DETAILED ACTION
Claims 1-20 are pending in this 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 .
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 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 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 1-3, 5-10 and 12-20 are rejected under 35 U.S.C. 103 as being unpatentable over Dill (US PGPUB No. 2010/0274597) in view of Cheng et al. (US PGPUB No. 11,334,565) [hereinafter “Cheng”] in further view of Jones (US PGPUB No. 2003/0139994) in further view of Kumari et al. (US Patent No. 10,475,125) [hereinafter “Kumari”].
As per claim 1, Dill teaches a computing platform comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: receive, from a computing device of a user, first identity information associated with the user (Abstract, gathering identity information about a customer); based on the first identity information, generate an identity confidence model associated with the user (Abstract, generating a confidence score using the identity information), wherein the identity confidence model indicates a level of confidence that the user is authentic ([0014], confidence score relating to proper identification of a customer); receive, from the computing device of the user, user activity data associated with transactions and interactions of the user, wherein the user activity data comprises temporal information associated with the user transacting ([0026], receiving transaction information of the customer over time see also [0043]) and interacting with an entity at one or more touchpoints ([0024], remote locations including atm machines); store the first identity information and the second identity information in a database of prior identity information associated with the user ([0017], confidence score and identity information including transaction history, stored in database as identity profile); compare the second identity information to the first identity information ([0006], comparing the transaction amount to a threshold associated with the stored identity of the customer); based on comparing the second identity information to the first identity information associated with the user, identify one or more anomalies ([0006], discovering that the transaction amount has exceeded the threshold); request authentication information associated with the identified one or more anomalies ([0006], requesting further verification from the customer regarding exceeded threshold); and automatically and continuously update the identity confidence model associated with the user based at least in part on the comparison ([0006], updating confidence score using verification response).
Dill does not explicitly teach responsive to receiving the user activity data, extracting using a machine learning model and a parser, second identity information associated with the user from the user activity data. Cheng teaches responsive to receiving the user activity data, extracting using a machine learning model and a parser, second identity information associated with the user from the user activity data (Col. 4, lines 45-50, using a NLP parser to extract and query user activity data from past activity data based on a trained machine-learning model see Col. 9, lines 52-60).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Dill with the teachings of Cheng, responsive to receiving the user activity data, extracting using a machine learning model and a parser, second identity information associated with the user from the user activity data, to use the advances in learning models to collate and analyze a variety of identity and transaction events and data which would take too long to predict or hardcode into the system.
The combination of Dill and Cheng does not explicitly teach wherein the first identity information associated with the user comprises a physical written signature in connection with opening an account associated with a financial institution. Jones teaches wherein the first identity information associated with the user comprises a physical written signature in connection with opening an account associated with a financial institution ([0035], [physically writing a signature on a card and recording the signing for use by the system of a financial institution in connection with opening a bank account).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Dill and Cheng with the teachings of Jones, wherein the first identity information associated with the user comprises a physical written signature in connection with opening an account associated with a financial institution, to use well known user data that are difficult to forge but readily available to the user.
The combination of Dill, Cheng and Jones does not explicitly teach extract, using a machine learning model trained using historical transaction data and historical interaction data, second identity information associated with the user. Kumari teaches extract, using a machine learning model trained using historical transaction data and historical interaction data (Col. 6, lines 32-62, training classification model on historical transaction data of user), second identity information associated with the user (Col. 8, lines 20-30, extracting user test dataset to apply classification model).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Dill, Cheng and Jones with the teachings of Kumari, extract, using a machine learning model trained using historical transaction data and historical interaction data, second identity information associated with the user, to provide the requisite data to make authentication and confidence determinations.
As per claim 2, the combination of Dill, Cheng, Jones and Kumari teaches the computing platform of claim 1, wherein generating the identity confidence model associated with the user comprises: identifying one or more types of identity information (Dill; [0028], various types of identity information); assigning a weighting to each type of identity information (Dill; [0028], assigning weight to a type of identity information); and based on the assigned weighting, generating an identity confidence score (Dill; [0028], generating confidence score using weights).
As per claim 3, the combination of Dill, Cheng, Jones and Kumari teaches the computing platform of claim 1, wherein the first identity information associated with the user comprises one or more of: a facial photo (Dill; [0041], photograph of customer stored in identity profile), or biometric data (Examiner Note: this is an optional feature but to expedite prosecution a potential citation is provided) (Dill; [0041], photograph is considered a biometric).
As per claim 5, the combination of Dill, Cheng, Jones and Kumari teaches the computing platform of claim 1, wherein the temporal information comprises time stamps associated with the transactions and interactions of the user (Cheng; Col. 2, lines 25-30, tracking date transactions are made by users).
