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 .
Response to Arguments
Applicant’s arguments, filed 6 February 2026, with respect to the rejections of claims 1-20 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further search and consideration, a new grounds of rejection is made, as outlined 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 (i.e., changing from AIA to pre-AIA ) 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 1-5, 10-15, and 20, are rejected under 35 U.S.C. 103 as being unpatentable over Baldwin et al., USPN 2019/0311098 in view of Gaubitch et al., USPN 2019/0238956.
With regard to claims 1, 3, 4, 11, 13, and 14, Baldwin discloses a computer-implemented method (claim1) including obtaining, by a computer, enrollment contact data for an enrollee including enrollment keypress data for an enrollment contact event (0006, 0030), generating, by the computer, a plurality of enrollment keypress features using the enrollment keypress data of the enrollment contact data (vector samples 0030), the plurality of enrollment keypress features including one or more temporal keypress features (0019), extracting, by the computer, an enrolled behaviorprint vector for the enrollee based upon the plurality of enrollment keypress features using a neural network architecture for embedding extraction having hyperparameters trained for extracting a behaviorprint vector embedding using a set of keypress features as input (0091), the plurality of enrollment keypress features including the one or more enrollment temporal keypress features (0088-0091, 0019, 0030), generating, by the computer, a plurality of inbound keypress features using inbound keypress data of an inbound contact data (0092), the plurality of inbound keypress features including one or more inbound temporal keypress features (0019), extracting, by a computer, an inbound behaviorprint vector for an inbound user based upon the plurality of inbound keypress features using the neural network architecture for embedding extraction (0094, 0092, 0086), the plurality of inbound keypress features including the one or more inbound temporal keypress features (0019), and authenticating, by the computer, the inbound user as the enrollee in accordance with an authentication score based upon a distance between the enrolled behaviorprint vector and the inbound behaviorprint vector (0094-0096, 0033, 0019). Baldwin does not disclose at least one of the one or more enrollment temporal keypress features is extracted from audio data of the enrollment contact data. Gaubitch discloses a method of authenticating a user using vectors from AI training (0071-0073, 0035, 0007), similar to that of Baldwin, and further discloses the enrollment temporal keypress features is extracted from audio data of the enrollment contact data (0071-0075), obtained as one or more dual-tone multi-frequency (DTMF) (0014, 0071, 0078), including the inbound keypress features for the inbound contact event via a set of one or more keypress responses corresponding to a set of one or more prompts of an interactive voice response program (0014, 0075). It would have been obvious for one of ordinary skill in the art, prior to the instant effective filing date, to use the voice prompt and DTMF response collection techniques of Gaubitch in the method of Baldwin for the motivation of improved authentication of telephone users, as taught by Gaubitch (0035, 0071).
With regard to claims 2 and 12, Baldwin in view of Gaubitch discloses the method of claim 1, as outlined above, and Baldwin further discloses a temporal keypress feature of the enrollment temporal keypress features or the inbound temporal keypress features includes at least one of a keypress duration or a keypress interval between successive keypress (0019).
With regard to claims 5 and 15, Baldwin in view of Gaubitch discloses the method of claim 1, as outlined above, and Baldwin further discloses the computer obtains the enrollment contact data for the enrollee including the enrollment keypress data for a plurality of enrollment contact events, and wherein the computer extracts the enrolled behaviorprint vector for the enrollee based upon the plurality of enrollment keypress features for the plurality of enrollment contact data events (0091, 0019, 0026).
With regard to claims 10 and 20, Baldwin in view of Gaubitch discloses the method of claim 1, as outlined above, and Baldwin further discloses training the computer on keypress data using machine learning (0004, 0091).
Claims 6, 7, 16, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Baldwin in view of Gaubitch in further view of Alharbi et al, “Demographic Group Prediction Based on Smart Device User Recognition Gestures”, as supplied by applicant along with IDS filed 22 March 2024.
With regard to claims 6, 7, 16, and 17, Baldwin in view of Gaubitch discloses the method of claim 1, as outlined above, but does not disclose generating, by the computer, a predicted age or gender for the inbound user based upon the inbound temporal keypress features, wherein the computer further authenticates the inbound user as the enrollee based upon comparing the predicted age or gender for the inbound user and an expected age of the enrollee. Alharbi discloses a method of identifying a user (page 3 column 2), similar to that of Baldwin in view of Gaubitch, and further discloses generating, by the computer, a predicted age or gender for the inbound user based upon the inbound temporal keypress features (page 3 column 3, page 4 columns 1-2), wherein the computer further authenticates the inbound user as the enrollee based upon comparing the predicted age or gender for the inbound user and an expected age of the enrollee (page 3 columns 1 and 2, page 7 columns 1 and 2). It would have been obvious for one of ordinary skill in the art, prior to the instant effective filing date, to use the age and gender classification of Alharbi in the method of Baldwin in view of Gaubitch for the motivation of improved authentication of users and better prevention of malicious and unwanted access attempts, as taught by Alharbi (page 3 columns).
Claims 8, 9, 18, and 19, are rejected under 35 U.S.C. 103 as being unpatentable over Baldwin in view of Gaubitch in further view of Grabowski et al., USPN 2021/0092228.
With regard to claims 8, 9, 18, and 19, Baldwin in view of Gaubitch discloses the method of claim 1, as outlined above, but does not disclose identifying whether a user is a robocall. Grabowski discloses a method of using AI to identify a user (180, 0206, 0197-0198, 0022, 0251), similar to that of Baldwin and Gaubitch, identifying whether a user is a robocall (0202-0216). It would have been obvious for one of ordinary skill in the art, prior to the instant effective filing date, to use robocall detection, as taught by Grabowski, in the method of Baldwin in view of Gaubitch for the motivation of improved authentication of telephone users and better prevention of malicious and unwanted telephone calls.
References Cited
Merchant et al., USPN 2023/0284016, discloses a method of training machine learning to authenticate a user (0039), including using acoustic factors used in a DTMF and IVR system (0044).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JACOB LIPMAN whose telephone number is (571)272-3837. The examiner can normally be reached 5:30AM-6:00PM.
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/JACOB LIPMAN/Primary Examiner, Art Unit 2434
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