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
This Action is in response to the amendment filed on September 12, 2025. Claims 1-15 and 18-21 are currently pending and have been fully examined. Claims 16-17 have been cancelled by Applicant and claim 21 is newly added.
Response to Arguments
With respect to the 101 rejection, Applicant argues that claims recite additional features that are not directed to the abstract idea. Applicant further argues that the additional features include creating a learning model to estimate fraud and that the system comprises a user terminal which is used to execute possession authentication via and NFC or a camera. The examiner respectfully disagrees and notes that the feature of “creating a learning model,” by itself is an abstract idea, because the learning model is used as an abstract black box which is entered input data and output data is received from. No further details is provided on the structure or functioning of the learning model. In Recentive Analytics, Inc. v. Fox Corp., the Federal Circuit addressed questions about the scope of patent eligibility under 35 U.S.C. § 101, evaluating whether patents that apply machine learning to optimize a technology meet the threshold for patent-eligible subject matter. The Federal Circuit held that the patents were directed to abstract ideas and lacked any inventive concept that would transform them into patent-eligible subject matter.
The examiner further notes that a user terminal having NFC or camera as additional elements would merely use one or more computers as tools to perform the abstract idea. In addition, the additional elements, would not be sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test, the additional elements would amount to no more than using computing devices or processors to automate and/or implement the abstract idea.
Applicant further argues that the claims overcome a technical problem with existing systems that use machine learning to detect fraud, because accuracy of a machine learning model decreases as fraudulent trends change. The examiner respectfully disagrees and notes that the claims merely recite measuring accuracy of a learning model without reciting any details or features of the learning model. The examiner notes that claim 1, for example, recites functions associated with a generic process for evaluating a learning model, without an association with a technology being referred to in the claims. The examiner further notes that considering the claims “as a whole” the claims merely recite evaluating accuracy of the learning model without significantly more.
Applicant further argues that with regards to “improvement consideration” the claims include features on how the accuracy of a learning model is achieved. The examiner respectfully notes that accuracy of a learning model is not a technology or a technical field that its improvement could overcome the ineligibility under 101.
Applicant further argues that claims 3 and 18 are eligible because they recite practical applications.
The examiner respectfully disagrees and notes that claim 3 recites “recreating the learning model by using a latest action in the predetermined service when the accuracy of the learning model becomes less than a predetermined accuracy.” The examiner respectfully disagrees and notes that recreating a learning model using data is still an abstract idea that may improve a model accuracy but not a technology. In addition, claim 18 recites “restrict the user's access to the predetermined service if the learning model detects fraud.” Claim 18 merely automates service restriction based on detecting fraud, which can be done, for example, by a security guard who checks an ID card and presses a button to activate or deactivate a gate.
Applicant further argues that new claim 21 is eligible. The examiner respectfully disagrees and notes that claim 21 recites “wherein the accuracy indicates a probability of a desired result being obtained as an output from the learning model; and wherein the accuracy is measured as a precision rate, a to use a correct answer rate, a reproducibility rate, a false positive rate, a log loss, or an area under the curve,” which is merely a definition of accuracy in terms of mathematical/statistical concepts and therefore is not eligible under 101.
With respect to the 103 rejections, Applicants arguments were fully considered but are moot in light of new grounds of rejection.
Claim Objections
With respect to claims 1, 14 and 15, the claims recite “acquire information relating to an action of the authenticated user on a predetermined service after the user has been authenticated according to the second authentication method; wherein the information comprises …” A “wherein” clause is a continuation of its previous claimed feature and not an independent feature, therefore, a “wherein” clause should be separated from its previous feature with a comma (“,”) and not a semicolon (“;”). Appropriate correction is required.
With respect to claim 21, the claim recites “wherein the accuracy is measured as a precision rate, a to use a correct answer rate,” The underlined phrase is grammatically incorrect and is required to be corrected.
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 1-15 and 17-20 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.
Claims 1-13 and 17-20 are directed to a system (product), claim 14 is directed to a method (process) and claim 15 is directed to a non-transitory computer-readable information storage (product.) Therefore, these claims fall within the four statutory categories of invention.
