DETAILED ACTION
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
This action is in reply to Application 19/196,366 filed on 15 May 2024.
Claims 1-26 are currently pending and have been examined.
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 1-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In the instant case, representative method claim 19 is directed towards facilitating predicting alerts generated by money laundering scenario detection models over a time period. Claim 19 recites the abstract idea of utilizing rules and/or instructions for performing the existing commercial practice (e.g., illegal financial/economic activity) comprising the steps of merely receiving (“obtaining … alert data”), evaluating (“training … producing a prediction”), and calculating (“predicting … alerts over a future time period”) occurrence of a financial-related event in an automatic manner, which is grouped under the certain methods of organizing human activity – fundamental economic principles, practices or concepts; sales activity; following set of instructions; commercial interactions; managing interactions between people (including social activities, teachings, following rules or instructions) grouping, in prong one of step 2A
Claim 19 recites:
“obtaining, by a compute device, historical alert data indicative of alerts produced by each of multiple money laundering scenario detection models associated with deposit accounts;
training, by the compute device and prior to producing a prediction, at least one alert volume prediction model with the obtained historical alert data; and
predicting, by the compute device and with the at least one alert volume prediction model, a number of alerts to be generated by the money laundering scenario detection models over a future time period”.
Based on the underlined elements above, abstract ideas and/or concepts are identified. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A, the additional elements of the claim such as a “compute device”, represent the use of a computer-related device as a tool (intermediary) to perform an abstract idea and/or does no more than generally apply 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) implement the acts of utilizing rules and/or instructions for performing the existing commercial practice (e.g., illegal financial/economic activity) comprising the steps of merely receiving (“obtaining … alert data”), evaluating (“training … producing a prediction”), and calculating (“predicting … alerts over a future time period”) occurrence of a financial-related event in an automatic manner.
When analyzed under step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception itself. Viewed as a whole, the combination of elements recited in the claims merely describe the concept of utilizing rules and/or instructions for performing the existing commercial practice (e.g., illegal financial/economic activity) comprising the steps of merely receiving (“obtaining … alert data”), evaluating (“training … producing a prediction”), and calculating (“predicting … alerts over a future time period”) occurrence of a financial-related event in an automatic manner using computer computer-related technology and/or devices that merely perform as designed to function. 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)). Hence, claim 19 is not patent eligible.
Independent claim 1 recites substantially the same limitations as claim 14 above and is ineligible for the same reasons. The subject matter of claim 1 corresponds to the subject matter of claim 14 in terms of a compute device (e.g., manufacture). Therefore the reasoning provided for claim 14 applies to claim 1 accordingly.
Dependent claims 2-13 and 15-26 add further details and contain limitations that narrow the scope of the invention. However, these details do not result in significantly more than the abstract idea itself. As explained in the December 16, 2014 Interim Eligibility Guidance from the USPTO (in reference to the BuySAFE, Inc. v. Google, Inc. decision), further narrowing the details of an abstract idea does not change the § 101 analysis since a more narrow abstract idea does not make it any less abstract.
The step(s) recited are a further refinement of methods of organizing human activity – – fundamental economic principles, practices or concepts; sales activity; following set of instructions; commercial or legal interactions (agreements in the form of contracts; business relations); managing interactions between people (including social activities, teachings, following rules or instructions), because it merely describes intermediate steps and/or rules/instructions of the process.
Viewed individually and in combination, these additional elements do not provide meaningful limitations to transform the abstract idea such that the claims amount to significantly more than the abstraction itself.
Accordingly, the present pending claims are not patent eligible and are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections – 35 USC § 103
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 may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claims 1-3, 5-8. 11-16, 18-21, and 24-26 are rejected under 35 U.S.C. 103 as being unpatentable over Wadhwa et al., US 2022/0020026 A1 (“Wadhwa”), in view of Juban et al., US 2021/0224922 A1 (“Juban”).
Re Claim 1: Wadhwa discloses a compute device comprising:
circuitry configured to: ([0049] “Examples of the processor 202 include, but are not limited to, an application-specific integrated circuit (ASIC) processor…”)
obtain historical alert data indicative of alerts produced by each of multiple money laundering scenario detection models associated with deposit accounts; (FIG. 6 [600] “provide, by the server system, an alert notification to the at least one issuer associated with the money laundering financial transaction based at least on a step of the predicting”;
[0033] “… server system is configured to generate a suspicious activity report (SAR) file associated with the suspicious cluster and provide the SAR file to the regulators for further actions”; [0075] “… processor 202 is configured to generate a suspicious activity report (SAR) file and alert the identified issuer 102 for preventing fraudulent financial transactions based on the SAR file”)
train, prior to producing a prediction, at least one alert volume prediction model with the obtained historical alert data; ([0069] “… training engine 218 implements a sequence neural network for training the data model 224 … LSTM) network (or other sequence neural network) to train the data model … Based on the trained data model, the LSTM network may predict next money laundering financial transactions”)
Regarding the limitation comprising:
predict, with the at least one alert volume prediction model, a number of alerts to be generated by the money laundering scenario detection models over a future time period.
