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 11 March 2026, along with the amendments filed along with them, have been fully considered and are persuasive. Therefore, the prior rejection has been withdrawn. However, upon further search 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 1-6, 8, 9, 11, 12, 14, 15, 17, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Novik, et al., USPN 2020/0327422, in view of Lee et al., USPN 2017/0227995.
With regard to claims 1 and 6, Novik discloses a computer-implemented method for identifying a user of a mobile device (0007, 0021), the method being executable by one or more server communicatively coupled with the mobile device (0046), the method including receiving (i) user identification data of the user including: a user login and a user password associated with the user (0027), and (ii) a device identifier of the mobile device (0056), transmitting executable instructions to the mobile device, thereby causing the mobile device to execute (0046): retrieving a list of sensors available on the mobile device, selecting, from the list of sensors, at least one available sensor (0031-0036); iteratively polling the at least one available sensor for generating sensed data for a given occurrence of a synchronization event (0034-0036), the sensed data being indicative of user interactions of the user with the mobile device (0035-0036), analyzing the sensed data to generate a vector of behavioral parameters (0036), and transmitting the vector of behavioral parameters to the one or more server (0037, 0046), receiving the vector of behavioral parameters (0037), aggregating respective vectors of behavioral parameters associated with the user and generated by the at least one available sensor over other occurrences of the synchronization event into behavioral data (0036-0037, 0056); clustering a given behavioral parameter within the behavioral data into a respective cluster of a plurality of clusters based on a respective interaction mode of a predetermined plurality of interaction modes of the user with the mobile device at a respective occurrence of the synchronization event, responsive to which the given behavioral parameter was generated (0031-0046), training, based on behavioral parameters of the respective cluster, a given classifier to determine whether in-use user interactions with the mobile device are performed by the user or not (0037-0046), and storing, in a database, the given classifier in association with the user identification data of the user, the device identifier of the mobile device, and the respective interaction mode associated with the given classifier for further use in detecting a suspicious activity on the mobile device (0056, 0059, 0062). Novik does not disclose the generating the sensed data for the given occurrence of the synchronization event including, prior to the given occurrence of the synchronization event, recording, in a first stack within a memory of the mobile device, current readings from the at least one sensor, in response to the given occurrence of the synchronization event, ceasing to record the current readings from the at least one sensor in the first stack, thereby generating a first plurality of readings, and continuing to record, in a second stack within the memory of the mobile device, further current readings from the at least one sensor, and in response to receiving an indication the synchronization event having ended, ceasing to record the further current readings in the second stack, thereby generating a second plurality of readings, and continuing to record, in a third stack within the memory of the mobile device, yet further current readings from the at least one sensor, thereby generating a third plurality of readings, and combining the first, second, and third plurality of readings of the sensed data to generate a vector of behavioral parameters. Lee discloses a method of identifying a user of a mobile device using trained behavior data (0007-0009), similar to that of Novik, and further discloses generating the sensed data for the given occurrence of the synchronization event including, prior to the given occurrence of the synchronization event, recording, in a first stack within a memory of the mobile device, current readings from the at least one sensor, in response to the given occurrence of the synchronization event, ceasing to record the current readings from the at least one sensor in the first stack, thereby generating a first plurality of readings (before period 0035, 0096), and continuing to record, in a second stack within the memory of the mobile device, further current readings from the at least one sensor, and in response to receiving an indication the synchronization event having ended, ceasing to record the further current readings in the second stack, thereby generating a second plurality of readings (during period 0035, 0096), and continuing to record, in a third stack within the memory of the mobile device, yet further current readings from the at least one sensor, thereby generating a third plurality of readings (after period 0035, 0096), and combining the first, second, and third plurality of readings of the sensed data to generate a vector of behavioral parameters (0035, 0096). It would have been obvious for one of ordinary skill in the art, prior to the instant effective filing date, to use multiple period readings of Lee in the method of Novik, for the motivation of better identifying total user behavior data.
With regard to claim 2, Novik in view of Lee discloses the method of claim 1, and Novik further discloses the at least one available sensor of the mobile device is one of: an accelerometer, a gyroscope, and a gravity sensor (0036).
With regard to claim 3, Novik in view of Lee discloses the method of claim 1, and Novik further discloses the synchronization event includes the user tapping on a touchscreen of the mobile device (0032-0033).
