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 Office Action is in response to the Amendment filed 07/24/2026. In the instant Amendment, claims 1, 12 and 19 were amended; claims 1, 12 and 19 are independent claims. Claims 1-20 are pending in this application. THIS ACTION IS MADE FINAL.
Response to Arguments
Applicant's arguments with respect to claims 1, 12 and 19 with regard to the limitation “detecting a set of gestures performed by a specific user; determining, using a system, whether the set of gestures both: designates a selected action from, among a plurality of actions available in the computer system,” have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant’s arguments filed 07/24/2026 have been fully considered but they are not persuasive.
Applicant argues that (on pages: 8-9): that Vidmar fails to explicitly disclose determining that a gesture designates one action among a plurality of available system actions; conditioning execution of a specific system action on a unified determination of both action designation and user-specific biomechanics.
The Examiner respectfully disagrees with the applicant. Applicant’s arguments regarding Vidmar are not persuasive because they address Vidmar as though it must independently disclose the entirety of the amended conjunctive limitation. Vidmar is relied upon for its teachings concerning gesture recognition and biomechanical user authentication, not necessarily for every aspect of action selection.
Vidmar expressly teaches that the system (1) recognizes a gesture from gesture data, (2) identifies the wearer from the gesture data using a machine learning model trained to identify the wearer’s biomechanical characteristics, and (3) executes a security action based on both the recognized gesture and identification of the wearer (See Vidmar, [0025], [0032] and [0042]).
Thus contrary to applicant’s assertion that Vidmar performs authentication “independent of action selection.” Vidmar expressly links recognition of the performed gesture and biomechanical identification of the specific wearer to execution of the security action.
Applicant argues that (on pages: 8-9): Vidmar merely authenticates identity does not account for Vidmar’s combined used of gesture recognition and biomechanical wearer identification as predicates to execution of an action.
In response, the Examiner respectfully disagrees with the applicant. To the extent Vidmar does not expressly disclose that the recognized gesture selects one action from a plurality of available actions, that feature need not be supplied by Vidmar alone in the stated 35 U.S.C.103 combination, (See Vidmar, [0025], [0032] and [0042]).
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, 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Krishnamurthi et al (“Krishnamurthi,” US 20130159939) and further in view of Vidmar et al (“Vidmar,” US 20250094961).
Regarding claim 1, Krishnamurthi discloses a method of performing an authenticated action in a computer system, the method comprising: (Krishnamurthi discloses in [0004]-[0006], FIG 2 describes authenticated gesture recognition in computing devices and executing commands responsive to authenticated gestures)
detecting a set of gestures performed by a specific user; (Krishnamurthi, [0006] describes a gesture performed by a user may be detected; [0024] describes cameras/sensors captured hand, arm and whole-body movements)
determining, using a system, whether the set of gestures both: (Krishnamurthi, FIG 2.[0033], Table A describes determining by training a system whether the set of gestures both perform commands)
designates a selected action from, among a plurality of actions available in the computer system; and (Krishnamurthi, [0006] describes determining that the detected gesture corresponds to at least one command of a plurality of commands; [0033], Table A describes a gesture map associates gesture+user identifier J_Doe_123+particular command; examples are Left Swipe to next page, left swipe to back page, shake to undo; FIG 2, describes training steps 210-214: receive gesture input, receive selection of command to be mapped, map gesture to selected command; [0037] describes determining what action or command should be executed for the particular user; [0047]-[0048] describes at step 206 the command is determined based on the identity of the authenticated user and the detected gesture; at step 207, that determined command is executed)
Krishnamurthi fails to explicitly disclose determining, using a machine learning model system, whether the set of gestures both: (ii) is performed by the specific user for the selected action based on biomechanics of the specific user; and performing the selected action in the computer system only in response to determining that both (i) and (ii) are satisfied.
However, in an analogous art, Vidmar discloses determining, using a machine learning model system, whether the set of gestures both: (Vidmar, [0032] describes that the Machine Learning ML model 148 is trained to identify biomechanical characteristics of wearers based on input data (e.g. gesture data); [0033] describes teaching learning from motion/accelerometer/gyroscope/magnetometer gesture data; also see [0025], FIG 1, [0034]-[0035]).
