DETAILED ACTION
This action is in response to the filing of 6-02-2026. Claims 1-2, 4-7, 9-16 and 20-25 are pending and have been considered below:
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.
Claims 1 3-7, 9, 14, 20, 22 and 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Leitner et al. (“Leitner” 20180114017 A1) in view of Gilbertson et al. (“Gilbertson” 20240370935 A1) and Triantafillos et al. (“Triantafillos” 20210084063 A1).
Claim 1: Leitner discloses a computing platform comprising: at least one processor; a communication interface communicatively coupled to the at least one processor(Paragraph 70; memory/processor);
and memory storing computer-readable instructions that, when executed by the at least one processor (Paragraph 70; memory/processor),
cause the computing platform to:
receive identity information corresponding to an identity generation request (Paragraph 25; request for transaction for subscriber);
input, into the synthetic identity detection model, the identity information, wherein inputting the identity information into the synthetic identity detection model causes the synthetic identity detection model to: generate information clusters corresponding to the identity information (Paragraphs 32 and 49 (synthetic detection performs clustering)),
identify a difference between a number of the information clusters and an anticipated number of information clusters, compare the difference in information clusters to an anomaly detection threshold (Paragraph 32; cluster of node comparison),
based on identifying that the difference in information clusters meets or exceeds the anomaly detection threshold (abstract, Paragraphs 6, 50-51 and 53; threshold determines possible fraud/fake entity);
compare a synthetic identity detection threshold, and based on identifying that the threat score meets or exceeds the synthetic identity detection threshold, identify a synthetic identity generation attempt (Paragraph 61; threshold exceeds level, synthetic attempt); prevent the requested identity generation; and send, to an administrator computing device, a notification indicating the synthetic identity generation attempt (abstract, Paragraphs 25 and 55-56; user must be authenticated, will not be if attempt is possible synthetic and alert provided).
Leitner may not explicitly disclose all the features below and therefore Gilbertson is provided to address train, using unsupervised learning techniques, a synthetic identity detection model, wherein training the synthetic identity detection model configures the synthetic identity detection model to detect attempts to generate synthetic identities (Figure 5 and Paragraphs 33, 39 and 137; train using datasets for unsupervised learning for synthetic/new (fake) identities Paragraph 20); and
generate a threat score corresponding to the identity information, compare the threat score (Gilbertson: Paragraphs 23, 31 and 57 (fraud association score)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device in the same way and provide training with an unsupervised model in order to enhance the synthetic detection algorithm of Leitner. One would have been motivated to provide the functionality to efficiently address fraudulent activities.
Gilbertson also may add functionality to further read with Leitner, wherein inputting the identity information into the synthetic identity detection model causes the synthetic identity detection model to: generate information clusters corresponding to the identity information (Gilbertson: Paragraphs 39; clustering of information and 42; detection with association scoring and Leitner: Paragraph 32; cluster).
Leitner also may not explicitly disclose wherein generating the threat score comprises analyzing the difference in information clusters to identify whether a valid reason exists for the difference, wherein the valid reason comprises identifying that a user corresponding to the identity information moved or changed jobs,
Triantafillos is provided because it discloses a threat management functionality that determines a score based on features including job changes (Paragraphs 61).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device in the same way and provide threat analysis based on job changes in Leitner. One would have been motivated to provide the functionality for enhanced analysis for more effective evaluation of risk within a system.
Claim 2: Leitner, Gilbertson and Triantafillos disclose a computing platform of claim 1, wherein the synthetic identity detection model comprises a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) model (Leitner: Paragraph 49; DBSC).
Claim 4: Leitner, Gilbertson and Triantafillos disclose a computing platform of claim 1, wherein the anomaly detection threshold is automatically identified based on profile information of a valid user corresponding to the synthetic identity (Leitner: Paragraphs 6 and 32; threshold of connectivity and Gilbertson: Paragraph 47; identify good actor (valid)).
Claim 5: Leitner, Gilbertson and Triantafillos disclose a computing platform of claim 1, wherein the anomaly detection threshold is configurable by a valid user corresponding to the synthetic identity (Leitner: Paragraphs 17 and 32; customizable thresholds and Gilbertson: Paragraphs 47 and 132; good actor association).
