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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/11/2026 has been entered.
Response to Amendment
The Amendment filed 3/11/2026 has been entered. Claim 6 is cancelled. Claim 21 has been added. Claims 1-5, 7-21 remain pending in the application.
Response to Arguments
Applicant's arguments filed with the Amendment have been fully considered but they are not persuasive.
Applicant argues that:
Claim 11 is rejected within the Office Action under 35 U.S.C. §112 as being indefinite. The claim has been amended herein to overcome the rejection.
The examiner cannot concur with the Applicant. Claim 11 still recites “higher importance” and “low importance” which is unclear based on the context of the claim and specification.
Applicant argues that:
Applicant respectfully maintains the arguments presented in the amendment of November 12, 2025 that Berenzweig does not appear to disclose an optical sensor as in feature (a) or receiving data streams from at least one optical sensor as in feature (b) of claim 15.
The Examiner cannot concur with the Applicant. Berenzweig (e.g. ¶0157, ¶0162) recites that the camera and “imaging devices” are included among auxiliary sensors, which are used to determine gestures. Similarly, Kim at ¶0110 recites that the signals detected by the detection units may come from sensors including a PPG sensor. At ¶0112, The signals are then used to recognize the motion.
The remainder of applicant’s arguments 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.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 11 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 11 recites “the optical sensor value has a higher importance at the beginning and end of a gesture time window and a lower importance otherwise”. It is unclear what is meant by “higher importance”, which would appear to be a matter of opinion and upon with reasonable persons of ordinary skill in the art can differ. Applicant’s Specification discusses importance at ¶0043, but only in so far as to link it to an optical sensor “value”. It is not clear from this statement what is implied by importance, or how a value aligns with the importance. Further, it is unclear what the claimed importance is measured relative to. The claim recites “higher” and “lower” but it is not clear whether it is higher than some other level of importance or if the claim is intended to recite that the higher importance is simply higher than the lower importance. These ambiguities render the scope of the claim indefinite.
Prior Art
Listed herein below are the prior art references relied upon in this Office Action:
Berenzweig et al. (US Patent Application Publication 2020/0097082), referred to as Berenzweig herein [previously cited].
Kim et al. (US Patent Application Publication 2014/0368474), referred to as Kim herein [previously cited].
Forutanpour et al. (US Patent Application Publication 2015/0091790), referred to as Forutanpour herein [previously cited].
Yokokawa (US Patent Application Publication 2019/0391662), referred to as Yokokawa herein [previously cited].
Xiong (US Patent Application Publication 2014/0198031), referred to as Xiong herein [previously cited].
Ding et al. (US Patent Application Publication 2015/0205521), referred to as Ding 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, 3-5, 7-8, 10, 12-14, 18, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Berenzweig in view of Kim in further view of Ding.
Regarding claim 1, Berenzweig discloses a wrist-wearable apparatus at least to (Berenzweig, Fig. 1 with ¶0128 – system includes sensors and processor. ¶0122 – wrist-wearable IMU, EMG device. ¶0188-¶0189 – processor executing instructions stored in hardware memory):
receive data streams from at least one optical sensor and at least one IMU, wherein the sensors are configured to measure a user (Berenzweig, ¶0026-¶0027, ¶0157, ¶0162 – processing output from IMU sensors and camera),
provide the received data streams to a gesture classifier, and determine, using the gesture classifier, gesture state by detecting and classifying gesture transitions based on transients within the sensor data of the received data streams, the gesture classifier comprising a neural network classifier trained to detect said gesture transitions (Berenzweig, ¶0140-¶0144 – providing sensor signals to a trained neural network classifier to identify handstate, movements, and gestures. ¶0119, ¶0135 – accumulating estimations of individual motor unit firing or combinations of motor units to determine a pattern comprising a gesture. Time-varying movement signals)
and
However, Berenzweig appears not to expressly disclose the limitations in strikethrough above. However, in the same field of endeavor, Kim discloses a wrist-wearable motion recognition device for detecting input movements (Kim, Abstract with Fig. 1 with ¶0057),
wrist-wearable apparatus comprising a processing core (Kim, Fig. 12 with ¶0061, ¶0109-¶0112, ¶0118 – wrist-wearable motion recognizing device includes processor. ¶0123 – processor executing instructions stored in hardware memory).
