Detailed Action
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Acknowledgments
The amendment filed on 03/16/2026 and the IDS filed on 03/10/2026 are acknowledged.
Status of Claims
Claims 22-29, 31-39 and 41 are pending.
In the amendment filed on 03/16/2026, claims 21, 23, 32, 33 and 41 were amended, claims 30 and 40 were cancelled, and no claims were added.
Claims 22-29, 31-39 and 41 are rejected.
Response to Arguments
Regarding the rejection under 35 U.S.C. 101
Applicant’s arguments have been fully considered but are not persuasive.
The Office responds to Applicant’s arguments below. In the discussion below, page numbers refer to Applicant’s Response, unless otherwise indicated.
Step 1 (p. 9)
The Examiner agrees with applicant that the claims fall within the statutory categories under 35 U.S.C. 101.
Step 2A, Prong 1 (pp. 10-11)
Applicant argues that independent claim 22 is directed to a method for determining adaptation of a machine learning model and therefore does not cover methods of organizing human activity. The Office disagrees. To be sure, Applicant has amended the preamble and added additional machine learning limitations. However, this does not detract from the fact that the claims still recite an abstract idea. Moreover, the machine learning limitations are generic computer limitations, recited at a high level of generality, not described, and their relationship to the abstract idea is that they simply apply the abstract idea, rather than integrate it into a practical application. It is noted that the machine learning limitations constitute merely ordinary, basic operations of machine learning, not any improvement in machine learning.
Further, while Applicant asserts that the claim cannot be performed in the mind, the Office notes that the claims were not rejected on this ground.
Step 2A, Prong 2 (pp. 11-13)
Applicant argues that the model fit is not generic but provides an improvement to machine learning. The Office respectfully disagrees. The model fit limitations represent merely an ordinary, basic element of machine learning, not any improvement in machine learning. As seen from specification 0079, Applicant's claims merely recite an off-the-shelf generic machine learning element.
Contrary to Applicant's allegation, the recited model fit limitations, as generic computer elements and basic elements of machine learning, are not comparable to the machine learning improvement in Desjardins.
The alleged concreteness of the limitations is not grounds for eligibility.
Step 2B (pp. 13-14)
Applicant generally argues the same subject matter as previously presented. Again, the machine learning limitations, including the newly added model fit limitations, are merely generic computer elements, used in their ordinary capacities, constituting what machine learning is, not any improvement to machine learning. The training of the model based on additional data (as per the specification 0071, feedback data), so as to improve performance of the model, including quantifying the model's performance/ improvement (such as minimizing a loss function), is merely a basic aspect of what machine learning is, not an improvement in machine learning. Thus, the machine learning limitations merely apply the abstract idea and, as such, cannot provide 'significantly' more than the abstract idea.
Regarding the rejections under 35 U.S.C. 102 and 103
Applicant’s arguments have been fully considered but are moot in view of the new combinations of references being used in the current rejections.
Claim Rejections - 35 U.S.C. § 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.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
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 of carrying out his invention.
Claims 22, 32 and 41 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, 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, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Lack of Written Description/Not in Specification
Claims 22, 32 and 41 recite:
generate, by the model fit, a measurement of adaptation of the trained machine learning model relative to the at least one additional training input;
Support in the disclosure is not found for the above-indicated recitation.
The sole discussion of model fit in the specification is at 0079 (Fig. 14, 1409). In pertinent part, 0079 reads as follows:
Inputs 1408 are all fed into a model fit 1409. Model fit 1409 represents a measurement of how well the machine learning model adapts to data that is similar to the data on which it was trained. Model fit 1409 accurately approximates the output, target model 1410, once provided with inputs 1408.
Thus, as per 0079 the specification describes the model fit as representing a measurement of how well the machine learning model adapts to data that is similar to the data on which it was trained, and as accurately approximating the output, target model 1410, once provided with inputs.
Neither the disclosed representing nor the disclosed approximating teach generating a measurement of adaptation of the trained machine learning model relative to the at least one additional training input. For example, representing a measurement of adaptation is not the same as, nor does it suggest, generating a measurement of adaptation. If something represents something, this does not suggest that it has the ability to generate it; and in the context of the instant disclosure it may even suggest that there is no need to generate it. For another example, approximating an output does not suggest generating a measurement of adaptation (in context, a measurement of adaptation would evaluate accuracy of the output).