Claims 1-15 and 17-20 are directed to the abstract idea of determining accuracy of a model by comparison between real world collected data and the model output. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Analysis
In the following analysis, bolded text indicates abstract idea, and the rest of the text indicates additional elements. Independent claims 1, 14 and 15 recite:
creating a learning model configured to detect fraud in a service, wherein the learning model estimates whether an action by a target user is fraudulent or valid;
authenticating a user according to a first authentication method, wherein the first authentication method is a login authentication to a predetermined service;
authenticating the user according to a second authentication method, wherein the second authentication method is a predetermined authentication method comprising possession authentication to confirm the user is in physical possession of a tangible object, by using the NFC unit to acquire an individual number on the tangible object or the camera to image the tangible object and perform optical character recognition to recognize the individual number;
acquiring information relating to an action of the authenticated user on the predetermined service after the user has been authenticated according to the second authentication method;
wherein the information comprises a location information, a date and time information, or a usage information;
inputting the information into the learning model, and acquiring an output from the learning model indicating whether the action of the authenticated user in the predetermined service is fraudulent or valid; and
evaluating an accuracy of the learning model by comparing the output of the learning model to the information wherein the accuracy of the learning model is evaluated as higher when the output indicates the action in the information is valid than if the output indicates the action in the information fraudulent.
Therefore, claims 1, and 14-15 are directed to the abstract idea of determining accuracy of a model by comparison between real world collected data and the model output, which is a “fundamental economic principle or practice” grouped within the “certain methods of organizing human activity” and an “observation, evaluation and judgement” grouped within the “mental process” groupings of abstract ideas in prong one of step 2A of the Alice/Mayo test (See MPEP 2106) because the claims involve a series of steps for determining accuracy of a model by comparison between real world collected data and the model output. Accordingly, the claims recite an abstract idea (See pages 7, 10, Alice Corporation Pty. Ltd. v. CLS Bank International, et al., US Supreme Court, No. 13-298, June 19, 2014; MPEP 2106).
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A of the Alice/Mayo test (See MPEP 2106), the additional elements of at least one processor, a user terminal comprising an NFT unit or a camera, perform optical character recognition, a non-transitory computer-readable information storage medium, and a computer, merely use one or more computers as tools to perform the abstract idea. The use of at least one processor, a user terminal comprising an NFT unit or a camera, perform optical character recognition, a non-transitory computer-readable information storage medium, and a computer, does not integrate the abstract idea into a practical application because it requires no more than one or more computing devices performing functions that correspond to acts required to carry out the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See MPEP 2106), the additional elements amount to no more than using computing devices or processors to automate and/or implement the abstract idea.
As discussed above, taking the claim elements separately, these additional elements perform the steps or functions that correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the abstract idea.
Dependent claim 2 recites:
acquire a plurality of pieces of the information,
acquire the output corresponding to each of the plurality of pieces of the information, and
evaluate the accuracy of the learning model based on the output corresponding to each of the plurality of pieces of the information, which further describes the abstract idea.
The claim does not recite any new additional elements for consideration under Step 2A, prong 2 or Step 2B, and therefore is ineligible.
Dependent claim 3 recites:
execute processing for recreating the learning model by using a latest action in the predetermined service when the accuracy of the learning model becomes less than a predetermined accuracy, which further describes the abstract idea.
The claim does not recite any new additional elements for consideration under Step 2A, prong 2 or Step 2B, and therefore is ineligible.
Dependent claim 4 recites:
acquire confirmed information relating to an action of a confirmed user for which the action has been confirmed as being fraudulent or not fraudulent, and
evaluate the accuracy of the learning model based on the information and the confirmed information, which further describes the abstract idea.
The claim does not recite any new additional elements for consideration under Step 2A, prong 2 or Step 2B, and therefore is ineligible.
Dependent claim 5 recites:
wherein the predetermined authentication is possession authentication for confirming whether the user possesses a predetermined card through use of the user terminal, and
wherein the authenticated user is a user who has executed the possession authentication from the user terminal, which further describes the abstract idea.
