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 .
This action is responsive to communication filed on 1/30/2026.
Claims 1-20 are subject to examination.
An IDS filed on 11/17/2025 has been fully considered and entered by the Examiner.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over McDougall et al. U.S. Patent # 11,316,830 (hereinafter McDougall) in view of Agrawal et al. U.S. Patent Publication # 2021/0165862 (hereinafter Agrawal)
With respect to claim 1, McDougall teaches an apparatus comprising: a processor (column 7 lines 55-67); and a memory (i.e. servers which have memory and processor)(column 7 lines 55-67), wherein the processor and the memory are communicatively coupled (i.e. servers which have memory and processor)(column 7 lines 55-67), wherein the processor is configured to:
-receive application data via at least one data prompt on an application form on a computing device (i.e. user pushing on apply now button which will start an application & also receive data associated with user interacting with an online application and receive data from a data source)(column 15 lines 49-62)(column 22 lines 39-47)
-receive device data from the computing device (i.e. receive data from a data source which can be a website, server, a mobile device a reporting system)(column 22 lines 39-47)
-execute a trained artificial intelligence (AI) model to predict an identity risk level(column 5 lines 11-34) based on the application data and the device data (column 10 lines 49-58) (column 22 lines 47-58)
receive the image of the applicant (i.e. receive facial image/biometric data)(Fig. 5d)(column 18 lines 21-46); and
-validate the image of the applicant (column 17 lines 1-10)(column 21 lines 62-67)(column 22 lines 1-11)
McDougall does not explicitly teach determine at least one needed detail of an applicant associated with the application data, wherein the at least one needed detail is an image of the applicant in a specific setting based on the predicted identity risk level; augment the application form on the computing device with at least one additional data prompt for the image of the applicant in the specific setting.
Agrawal teaches execute a trained artificial intelligence (AI) model to predict an identity risk level (i.e. AI predictive analysis based on biometric identification and corresponding user data and a risk score is assigned to the user)based on the application data (i.e. user data/identity data, data uploaded by the user and from other sources)(Fig. 3 element 306, 308, 318, 320) and the device data (i.e. biometric data obtained using the user owned device and stored in the user device and also using bio pass authenticates itself using their own mobile phone along with geo-location that is recorded)(Paragraph 33-35, 38, 40, 41)
determine at least one needed detail of an applicant associated with the application data (Paragraph 38-39, 40), wherein the at least one needed detail is an image of the applicant in a specific setting (i.e. thermal imaging system used for screening) (Fig. 4 element 418)based on the predicted identity risk level (Paragraph 38, 39-41); augment the application form on the computing device (i.e. user registers on the client application installed on their user device) with at least one additional data prompt for the image of the applicant in the specific setting (i.e. then user registers biometric identification on his or her mobile phone and the user then provides user data that includes documents that contain ID details that are verified from government source database) (Paragraph 38-40)(Fig. 4 element 402, 404, 406, 408, 410, 416, 418); validate the image of the applicant (Paragraph 38, 40)(Fig. 4 element 418, 420, 422). It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to implement Agrawal’s teaching in McDougall’s teaching to come up with executing a trained AI model to predict identity risk, determining at least one needed detail of an applicant associated with the application data, augment the application form with at least one additional data prompt and validate the image of the applicant. The motivation for doing to provide access to only authorized and verified individual/users and providing a bio pass wherein the user is authorized access for any business transaction after which the bio pass expires.
With respect to claim 2, McDougall and Agrawal teaches the apparatus of claim 1, but McDougall further teaches wherein the specific setting is based on an amount the predicted identity risk level is above a threshold (i.e. score above the threshold)(column 21 lines 62-67)(column 22 lines 1-11)
With respect to claim 2, McDougall and Agrawal teaches the apparatus of claim 1, but Agrawal further teaches wherein the specific setting includes at least one of: the image of the applicant with a specific background; an image of a specific foreground; an image of an individual associated with the applicant (i.e. thermal imaging system used for screening as well as biometric identification on the phone and user provides user data that includes ID details from government verified source database) (Fig. 4 element 418)(Paragraph 40-42); and an image of the applicant and an image of an individual associated with the applicant with a specific background.
With respect to claim 4, McDougall and Agrawal teaches the apparatus of claim 1, but Agrawal further teaches wherein the processor is configured to: confirm the specific setting (Paragraph 40-42); and validate the image of the applicant based on the confirmed specific setting (Paragraph 40-42)
With respect to claim 5, McDougall and Agrawal teaches the apparatus of claim 1, but Agrawal further teaches wherein the processor is configured to: display an option in an area approximate to the application form, to communicate with an entity to assist when the image of the applicant cannot be validated (Paragraph 37); identify that at least one further needed detail is needed to collect further application data (Paragraph 38-40); collect the at least one further needed detail of the applicant by the entity; and validate the collected at least one further needed detail by the entity (paragraph 38-40)
With respect to claim 6, McDougall and Agrawal teaches the apparatus of claim 1, but McDougall further teaches wherein the processor is further configured to: add a model feedback record, which includes the predicted identity risk level and a final application identity check result, to model feedback data (column 5 lines 11-34)(column 21 lines 62-67)(column 22 lines 1-11); and retrain the trained AI model with the model feedback data that includes the added model feedback record (column 21 lines 1-15).
With respect to claim 7, McDougall and Agrawal teaches the apparatus of claim 1, but McDougall further teaches wherein the application form is displayed on a graphical user interface (GUI) on the computing device (Fig. 5C)(column 18 lines 1-20), wherein the at least one additional data prompt for the image of the applicant (Fig. 5D)(column 18 lines 21-34), and at least one instruction related to the at least one additional data prompt for the image of the applicant is displayed on the GUI (Fig. 5D)(column 18 lines 21-34).