(ii) is performed by the specific user for the selected action based on biomechanics of the specific user; and (Vidmar, [0025], describes an identifying module identifies the wearer using a machine learning model trained to identify biomechanical characteristics of wearers based on gesture data; [0032]-[0033] describes identifies specific movement patterns, pace, strength, flexibility, and idiosyncrasies in how gesture are performed; [0063] describes biomechanical characteristics include unique physical/mechanical traits related to how individuals move, including gait, posture/movement and gesture dynamics such as speed, strength, fluidity and sequence; [0042], the recognizing module recognizes the gesture from the gesture data; identifying module identifies the wearer based on that gesture data via the ML model; executing module executes a security action based on the recognized gesture and identifying the wearer; also see FIG 3, steps 310-340).
performing the selected action in the computer system only in response to determining that both (i) and (ii) are satisfied (Vidmar, [0025], [0042], FIG 3 describes an execution module executes a security action based on the gesture executed by the wearer and identifying the wearer; FIG 3 describes receive to recognize gesture to identify wearer to identify the wearer by biomechanical ML to execute security action)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Vidmar with the method/system of Krishnamurthi to include determining, using a machine learning model system, whether the set of gestures both: (ii) is performed by the specific user for the selected action based on biomechanics of the specific user; and performing the selected action in the computer system only in response to determining that both (i) and (ii) are satisfied. One would have been motivated to identify the user by providing a machine-learning model trained on biomechanical characteristics derived from gesture data to authenticate the user from the same gesture used to designate the command thereby providing a more integrated gesture based authentication technique with predictable results (Vidmar, [0032]-[0033]).
Regarding claim 12, claim 12 is a directed to a computer system. Claim 12 is similar in scope to claim 1 and is therefore rejected under the same rationale.
Regarding claim 19, claim 19 is a directed to a computer program product.
Claim 19 is similar in scope to claim 1 and is therefore rejected under the same rationale.
Claims 2, 6, 9-11, 13, 17-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable Krishnamurthi et al (“Krishnamurthi,” US 20130159939) in view of Vidmar et al (“Vidmar,” US 20250094961) and further in view of Lu et al ("Lu," US 20200250413).
Regarding claim 2, Krishnamurthi and Vidmar disclose the method of claim 1.
Krishnamurthi and Vidmar fail to explicitly disclose wherein the set of gestures is performed in air.
However, in an analogous art, Lu discloses wherein the set of gestures is performed in air, (Lu discloses in [0033] that the user write the ID string and pass-code in the air for in-air handwriting)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Lu with Krishnamurthi and Vidmar to include wherein the set of gestures is performed in air. One would have been motivated to provide a three-dimensional in-the-air finger motion based user login framework for gesture interface which tracks finger motion and hand geometry of the user (Lu, [0002]).
Regarding claim 6, Krishnamurthi and Vidmar disclose the method of claim 1.
Krishnamurthi and Vidmar fail to explicitly disclose further comprising: training the machine learning model system to recognize the set of gestures comprising an anatomy of the specific user and movements of a number of body parts by the specific user
However, in an analogous art, Lu discloses further comprising: training the machine learning model system to recognize the set of gestures comprising an anatomy of the specific user and movements of a number of body parts by the specific user, (Lu discloses [0087]-[0088] training/using a neural network to recognize gestures comprising hand geometry [0033] [anatomy] and [0038], [0035] captured motion signal/trajectory while writing [movements of a number of body parts by a specific user]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Lu with Krishnamurthi and Vidmar include further comprising: training the machine learning model system to recognize the set of gestures comprising an anatomy of the specific user and movements of a number of body parts by the specific user. One would have been motivated to provide a three-dimensional in-the-air finger motion based user login framework for gesture interface which tracks finger motion and hand geometry of the user (Lu, [0002]).
Regarding claim 9, Krishnamurthi and Vidmar disclose the method of claim 1.