The claim does not recite any new additional elements for consideration under Step 2A, prong 2 or Step 2B, and therefore is ineligible.
Dependent claim 6 recites:
wherein each of a first card and a second card, each of which is the predetermined card, is usable in the predetermined service by the authenticated user,
wherein the at least one processor is configured to:
acquire the information corresponding to the first card,
acquire the output corresponding to the first card based on the information corresponding to the first card, and
evaluate the accuracy of the learning model based on the output corresponding to the first card, which further describes the abstract idea.
The claim does not recite any new additional elements for consideration under Step 2A, prong 2 or Step 2B, and therefore is ineligible.
Dependent claim 7 recites:
compare first name information relating to a name of the first card and second name information relating to a name of the second card,
acquire the information corresponding to the second card when a result of the comparison is a predetermined result,
acquire the output corresponding to the second card based on the information corresponding to the second card, and
evaluate the accuracy of the learning model based on the output corresponding to the first card and the output corresponding to the second card, which further describes the abstract idea.
The claim does not recite any new additional elements for consideration under Step 2A, prong 2 or Step 2B, and therefore is ineligible.
Dependent claim 8 recites:
wherein the second card is a card other than a card which supports the possession authentication, and
wherein the information corresponding to the second card is information relating to the action of the authenticated user who has used the second card on which the possession authentication has not been executed, which further describes the abstract idea.
The claim does not recite any new additional elements for consideration under Step 2A, prong 2 or Step 2B, and therefore is ineligible.
Dependent claim 9 recites:
create, based on the information, the learning model for detecting fraud in the predetermined service such that the action of the authenticated user is estimated to be valid.
The claim does not recite any new additional elements for consideration under Step 2A, prong 2 or Step 2B, and therefore is ineligible.
Dependent claim 10 recites:
wherein the learning model is a supervised learning model, and
wherein the at least one processor is configured to create the learning model by creating first training data indicating that the action of the authenticated user is valid based on the information, and training the learning model based on the first training data, which further describes the abstract idea.
The claim does not recite any new additional elements for consideration under Step 2A, prong 2 or Step 2B, and therefore is ineligible.
Dependent claim 11 recites:
acquire unauthenticated information relating to an action of an unauthenticated user who is yet to execute the predetermined authentication, and
create second training data indicating that the action of the unauthenticated user is valid or fraudulent based on the unauthenticated information, and to train the learning model based on the second training data, which further describes the abstract idea.
The claim does not recite any new additional elements for consideration under Step 2A, prong 2 or Step 2B, and therefore is ineligible.
Dependent claim 12 recites:
acquire an output from the trained learning model based on the unauthenticated information, and to create the second training data based on the output, which further describes the abstract idea.
The claim does not recite any new additional elements for consideration under Step 2A, prong 2 or Step 2B, and therefore is ineligible.
Dependent claim 13 recites:
wherein the predetermined service is an electronic payment service usable from the user terminal,
wherein the predetermined authentication is authentication of the electronic payment service executed from the user terminal,
wherein the information is information relating to the action of the authenticated user in the electronic payment service, and
wherein the learning model is a model for detecting fraud in the electronic payment service, which further describes the abstract idea.
The judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A of the Alice/Mayo test (See MPEP 2106), the additional element of an electronic payment service, merely use one or more computers as tool to perform the abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See MPEP 2106), the additional element amount to no more than using computing devices or processors to automate and/or implement the abstract idea.
Dependent claim 18 recites:
wherein the at least one processor is configured to restrict the user's access to the predetermined service if the learning model detects fraud, which further describes the abstract idea.
The claim does not recite any new additional elements for consideration under Step 2A, prong 2 or Step 2B, and therefore is ineligible.
Dependent claim 19 recites:
wherein if the accuracy of the learning model is more than a threshold, a notification is sent indicating that the accuracy of the learning model is high, which further describes the abstract idea.
The claim does not recite any new additional elements for consideration under Step 2A, prong 2 or Step 2B, and therefore is ineligible.