Accordingly, support in the disclosure is not found for the above-indicated recitation of claims 22, 32 and 41.
Claims 23-29, 31 and 33-39 are rejected by virtue of their dependency from a rejected claim.
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 22-29, 31-39 and 41 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.
Claims 22-29, 31-39 and 41 are directed to a method, system, or non-transitory computer-readable medium, which are/is one of the statutory categories of invention. (Step 1: YES)
Claims 22, 32 and 41 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a method, system, and non-transitory computer-readable medium for determining a likelihood of fraud.
For claims 22, 32 and 41 (claim 32 being deemed representative), the limitations (indicated below in bold) of:
one or more processors configured to:
train the machine learning model using at least one initial training input to generate a trained machine learning model;
receive, by the trained machine learning model, at least one additional training input;
feed the at least one additional training input into a model fit;
generate, by the model fit, a measurement of adaptation of the trained machine learning model relative to the at least one additional training input;
train the trained machine learning model based on the at least one additional training input;
receive, by the trained machine learning model, a processed action of a user;
compare, using the trained machine learning model, the processed action with the at least one training input; and
generate, using the trained machine learning model, a risk indicator to predict a likelihood of unauthorized activity for the processed action based on the comparison.
as drafted, constitute a process that, under the broadest reasonable interpretation, covers "certain methods of organizing human activity," specifically, "fundamental economic practices or principles" and/or "commercial or legal interactions," but for recitation of generic computer components and generally linking the use of a judicial exception to a particular technological environment or field of use. The Examiner notes that "fundamental economic practices" or "fundamental economic principles" describe concepts relating to the economy and commerce, including hedging, insurance, and mitigating risks, and "commercial interactions" or "legal interactions" include agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations. MPEP 2106.04(a)(2)II.A.,B. If a claim limitation, under its broadest reasonable interpretation, covers "fundamental economic practices or principles" and/or "commercial or legal interactions," but for recitation of generic computer components and generally linking the use of a judicial exception to a particular technological environment or field of use, then it falls within the "certain methods of organizing human activity" grouping of abstract ideas. Accordingly, claims 22, 32 and 41 recite an abstract idea. (Step 2A - Prong 1: YES. The claims recite an abstract idea.)
This judicial exception is not integrated into a practical application. Claims 22, 32 and 41 recite the additional elements of at least one processor (claim 22); one or more processors (claim 32); one or more instructions that, when executed by one or more processors of a computing system, cause the computing system to (perform actions) (claim 41); and train the machine learning model using at least one initial training input to generate a trained machine learning model, (receive) by the trained machine learning model, feed the at least one additional training input into a model fit, generate, by the model fit, a measurement of adaptation of the trained machine learning model relative to the at least one additional training input, train the trained machine learning model based on the at least one additional training input, (receive) by the trained machine learning model, and (compare and generate) using the trained machine learning model (claims 22, 32 and 41), that implement the abstract idea. These additional elements are not described by the applicant and they are recited at a high level of generality (i.e., one or more generic computer elements performing generic computer functions, or generally linking the use of a judicial exception to a particular technological environment or field of use), such that they amount to no more than mere instructions to apply the exception using generic computer elements (namely, all of the additional elements), or such that they amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (namely, the additional elements involving machine learning/training/model fit). Accordingly, even in combination these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (Step 2A - prong 2: NO. The additional elements do not integrate the abstract idea into a practical application.)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception itself. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of at least one processor (claim 22); one or more processors (claim 32); one or more instructions that, when executed by one or more processors of a computing system, cause the computing system to (perform actions) (claim 41); and train the machine learning model using at least one initial training input to generate a trained machine learning model, (receive) by the trained machine learning model, feed the at least one additional training input into a model fit, generate, by the model fit, a measurement of adaptation of the trained machine learning model relative to the at least one additional training input, train the trained machine learning model based on the at least one additional training input, (receive) by the trained machine learning model, and (compare and generate) using the trained machine learning model (claims 22, 32 and 41), to perform the noted steps amount to no more than mere instructions to apply the exception using generic computer elements or generally linking the use of a judicial exception to a particular technological environment or field of use. Mere instructions to apply an exception using generic computer elements or generally linking the use of a judicial exception to a particular technological environment or field of use cannot provide an inventive concept ("significantly more"). Accordingly, even in combination, these additional elements do not provide significantly more. As such, claims 22, 32 and 41 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more.)
