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 Amendment
The Amendment filed 8/7/2026 has been entered. Claims 1-3, 5-13, and 15-20 remain pending in the application. Applicant’s amendments to the claims have overcome the objection and rejections under 112(b) noted in the previous Office Action.
Response to Arguments
Applicant’s arguments filed with the Amendment, with respect to rejections under prior art have been fully considered and are moot upon a new ground(s) of rejection, as necessitated by amendment, as outlined below.
Prior Art
Listed herein below are the prior art references relied upon in this Office Action:
Mao et al. (US Patent Application Publication 2020/0097083), referred to as Mao herein [previously cited].
Valafar et al. (US Patent Application Publication 2018/0292910), referred to as Valafar herein [previously cited].
Wei et al. (US Patent Application Publication 2022/0265168), referred to as Wei herein [previously cited].
Chang et al. (US Patent Application Publication 2018/0360329), referred to as Chang herein [previously cited].
Zeng et al. (US Patent Application Publication 2024/0338085), referred to as Zeng herein [previously cited].
Liu et al. (US Patent Application Publication 2023/0333209), referred to as Liu herein [previously cited].
Lefebvre et al. (US Patent Application Publication 2014/0232642), referred to as Lefebvre herein.
Examiner’s Note
Strikethrough notation in the pending claims has been added by the Examiner.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1, 2, 5, 9, 11, 12, 15, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mao in view of Lefebvre.
Regarding claim 1, Mao discloses a method comprising: receiving, with at least one processor, sensor signals indicative of a hand gesture made by a user, the sensor data obtained from at least one sensor of a wearable device worn by the user (Mao, Abstract with Figs. 7A-7B with ¶0130 – computer processor analyzing signals from wearable sensors. ¶0112-¶0113 – hand gesture identification);
generating, with the at least one processor, a first embedding of first features extracted from the sensor signals; predicting, with the at least one processor, a first part of a hold gesture based on a first machine learning (ML) gesture classifier and the first embedding (Mao, ¶0114, ¶0116, ¶0142-¶0143, ¶0163 – feature extraction for gesture classification is performed based on training from prior sensor feature extraction. ¶0151-¶0152, ¶0163 - model and object prediction. ¶0189-¶0190 – example hold gesture is sending control instructions to a held object. ¶0113 – grasping a ball);
generating, with the at least one processor, a second embedding of second features extracted from the sensor signals (Mao, ¶0114, ¶0116 ¶0142-¶0143, ¶0163 – feature extraction for gesture classification is performed based on training from prior sensor feature extraction. Training is performed on multiple features. ¶0112-¶0113 – multiple gestures are recognized);
predicting, with the at least one processor, a second part of the hold gesture based on a second ML gesture classifier and the second embedding (Mao, ¶0189 – a release of the clenched fist results in disabling the command instruction state, completing the interaction. In this example, a user interface is presented in response to an object being held, the user interface can receive a variety of second gestures, and be dismissed by a second gesture. ¶0151-¶0152 – user body state prediction via inference model. ¶0135 – multiple machine learning algorithms used to train multiple inference models for processing sensor signals. ¶0139, ¶0142 – inference model implemented as a classifier);
determining, with the at least one processor, whether or not hold state logic is in a hold state (Mao, ¶0189 – detecting whether the user’s fist is in a clenched state to determine whether to activate or complete listening mode);
in accordance with the hold state logic not being in a hold state and the first ML gesture classifier predicting hand closing, generating a hold gesture
in accordance with the hold state logic being in a hold state and the second ML gesture classifier predicting a hand opening gesture, generating a hold gesture
predicting, with the at least one processor, a hold gesture based at least in part on outputs of the first and second ML gesture classifiers
However, Mao appears not to expressly disclose the limitations in strikethrough above. However, in the same field of endeavor, Lefebvre discloses motion gesture recognition (Lefebvre, Abstract), including
exceeding a threshold within a gesture window defined by the gesture start time and the gesture end time (Lefebvre, Abstract – gesture indicator threshold is used to determine the gesture between the start and end time. See also ¶0033-¶0037).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the gesture detection of Mao to include a threshold gesture indicator during the gesture window based on the teachings of Lefebvre. The motivation for doing so would have been to improve accuracy in recognizing the gesture (Lefebvre, ¶0004).
Regarding claim 2, Mao as modified discloses the elements of claim 1 above and further discloses wherein the sensor signals include a bio signal and at least one motion signal (Mao, ¶0116-¶0117 – sensor signals represent muscular activation states, including movement states of the body).