Dependent claim 20 recites:
wherein if the accuracy of the learning model is less than a threshold, a notification is sent indicating that the accuracy of the learning model is low. which further describes the abstract idea.
The claim does not recite any new additional elements for consideration under Step 2A, prong 2 or Step 2B, and therefore is ineligible.
Dependent claim 21 recites:
wherein the accuracy indicates a probability of a desired result being obtained as an output from the learning model; and
wherein the accuracy is measured as a precision rate, to use a correct answer rate, a reproducibility rate, a false positive rate, a log loss, or an area under the curve, which further describes the abstract idea.
The claim does not recite any new additional elements for consideration under Step 2A, prong 2 or Step 2B, and therefore is ineligible.
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.
This application currently names joint inventors. In considering patentability of the claims, the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 Graham 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.
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:
Claims 1-4, 9-12 and 14-15 and 19-21 are rejected under 35 U.S.C. 103 as being unpatentable over Streit (US Patent Publication No. 2021/0141896,) in view of Jass (US Patent Publication No. 2022/0116390 ,) further in view of Machani (US Patent Publication No. 2020/0242222.)
With respect to claims 1, 14 and 15, Streit et al. teach:
creating a learning model…(model is generated: [0091] )
wherein the learning model estimates whether an action by a target user is fraudulent or valid; (prediction by the classification network (i.e., model): [0050], determine whether authentication data meets a validation threshold (i.e. estimated validity: [0070]-[0074] )
The examiner notes that the claim recitation: “…configured to detect…,” indicates intended use of the model and therefore does not further limit the scope of the claim.
authenticating a user according to a first authentication method… (authentication function: FIG. 2A, [0248], FIG. 9, [0185])
acquire information relating to an action of an authenticated user…(FIG. 7, behavioral input (i.e., action) from a user is received by a mobile device (user terminal) performing authentication service: [0060], [0062])
wherein the information comprises a location information, a date and time information, or a usage information; (timing or location of the activity: [0006], App usage:[0197], Table XI)
The examiner notes that the claim recitation: “the information comprises a location information, a date and time information, or a usage information,” indicates non-functional descriptive material and therefore does not further limit the scope of the claim.
inputting the information into the learning model, and acquire an output from the learning model indicating whether the action of the authenticated user in the predetermined service is fraudulent or valid; (a classifier component (learning model) receives an input [0052] and generates an output identifying a person: [0053], [0069], [0075]-[0076], [0100])
evaluate an accuracy of the learning model by comparing the output of the learning model to the information. (validating output of a classification network (i.e., learning model) by calculating probability of match (i.e., comparison) of classification output (item 1102 in FIG. 11) with corresponding encrypted credential (information.) (item 1110 in FIG. 11): [0049]-[0050], FIG. 11, [0081], [0206], [0226]-[0228])
wherein the accuracy of the learning model is evaluated as higher when the output indicates the action in the information is valid than if the output indicates the action in the information fraudulent. (accuracy percentage is calculated based on validation thresholds: [0073], [0082], [0116])
In addition, with respect to claim 1, Streit teaches:
a learning model evaluation system, comprising at least one processor, ([0330])
In addition, with respect to claim 15, Streit teaches:
a non-transitory computer-readable information storage medium for storing a program ([0335])
Streit does not explicitly teach:
a user terminal comprising an NFC unit or a camera;
wherein the first authentication method is a login authentication to a predetermined service
authenticate the user according to a second authentication method, wherein the second authentication method is a predetermined authentication method comprising possession authentication to confirm the user is in physical possession of a tangible object, by using the NFC unit to acquire an individual number on the tangible object or the camera to image the tangible object and perform optical character recognition to recognize the individual number;
acquire information relating to an action of the authenticated user on the predetermined service after the user has been authenticated according to the second authentication method;
However, Jass teaches:
a user terminal comprising an NFC unit or a camera; (computing device 950-a: [0061])
wherein the first authentication method is a login authentication to a predetermined service, (FIG. 9, login screen 905: [0061])
authenticate the user according to a second authentication method, wherein the second authentication method is a predetermined authentication method comprising possession authentication to confirm the user is in physical possession of a tangible object, by using the NFC unit… (Multifactor authentication verifies user using two or more factors, such as possession factor of a security token or NFC dongle: [0061], [0074])
Jass does not explicitly teach:
…to acquire an individual number on the tangible object or the camera to image the tangible object and perform optical character recognition to recognize the individual number;
However, the claim recitation indicates intended use of the NFC unit and does not further limit the scope of the claim.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate multi-factor authentication where the first authentication is a login and a second authentication is based on a possession factor determined using NFC, as taught by Jass, into the learning model accuracy evaluation system of Streit based on a first authentication method (biometrics), in order to ensure model accuracy is evaluated based on an authentic user data.