Dependent claims 23-29, 31 and 33-39 are similarly rejected because they further define/narrow the abstract idea of independent claims 22, 32 and 41 as discussed above, and/or do not integrate the abstract idea into a practical application or provide an inventive concept such as would render the claims eligible, whether each is considered individually or as an ordered combination.
As for further defining/narrowing the abstract idea:
Dependent claims 23 and 33 merely further describe wherein the at least one additional training input includes at least one of transactional data, a customer characteristic, or historical data.
Dependent claims 24 and 34 merely further describe retaining a previous risk indicator ….
Dependent claims 25 and 35 merely further describe appending at least one of transactional data, a customer characteristic, or historical data to the processed action.
Dependent claims 26 and 36 merely further describe generate an alert based on the risk indicator.
Dependent claims 28 and 38 merely further describe enriching the processed action in real time based on the at least one training input.
Dependent claims 29 and 39 merely further describe deriving the risk indicator from a model probability.
As for additional elements:
Dependent claims 24 and 34 recite "tuning the machine learning model based on the previous risk indicator." This recitation is at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer element or such that it amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. Even in combination these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself.
Dependent claims 26 and 36 recites "training the machine learning model to …." This recitation is at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer element or such that it amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. Even in combination these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself.
Dependent claims 27 and 37 recite "training the machine learning model based on at least one of an instrument propensity, a device propensity, or a transaction channel type." This recitation is at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer element or such that it amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. Even in combination these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself.
Dependent claim 31 recites "benchmarking the machine learning model based on a relative precision, wherein the relative precision is based on at least one of a true positive, a false positive, or a false negative." This recitation is at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer element or such that it amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. Even in combination these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself.
Claims 23, 25, 28, 29, 33, 35, 38 and 39 do not recite any additional elements, and accordingly, for the reasons provided above with respect to the independent claims, are not patent eligible.
Therefore, dependent claims 23-29, 31 and 33-39 are not patent eligible.
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.
The factual inquiries set forth in Comeaux v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 22-24, 26, 27, 29, 32-34, 36, 37, 39 and 41 are rejected under 35 U.S.C. 103 as being unpatentable over Comeaux et al. (U.S. Patent No. 11,669,844), hereafter Comeaux, in view of Faibish (U.S. Patent Application Publication No. 2021/0125053 A1).
Regarding Claims 22, 32 and 41
Comeaux teaches:
(claim 22) A computer-implemented method for … a machine learning model (4:49-52 teaches that the alert generation model is a machine-learning model) to predict a likelihood of unauthorized activity, the method being performed by at least one processor and comprising: (claim 1: steps of the method are performed "by a/the computing system"; claim 11: "one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the computing system to perform a process comprising"; claim 1 also teaches to predict a likelihood of unauthorized activity)
(claim 32) A computing system for … a machine learning model (4:49-52 teaches that the alert generation model is a machine-learning model) to predict a likelihood of unauthorized activity comprising: one or more processors configured to: (5:53-57; claim 11)
(claim 41) A non-transitory computer-readable medium storing a set of instructions for … a machine learning model (4:49-52 teaches that the alert generation model is a machine-learning model) to predict a likelihood of unauthorized activity, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a computing system, cause the computing system to: (5:53-57, 20:5-35, claim 6)
training the machine learning model (4:49-52 teaches that the alert generation model is a machine-learning model) using at least one initial training input to generate a trained machine learning model; (4:56-64, 6:59-67, 8:7-11, 8:62-9:19)
