DETAILED ACTION
Status of Claims
Claims 1-20 are pending. Objections and rejection are recited below. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
This application, filed on 08 May 2024 is given priority from 08 May 2024.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 4 and 5 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention.
Regarding claims 4 and 15, the representative limitation recites:
the TM Index model comprises iteratively adding decision trees until a next tree in the iteration does not improve performance of the TM Index model beyond a threshold.
However, Applicant's specification does not describe what “not improving performance of the TM Index model beyond a threshold” is.
As per MPEP 2173.05
2173.05(i) Negative Limitations [R-07.2022]
The current view of the courts is that there is nothing inherently ambiguous or uncertain about a negative limitation. So long as the boundaries of the patent protection sought are set forth definitely, albeit negatively, the claim complies with the requirements of 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Some older cases were critical of negative limitations because they tended to define the invention in terms of what it was not, rather than pointing out the invention. Thus, the court observed that the limitation “R is an alkenyl radical other than 2-butenyl and 2,4-pentadienyl” was a negative limitation that rendered the claim indefinite because it was an attempt to claim the invention by excluding what the inventors did not invent rather than distinctly and particularly pointing out what they did invent. In re Schechter, 205 F.2d 185, 98 USPQ 144 (CCPA 1953).(emphasis added)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 1 and 12 are vague and indefinite in that they are structured such that it is hard to determine what Applicant is claiming as his invention. For example:
Regarding claims 1, 6, 11, 12, 17 and 20, in the representative limitation:
the TM Index model comprises a series of multiple decision trees, such that the TM Index score for an individual is a sum of an output of each of the series of multiple decision trees;
the phrase “such that the TM Index score for an individual …” is conclusory and does not clearly convey a method step.
Regarding claims 1 and 12, in the representative limitation:
training, by a TM Index model training computer system, via machine learning, a TM Index model to compute an TM Index score for customers of the financial institution, wherein:the TM Index scores are numerical values over a range, where higher scores indicate more interestingness of the customer in terms of potential money-laundering activities;the TM Index model comprises a series of multiple decision trees, such that the TM Index score for an individual is a sum of an output of each of the series of multiple decision trees;
the term “the customer” lacks antecedent basis. It is also not clear if “an individual“ refers to “the customer” or some other entity. Similarly, in the representative limitation:
for each of the one more scenario alerts, determining, by an alerting computer system, whether a financial crime alert should be issued based on, at least, transaction data for the customer pertaining to the scenario alert and the TM Index score for the customer.
the term “the customer pertaining to the scenario alert” lacks antecedent basis. It is also not clear what the relationship is between “the customer pertaining to the scenario alert” and “the customer” at the end of the limitation. Applicant should refer to “a plurality of customers” and “a first customer of the plurality of customers”, etc., if that is indeed what the Applicant wishes to convey.
Regarding claims 1, 4, 12 and 15, in the representative limitation:
determining a set of feature variables for the TM Index model, wherein determining the set of feature variables comprises iteratively determining, for each of a plurality of potential feature variables, whether performance of the model improves due to inclusion of the potential feature variable;
the term "improves" is a relative term which renders the claim indefinite. The term “improves” (performance of the model) is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Regarding claims 4 and 15, the representative limitation:
the TM Index model comprises iteratively adding decision trees until a next tree in the iteration does not improve performance of the TM Index model beyond a threshold.
is vague and indefinite in that it is a negative limitation – i.e. “does not improve performance of the TM Index model beyond a threshold” such that it is not clear what the metes and bounds are of “does not improve performance”. Paragraph [0055] of US Pub. No. 20250348880 A1 of Applicant’s specification recites:
[0055] In various implementations, training the TM Index model comprises iteratively adding decision trees until a next tree in the iteration does not improve performance of the TM Index model beyond a threshold.
However, this section of the specification does not convey boundaries of the patent protection sought.
Moreover, the Examiner finds that because particular claims are rejected as being indefinite under 35 U.S.C. § 112(b), it is impossible to properly construe claim scope at this time (See Honeywell International Inc. v. ITC, 68 USPQ2d 1023, 1030 (Fed. Cir. 2003) “Because the claims are indefinite, the claims, by definition, cannot be construed.”). However, in accordance with MPEP § 2173.06 and the USPTO’s policy of trying to advance prosecution by providing art rejections even though the claims are indefinite, the claims are construed and the art is applied as much as practically possible.
Claims 2-11 and 13-20 are rejected by way of dependency on a rejected independent claim.
The art rejections below are in view of the 112(b) rejections stated above.
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.
In the instant case, claims 1-11 are directed to a “method” which is one of the four statutory categories of invention.
Claims are directed to the abstract idea of determining an alert for a financial crime which is a fundamental business practice of mitigating risk, grouped under Methods Of Organizing Human Activity
in prong one of step 2A (See 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 5, p.p. 50-57 (Jan. 7, 2019))). Claims recite:
determining a set of feature variables for a “model”, wherein determining the set of feature variables comprises iteratively determining, for each of a plurality of potential feature variables, whether performance of the model improves due to inclusion of the potential feature variable; and
selecting a set of training samples on which to train the “model”, wherein selecting the set of training samples includes identifying, based at least in part on transaction data of the customers, customers in an area of interest for one or more money-laundering scenarios; and
computing TM Index scores for the customers using the trained “model”;
identifying one or more scenario alerts of potential financial crime activity by customers of the financial institution based on transaction data for the customers; and
for each of the one more scenario alerts, determining, whether a financial crime alert should be issued based on, at least, transaction data for the customer pertaining to the scenario alert and the TM Index score for the customer.