Streit and Jass do not explicitly teach:
acquire information relating to an action of the authenticated user on the predetermined service after the user has been authenticated according to the second authentication method;
However, Machani teaches:
acquire information relating to an action of the authenticated user on the predetermined service after the user has been authenticated according to the second authentication method; (behavior model collects behavioral data before or after authentication: [0081])
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate calculating behavior score of a user based on authentication data, as taught by Machani, into the learning model accuracy evaluation system of Streit and Jass, in order to consider user authenticity into the behavior data.
With respect to claim 2, Streit Jass and Machani teach the limitations of claim 1.
Moreover, Streit teaches:
acquire a plurality of pieces of the information, (various authentication data: FIG. 14, [0094]-[0097])
acquire the output corresponding to each of the plurality of pieces of the information, (credential processing component: [0094]-[0097])
evaluate the accuracy of the learning model based on the output corresponding to each of the plurality of pieces of the information. (classification component: [0094]-[0097])
With respect to claim 3, Streit Jass and Machani teach the limitations of claim 1.
Moreover, Streit teaches:
execute processing for recreating the learning model by using a latest action in the predetermined service when the accuracy of the learning model becomes less than a predetermined accuracy. (determine whether authentication data meets a validation threshold: [0070]-[0074], model is updated (i.e. recreated): [0091]-[0092])
With respect to claim 4, Streit Jass and Machani teach the limitations of claim 1.
Moreover, Streit teaches:
acquire confirmed information relating to an action of a confirmed user for which the action has been confirmed as being fraudulent or not fraudulent, (known or unknown result: [0074]-[0077])
evaluate the accuracy of the learning model based on the information and the confirmed information. (results are used to determine model accuracy: [0178], [0205], [0216])
With respect to claim 9, Streit Jass and Machani teach the limitations of claim 1.
Moreover, Streit teaches:
create, based on the information, the learning model for detecting fraud in the predetermined service… (model is generated: [0091] )
The examiner notes that the claim recitation: “…for detecting…,” indicates intended use of the model and therefore does not further limit the scope of the claim.
such that the action of the authenticated user is estimated to be valid. (prediction by the classification network (i.e., model): [0050], determine whether authentication data meets a validation threshold (i.e. estimated validity: [0070]-[0074] )
The examiner notes that the claim recitation: “…such that the action of the authenticated user is estimated to be valid,” indicates an intended result of detecting fraud and therefore does not further limit the scope of the claim.
With respect to claim 10, Streit Jass and Machani teach the limitations of claim 9.
Moreover, Streit teaches:
wherein the learning model is a supervised learning model, (the model is trained (i.e., supervised): [0045])
wherein the at least one processor is configured to create the learning model by creating first training data indicating that the action of the authenticated user is valid based on the information, and training the learning model based on the first training data. ([0065]-[0067], [0088]-0092])
With respect to claim 11, Streit Jass and Machani teach the limitations of claim 10.
Moreover, Streit teaches:
acquire unauthenticated information relating to an action of an unauthenticated user who is yet to execute the predetermined authentication, (enroll new user: [0065])
create second training data indicating that the action of the unauthenticated user is valid or fraudulent based on the unauthenticated information, and to train the learning model based on the second training data. (additional training data for new user: [0065], update the model with new user data: [0091])
The examiner notes that the claim recitation “to train the learning model…” indicates intended use and therefore does not further limit the scope of the claim because the function “training” is not positively recited.