receiving, by the machine learning model (4:49-52 teaches that the alert generation model is a machine-learning model), at least one additional training input; (training input is taught by "a data sample" (4:63-64, see 4:56-64), "data associated with the new fraudulent network activity" (8:45-50, see 8:39-61), "other known fraudulent users and/or activities" (9:19-22, see 9:7-28) "updated behavior profile" (19:19-21, see 19:12-35))
…
training the trained machine learning model based on the at least one additional training input; (4:56-64, 8:39-61, 9:19-28)
receiving, by the trained machine learning model, a processed action of a user; (11:14-16, 11:26-27, 12:6-27; Fig. 2, 204, 16:40-42)
comparing, using the trained machine learning model, the processed action with the at least one training input; and (10:21-11:13 detected fraudulent/malicious event (the processed action) is compared with past user data of the user's behavior profile, e.g., remote network logon logs (10:21-39), prior traveling destination (10:40-50), historical failed authentication event data, such as device ID (10:51-11:13); and the past user data of the user's behavior profile can have served as training input, as taught by 2:26-31, 8:7-27 and 9:7-10 (note: in order for the neurons to represent the user attributes, the user attributes must have been inputted to train the model, and hence the user attributes constitute training input), 16:1-39, 17:67-18:3)
generating, using the trained machine learning model, a risk indicator (alert probability score) to predict a likelihood of unauthorized activity for the processed action based on the comparison. (2:43-49, 11:26-32, Fig. 2, 210, 17:60-18:14)
Comeaux (8:39-61, 19:12-35) teaches retraining the machine learning model based on new data, including reconfiguring the model using a variety of methods, but does not explicitly disclose the model fit limitations (below) in their entirety. However, Faibish teaches:
… determining adaptation of a machine learning model …: (0088-0100 e.g., model fitting as explained in following bullet points)
feeding the at least one additional training input into a model fit; (0088-0100 (with reference to Fig. 12) the additional data is taught by the validation data set of 0092, the test dataset of 0094, and/or the "additional data sets" of 0098; the model fit is taught by the validation evaluation process 1208 (and/or the "error function" thereof), the test (confirm) evaluation process 1212, and/or the training, validation and/or testing (confirmation) process performed using the "additional data sets" (0098))
generating, by the model fit, a measurement of adaptation of the trained machine learning model relative to the at least one additional training input; (0088-0100 (with reference to Fig. 12) the generating the measurement of adaptation is taught by "generating a measured error rate," which is checked against a threshold/criterion, (0092), the determination whether the neural network meets a validation criterion (0093, 1212), the determination of the error rate and specification of a criterion/ threshold for determining whether the model should be confirmed (0094), the determination whether the confirmation was successful (0095, 1214), the determination of evaluation criteria (e.g., re validating or testing/confirmation) and of whether the neural network meets the criteria (0096), and/or any of the foregoing generation/determinations that is made in the corresponding further training/ retraining that is carried out with respect to the additional data sets (0098 e.g., "the resulting predictive performance of the neural network is again validated and confirmed meeting any specified criteria"); note Faibish describes the foregoing content as "model fitting" (0091) and as "the fitness or goodness of the current neural network model may be evaluated" (0092); note here regarding 0091 this "model fitting" refers, e.g., to "the result generated by the neural network during the training [i.e., 1206] is compared to the expected output of the training dataset [and based on this result] the parameters of the neural network model are adjusted" (0091); this comparison constitutes an evaluation of model performance, i.e., a measurement of adaptation of the machine learning model relative to the training input is generated; note further that the initial training (1206) carried out per 0091 is carried out again qua further training/retraining on the additional data sets per 0098, so that while 0091 teaches a measurement of adaptation of the machine learning model relative to the (initial) training input is generated, 0098 teaches a measurement of adaptation of the trained machine learning model relative to the at least one additional training input is generated)
It would have been obvious to one of ordinary skill in the art not later than the effective filing date of the claimed invention to have modified Comeaux's systems and methods for detecting and preventing fraudulent events, by incorporating therein these teachings of Faibish regarding model fitting on new data, because it would improve model performance/results.