Krishnamurthi and Vidmar fail to explicitly disclose wherein the set of gestures spells out at least one of a secret or a password,
However, in an analogous art, Lu discloses wherein the set of gestures spells out at least one of a secret or a password, (Lu discloses [0033] the in-air handwriting [gestures] spells out the passcode spells out a secret)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Lu with Krishnamurthi and Vidmar to include wherein the action is a lexical item spelled out by the specific user. One would have been motivated to provide a three-dimensional in-the-air finger motion based user login framework for gesture interface which tracks finger motion and hand geometry of the user (Lu, [0002]).
Regarding claim 10, Krishnamurthi and Vidmar disclose the method of claim 1.
Krishnamurthi and Vidmar fail to explicitly disclose wherein the set of gestures is a signature of the specific user
However, in an analogous art, Lu discloses wherein the set of gestures is a signature of the specific user, (Lu discloses [0033] the in-air handwriting [gestures] is a signature of the user)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Lu with Krishnamurthi and Vidmar include wherein the action is a lexical item spelled out by the specific user. One would have been motivated to provide a three-dimensional in-the-air finger motion based user login framework for gesture interface which tracks finger motion and hand geometry of the user (Lu, [0002]).
Regarding claim 11, Krishnamurthi and Vidmar disclose the method of claim 1.
Krishnamurthi and Vidmar fail to explicitly disclose wherein the machine learning model system comprises at least one of a neural network, a convolutional neural network, a recurrent neural network, a random forest, a support vector machine, a large language model, or a transformer model.
However, in an analogous art, Lu discloses wherein the machine learning model system comprises at least one of a neural network, (Lu, [0032], deep neural network)
a convolutional neural network, (Lu discloses [0035] a deep convolutional neural network)
a recurrent neural network,
a random forest,
a support vector machine, a large language model, or a transformer model.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Lu with Krishnamurthi and Vidmar to include wherein the machine learning model system comprises at least one of a neural network, a convolutional neural network, a recurrent neural network, a random forest, a support vector machine, a large language model, or a transformer model. One would have been motivated to provide a three-dimensional in-the-air finger motion based user login framework for gesture interface which tracks finger motion and hand geometry of the user (Lu, [0002]).
Regarding claim 13, claim 13 is a directed to the computer system of claim 12.
Claim 13 is similar in scope to claim 2 and is therefore rejected under the same rationale.
Regarding claim 17, claim 17 is a directed to the computer system of claim 12.
Claim 17 is similar in scope to claim 6 and is therefore rejected under the same rationale.
Regarding claim 18, Krishnamurthi and Vidmar disclose the computer system of claim 12.
Krishnamurthi and Vidmar fail to explicitly disclose wherein the set of gestures comprises letters and wherein the operations further comprise: training the machine learning model system to recognize handwriting of the specific user based on biomechanics of the specific user.
However, in an analogous art, Lu discloses wherein the set of gestures comprises letters and wherein the operations further comprise: training the machine learning model system to recognize handwriting of the specific user based on biomechanics of the specific user, (Lu discloses in [0032]-[0033], [0088], [0091] wherein the set of gestures comprises letters and wherein the operations further comprise: training the machine learning model system to recognize handwriting of the specific user based on biomechanics of the specific user)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Lu with Krishnamurthi and Vidmar to include wherein the set of gestures comprises letters and wherein the operations further comprise: training the machine learning model system to recognize handwriting of the specific user based on biomechanics of the specific user. One would have been motivated to provide a three-dimensional in-the-air finger motion based user login framework for gesture interface which tracks finger motion and hand geometry of the user (Lu, [0002]).
Regarding claim 20, claim 20 is a directed to the computer program product of
claim 19. Claim 20 is similar in scope to claim 2 and is therefore rejected under the same rationale.
Claims 3 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Krishnamurthi et al (“Krishnamurthi,” US 20130159939) in view of Vidmar et al (“Vidmar,” US 20250094961) and further in view of lmura et al ("lmura," "A Hand Gesture-Based Method for Biometric Authentication," 2018, Pages 554-566).
Regarding claim 3, Krishnamurthi and Vidmar disclose the method of claim 1.