With respect to claim 12, Streit Jass and Machani teach the limitations of claim 11.
Moreover, Streit teaches:
acquire an output from the trained learning model based on the unauthenticated information, and to create the second training data based on the output. (the model is incrementally retrained: [0087]-[0092])
With respect to claim 19, Streit Jass and Machani teach the limitations of claim 1.
Moreover, Streit teaches:
wherein if the accuracy of the learning model is more than a threshold, a notification is sent… (FIG. 11, [0228])
The examiner notes that the claim recitation “indicating that the accuracy of the learning model is high,” merely indicates content of the notification which is non-functional descriptive material and therefore does not further limit the scope of the claim.
With respect to claim 20, Streit Jass and Machani teach the limitations of claim 1.
Moreover, Streit teaches:
wherein if the accuracy of the learning model is less than a threshold, a notification is sent…(FIG. 11, [0228])
The examiner notes that the claim recitation “indicating that the accuracy of the learning model is low,” merely indicates content of the notification which is non-functional descriptive material and therefore does not further limit the scope of the claim.
With respect to claim 21, Streit Jass and Machani teach the limitations of claim 1.
Moreover, Streit teach:
wherein the accuracy indicates a probability of a desired result being obtained as an output from the learning model; ([0076]-[0077])
Streit Jass and Machani do not explicitly teach:
wherein the accuracy is measured as a precision rate, a to use a correct answer rate, a reproducibility rate, a false positive rate, a log loss, or an area under the curve.
However, the claim recitation indicates non-functional descriptive material that merely describes data and does not further limit the scope of the claim.
Claims 5-8, 13 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Streit, in view of Jass and Machani, further in view of Zarakas et al. (US Patent Publication No. 2021/0383394)
With respect to claim 5, Streit Jass and Machani teach the limitations of claim 1.
Streit Jass and Machani do not explicitly teach:
wherein the predetermined authentication is possession authentication for confirming whether a user possesses a predetermined card through use of the user terminal, and
wherein the authenticated user is a user who has executed the possession authentication from the user terminal.
However, Zarakas et al. teach:
wherein the predetermined authentication is possession authentication for confirming whether the user possesses a predetermined card through use of the user terminal, (authenticating user using a transaction card: [0014]-[0015], [0026], [0031])
The examiner notes that the claim recitation “…the predetermined authentication is possession authentication for confirming…” indicate non-functional descriptive material which does not further limit the scope of the claim. In addition, the recitation “to confirm…” indicates intended use of the authentication and does not further limit the scope of the claim.
wherein the authenticated user is a user who has executed the possession authentication from the user terminal. ([0014]-[0015], [0026], [0031])
The examiner notes that the claim recitation indicates non-functional descriptive material that describes the user and therefore does not further limit the scope of the claim.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate authentication using transaction cards in a machine learning environment, as taught by Zarakas, into the learning model accuracy evaluation system of Streit Jass and Machani in order to verify model accuracy based on transaction card information. (Zarakas et al.: Abstract, [0038])
With respect to claim 6, Streit, Jass Machani and Zarakas teach the limitations of claim 5.
Moreover, Zarakas et al. teach:
wherein each of a first card and a second card, each of which is the predetermined card, is usable in the predetermined service by the authenticated user, ([0031], [0034]-[0038])
The examiner notes that the claim recitation “each of a first card and a second card, each of which is the predetermined card, is usable in the predetermined service by the authenticated user…” indicates non-functional descriptive material that describes intended use of the cards and therefore does not further limit the scope of the claim.
acquire the information corresponding to the first card, (training learning model using card data: FIG. 2, [0044]-[0049])
acquire the output corresponding to the first card based on the information corresponding to the first card, (training learning model using card data: FIG. 2, [0044]-[0049])
evaluate the accuracy of the learning model based on the output corresponding to the first card. (FIG. 2, [0049]-[0051])
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate authentication using transaction cards in a machine learning environment, as taught by Zarakas, into the learning model accuracy evaluation system of Streit Jass and Machani in order to verify model accuracy based on transaction card information. (Zarakas et al.: Abstract, [0038])
With respect to claim 7, Streit, Jass Machani and Zarakas et al. teach the limitations of claim 6.
