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 .
DETAILED ACTION
This action is responsive to amendment filed September 19, 2025.
Status of Claims
Applicant amended the claims in the filed amendment. Claims 1-20 remain pending.
Response to Arguments
Applicant’s arguments, filed 9/19/25, with respect to the previous rejections have been fully considered and are persuasive. Therefore, the rejections have been withdrawn. However, upon further consideration, a new grounds of rejection is made under 103 based on Kedem in view of ElDokany in view of Wright-Freeman.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kedem et al (US Publication 20180115899) in view of ElDokany et al (US Publication 20250113192) in further view of Wright-Freeman (US Publicaiton 20200364779).
In reference to claim 1, Kedem teaches a platform for enhanced behavioral analysis and classification with mobile data collection, processing, and analysis, comprising:
a plurality of mobile devices each comprising a processor, a memory, a network interface, a touch-sensitive screen, a movement sensor, and a plurality of programming instructions stored in the memory and operating on the processor (see at least ¶ 22, which teaches mobile devices with touch screen and sensors) which causes the mobile device to:
collect responses by a user to queries on a user interface displayed on the touch-sensitive screen of a mobile device; (see at least ¶s 19,34, which teaches collecting responses from forms and questionnaires)
during collection of the responses, collect behavioral data of the user associated with the responses, the behavioral data comprising: one or more mobile device activities comprising swiping, clicking, or dragging of an element of the user interface on the touch-sensitive by the user while the user is providing the responses; (see at least ¶ 25, which teaches collecting user activities during the responses comprising clicking behavior) and
one or more mobile device movements captured by the movement sensor occurring while the user is providing the responses; (see at least ¶ 26, which teaches collecting device movements during the responses) and
send the behavioral data to a cloud-based computer for processing; (see at least ¶s 27,98, which teaches sending the behavior data to a remote server/cloud) and
a cloud-based computer comprising a memory, a processor, and a second plurality of programming instructions stored in the memory and operating on the processor which causes the cloud-based computer to: receive the behavioral data from the plurality of mobile devices (see at least ¶ 28, which teaches receiving the behavior data):
process the behavioral data through a model: determine a level of confidence of the user in the responses; and determine a level of veracity of the user in the responses; (see at least ¶s 56-58, which teaches processing the behavior data to determine a confidence evaluation of the users behavior and determining the accuracy of the responses)
generate a model output; determine a level of cognitive dissonance of the user from the model output; and output the level of cognitive dissonance of the user (see at least ¶s 28,40,42,48, which teaches generating and outputting a level of internal contradiction of the users).
Kedem fails to explicitly teach process the behavioral data through a two-part model comprising: a machine learning conviction model trained to determine a level of confidence of the user in the responses; and a machine learning veracity model trained to determine a level of veracity of the user in the responses; and generate a model output. However, ElDokany teaches application behavior monitoring (see ElDokany, at least Abstract and ¶ 34), and discloses processing behavior data through two machine learning models that are trained to make a determination and generate an output (see ElDokany, at least ¶s 38,40,43,45). It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify Kedem based on the teachings of ElDokany for the purpose of making intelligent evaluations of behavior data that gives relevant information to administrators and managers.
Kedem fails to explicitly teach monitoring cognitive dissonance of the user. However, Wright-Freeman discloses user behavior monitoring, specifically cognitive dissonance (see Wright-Freeman, at least Abstract and ¶s 2-4,35). It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to modify Kedem based on the teachings of Wright-Freeman for the purpose of making intelligent evaluations of user cognitive state.
In reference to claim 2, this is taught by Kedem, see at least ¶ 58, which teaches generating a behavioral historic baseline associated with a plurality of user responses by other users.
In reference to claim 3, this is taught by Kedem, see at least ¶ 58, which teaches assessing the user behavior and cognitive state based on comparison of the behavioral data and historical data.
In reference to claim 4, this is taught by Kedem, see at least ¶ 22, which teaches accelerometer or gyroscope.
In reference to claim 5, this is taught by Kedem, see at least ¶ 22, which teaches mobile phones.
In reference to claim 6, this is taught by Kedem, see at least ¶s 24,45,48, which teaches behavioral data comprising a plurality of timing data.
In reference to claim 7, this is taught by Kedem, see at least ¶s 48,54, which teaches behavioral data comprising keyboard input data.
In reference to claim 8, this is taught by Kedem, see at least ¶s 32,44, which teaches behavioral data comprising changing answers.
In reference to claim 18, this is taught by Kedem, see at least ¶s 13,47, which teaches an interface for collecting behavioral response data for a game.
In reference to claim 20, this is taught by Kedem, see at least ¶s 32,44, which teaches arranging user interface elements for collecting behavioral data.
Claims 9-16,17,19 are slight variations of the rejected claims 1-8,18,20 above, and are therefore rejected based on the same rationale.
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.
For any subsequent response that contains new/amended claims, Applicant is required to cite its corresponding support in the specification. (See MPEP chapter 2163.03 section (I.) and chapter 2163.04 section (I.) and chapter 2163.06) Applicant may not introduce any new matter to the claims or to the specification.
In formulating a response/amendment, Applicant is encouraged to take into consideration the prior art made of record but not relied upon, as it is considered pertinent to applicant's disclosure. See attached Form 892.
Contact & Status
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAMY M OSMAN whose telephone number is (571)272-4008. The examiner can normally be reached Mon-Fri, 9AM-5PM.
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/Ramy M Osman/
Primary Examiner, Art Unit 2457
December 4, 2025