Claim 6: Leitner, Gilbertson and Triantafillos disclose a computing platform of claim 5, wherein the memory stores additional computer readable instructions that, when executed by the at least one processor, cause the computing platform to: receive, from the valid user, a request for a financial product; and identify whether or not to grant the request for the financial product, wherein the identification of whether or not to grant the request for the financial product is based on the anomaly detection threshold (Leitner: Paragraphs 17, 19, 35 (catch before fraud) 55-56; alert provided to analyst to take proper actions).
Claim 7: Leitner, Gilbertson and Triantafillos disclose a computing platform of claim 1, wherein the anomaly detection threshold corresponds to a percentage change in the number of the information clusters over time (Leitner: Paragraph 37 and 52; analyzed over time).
Claim 9: Leitner, Gilbertson and Triantafillos disclose a computing platform of claim 1, wherein generating the threat score comprises identifying a likelihood that the identity generation request is valid based on known identity information of a valid user corresponding to the identity generation request (Leitner: Paragraphs 18; threshold of connectivity, 34; ratio can represent a threat score).
Claim 14: Leitner, Gilbertson and Triantafillos disclose a computing platform of claim 1, wherein the memory stores additional computer readable instructions that, when executed by the at least one processor, cause the computing platform to: based on identifying that the threat score does not meet or exceed the synthetic identity detection threshold, generating an identity corresponding to the identity generation request (Leitner: Paragraphs 25 (generate request) and 32-33; threshold of connectivity, can indicate likely real individual).
Claims 16 and 20 are similar in scope to claim 1 and therefore rejected under the same rationale.
Regarding a method of claim 16 (Paragraphs 73 and 78)and the non-transitory computer readable medium of claim 20 (Paragraph 42)
Claim 22: Leitner, Gilbertson and Triantafillos disclose a computing platform of claim 1, wherein generating the threat score further comprises analyzing whether an area code of a phone number included in the identity information matches an address associated with a valid user corresponding to the identity generation request (Leitner: Figure 2 and Paragraphs 31 and 33; looks at connectivity of phone numbers (would include area code) and addresses connection entities (Paragraph 30)).
Claim 24: Leitner, Gilbertson and Triantafillos disclose a computing platform of claim 1, wherein the memory stores additional computer readable instructions that, when executed by the at least one processor, cause the computing platform to: based on identifying the synthetic identity generation attempt, trigger a plurality of automated security actions, wherein the plurality of automated security actions includes: putting a temporary hold on a user account, blocking login attempts, and prompting for additional authentication mechanisms(Gilbertson: Paragraphs 20, 23 and 47; hard deny puts hold on account/block attempt).
Claims 10-11 is/are rejected under 35 U.S.C. 103 as being
unpatentable over Leitner et al. (“Leitner” 20180114017 A1), Gilbertson et al. (“Gilbertson” 20240370935 A1) and Triantafillos et al. (“Triantafillos” 20210084063 A1) in further view of Edwards et al. (“Edwards” 10685347 B1).
Claim 10: Leitner, Gilbertson and Triantafillos disclose a computing platform of claim 9, but may not explicitly disclose wherein generating the threat score comprises prompting for additional identity information, and wherein generating the threat score is further based on the additional identity information.
Edwards is provided because it discloses a functionality where a score determines if additional information is required (Edwards: Column 13, Line 54-Column 14, Line 25).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device in the same way and provide functionality to request additional information in Leitner. One would have been motivated to provide the functionality to more effectively evaluate fraudulent activities .
Claim 11: Leitner, Gilbertson, Triantafillos and Edwards disclose a computing platform of claim 10, wherein the additional identity information includes an amount of time elapsed between prompting for the additional identity information and receiving the additional identity information (Edwards: Column 5, Lines 40-60 (time frame) Column 13, Line 54-Column 14, Line 25).
Claims 12-13 is/are rejected under 35 U.S.C. 103 as being
unpatentable over Leitner et al. (“Leitner” 20180114017 A1), Gilbertson et al. (“Gilbertson” 20240370935 A1) and Triantafillos et al. (“Triantafillos” 20210084063 A1) in further view of Inmaneni et al. (“Inmaneni” 20220180368 A1).
Claim 12: Leitner, Gilbertson and Triantafillos disclose a computing platform of claim 1, but may not explicitly disclose all features wherein generating the threat score includes identifying a data collision between the identity information and known identity information, wherein the known identity information corresponds to a user other than a valid user corresponding to the identity generation request, and wherein the known identity information comprises one or more of: internally stored information or third party information (Gilbertson: Paragraph 47; identify good actor (valid)).