Moreover, Zarakas et al. teach:
compare first name information relating to a name of the first card and second name information relating to a name of the second card, (cross-validation the learning model over features set which includes user data on the first card and second card: FIG. 2, [0053]-[0055])
acquire the information corresponding to the second card when a result of the comparison is a predetermined result, (using a trusted card to authenticate another card: [0031], [0034], [0058]-[0060])
acquire the output corresponding to the second card based on the information corresponding to the second card, (FIG. 2, [0044]-[0049])
evaluate the accuracy of the learning model based on the output corresponding to the first card and the output corresponding to the second card. (FIG. 2, [0049]-[0051])
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate authentication using transaction cards in a machine learning environment, as taught by Zarakas, into the learning model accuracy evaluation system of Streit Jass and Machani in order to verify model accuracy based on transaction card information. (Zarakas et al.: Abstract, [0038])
With respect to claim 8, Streit, Jass Machani and Zarakas et al. teach the limitations of claim 6.
Moreover, Zarakas et al. teach:
wherein the second card is a card other than a card which supports the possession authentication, (each card has a different trust score: [0022])
wherein the information corresponding to the second card is information relating to the action of the authenticated user who has used the second card on which the possession authentication has not been executed. (authentication user based on trust score of the card: [0059])
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate authentication using transaction cards in a machine learning environment, as taught by Zarakas, into the learning model accuracy evaluation system of Streit Jass and Machani in order to verify model accuracy based on transaction card information. (Zarakas et al.: Abstract, [0038])
The examiner notes that the claim recitation “…wherein the second card is a card other than a card which supports the possession authentication…” indicate non-functional descriptive material which merely describes the card and does not further limit the scope of the claim.
The examiner further notes that the claim recitation “…wherein the information corresponding to the second card is information relating to the action of the authenticated user who has used the second card on which the possession authentication has not been executed…” indicate non-functional descriptive material which merely describes the data but does not affect the functions of the claim and therefore does not further limit the scope of the claim.
With respect to claim 13, Streit Jass and Machani teaches the limitations of claim 1.
Streit Jass and Machani do not explicitly teach:
wherein the predetermined service is an electronic payment service usable from the user terminal,
wherein the predetermined authentication is authentication of the electronic payment service executed from the user terminal,
wherein the information is information relating to the action of the authenticated user in the electronic payment service, and
wherein the learning model is a model for detecting fraud in the electronic payment service.
However, Zarakas et al. teach:
wherein the predetermined service is an electronic payment service usable from the user terminal, ([0075])
The examiner notes that the claim recitation “usable from the user terminal…” indicates intended use of the service and therefore does not further limit the scope of the claim.
wherein the predetermined authentication is authentication of the electronic payment service executed from the user terminal, ([0034])
wherein the information is information relating to the action of the authenticated user in the electronic payment service, ([0033]-[0034])
wherein the learning model is a model for detecting fraud in the electronic payment service. ([0034], [0038], [0040], [0075])
The examiner notes that claim recitation “for detecting fraud…” indicates intended use of the learning model and therefore does not further limit the scope of the claim, because the “detecting” function is not performed.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate system of detecting fraud in payment services using machine learning models, as taught by Zarakas, into the learning model accuracy evaluation system of Streit Jass and Machani, in order to evaluate fraud detection models used in transactions. (Zarakas et al.: Abstract, [0015])
With respect to claim 18, Streit, Jass Machani and Zarakas et al. teach the limitations of claim 13
Moreover, Streit teaches:
restrict the user's access to the predetermined service if the learning model detects fraud. ([0057], [0115], [0185]
Conclusion
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 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 SIMA ASGARI whose telephone number is (571)272-2037. The examiner can normally be reached M-F 9am-6pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patrick McAtee can be reached at (571)272-7575. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SIMA ASGARI/Examiner, Art Unit 3698
/PATRICK MCATEE/Supervisory Patent Examiner, Art Unit 3698