodes, adding edges, removing nodes, removing edges, adding additional metadata for existing nodes, removing metadata for existing nodes, and/or the like. As shown as an example in FIG. 1C, assume the AML platform analyzes the additional information to determine that officer B is a wife of officer A, and that target entity A has a multi-million dollar contract with related entity B. In this case, the AML platform may update the data structure by adding two additional edges that identify the new relationships.” These figures and passages clarify that Han analyses the additional information to determine the importance of the data. In this example, Officer A is married to Officer B which is very important information in the determination of the likelihood that money laundering is occurring. Therefore, when Han adds the edges to the nodes of Officer A to Officer B to connect them, it is showing the importance of this data for determining the score. This is one of Hans examples of the inventions the feature importance values and how it relates to determining the score. Therefore, Han teaches both the score and the feature importance value.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zoldi et al. (US 20170270534 A1) - Advanced Learning System For Detection And Prevention Of Money Laundering
Song et al. (US 20140058914 A1) - TRANSACTIONAL MONITORING SYSTEM
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PATRICIA H MUNSON whose telephone number is (571)270-5396. The examiner can normally be reached M-F 7:30am-4:30pm.
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/PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624
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