Inmaneni is provided because it discloses a fraud risk detection functionality that further looks at collision data (Paragraph 34; recycled phone number (collision)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device in the same way and provide functionality to determine collision data for a valid/good actor in the modified Leitner. One would have been motivated to provide the functionality for enhanced analysis for more effective evaluation of fraudulent activities.
Claim 13: Leitner, Gilbertson, Triantafillos and Inmaneni disclose a computing platform of claim 12, wherein identifying the data collision comprises identifying that a phone number included in the identity information corresponds to the user other than the valid user (Inmaneni: Paragraph 34; recycled phone number (collision)).
Claims 15 and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Leitner et al. (“Leitner” 20180114017 A1), Gilbertson et al. (“Gilbertson” 20240370935 A1) and Triantafillos et al. (“Triantafillos” 20210084063 A1) in further view of Li et al. (“Li” 20100036672 A1).
Claim 15: Leitner, Gilbertson and Triantafillos disclose a computing platform of claim 1, but may not explicitly disclose each feature wherein the memory stores additional computer readable instructions that, when executed by the at least one processor, cause the computing platform to: update, using a dynamic feedback loop and based on the number of information clusters, the threat score, and the identity information, the synthetic identity detection model(Leitner: Paragraphs 24; update database 56).
Li is provided because it discloses a fraud risk detection functionality that further provides a fraud feedback loop (Li: Figure 1, Paragraphs 37 and 56; feedback loop, to update pattern).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device in the same way and provide feedback of detected fraud/synthetic generation in Leitner. One would have been motivated to provide the functionality for enhanced analysis for more effective evaluation of fraudulent activities .
Claim 23: Leitner, Gilbertson, Triantafillos and Li disclose a computing platform of claim 15, wherein the memory stores additional computer readable instructions that, when executed by the at least one processor, cause the computing platform to: maintain an accuracy threshold for the synthetic identity detection model; pause refinement of the synthetic identity detection model through the dynamic feedback loop based on identifying that an accuracy of the synthetic identity detection model is greater than the accuracy threshold; and resume refinement of the synthetic identity detection model through the dynamic feedback loop based on identifying that the accuracy of the synthetic identity detection model fails to be greater than the accuracy threshold (Gilbertson: Paragraphs 39 (accuracy measure) 124 and 137-138; update model).
Claims 21 and 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Leitner et al. (“Leitner” 20180114017 A1), Gilbertson et al. (“Gilbertson” 20240370935 A1) and Triantafillos et al. (“Triantafillos” 20210084063 A1) in further view of Kikinis et al. (“Kikinis” 20230017855 A1).
Claim 21: Leitner, Gilbertson and Triantafillos disclose a computing platform of claim 1, however may not explicitly disclose wherein generating the threat score further comprises: prompting, via a user device, for additional identity information; analyzing one or more characteristics corresponding to input of the additional identity information, wherein the one or more characteristics comprise: pauses in speech of a user of the user device, speech patterns of the user, and a depth of a response provided by the user; and based on the one or more characteristics, distinguishing between a valid user and a fraudulent impersonator or bot.
Kikinis is provided because it discloses a fraud risk functionality that analyzes speech patterns and voice/tone (Paragraphs 176; speech/voice information).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device in the same way and provide speech analysis in Leitner. One would have been motivated to provide the functionality for enhanced analysis for more effective evaluation of fraudulent activities.
Claim 25: Leitner, Gilbertson and Triantafillos disclose a computing platform of claim 1, however may not explicitly disclose each feature wherein the anomaly detection threshold is based on a risk tolerance of a valid user corresponding to the identity information (Leitner: Paragraph 63; high connectivity represents higher risk), and wherein the risk tolerance informs one or more terms of a financial product comprising one or more of: an interest rate, a loan amount, or a line of credit amount.
Kikinis is provided because it discloses a fraud risk functionality that analyzes data to determine credit worthiness (Paragraphs 176; credit worthiness would include determination of interest rates).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to use a known technique to improve a similar device in the same way and provide credit worthiness analysis in Leitner. One would have been motivated to provide the functionality for enhanced analysis for more effective customer service options.
Response to Arguments
Applicants’ arguments have been considered but are moot in view of the new ground(s) of rejection. Triantafillos and Kikinis are now incorporated to address the amendments and new claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
11178179 B2 FOX ANSTRACT
Applicants’ 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).
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/SHERROD L KEATON/Primary Examiner, Art Unit 2148 8-17-2026
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