Juban makes this teaching in a related endeavor ([0003] “… systems and methods that may advantageously apply machine learning to accurately manage and predict accounts and account holders with money laundering risk. 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”). It would have been obvious to one of ordinary skill in the art at the time of the invention to incorporate the teachings of Juban to the invention of Wadhwa as described above for the motivation of facilitating the further investigation of accounts and/or account holders suspected of engaging in illicit financial activities.
Re Claim 2: Wadhwa in view of Juban discloses the compute device of claim 1. Wadhwa further discloses:
wherein the circuitry is further configured to retrain, based on subsequent historical alert data, the at least one alert volume prediction model prior to producing a subsequent prediction of a number of alerts to be generated by the money laundering scenario detection models. ([0069] “… training engine 218 implements a sequence neural network for training the data model 224 … LSTM) network (or other sequence neural network) to train the data model … Based on the trained data model, the LSTM network may predict next money laundering financial transactions”)
Re Claim 3: Wadhwa in view of Juban discloses the compute device of claim 1. Wadhwa further discloses:
wherein the circuitry is further configured to retrain the at least one alert volume prediction model on a weekly basis. ([0032] “… server system is configured to determine time-based probabilities of next edge formation within the suspicious cluster and next edge formation outside the suspicious cluster.”)
Re Claim 5: Wadhwa in view of Juban discloses the compute device of claim 1. Wadhwa further discloses:
wherein to train at least one alert volume prediction model comprises to create features to be used as input variables to the at least one alert volume prediction model. ([0116] “… user device 800 includes a controller or a processor 802 (e.g., a signal processor, microprocessor, ASIC, or other control and processing logic circuitry) for performing such tasks as signal coding, data processing, image processing, input/output processing, power control, and/or other functions”)
Re Claim 6: Wadhwa in view of Juban discloses the compute device of claim 5. Wadhwa further discloses:
wherein to create features comprises to create lag-based features and date-based features by (i) creating features indicative of a lag, a lag first difference, a lag second difference, a moving average, and an exponential weighted mean; and/or
(ii) creating features indicative of a month of a year, a week of a year, a week of a month, a quarter of a year, a beginning of a month, an end of a month, summer, a school opening, one or more holidays, and a long weekend.
([0032] “… server system is configured to determine time-based probabilities of next edge formation within the suspicious cluster and next edge formation outside the suspicious cluster.”)
Re Claim 7: Wadhwa in view of Juban discloses the compute device of claim 6. Regarding the limitation feature comprising:
wherein the circuitry is further configured to adjust a significance of each feature for each of multiple alert volume prediction models, wherein each alert volume prediction model is associated with a corresponding money laundering scenario detection model.
Juban makes this teaching in a related endeavor ([0092] “… Using the AML platform and an Alerts Engine, analysts and other application users may adjust the alert date, manage the notifications, or add additional alert triggers on incoming data”). It would have been obvious to one of ordinary skill in the art at the time of the invention to incorporate the teachings of Juban to the invention of Wadhwa as described above for the motivation of facilitating the further investigation of accounts and/or account holders suspected of engaging in illicit financial activities.
Re Claim 8: Wadhwa in view of Juban discloses the compute device of claim 1. Regarding the limitation feature comprising:
wherein to train at least one alert volume prediction model comprises to adjust one or more hyper parameters associated with the at least one alert volume prediction model.
Juban makes this teaching in a related endeavor ([0092] “… Using the AML platform and an Alerts Engine, analysts and other application users may adjust the alert date, manage the notifications, or add additional alert triggers on incoming data”). It would have been obvious to one of ordinary skill in the art at the time of the invention to incorporate the teachings of Juban to the invention of Wadhwa as described above for the motivation of facilitating the further investigation of accounts and/or account holders suspected of engaging in illicit financial activities.
Re Claim 11: Wadhwa in view of Juban discloses the compute device of claim 1. Wadhwa further discloses:
wherein to train the at least one alert volume prediction model comprises to train the at least one alert volume prediction model based on 80% of the historical alert data and allocate a remainder of the historical alert data to validation and out-of-time testing.