Regarding claim 5, Mao as modified discloses the elements of claim 1 above, and further discloses wherein the first and second ML gesture classifiers are convolutional neural networks (Mao, ¶0136 – CNN).
Regarding claim 9, Mao as modified discloses the elements of claim 1 above, and further discloses wherein the first and second ML gesture classifiers have separate networks for generating the first and second embeddings, respectively (Mao, Fig. 1 with ¶0135-¶0136 – one or more inference models. Inference model is implemented as a neural network).
Regarding claim 11, Mao discloses a system comprising: at least one processor; memory storing instructions, that when executed by the at least one processor, cause the at least one process to perform operations comprising: receiving sensor signals indicative of a hand gesture made by a user, the sensor data obtained from at least one sensor of a wearable device worn by the user (Mao, Abstract with Figs. 7A-7B with ¶0130, ¶0203 – computer processor analyzing signals from wearable sensors. Computer memory. ¶0112-¶0113 – hand gesture identification);
generating a first embedding of first features extracted from the sensor signals; predicting a first part of a hold gesture based on a first machine learning (ML) gesture classifier and the first embedding (Mao, ¶0114, ¶0116 ¶0142-¶0143, ¶0163 – feature extraction for gesture classification is performed based on training from prior sensor feature extraction. ¶0151-¶0152, ¶0163 - model and object prediction. ¶0189-¶0190 – example hold gesture is sending control instructions to a held object. ¶0113 – grasping a ball);
generating a second embedding of second features extracted from the sensor signals (Mao, ¶0114, ¶0116 ¶0142-¶0143, ¶0163 – feature extraction for gesture classification is performed based on training from prior sensor feature extraction. Training is performed on multiple features. ¶0112-¶0113 – multiple gestures are recognized);
predicting, with the at least one processor, a second part of the hold gesture based on a second ML gesture classifier and the second embedding (Mao, ¶0189 – a release of the clenched fist results in disabling the command instruction state, completing the interaction. In this example, a user interface is presented in response to an object being held, the user interface can receive a variety of second gestures, and be dismissed by a second gesture. ¶0151-¶0152 – user body state prediction via inference model. ¶0135 – multiple machine learning algorithms used to train multiple inference models for processing sensor signals. ¶0139, ¶0142 – inference model implemented as a classifier);
determining, with the at least one processor, whether or not hold state logic is in a hold state (Mao, ¶0189 – detecting whether the user’s fist is in a clenched state to determine whether to activate or complete listening mode);
in accordance with the hold state logic not being in a hold state and the first ML gesture classifier predicting hand closing, generating a hold gesture clenched state to determine to activate listening mode. ¶0151-¶0152 – user body state prediction via inference model. ¶0075 – first and second time periods of gestures);
in accordance with the hold state logic being in a hold state and the second ML gesture classifier predicting a hand opening gesture, generating a hold gesture
predicting a hold gesture based at least in part on outputs of the first and second ML gesture classifiers
However, Mao appears not to expressly disclose the limitations in strikethrough above. However, in the same field of endeavor, Lefebvre discloses motion gesture recognition (Lefebvre, Abstract), including
exceeding a threshold within a gesture window defined by the gesture start time and the gesture end time (Lefebvre, Abstract – gesture indicator threshold is used to determine the gesture between the start and end time. See also ¶0033-¶0037).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the gesture detection of Mao to include a threshold gesture indicator during the gesture window based on the teachings of Lefebvre. The motivation for doing so would have been to improve accuracy in recognizing the gesture (Lefebvre, ¶0004).
Regarding claim 12, Mao as modified discloses the elements of claim 11 above and further discloses wherein the sensor signals include a bio signal and at least one motion signal (Mao, ¶0116-¶0117 – sensor signals represent muscular activation states, including movement states of the body).
Regarding claim 15, Mao as modified discloses the elements of claim 11 above, and further discloses wherein the first and second ML gesture classifiers are convolutional neural networks (Mao, ¶0136 – CNN).
Regarding claim 19, Mao as modified discloses the elements of claim 11 above, and further discloses wherein the first and second ML gesture classifiers have separate networks for generating the first and second embeddings, respectively (Mao, Fig. 1 with ¶0135-¶0136 – one or more inference models. Inference model is implemented as a neural network).
Claim(s) 3, 8, 13, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mao in view of Lefebvre in further view of Valafar.
Regarding claim 3, Mao as modified discloses the elements of claim 1 above. However, Mao appears not to expressly disclose wherein the first and second ML gesture classifiers are run concurrently in parallel.