With regard to claims 4 and 5, Novik in view of Lee discloses the method of claim 1, and Novik further discloses the synchronization event includes one of opening a new window in an application interface, in which the user has input the user login and the user password (0031-0033).
With regard to claim 8, Novik in view of Lee discloses the method of claim 1, and Novik further discloses the vector of behavioral parameters includes a minimum value (min) of sensor readings of the at least one available sensor in an entirety of the sensed data or a median (median) of the sensor readings of the at least one available sensor in the entirety of the sensed data (0036).
With regard to claim 9, Novik in view of Lee discloses the method of claim 1, and Novik further discloses the clustering the given behavioral parameter is further based on values of an arithmetic mean value (mean) of sensor readings of the at least one available sensor (0039-0040).
With regard to claims 11 ad 14, Novik in view of Lee discloses the method of claim 1, and Novik further discloses determining an orientation of a screen of the mobile device as being one of portrait and landscape (0030-0031, 0037).
With regard to claims 12, Novik in view of Lee discloses the method of claim 1, and Novik further discloses the clustering the given behavioral parameter is further based on the values of a median (median) of the sensor readings of the at least one available sensor (0036-0040).
With regard to claim 15, Novik in view of Lee discloses the method of claim 1, and Novik further discloses the predetermined plurality of interaction modes of the user with the mobile device includes a first interaction mode while the user is in a standing position, a second interaction mode while the user is in a lying position, and a third interaction mode while the suer is in a sitting position (0031-0034). Lee further discloses reading sensor data while a user is standing or sitting (0060). The examiner further takes official notice that it is well known in the art that a user of a mobile device can be standing sitting or lying down. It would have been obvious for one of ordinary skill in the art, prior to the instant effective filing date, to use multiple period readings of Lee in the method of Novik for situations where the user was in a different position before, during, and after a usage event, for the motivation of better identifying total user behavior data.
With regard to claim 17, Novik in view of Lee discloses the method of claim 1, and Novik further discloses using the given classifier for identifying the user of the mobile device by analyzing a current activity thereon, the using comprising: causing the mobile device to execute: iteratively polling the at least one available sensor for generating in-use sensed data for a given in-use occurrence of the synchronization event, the in-use sensed data being indicative of current user interactions with the mobile device (0055), analyzing the in-use sensed data to generate an in-use vector of behavioral parameters (0055, 0036-0037), and transmitting the in-use vector of behavioral parameters to the one or more server (0037, 0056), based on the in-use vector of behavioral parameters, determining, a current interaction mode of the predetermined plurality of interaction modes with the mobile device (0050-0052), searching the database to identify a respective classifier corresponding to the current interaction mode with the mobile device ;in response to failing to identify the respective classifier corresponding to the current interaction mode: determining the current activity on the mobile device as being suspicious, and causing execution of remedial actions against the suspicious activity (0052-0053), in response to identifying the respective classifier corresponding to the current interaction mode: applying the respective classifier to the in-use vector of behavioral parameters to generate a likelihood value representative of a likelihood of the current activity being suspicious; in response to the likelihood value being greater than a predetermined likelihood threshold: determining the current activity on the mobile device as being suspicious; and causing execution of the remedial actions against the suspicious activity (0052-0053).
Claims 10 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Novik in view of Lee, in further view of Progonov et al., USPN 2022/0350869.
With regard to claims 10 and 13, Novik in view of Lee discloses the method of claim 1, but does not disclose using a gravity sensor. Progonov discloses a method of using sensed behavior to identify a mobile system (0002, 0055-0059) similar to that of Novik and Lee, and further discloses the at least one available sensor is a gravity sensor of the mobile device (0022, 0080). It would have been obvious for one of ordinary skill in the art, prior to the instant effective filing date, to use a gravity sensor of Progonov in the method of Novik in view of Lee, in addition to the disclosed other sensors (0033) taught by Novik, for the motivation of better identifying behavior data.
References Cited
Whaley, USPN 2018/0181741, discloses reading sensors data from different periods before, during, and after an event (0041), and walking, standing, and sitting behavior (0059).
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.
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ali Shayanfar can be reached at 571-270-1050. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JACOB LIPMAN/Primary Examiner, Art Unit 2434
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