([0069] “… training engine 218 implements a sequence neural network for training the data model 224 … LSTM) network (or other sequence neural network) to train the data model … Based on the trained data model, the LSTM network may predict next money laundering financial transactions”; [0072] “… if the time-based probability of the next edge formation leading to a source node is greater than a predetermined threshold value, the prediction engine 220 identifies an issuer associated with a particular node (i.e., a trailing node) related to the next edge (i.e., link) which may be linked in future money-laundering activities”)
Re Claim 12: Wadhwa in view of Juban discloses the compute device of claim 1. Wadhwa further discloses:
wherein to predict the number of alerts comprises to:
predict a number of alerts to be produced by each scenario detection model; and
determine a total number of alerts to be produced across the scenario detection models.
([0069] “… training engine 218 implements a sequence neural network for training the data model 224 … LSTM) network (or other sequence neural network) to train the data model … Based on the trained data model, the LSTM network may predict next money laundering financial transactions”)
Re Claim 13: Wadhwa in view of Juban discloses the compute device of claim 1. Wadhwa further discloses:
wherein to predict the number of alerts comprises
to produce a multi-step forecast over a one-year time period based on recursive forecasts over multiple one-week time periods; and/or
to provide the predicted number of alerts to a staffing model for use in determining a number of personnel to be allocated to review the alerts.
([0069] “… training engine 218 implements a sequence neural network for training the data model 224 … LSTM) network (or other sequence neural network) to train the data model … Based on the trained data model, the LSTM network may predict next money laundering financial transactions”)
Re Claim 14: Claim 14, as best understood by the Examiner, encompasses the same or substantially the same scope as claim 1. Accordingly, claim 14 is rejected in the same or substantially the same manner as claim 1.
Re Claim 15: Claim 15, as best understood by the Examiner, encompasses the same or substantially the same scope as claim 2. Accordingly, claim 15 is rejected in the same or substantially the same manner as claim 2.
Re Claim 16: Claim 16, as best understood by the Examiner, encompasses the same or substantially the same scope as claim 3. Accordingly, claim 16 is rejected in the same or substantially the same manner as claim 3.
Re Claim 18: Claim 18, as best understood by the Examiner, encompasses the same or substantially the same scope as claim 5. Accordingly, claim 18 is rejected in the same or substantially the same manner as claim 5.
Re Claim 19: Claim 19, as best understood by the Examiner, encompasses the same or substantially the same scope as claim 6. Accordingly, claim 19 is rejected in the same or substantially the same manner as claim 6.
Re Claim 20: Claim 20, as best understood by the Examiner, encompasses the same or substantially the same scope as claim 7. Accordingly, claim 20 is rejected in the same or substantially the same manner as claim 7.
Re Claim 21: Claim 21, as best understood by the Examiner, encompasses the same or substantially the same scope as claim 8. Accordingly, claim 21 is rejected in the same or substantially the same manner as claim 8.
Re Claim 24: Claim 24, as best understood by the Examiner, encompasses the same or substantially the same scope as claim 11. Accordingly, claim 24 is rejected in the same or substantially the same manner as claim 11.
Re Claim 25: Claim 25, as best understood by the Examiner, encompasses the same or substantially the same scope as claim 12. Accordingly, claim 25 is rejected in the same or substantially the same manner as claim 12.
Re Claim 26: Claim 26, as best understood by the Examiner, encompasses the same or substantially the same scope as claim 13. Accordingly, claim 26 is rejected in the same or substantially the same manner as claim 13.
Claims 4, 9-10, 17, 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Wadhwa et al., US 2022/0020026 A1 (“Wadhwa”), in view of Juban et al., US 2021/0224922 A1 (“Juban”), as applied to claims 1-3, 5-8. 11-16, 18-21, and 24-26 described above, further in view of Zuberi et al., US 2022/0343422 A1 (“Zuberi”).
Re Claim 4: Wadhwa in view of Juban discloses the compute device of claim 1. Regarding the limitation comprising:
wherein to train at least one alert volume prediction model comprises to train an ensemble of alert volume prediction models comprises (i) training an alert volume prediction model for each money laundering scenario detection model; and/or
(ii) utilizing gradient boosting to produce the ensemble of decision tree models as the alert volume prediction models.
Zuberi makes this teaching in a related endeavor ([0024] “As described herein, the machine-learning or artificial-intelligence process may include an ensemble or decision- tree process, such as a gradient-boosted decision-tree process ( e.g., XGBoost model), and certain of the exemplary
training and validation processes described herein may generate, and utilize, training datasets …”). It would have been obvious to one of ordinary skill in the art at the time of the invention to incorporate the teachings of Zuberi to the invention of Wadhwa as described above for the motivation of facilitating predicting the occurrence of future events in an accurate manner.