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 wrist-wearable device of Berenzweig to an optical sensor based on the teachings of Kim. The motivation for doing so would have been to improve gesture and error detection and classification accuracy through additional sensor combinations, and to reduce consumption of computing resources at other devices, thereby reducing processing requirements at those devices.
However, Berenzweig appears not to expressly disclose and wherein an adaptive time window is used as part of the detecting, wherein a typical gesture duration is determined based on training data, where the adaptive time window is based on said typical gesture duration. However, in the same field of endeavor Ding discloses identification of gestures, including non-contact gestures (Ding, Abstract), including
and wherein an adaptive time window is used as part of the detecting, wherein a typical gesture duration is determined based on training data, where the adaptive time window is based on said typical gesture duration (Ding, ¶0034, ¶0062, ¶0065, ¶0080, ¶0083 – gesture duration is learned from training data obtained from user input. A gesture habit (typical) is calculated from the observed gesture inputs and used to calculate the period of time).
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 Berenzweig to include adapting based on learning from typical user gesture times based on the teachings of Ding. The motivation for doing so would have been to enable active adaptation to the operation habit of the user, improving gesture recognition accuracy and computational effort.
Regarding claim 3, Berenzweig as modified discloses the elements of claim 1 above, and further discloses wherein the apparatus is further configured to infer pinch state from the received data streams based on the determined gesture state (Berenzweig, ¶0111, ¶0118, ¶0156-¶0157 – pinch gesture).
Regarding claim 4, Berenzweig as modified discloses the elements of claim 1 above, and further wherein the apparatus is further configured so that the interaction and/or state interpreter is configured to provide the determined gesture state, to an extended reality, XR, application (Berenzweig, ¶0032-¶0033, ¶0111-¶0114 – gestures provided to XR system).
Regarding claim 5, Berenzweig as modified discloses the elements of claim 1 above, and further discloses wherein the apparatus is further configured to receive, from a XR application, at least one of: an intent estimate or an affordance profile (Berenzweig, ¶0111-¶0112, ¶0187 – two-handed, one-handed, writing, typing, drawing modes. In this case, the modes are both an intent and an affordance profile for the interface).
Regarding claim 7, Berenzweig as modified discloses the elements of claim 1 above, and further discloses wherein the apparatus is further configured to receive, from the XR application, contextual information and wherein the apparatus is configured to use the received contextual information to adjust statefulness detection (Berenzweig, ¶0140-¶0144 – providing sensor signals to identify handstate, movements, and gestures. ¶0166-¶0167 – input mode detection based on user patterns. ¶0171-¶0172, ¶0174-¶0176 – mode is used to determine recognized gestures).
Regarding claim 8, Berenzweig as modified discloses the elements of claim 1 above, and further discloses wherein the apparatus is further configured to receive gaze tracking information, wherein the apparatus is configured to use the received gaze tracking information to adjust statefulness detection (Berenzweig, ¶0121 – sensors include eye trackers. ¶0167-¶0168 – sensor signals are used to trigger mode switching).
Regarding claim 10, Berenzweig as modified discloses the elements of claim 1 above, and further discloses wherein the apparatus is further configured to use a recurrent model to capture gesture history and improve statefulness detection (Berenzweig, ¶0134-¶0135 – recurrent neural network trained on training data. ¶0128, ¶0140-¶0142 – training/retraining is performed on received gesture signals).
Regarding claim 12, Berenzweig as modified discloses the elements of claim 1 above, and further discloses wherein the apparatus is further configured to transform discrete temporal events, such as taps and releases, into a state, in order to detect transitions, (Berenzweig, ¶0140-¶0144 – providing sensor signals to a trained neural network classifier to identify handstate, movements, and gestures. ¶0119, ¶0135 – accumulating estimations of individual motor unit firing or combinations of motor units to determine a pattern comprising a gesture. Time-varying movement signals. ¶0111, ¶0118, ¶0156 – pinch and tap gestures between various and/or combinations of fingers. ¶0113 – ceasing tapping gesture. ¶0122, ¶0152 – open hand configuration).
Regarding claim 13, Berenzweig as modified discloses the elements of claim 1 above, and further discloses wherein the apparatus comprises the IMU and the optical sensor (Berenzweig, ¶0122 – wrist-wearable IMU, EMG device. Kim, Fig. 12 with ¶0109-¶0110 – photoplethysmography (PPG) sensor. Applicant’s Specification at ¶0026-¶0027 describes a PPG sensor as an example of the optical sensor).