Limitations such as:
the TM Index scores are numerical values over a range, where higher scores indicate more interestingness of the customer in terms of potential money-laundering activities
are merely a description of data and does not impose any meaningful limit on the computer implementation of the abstract idea.
Accordingly, the claim recites an abstract idea (See 2019 Revised Patent Subject Matter Eligibility Guidance).
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A (See 2019 Revised Patent Subject Matter Eligibility Guidance), the additional elements of the claim such as
a computer system,
a TM Index model comprising a series of multiple decision trees;
various computer systems with named as a TM Index computation system, a scenario generation system, and an alerting computer system; and
training, by a TM Index model training computer system, via machine learning, a TM Index model to compute “score” for customers of the financial institution,
represent the use of a computer as a tool to perform an abstract idea and/or does no more than generally link the abstract idea to a particular field of use. Therefore, the additional elements do not integrate the abstract idea into a practical application as they do no more than represent a computer performing functions that correspond to (i.e. automate) the acts of “collecting information, analyzing the information and providing the results of the analysis”.
When analyzed under step 2B (See 2019 Revised Patent Subject Matter Eligibility Guidance), the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception itself because the ordered combination does not offer substantially more than the sum of the functions of the elements when each is taken alone.
The computer and computer program instructions are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. The elements together execute in routinely and conventionally accepted coordinated manners and interact with their partner elements to achieve an overall outcome which, similarly, is merely the combined and coordinated execution of generic computer functionalities. These functionalities are well-understood, routine and conventional activities previously known to the industry. Therefore, the use of these additional elements does no more than employ a computer as a tool to automate and/or implement the abstract idea, which cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)).
Thus, viewed as a whole, the combination of elements recited in the claims merely describe the concept of determining an alert for a financial crime using computer technology (e.g. the processor).
Hence, claims are not patent eligible.
Dependent claims 2-11 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to a judicial exception (Step 2A- Prong One). Nor are the claims directed to a practical application to a judicial exception (Step 2A- Prong Two).
For example, claims 5-11 are silent as to “additional elements” which integrate the abstract idea into a practical application of a judicial exception, or that are sufficient to amount to significantly more than the judicial exception. They merely further describe the abstract idea of determining an alert for a financial crime.
In claims 2-4, the features:
a gradient boosting machine learning framework;
a Light Gradient Boosted Tree model; and
iteratively adding decision trees.
add technology to the abstract idea of the independent claim. However, a these components amount to no more than standard Boolean tree analysis and convey generic technological components. Their use is in their normal, expected, and routine manner. The components are recited at a high level of generality which do not improve another technology or technical field nor the functioning of the computer itself.
Accordingly, none of the dependent claims add a technological solution to the fundamental business practice in the independent claim.
Note: The analysis above applies to all statutory categories of invention. As such, the presentment of claims 12-20 otherwise styled as a system, would be subject to the same analysis.
Conclusion
The claims as a whole do not amount to significantly more than the abstract idea itself. This is because the claims do not affect an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer system itself; and the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment.
Accordingly, there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
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 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.
Claims 1, 2, 4-9, 12, 13 and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Juban et al (US Pub. No. 20210224922 A1) in view of Guo (US Pub. No. 20200394707 A1).
Regarding claims 1 and 12, Juban teaches systems and methods that may advantageously apply machine learning to accurately manage and predict accounts and account holders with money laundering risk [0003]. Such systems and methods may allow accurate predictions of money laundering risk based on analysis of account variables based on aggregated data from multiple disparate data source systems, identification of suspicious accounts or account holders for investigation, and identification of actionable recommendations to users, all in real time, near real-time, just-in-time, at regular intervals (e.g., every week, every day, every four hours, etc.), upon the request of a user, or the like. He teaches:
training, by a TM Index model training computer system, via machine learning, a TM Index model to compute an TM Index score for customers of the financial institution – [0006], [0020], [0064] “Classification of illegal activity can be improved through machine learning training on a set of confirmed money laundering cases and associated transaction and account information or account holder information”, and [0100], wherein:the TM Index scores are numerical values over a range, where higher scores indicate more interestingness of the customer in terms of potential money-laundering activities – [0014];the TM Index model comprises a series of multiple decision trees, such that the TM Index score for an individual is a sum of an output of each of the series of multiple decision trees – [0105]; and
training the TM Index model includes:
determining a set of feature variables for the TM Index model, wherein determining the set of feature variables comprises iteratively determining, for each of a plurality of potential feature variables, whether performance of the model improves due to inclusion of the potential feature variable – [0025] “implements a method for anti-money laundering (AML) analysis”, [0062] “the AML (anti-money laundering) application can track key performance metrics of AML activity to ensure operational improvement over time and provide summary-level information about recent verified illegal activity and current suspicious case” and [0074]; and
periodically, after training the TM Index model – [0003] “at regular intervals (e.g., every week, every day, every four hours, etc.)”, [0017] and [0024]:
computing, by a TM Index computation system, TM Index scores for the customers using the trained TM Index model – [0006], [0007], [0008], [0015], [0017], [0018] and [0024] “a money laundering risk score”;
identifying, by a scenario generation system, one or more scenario alerts of potential financial crime activity by customers of the financial institution based on transaction data for the customers – [0079] and [0100] “training the classification model using the features of prior confirmed illegal activity cases (e.g., known financial crimes)”; and
for each of the one more scenario alerts, determining, by an alerting computer system, whether a financial crime alert should be issued based on, at least, transaction data for the customer pertaining to the scenario alert and the TM Index score for the customer – [0046], [0075], [0078], [0091], [0092] and [0107].