Re Claim 9: Wadhwa in view of Juban discloses the compute device of claim 8. Regarding the limitation comprising:
wherein to adjust one or more hyper parameters comprises to adjust:
(i) a number of estimators;
(ii) a decision tree depth limit;
(iii) a number of leaves in a decision tree;
(iv) one or more regularization parameters to control a level of fit to training data; and/or
(v) one or more hyper parameters for multiple alert volume prediction models in an ensemble.
Zuberi makes this teaching in a related endeavor ([0024] “As described herein, the machine-learning or artificial-intelligence process may include an ensemble or decision- tree process, such as a gradient-boosted decision-tree process ( e.g., XGBoost model), and certain of the exemplary
training and validation processes described herein may generate, and utilize, training datasets …”;
[0032] “… the distributed components of Fl computing system 130 may perform operations in parallel that not only train adaptively a machine learning or artificial intelligence process (e.g., the gradient-boosted, decision-tree process described herein) using corresponding training and validation datasets extracted from temporally distinct subsets of the preprocessed data elements, but also apply the trained machine learning or artificial intelligence process to customer-specific input datasets”). It would have been obvious to one of ordinary skill in the art at the time of the invention to incorporate the teachings of Zuberi to the invention of Wadhwa as described above for the motivation of facilitating predicting the occurrence of future events in an accurate manner.
Re Claim 10: Wadhwa in view of Juban discloses the compute device of claim 1. Regarding the limitation comprising:
wherein to train the at least one alert volume prediction model comprises to train the at least one alert volume prediction model based on mean absolute percentage error.
Zuberi makes this teaching in a related endeavor ([0125} “… executed treatment determination engine 252 may compute the exposure score as an arithmetic mean, a geometric mean, or a weighted average of a plurality of inputs that characterize, among other things, the obtained numerical value (e.g., 0.84) and one or more of the computed metric values”; [0067] “… each of the prior temporal
intervals may correspond to a one-month interval, and executed training engine 172 may perform operations that establish adaptively the splitting point between the corresponding temporal boundaries such that a predetermined first percentage of the consolidated data records are associated with temporal intervals (e.g., as specified by corresponding ones of the temporal identifiers) disposed within the training interval, and such that a predetermined second percentage of the consolidated data records are associated with temporal intervals ( e.g., as specified by corresponding ones of the temporal identifiers) disposed within the validation interval.”). It would have been obvious to one of ordinary skill in the art at the time of the invention to incorporate the teachings of Zuberi to the invention of Wadhwa as described above for the motivation of facilitating predicting the occurrence of future events in an accurate manner.
Re Claim 17: Claim 17, as best understood by the Examiner, encompasses the same or substantially the same scope as claim 4. Accordingly, claim 17 is rejected in the same or substantially the same manner as claim 4.
Re Claim 22: Claim 22, as best understood by the Examiner, encompasses the same or substantially the same scope as claim 9. Accordingly, claim 22 is rejected in the same or substantially the same manner as claim 9.
Re Claim 23: Claim 23, as best understood by the Examiner, encompasses the same or substantially the same scope as claim 10. Accordingly, claim 23 is rejected in the same or substantially the same manner as claim 10.
Conclusion
The prior art(s) made of record and not relied upon is/are considered pertinent to applicant's disclosure.
Kaznady (US) discloses a system and method for prediction using synthetic features and gradient boosted decision tree. A machine learning system and method are disclosed in which a plurality of synthetic features are created from input data, and a gradient boosted decision tree algorithm is then
executed by the computer to process both the synthetic features and at least some of the input data to produce an output that is a probability.
Adjaoute (US 2019/0325528 A1) discloses increasing performance in anti-money laundering
transaction monitoring using artificial intelligence. Provided is an artificial-intelligence based, electronic computer implemented system for generating an alert to a likelihood of money laundering activity within a financial environment, comprising at least one computer that includes both hardware and software components. The components form: a smart agent generating means for generating a smart
agent for each entity capable of acting by itself or in concert with another in furtherance of money laundering activity; an updating means for updating each smart agent with transaction based data (financial and/or non-financial) associated therewith so that each smart agent models an individual
entity behavior profile; a supervised learning model for a first-pass detection of potential money laundering activity; an unsupervised learning model for reducing false positive and enhancing detection of potential money laundering activity; and an alerting means for generating an alert to a
likelihood of money laundering activity within the financial environment. Alternative systems are also provided.
Claims 1-26 are rejected.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Clifford Madamba whose telephone number is 571-270-1239. The examiner can normally be reached on Mon-Thu 7:30-5:00 EST Alternate Fridays.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan Donlon, can be reached at 571-272-3602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CLIFFORD B MADAMBA/Primary Examiner, Art Unit 3692
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