However, in the same field of endeavor, Valafar discloses machine learned gesture recognition (Valafar, Abstract), including
wherein the first and second ML gesture classifiers are run concurrently in parallel (Valafar, Fig. 4 with ¶0019, ¶0049, ¶0053 – parallel machine learning classification instances).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the first and second ML of Mao as modified to include parallel processing based on the teachings of Valafar. The motivation for doing so would have been to perform multiple identification analyses (Valafar, ¶0019) while avoiding a corresponding increase in computation time delay.
Regarding claim 8, Mao as modified discloses the elements of claim 1 above, and further discloses wherein the first and second ML gesture classifiers
However, Mao appears not to expressly disclose the limitations in strikethrough above. However, in the same field of endeavor, Valafar discloses machine learned gesture recognition (Valafar, Abstract), including
wherein the first and second ML gesture classifiers share a common network for generating the first and second embeddings (Valafar, Fig. 4 with ¶0019, ¶0049, ¶0053 – parallel machine learning classification instances of the same model. ¶0072 – one or more machine learned models including neural networks).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the first and second ML of Mao as modified to include a common neural network based on the teachings of Valafar. The motivation for doing so would have been to further leverage the neural network against additional data (Valafar, ¶0053), improving gesture detection accuracy.
Regarding claim 13, Mao as modified discloses the elements of claim 11 above. However, Mao appears not to expressly disclose wherein the first and second ML gesture classifiers are run concurrently in parallel.
However, in the same field of endeavor, Valafar discloses machine learned gesture recognition (Valafar, Abstract), including
wherein the first and second ML gesture classifiers are run concurrently in parallel (Valafar, Fig. 4 with ¶0019, ¶0049, ¶0053 – parallel machine learning classification instances).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the first and second ML of Mao as modified to include parallel processing based on the teachings of Valafar. The motivation for doing so would have been to perform multiple identification analyses (Valafar, ¶0019) while avoiding a corresponding increase in computation time delay.
Regarding claim 18, Mao as modified discloses the elements of claim 11 above, and further discloses wherein the first and second ML gesture classifiers
However, Mao appears not to expressly disclose the limitations in strikethrough above. However, in the same field of endeavor, Valafar discloses machine learned gesture recognition (Valafar, Abstract), including
wherein the first and second ML gesture classifiers share a common network for generating the first and second embeddings (Valafar, Fig. 4 with ¶0019, ¶0049, ¶0053 – parallel machine learning classification instances of the same model. ¶0072 – one or more machine learned models including neural networks).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the first and second ML of Mao as modified to include a common neural network based on the teachings of Valafar. The motivation for doing so would have been to further leverage the neural network against additional data (Valafar, ¶0053), improving gesture detection accuracy.
Claim(s) 6 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mao in view of Lefebvre in further view of Chang.
Regarding claim 6, Mao as modified discloses the elements of claim 1 above, and further discloses wherein the sensor signals are each filtered through a number of
However, Mao appears not to expressly disclose the limitations in strikethrough above. However, in the same field of endeavor, Chang discloses gesture recognition through physiological sensor signals (Chang, Abstract with ¶0101-¶0102, ¶0135), including
wherein the sensor signals are each filtered through a number of band-pass filters having adjacent and non-overlapping frequency bands (Chang, Fig. 14 with ¶0039-¶0041, ¶0135, ¶0141 – respiration and heartbeat band pass filters are arranged with non-overlapping and adjacent bands).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified filtering of Mao as modified to include a number of adjacent band-pass filters based on the teachings of Chang. The motivation for doing so would have been to more effectively identify multiple signals of interest with characteristic frequencies within the sensor data.
Regarding claim 16, Mao as modified discloses the elements of claim 11 above, and further discloses wherein the sensor signals are each filtered through a number of
However, Mao appears not to expressly disclose the limitations in strikethrough above. However, in the same field of endeavor, Chang discloses gesture recognition through physiological sensor signals (Chang, Abstract with ¶0101-¶0102, ¶0135), including
wherein the sensor signals are each filtered through a number of band-pass filters having adjacent and non-overlapping frequency bands (Chang, Fig. 14 with ¶0039-¶0041, ¶0135, ¶0141 – respiration and heartbeat band pass filters are arranged with non-overlapping and adjacent bands).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified filtering of Mao as modified to include a number of adjacent band-pass filters based on the teachings of Chang. The motivation for doing so would have been to more effectively identify multiple signals of interest with characteristic frequencies within the sensor data.
Claim(s) 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mao in view of Lefebvre in further view of Wei.
Regarding claim 7, Mao as modified discloses the elements of claim 1 above. However, Mao appears not to expressly disclose wherein the first and second ML gesture classifiers are trained by dissecting the sensor signals into N second input buffers overlapping by a prediction frequency.