Regarding claim 14, Berenzweig as modified discloses the elements of claim 1 above, and further discloses wherein the apparatus is further configured to perform preprocessing of the data streams, the gesture classification and the state interpretation (Berenzweig, ¶0119, ¶0135 – accumulating estimations of individual motor unit firing or combinations of motor units to determine a pattern comprising a gesture. Time-varying movement signals. Kim, Fig. 12 with ¶0061, ¶0109-¶0112 – wrist-wearable motion recognizing device includes processor).
Regarding claim 18, Berenzweig discloses the elements of claim 15 above, and further discloses wherein the method further comprises performing at least one of: preprocessing of the data stream; gesture classifying; transition detection; by an apparatus comprising: a processing core, at least one memory including computer program code and the at least one optical sensor
However, Berenzweig appears not to expressly disclose the limitations in strikethrough above. However, in the same field of endeavor, Kim discloses a wrist-wearable motion recognition device for detecting input movements (Kim, Abstract with Fig. 1 with ¶0057),
herein the method further comprises performing at least one of: preprocessing of the data stream; gesture classifying; transition detection; by an apparatus comprising: a processing core, at least one memory including computer program code and the at least one optical sensor and the at least one IMU (Kim, Fig. 12 with ¶0061-¶0062, ¶0095, ¶0109-¶0112, ¶0123 – wrist-wearable motion recognizing device includes processor, sensors, and memory storing instructions).
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 wrist-wearable device of Berenzweig to include the processor and memory based on the teachings of Kim. The motivation for doing so would have been to improve gesture and error detection and classification accuracy through additional sensor combinations, and to reduce consumption of computing resources at other devices, thereby reducing processing requirements at those devices.
Regarding claim 21, Berenzweig discloses the elements of claim 1 above, and further discloses wherein the optical sensor is arranged to face the user's hand (Berenzweig, Fig. 9C with ¶0146 – camera arranged such that it captures the views experienced by the user. The user can view their hand. ¶0162 – camera can capture images of a stylus, pen, fingertips, etc. ¶0172 – camera may be part of a headset).
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Berenzweig in view of Kim in further view of Ding in further view of Forutanpour.
Regarding claim 2, Berenzweig as modified discloses the elements of claim 1 above, and further discloses wherein the apparatus is further configured to provide the determined gesture state to an interaction and/or state interpreter configured to apply corrections
However, Berenzweig as modified appears not to expressly disclose the limitations in strikethrough above. However, in the same field of endeavor Forutanpour discloses multi-sensor gesture recognition (Forutanpour, Abstract), including
apply corrections based on application state and/or context (Forutanpour, ¶0037 – classifier retraining for improved gesture detection based on secondary gesture classification. ¶0038 – rejection of sensor signals based on user state/context of walking. ¶0027-¶0028, ¶0032 – incorporation/deactivation of secondary sensor signals (corrected sensor input) in reliable/unreliable contexts. Fig. 4 with ¶0054 – gesture error detection (gesture detection correction to an error state) and reporting in circumstances where sensors disagree and the environment provides for a reliable second sensor. ¶0022 – confidence table is corrected based on detected errors).
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 wrist-worn apparatus of Berenzweig as modified to include corrections based on secondary sensors in certain states/contexts based on the teachings of Forutanpour. The motivation for doing so would have been to improve gesture and error detection and classification accuracy in certain environments (Forutanpour, ¶0005).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Berenzweig in view of Kim in further view of Ding in further view of Yokokawa.
Regarding claim 9, Berenzweig as modified discloses the elements of claim 1 above, and further discloses wherein the apparatus is further configured to apply
However, Berenzweig appears not to expressly disclose the limitations in strikethrough above. However, in the same field of endeavor, Yokokawa disclose gaze detection of hand gestures (Yokokawa, Abstract), including
wherein the apparatus is further configured to apply dead reckoning corrections and context cues to improve statefulness detection (Yokokawa, ¶0053 – tracking hand motion via dead reckoning position algorithm).
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 motion tracking of Berenzweig as modified to include dead-reckoning based on the teachings of Yokokawa. The motivation for doing so would have been to more effectively incorporate additional sensors (Yokokawa, ¶0053), to avoid false detection of inputs (Yokokawa, ¶0002), for improved gesture detection, especially in circumstances where optical gesture detection is compromised such as when the gesture is obscured.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Berenzweig in view of Kim in further view of Ding in further view of Xiong.