Juban teaches training a classification model using the features of prior confirmed illegal activity cases (e.g., known financial crimes) [0100]. Juban does not explicitly disclose:
selecting a set of training samples on which to train the TM Index model, wherein selecting the set of training samples includes identifying, based at least in part on transaction data of the customers, customers in an area of interest for one or more money-laundering scenarios.
However, Guo teaches a method and system for detecting online money laundering [0005]. During operation, the system can obtain, from an online financial platform, online financial transaction records associated with a plurality of customer accounts and establish fund-transfer relationships among the plurality of customer accounts based on the transaction records. He teaches a money-laundering-detection system which includes an anomaly-detection module configured to detect node clusters with unknown risks [0050]. In addition to binary-classification model, feature vectors extracted from the node clusters can also be sent to anomaly-detection module, especially in the event of binary-classification model failing to generate a classification output. Anomaly-detection module can use a machine-learning based anomaly-detection technique (e.g., the isolation forest technique) to identify anomalous node clusters. The identified anomalous node cluster can further be provided to a security expert, which can perform further analysis on the customer accounts and transactions associated with these customer accounts to evaluate potential money-laundering risk. The evaluation results can further be sent to binary-classification-model-training module for training of binary-classification model [Id.].
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Juban’s disclosure to include machine-learning based anomaly-detection technique to identify anomalous node clusters as taught by Guo in order to quickly and effectively detect and prevent online money laundering needs - Guo [0004].
Regarding claims 2 and 13, Juban teaches the TM Index model as comprising a gradient boosting machine learning framework – [0006] and [0020].
Regarding claims 4 and 15, Juban teaches the TM Index model as comprising iteratively adding decision trees until a next tree in the iteration does not improve performance of the TM Index model beyond a threshold – [0118].
Regarding claims 5 and 16, Juban does not explicitly disclose each decision node in the series of multiple decision trees as comprising a test on one of the feature variables.
However, Guo teaches selecting two customer accounts identified by a transaction record, and then accumulate, based on all transaction records satisfying the statistical conditions, the total fund-transfer amount between the two customer accounts [0033].
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Juban’s disclosure to include accumulate, based on all transaction records satisfying the statistical conditions as taught by Guo in order to quickly and effectively detect and prevent online money laundering needs - Guo [0004].
Regarding claims 6 and 17, Juban teaches training the TM Index model as comprising training the TM index model such that the TM index score is the sum of an output from each of the series of decision trees – [0105].
Regarding claim 7, Juban teaches periodically computing the TM Index scores as comprising computing the TM Index scores weekly – [0017] and [0024].
Regarding claim 8, Juban teaches the customers as comprising persons – [0006]-[0008] “given account holder”, [0020] and [0022].
Regarding claim 9 and 18, Juban does not explicitly disclose:
training the TM index model with training samples; and
selecting the training samples for the training, wherein selecting the training samples comprises selecting training samples that are within an area of interest for one or more predefined suspicious financial crime scenarios.
However, Guo teaches this at [0050] as discussed in the rejection of claim 1. Accordingly, this claim is rejected for the same reasons.
Claims 3 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Juban in view of Guo, in further view of Shah et al (US Pub. No. 20220399132 A1).
Regarding claims 3 and 14, neither Juban nor Guo explicitly discloses the TM Index model as comprising a Light Gradient Boosted Tree model.
However, Shah teaches training a machine learning model as including training a light gradient boosted tree model [0009].
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Juban’s disclosure to include a light gradient boosted tree model as taught by Shah since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Additional Comments
Regarding claims 10, 11, 19 and 20, in view of pending rejections, the Examiner is unable to locate prior art references that anticipate the claimed invention or renders it obvious.
Conclusion
The prior art of record and not relied upon is considered pertinent to Applicant’s disclosure:
Adjaoute: “INCREASING PERFORMANCE IN ANTI-MONEY LAUNDERING TRANSACTION MONITORING USING ARTIFICIAL INTELLIGENCE”, (US Pub. No. 20190325528 A1).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDWARD J BAIRD whose telephone number is (571)270-3330. The examiner can normally be reached 7 am to 3:30 pm M-F.
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/EDWARD J BAIRD/Primary Examiner, Art Unit 3692