With respect to claims 8-15 respectively, recite similar claim limitations as claim 1-7 respectively, therefore rejected under same basis.
With respect to claims 15-20 respectively, recite similar claim limitations as claim 1-6 respectively, therefore rejected under same basis.
Response to Arguments
Applicant's arguments filed 1/30/2026 have been fully considered but they are not persuasive.
A). Applicant states McDougall and Agarwal do not disclose or suggest “determine at least one needed detail of an applicant associated with the application data, wherein the at least one needed….based on the predicted identity risk level” and “augment the application form on the computer device with at least….the applicant in the specific setting”.
With respect to remark A, First, Examiner would like to point out that McDougall already teaches execute a trained artificial intelligence (AI) model to predict an identity risk level(column 5 lines 11-34) based on the application data and the device data (column 10 lines 49-58) (column 22 lines 47-58). McDougall provides AI model for predictive modeling wherein generate an anonymized account for the user based on the data associated with the user and having a risk parameter associated with the potential threat of the user. The processor may provide application announcement option to the user when the risk parameter reaches a predetermined threshold. This shows using AI modeling to predict risk level (i.e. risk parameter) associated based on the user data.
Then, Examiner states that McDougall does not teach determine at least one needed detail of an applicant associated with the application data, wherein the at least one needed detail is an image of the applicant in a specific setting based on the predicted identity risk level; augment the application form on the computing device with at least one additional data prompt for the image of the applicant in the specific setting.
Applicant states Agarwal does not the above claimed limitations.
First, Examiner would like to point that McDougall teaches predicted identity risk level. Examiner respectfully disagrees with the applicant because in Paragraphs 33-35, 38-41, Agarwal teaches execute a trained artificial intelligence (AI) model to predict an identity risk level (i.e. AI predictive analysis based on biometric identification and corresponding user data and a risk score is assigned to the user)based on the application data (i.e. user data/identity data, data uploaded by the user and from other sources)(Fig. 3 element 306, 308, 318, 320) and the device data (i.e. biometric data obtained using the user owned device and stored in the user device and also using bio pass authenticates itself using their own mobile phone along with geo-location that is recorded)(Paragraph 33-35, 38, 40, 41)
In Paragraph 38-39, 40, Agarwal teaches determine at least one needed detail of an applicant associated with the application data (Paragraph 38-39, 40), wherein the at least one needed detail is an image of the applicant in a specific setting (i.e. thermal imaging system used for screening) (Fig. 4 element 418)based on the predicted identity risk level (Paragraph 38, 39-41). Examiner would first like to state that the claim language and the specification does not explicitly define predicted identity risk level. The claim language and the specification does not explicitly state the predict identity risk level as an arbitrary number (i.e. is the predicted identity risk at 0%, 1%, or is it above 5% or 50% or any number or 100%) One of ordinary skill in the art can say/interpret there is always a predicted identity risk level of even 0% or 100% which means risk is always there, hence always requiring user data beforehand. Hence, Agarwal teaches requiring user uploading user data including identity documents because there’s always predicted identity risk level. The identity is verified from source system using authentication engine and simultaneously documentation of the biometric identification and corresponding user data is performed and a risk score is associated to the user based on AI predictive analysis after the validation of the identity. Hence, Examiner recommends amending the claim language to further clarify/define the “predicted identity risk level”.
Furthermore, in Paragraphs 38-40, Agarwal teaches augment the application form on the computing device (i.e. user registers on the client application installed on their user device) with at least one additional data prompt for the image of the applicant in the specific setting (i.e. then user registers biometric identification on his or her mobile phone and the user then provides user data that includes documents that contain ID details that are verified from government source database) (Paragraph 38-40)(Fig. 4 element 402, 404, 406, 408, 410, 416, 418); validate the image of the applicant (Paragraph 38, 40)(Fig. 4 element 418, 420, 422). Examiner would like to point out to Paragraph 40 & 41 which provides two examples of augmenting the application form on the computing device. In Fig. 4 element 414, 404, and Fig. 5 518, 504 in both cases the photo of the user can be augmented by zooming in with two fingers which in this case, application form includes the photo and then in Fig. 5 element 516, it shows user has to click on “verify” which is the additional prompt which is for the image in (Fig. 5 element 504) , and then processing happens ( 506-508), to shopping (Fig. 5 element 510) and then to pay (Fig. 5 element 514), and then to verify clicking which is additional data prompt. It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to implement Agrawal’s teaching in McDougall’s teaching to come up with executing a trained AI model to predict identity risk, determining at least one needed detail of an applicant associated with the application data, augment the application form with at least one additional data prompt and validate the image of the applicant. The motivation for doing to provide access to only authorized and verified individual/users and providing a bio pass wherein the user is authorized access for any business transaction after which the bio pass expires.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
A). Izvekova et al. U.S. Patent Publication # 2021/0084028 which teaches about creating a multi-applicant account using unique link and one time password and also have ability for multi-applicant account profile and application.
B). Denning et al. U.S. Patent # 10,672,077 which teaches about proactive underwriting wherein evaluation, rating, offering, quoting and/or pricing of insurance for one or more entities using social data.
C). Welch et al. U.S. Patent Publication # 2012/0191594 which teaches about providing a financial service or product and using application form to obtain user identification and data.
D). Barth et al. U.S. Patent Publication # 2023/0164165.
THIS ACTION IS MADE FINAL. 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.
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DHAIRYA A. PATEL
Primary Examiner
Art Unit 2453
/DHAIRYA A PATEL/ Primary Examiner, Art Unit 2453