Vidmar further discloses analyzing biomechanics of the specific user to identify and authenticate the user (Vidmar, [0032] describes identification data can include machine-learning model 148 and that the model is trained to identify biomechanical characteristics of wearers based on input data such as gesture data. It further says the model can be trained to recognize and distinguish unique attributes of individual users; [0033] describes what those biomechanical characteristics are specific patterns of movement, pace, strength, flexibility, and idiosyncrasies in how certain gestures are performed; [0034] states that the trained model maps gesture data to specific wearer characteristics; it expressly states this identification can be used for user authentication; [0042] ties the analysis into operation of the system: the identifying module identifies the wearer from gesture data via the machine learning model trained to identify biomechanical characteristics of wearers based on gesture data, and the system then excuses a security action based on the recognized gesture and identified wearer).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Vidmar with the method/system of Krishnamurthi to include analyzing biomechanics of the specific user to identify and authenticate the user. One would have been motivated to predictably add user-specific authentication to an existing gesture-command system without changing the underlying gesture to action functionality (Vidmar, [0032]-[0034], [0042]).
Krishnamurthi and Vidmar fail to explicitly disclose wherein determining comprises: analyzing biomechanics of the specific user derived from the four-dimensional data representing positions of one or more body parts of the specific user over time.
However, in an analogous art, lmura discloses wherein determining comprises: analyzing biomechanics of the specific user derived from the four-dimensional data representing positions of one or more body parts of the specific user over time, (lmura, Pages 550, Section 4.2 under Representation of 30 Hand Gestures describes a time-based sequence of 30 positions including x-position, y-position, z-position and time [four-dimensional data, which is x, y, z, and t]; Page 555, Abstract, Page 564, Conclusion and Future Work describes fingertips and finger joints are one or more body parts. The use of those positions as biometric data for authentication [analyzing biomechanics of the specific user)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of lmura with Krishnamurthi and Vidmar to include wherein determining comprises: analyzing biomechanics of the specific user derived from the four-dimensional data representing positions of one or more body parts of the specific user over time. One would have been motivated to provide a new biometric method based on 3D hand gestures (lmura, Page 564, Last Paragraph).
Regarding claim 14, claim 14 is a directed to the computer system of claim 12.
Claim 14 is similar in scope to claim 3 and is therefore rejected under the same rationale.
Claims 4 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Krishnamurthi et al (“Krishnamurthi,” US 20130159939), Vidmar et al (“Vidmar,” US 20250094961) in view of lmura et al ("lmura," "A Hand Gesture-Based Method for Biometric Authentication," 2018, Pages 554-566) and further in view of Deng et al ("Deng," "Enhanced In-Air Signature Verification via Hand Skeleton Tracking to Defeat Robot-Level Replays," 2023, Pages 451-462).
Regarding claim 4, Krishnamurthi, Vidmar and Imura disclose the method of claim 3.
Krishnamurthi and Vidmar fail to explicitly disclose wherein the biomechanics comprises at least one of an anatomy or movement of the specific user.
However, in an analogous art, Deng discloses wherein the biomechanics comprises at least one of an anatomy or movement of the specific user, (Deng, Page 452, Left Column, Second paragraph discloses hand skeleton/multi-joint motion and kinematic structure; Page 455, Left Column, Second Paragraph describes with inter-joint motion relationships)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Deng with Krishnamurthi, Vidmar and Imura to include wherein the biomechanics comprises at least one of an anatomy or movement of the specific user. One would have been motivated to provide 3D hand skeleton signature verification system to address behavioral biometric security under the threat of emerging motion-copy robots (Deng, Page 461, Right Column, Under Section 7, Conclusion)
Regarding claim 15, claim 15 is a directed to the computer system of claim 14.
Claim 15 is similar in scope to claim 4 and is therefore rejected under the same rationale.
Claims 5 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Krishnamurthi et al (“Krishnamurthi,” US 20130159939), Vidmar et al (“Vidmar,” US 20250094961) in view of lmura et al ("lmura," "A Hand Gesture-Based Method for Biometric Authentication," 2018, Pages 554-566) and further in view of Lu et al ("Lu," US 20200250413).
Regarding claim 5, Krishnamurthi, Vidmar and lmura disclose the method of claim 3.