Regarding Claims 23 and 33
Comeaux in view of Faibish teaches the limitations of base claims 22 and 32 as set forth above. Comeaux further teaches:
wherein the at least one additional training input includes at least one of transactional data, a customer characteristic, or historical data. (16:1-3 "inputs [training input] from a behavior profile," where the behavior profile includes transactional data, a customer characteristic, or historical data, as taught by 2:26-31, 9:29-10:20, 10:21-11:13, 16:1-39 and 17:67-18:3; note as per 19:12-35 the behavior profile may be updated and the model may be trained using the updated behavioral profile, and the updated behavioral profile constitutes additional training input; 9:7-28 teaches that additional training input is user attributes, which per 8:14-16 are within the user profile and which per 8:16-20 include transactional data, a customer characteristic, or historical data)
Regarding Claims 24 and 34
Comeaux in view of Faibish teaches the limitations of base claims 22 and 32 as set forth above. Comeaux further teaches:
retaining a previous risk indicator and tuning the machine learning model based on the previous risk indicator. (8:62-9:6 "the alert-generating server 102 may train [tune] the alert-generation model based on known false positive fraud identification [previous risk indicator]. For example, when the alert-generation model determines that a user's account has been compromised, the alert-generating model 102 may transmit a notification to an administrator who then verifies whether the user's account has indeed been compromised. Upon receiving an indication from the administrator that the alert-generation model has yielded incorrect results [previous risk indicator], the alert-generation model may then train [tune] the alert-generation model, using the above-mentioned methods, to avoid future false positives"; 19:30-35 "the alert-generating server may also train [tune] the alert-generation model when the administrator determines that the alert-generation model has yielded an incorrect fraud probability score [previous risk indicator] and the user is not involved in fraudulent network activity")
Regarding Claims 26 and 36
Comeaux in view of Faibish teaches the limitations of base claims 22 and 32 as set forth above. Comeaux further teaches:
training the machine learning model to generate an alert based on the risk indicator. (Fig. 2, 212 (18:21-50) teaches generating an alert based on alert probability score (generate an alert based on the risk indicator); Fig. 2, 202 (15:65-16:39) teaches generating the alert generation model that is used in step 212; the training of this model is taught by 4:31-64, 6:59-67, 8:7-11 and 8:39-9:28; 4:49-52 teaches that the alert generation model is a machine-learning model)
Regarding Claims 27 and 37
Comeaux in view of Faibish teaches the limitations of base claims 22 and 32 as set forth above. Comeaux further teaches:
training the machine learning model based on at least one of an instrument propensity, a device propensity, or a transaction channel type.(16:1-3 teaches that the alert generation model (which per 4:49-52 is a machine learning model) is trained using "inputs from a behavior profile," and the behavior profile includes at least one of an instrument propensity, a device propensity, or a transaction channel type, as taught by 9:58-63 (account balance, credit score, any known flag for activity or known fraud (instrument propensity)), 10:30-39 (remote network logon logs (device propensity)), 10:51-11:13 (history of the user for failed authentication events (device propensity))
Regarding Claims 29 and 39
Comeaux in view of Faibish teaches the limitations of base claims 22 and 32 as set forth above. Comeaux further teaches:
deriving the risk indicator from a model probability. (2:43-49, 4:47-49, 11:26-32, Fig. 2, 210, 17:60-18:14 the alert generation model generates a fraud probability score; the fraud probability score is the risk indicator; as the term itself indicates, the score is a score based on, i.e., derived from, the probability; the probability is determined by the model and hence is a model probability)
Claims 25 and 35 are rejected under 35 U.S.C. 103 as being unpatentable over Comeaux et al. (U.S. Patent No. 11,669,844), hereafter Comeaux, in view of Faibish (U.S. Patent Application Publication No. 2021/0125053 A1), and further in view of Hart et al. (U.S. Patent Application Publication No. 2020/0294054 A1), hereafter Hart.
Regarding Claims 25 and 35
Comeaux in view of Faibish teaches the limitations of base claims 22 and 32 as set forth above. Comeaux in view of Faibish does not explicitly disclose but Hart teaches:
appending at least one of transactional data, a customer characteristic, or historical data to the processed action. (0025 " Additionally, the data cleansing method may include data enhancement, where data is made more complete by adding related information (e.g., appending an address with any phone number related to that address).")
It would have been obvious to one of ordinary skill in the art not later than the effective filing date of the claimed invention to have modified the combination of Comeaux's systems and methods for detecting and preventing fraudulent events, as modified by Faibish's teachings regarding model fitting on new data, by incorporating therein these teachings of Hart, because it would improve model performance/results by including more relevant information to analyze in making determinations about transactions, see Hart, 0025.
Claims 28 and 38 are rejected under 35 U.S.C. 103 as being unpatentable over Comeaux et al. (U.S. Patent No. 11,669,844), hereafter Comeaux, in view of Faibish (U.S. Patent Application Publication No. 2021/0125053 A1), and further in view of Yadav et al. (U.S. Patent Application Publication No. 2022/0164798 A1), hereafter Yadav.