As per claim 6, the combination of Dill, Cheng, Jones and Kumari teaches the computing platform of claim 1, further including instructions that, when executed, cause the computing platform to: receive input data based on the user transacting or interacting with a financial institution (Cheng; Col. 3, lines 35-40, request made for transactions with banking-institution categories).
As per claim 7, the combination of Dill, Cheng, Jones and Kumari teaches the computing platform of claim 1, wherein automatically and continuously updating the identity confidence model associated with the user comprises increasing or decreasing the level of confidence that a user identity is authentic by a predetermined value (Dill; [0004], updating confidence score if there is successful or failed verification, i.e. increasing/decreasing score see also [0026]).
As per claim 8, the substance of the claimed invention is identical or substantially similar to that of claim 1. Accordingly, this claim is rejected under the same rationale.
As per claim 9, the substance of the claimed invention is identical or substantially similar to that of claim 2. Accordingly, this claim is rejected under the same rationale.
As per claim 10, the substance of the claimed invention is identical or substantially similar to that of claim 3. Accordingly, this claim is rejected under the same rationale.
As per claim 12, the substance of the claimed invention is identical or substantially similar to that of claim 5. Accordingly, this claim is rejected under the same rationale.
As per claim 13, the substance of the claimed invention is identical or substantially similar to that of claim 6. Accordingly, this claim is rejected under the same rationale.
As per claim 14, the substance of the claimed invention is identical or substantially similar to that of claim 7. Accordingly, this claim is rejected under the same rationale.
As per claim 15, the substance of the claimed invention is identical or substantially similar to that of claim 1. Accordingly, this claim is rejected under the same rationale.
As per claim 16, the combination of Dill, Cheng, Jones and Kumari teaches the one or more non-transitory computer-readable media of claim 15, wherein the instructions, when executed by the computing platform, further cause the computing platform to: based on comparing the second identity information to the first identity information associated with the user, identify one or more anomalies (Dill; [0006], discovering that the transaction amount has exceeded the threshold); and request authentication information associated with the identified one or more anomalies (Dill; [0006], requesting further verification from the customer regarding exceeded threshold).
As per claim 17, the substance of the claimed invention is identical or substantially similar to that of claim 2. Accordingly, this claim is rejected under the same rationale.
As per claim 18, the substance of the claimed invention is identical or substantially similar to that of claim 5. Accordingly, this claim is rejected under the same rationale.
As per claim 19, the substance of the claimed invention is identical or substantially similar to that of claim 6. Accordingly, this claim is rejected under the same rationale.
As per claim 20, the substance of the claimed invention is identical or substantially similar to that of claim 7. Accordingly, this claim is rejected under the same rationale.
Claims 4 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Dill, Cheng, Jones and Kumari in further view of Caldera et al. (US PGPUB No. 2019/0122149) [hereinafter “Caldera”].
As per claim 4, the combination of Dill, Cheng, Jones and Kumari teaches the computing platform of claim 1.
The combination of Dill, Cheng, Jones and Kumari does not explicitly teach wherein the user activity data comprises geographical information associated with the transactions and interactions of the user. Caldera teaches wherein the user activity data comprises geographical information associated with the transactions and interactions of the user ([0060], including geographic information in the identity profile of a user/device).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Dill, Cheng, Jones and Kumari with the teachings of Caldera, wherein the user activity data comprises geographical information associated with the transactions and interactions of the user, to provide the requisite data to make authentication and confidence determinations.
As per claim 11, the substance of the claimed invention is identical or substantially similar to that of claim 4. Accordingly, this claim is rejected under the same rationale.
Response to Arguments
Applicant’s arguments with respect to the rejection of claims 1-20 under 35 U.S.C. 103 have been fully considered and in light of the new amendments, a new prior art reference, Cheng has been introduced and cited to.
To expedite prosecution, Examiner is open to conducting an interview to discuss claim amendments to overcome the current rejection and/or place the application in condition for allowance.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Brette et al. (US Patent No. 10,210,203), Anderson et al. (US Patent No. 10,303,683), Pan (US Patent No. 12,481,652), Qureshi et al. ("The Way of Machine Learning Based Solicit for Detecting Deceit in Online Based Transaction System with Security," 2024 (ICACITE), Greater Noida, India, 2024, pp. 1316-1321, doi: 10.1109/ICACITE60783.2024.10616595) and Temara et al. ("Using AI and Natural Language Processing to Enhance Consumer Banking Decision-Making," 2024 International Conference on E-mobility, Power Control and Smart Systems (ICEMPS), Thiruvananthapuram, India, 2024, pp. 1-6, doi: 10.1109/ICEMPS60684.2024.10559280) all disclose various aspects of the claimed invention including an identity confidence score tailored using machine learning.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PETER C SHAW whose telephone number is (571)270-7179. The examiner can normally be reached Max Flex.
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, Carl Colin can be reached on 571-272-3862. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PETER C SHAW/Primary Examiner, Art Unit 2493 June 25, 2026
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