However, in the same field of endeavor, Wei discloses limb motion tracking (Wei, Abstract), including
wherein the first and second ML gesture classifiers are trained by dissecting the sensor signals into N second input buffers overlapping by a prediction frequency (Wei, ¶0056-¶0057 – recordings are segmented into overlapping segments of a given length. The segments are used to train the RNN model. See also Fig. 6 with ¶0033, ¶0062 – ML training is performed by segmenting IMU data 1 second segments, the motion within the segments is detected and input to a classifier for detection of motion).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have training of Mao as modified to include segmented data based on the teachings of Wei. The motivation for doing so would have been to reduce the time consuming and laborious process of training (Wei, ¶0056).
Regarding claim 17, Mao as modified discloses the elements of claim 11 above. However, Mao appears not to expressly disclose wherein the first and second ML gesture classifiers are trained by dissecting the sensor signals into N second input buffers overlapping by a prediction frequency.
However, in the same field of endeavor, Wei discloses limb motion tracking (Wei, Abstract), including
wherein the first and second ML gesture classifiers are trained by dissecting the sensor signals into N second input buffers overlapping by a prediction frequency (Wei, ¶0056-¶0057 – recordings are segmented into overlapping segments of a given length. The segments are used to train the RNN model. See also Fig. 6 with ¶0033, ¶0062 – ML training is performed by segmenting IMU data 1 second segments, the motion within the segments is detected and input to a classifier for detection of motion).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have training of Mao as modified to include segmented data based on the teachings of Wei. The motivation for doing so would have been to reduce the time consuming and laborious process of training (Wei, ¶0056).
Claim(s) 10 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mao in view of Lefebvre in further view of Zeng in further view of Liu.
Regarding claim 10, Mao as modified discloses the elements of claim 1 above, and further discloses wherein generating the first and second embeddings, further comprises: extracting, using at least one
performing
However, Mao appears not to expressly disclose the limitations in strikethrough above. However, in the same field of endeavor, Zeng discloses gesture recognition (Zeng, Abstract), including
concatenating the features into a data structure and performing multilayer convolution on contents of the data structure; and generating the first and second embeddings based on results of the multilayer convolution (Zeng, ¶0395– multi-layer convolution performed on feature matrices within an CNN for classification).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the first and second ML of Mao as modified to include a multi-layer convolution based on the teachings of Zeng. The motivation for doing so would have been to improve accuracy in recognizing the gesture (Zeng, ¶0018).
However, Mao as modified appears not to expressly disclose a self-attention network. However, in the same field of endeavor Liu discloses a neural network, including for processing sensor input for gesture recognition (Liu, Abstract)
extracting, using at least one self-attention network, features from the sensor signals (Liu, ¶0160-¶0163, ¶0279 – feature extraction via self-attention network).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the feature extraction of Mao to include a self-attention network based on the teachings of Liu. The motivation for doing so would have been to improve the accuracy of subsequent gesture recognition (Liu, ¶0160).
Regarding claim 20, Mao as modified discloses the elements of claim 11 above, and further discloses wherein generating the first and second embeddings, further comprises: extracting, using at least one
performing multilayer convolution on contents of the data structure; and generating the first and second embeddings based on results of the
However, Mao appears not to expressly disclose the limitations in strikethrough above. However, in the same field of endeavor, Zeng discloses gesture recognition (Zeng, Abstract), including
concatenating the features into a data structure and performing multilayer convolution on contents of the data structure; and generating the first and second embeddings based on results of the multilayer convolution (Zeng, ¶0395– multi-layer convolution performed on feature matrices within an CNN for classification).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the first and second ML of Mao as modified to include a multi-layer convolution based on the teachings of Zeng. The motivation for doing so would have been to improve accuracy in recognizing the gesture (Zeng, ¶0018).
However, Mao as modified appears not to expressly disclose a self-attention network. However, in the same field of endeavor Liu discloses a neural network, including for processing sensor input for gesture recognition (Liu, Abstract)
extracting, using at least one self-attention network, features from the sensor signals (Liu, ¶0160-¶0163, ¶0279 – feature extraction via self-attention network).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the feature extraction of Mao to include a self-attention network based on the teachings of Liu. The motivation for doing so would have been to improve the accuracy of subsequent gesture recognition (Liu, ¶0160).
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 DANIEL W PARCHER whose telephone number is (303)297-4281. The examiner can normally be reached Monday - Friday, 9:00am - 5:00pm, Mountain Time.
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/DANIEL W PARCHER/ Primary Examiner, Art Unit 2174
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