Regarding Claims 28 and 38
Comeaux in view of Faibish teaches the limitations of base claims 22 and 32 as set forth above. Comeaux in view of Faibish does not explicitly disclose but Yadav teaches:
enriching the processed action in real time (0044-0045, 0041)
based on the at least one training input. (per claim 2, the transaction data is enriched with historical transaction data, and per 0042-0043, the historical transaction data is training input, therefore the enriching is based on training input; per 0041, Fig. 2, the transaction data is enriched with features extracted during training, per 0043, Fig. 3, the features are extracted from historical transaction data which in turn, again per 0042-0043, is training input, therefore the enriching is based on the training)
It would have been obvious to one of ordinary skill in the art not later than the effective filing date of the claimed invention to have modified the combination of Comeaux's systems and methods for detecting and preventing fraudulent events, as modified by Faibish's teachings regarding model fitting on new data, by incorporating therein these teachings of Yadav, because enrichment with the historical transaction data and/or the features based on training would improve model performance/results and real-time processing would improve processing speed/decrease processing time, which benefits purchasers (by reducing wait time) and sellers (by reducing drop-off).
Claim 31 is rejected under 35 U.S.C. 103 as being unpatentable over Comeaux et al. (U.S. Patent No. 11,669,844), hereafter Comeaux, in view of Faibish (U.S. Patent Application Publication No. 2021/0125053 A1), and further in view of Lu (U.S. Patent Application Publication No. 2019/0197442 A1).
Regarding Claim 31
Comeaux in view of Faibish teaches the limitations of base claim 22 as set forth above. Comeaux in view of Faibish does not explicitly disclose but Lu teaches:
benchmarking the machine learning model based on a relative precision, wherein the relative precision is based on at least one of a true positive, a false positive, or a false negative. (0054; note Applicant's specification 0086 defines "relative precision" as "[t]he number of true positives divided by the sum of the true positives and false negatives"; Lu 0054 teaches inter alia benchmarking based on "recall"; the definition of "recall" in the art is the same as Applicant's definition of "relative precision"1; therefore, Lu's teaching of "recall" teaches Applicant's recitation of "relative precision")
It would have been obvious to one of ordinary skill in the art not later than the effective filing date of the claimed invention to have modified the combination of Comeaux's systems and methods for detecting and preventing fraudulent events, as modified by Faibish's teachings regarding model fitting on new data, by incorporating therein these teachings of Lu, because it would improve model performance/results by having the model meet a standard/criterion/threshold.
Conclusion
The prior art made of record and not relied upon, as set forth in the accompanying Notice of References Cited (PTO-892), is considered pertinent to applicant's disclosure.
Comeaux (10,567,402) and Comeaux (11,722,502) teach fraud detection/prevention similar to Comeaux (11,699,844) but to greater depth in certain aspects;
Phatak (2022/0006899) and Anderson (12,136,096) teach a fraud alert queue that prioritizes fraud alerts based on fraud importance;
Vaswani (2022/0377090) teaches fraud detection/prevention (including risk scores and alerts) similar to Comeaux (11,699,844);
Karpovsky (2022/0191173) teaches determining fraud risk based on VPN and/or proprietary knowledge and periodic monitoring; and
Pavlovic ("Log-normal Distribution - A simple explanation”) teaches content about log-normal distribution similar to that of Applicant's disclosure (specification paragraph 0045)
Vimal (US-2023/0186311-A1) (qualifying as prior art based on Indian priority date) teaches inter alia benchmarking a machine learning model based on precision, recall, F1, and/or F2 scores, see 0097.
Thomas (US-10997596-B1) teaches appending a fraud accuracy tag to a declined transaction, where the fraud accuracy tag is indicative of whether the decline of the transaction is a true positive decline or a false positive decline, whereby the fraud accuracy tag is suitable to provide insight into accuracy of a fraud strategy implemented in connection with the declined transaction.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 extension fee 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 date of this final action.
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/DOUGLAS W PINSKY/
Examiner, Art Unit 3626
/JESSICA LEMIEUX/Supervisory Patent Examiner, Art Unit 3626
1 See, e.g., Wilber ("Precision and Recall"), p. 9.
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