Krishnamurthi, Vidmar and Imura fail to explicitly disclose further comprising: training the machine learning model system to recognize the set of gestures performed by the specific user based on the biomechanics of the specific user
However, in an analogous art, Lu discloses further comprising: training the machine learning model system to recognize the set of gestures performed by the
specific user based on the biomechanics of the specific user, (Lu discloses [0099],
[0035], training/using a deep CNN-based model of the in-air handwriting signal [0033] as part of the identification/authentication framework [0032]-[0033] and also discloses [0093] a registration template used for comparing captured signals [recognizing the set of gestures performed by the specific user based on user traits [biomechanics] of the specific user).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Lu with Krishnamurthi, Vidmar and Imura to include further comprising wherein the set of gestures is performed in air. One would have been motivated to provide a three-dimensional in-the-air finger motion based user login framework for gesture interface which tracks finger motion and hand geometry of the user (Lu, [0002]).
Regarding claim 16, claim 16 is a directed to the computer system of claim 14.
Claim 16 is similar in scope to claim 5 and is therefore rejected under the same rationale.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Krishnamurthi et al (“Krishnamurthi,” US 20130159939) in view of Vidmar et al (“Vidmar,” US 20250094961) and further in view of Bashir et al ("Bashir," "Person Authentication by Handwriting in Air Using Biometric Smart Pen Device," 2011, Pages 219-226).
Regarding claim 7, Krishnamurthi and Vidmar disclose the method of claim 1.
Krishnamurthi and Vidmar fail to explicitly disclose wherein the set of gestures comprise handwriting forming a lexical item corresponding to the authenticated action, and wherein determining includes analyzing biomechanics of the specific user associated with the handwriting.
However, in an analogous art, Bashir discloses wherein the set of gestures comprise handwriting forming a lexical item corresponding to the authenticated action, and wherein determining includes analyzing biomechanics of the specific user associated with the handwriting, (Bashir, Pages 220-222 describe handwriting in air which is a gesture set comprising handwriting; Pages 221 describes handwritten single characters and PIN [handwriting forming a lexical item]; Pages 221-222 describe handwritten PIN words are used for authentication/access control; Pages 222-225, describe measured acceleration, tilt, grip forces, pressure, time-series dynamics [biomechanical characteristics associated with handwriting])
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Bashir with Krishnamurthi and Vidmar to include wherein the set of gestures comprise handwriting forming a lexical item corresponding to the authenticated action, and wherein determining includes analyzing biomechanics of the specific user associated with the handwriting. One would have been motivated to provide biometric authentication using handwriting in air using a biometric smart pen device (Bashir, Pages 219, Abstract and Page 221, Section 3)
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Krishnamurthi et al (“Krishnamurthi,” US 20130159939), Vidmar et al (“Vidmar,” US 20250094961) in view of Bashir et al ("Bashir," "Person Authentication by Handwriting in Air Using Biometric Smart Pen Device," 2011, Pages 219-226) and further in view of Lu et al ("Lu," US 20200250413).
Regarding claim 8, Krishnamurthi, Vidmar and Bashir discloses the method of claim 7.
Krishnamurthi, Vidmar and Bashir fail to explicitly disclose wherein the action is a lexical item spelled out by the specific user
However, in an analogous art, Lu discloses wherein the action is a lexical item spelled out by the specific user, (Lu discloses [0008], [0038], [0033] the user writes an ID string [lexical item] and/or a meaningful phrase [0033] as part of a login [01051)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Lu with Krishnamurthi, Vidmar and Bashir to include wherein the action is a lexical item spelled out by the specific user. One would have been motivated to provide a three-dimensional in-the-air finger motion based user login framework for gesture interface which tracks finger motion and hand geometry of the user (Lu, [0002]).
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 JAMES J WILCOX whose telephone number is (571)270-3774. The examiner can normally be reached M-F: 8 A.M. to 5 P.M..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Luu T. Pham can be reached at (571)270-5002. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAMES J WILCOX/ Examiner, Art Unit 2439
/LUU T PHAM/ Supervisory Patent Examiner, Art Unit 2439