Regarding claim 11, Berenzweig as modified discloses the elements of claim 1 above, and further discloses
However, Berenzweig appears not to expressly disclose wherein the apparatus is further configured so that the optical sensor value has a higher importance at the beginning and end of a gesture time window and a lower importance otherwise.
However, in the same field of endeavor, Xiong discloses image-based gesture detection (Xiong, Abstract, ¶0001-¶0004, ¶0054), including
wherein the apparatus is further configured so that the optical sensor value has a higher importance at the beginning and end of a gesture time window and a lower importance otherwise (Xiong, ¶0099, ¶0104-¶0106 with Table 1 – detection of the start and end frames for the gestures are characterized as necessary (high importance) for identification of the gesture. Weights for the features of the start image frame are higher than other frames for first frame identification. End frame identification is performed in the same manner. ¶0104 – the degree of importance of the feature difference is expressed by the weight).
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 motion tracking of Berenzweig as modified to include prioritizing identification of the beginning and ending of the gesture based on the teachings of Xiong. The motivation for doing so would have been to more accurately identify the gesture, improving human-machine interaction (Xiong, ¶0013-¶0014).
Claim(s) 15, 17, 19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Berenzweig in view of Ding.
Regarding claim 15, Berenzweig discloses a method for identifying a selection gesture from obtained data, the method comprising (Berenzweig, ¶0140-¶0144 – providing sensor signals to a trained neural network classifier to identify handstate, movements, and gestures. ¶0152 – gestures mapped to interactions and commands):
receiving data streams from at least one optical sensor and at least one IMU, wherein the sensors are configured to measure a user (Berenzweig, Fig. 1 with ¶0003, ¶0128, ¶0133 – system includes sensors (including a camera) and processor. ¶0122 – wrist-wearable IMU, EMG device. ¶0188-¶0189 – processor executing instructions stored in hardware memory),
providing the received data streams to a gesture classifier, and determining, using the gesture classifier, gesture state by detecting and classifying gesture transitions based on transients within the sensor data of the received data streams, the gesture classifier comprising a neural network classifier trained to detect said gesture transitions (Berenzweig, ¶0140-¶0144 – providing sensor signals to a trained neural network classifier to identify handstate, movements, and gestures. ¶0119, ¶0135 – accumulating estimations of individual motor unit firing or combinations of motor units to determine a pattern comprising a gesture. Time-varying movement signals)
and
However, Berenzweig appears not to expressly disclose the limitations in strikethrough above. However, in the same field of endeavor Ding discloses identification of gestures, including non-contact gesetures (Ding, Abstract), including
and wherein an adaptive time window is used as part of the detecting, wherein a typical gesture duration is determined based on training data, where the adaptive time window is based on said typical gesture duration (Ding, ¶0034, ¶0062, ¶0065, ¶0080, ¶0083 – gesture duration is learned from training data obtained from user input. A gesture habit (typical) is calculated from the observed gesture inputs and used to calculate the period of time).
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 Berenzweig to include adapting based on learning from typical user gesture times based on the teachings of Ding. The motivation for doing so would have been to enable active adaptation to the operation habit of the user, improving gesture recognition accuracy and computational effort.
Regarding claim 17, Berenzweig discloses the elements of claim 15 above, and further discloses wherein the method further comprises detecting transitions between pinched and unpinched states (Berenzweig, ¶0119, ¶0135 – accumulating estimations of individual motor unit firing or combinations of motor units to determine a pattern comprising a gesture. Time-varying movement signals. ¶0111, ¶0118, ¶0156 – pinch gesture. ¶0152 – open hand configuration).
Regarding claim 19, Berenzweig discloses the elements of claim 15 above, and further discloses wherein the method further comprises receiving, from a XR application, contextual information, and using the received contextual information to adjust statefulness detection (Berenzweig, ¶0140-¶0144 – providing sensor signals to identify handstate, movements, and gestures. ¶0166-¶0167 – input mode detection based on user patterns. ¶0171-¶0172, ¶0174-¶0176 – mode is used to determine recognized gestures).
Regarding claim 20, Berenzweig discloses a non-transitory computer readable medium having stored thereon a set of computer readable instructions that, when executed by at least one processor, cause an apparatus to at least (Berenzweig, Fig. 1 with ¶0128 – system includes sensors and processor. ¶0122 – wrist-wearable IMU, EMG device. ¶0188-¶0189 – processor executing instructions stored in hardware memory):
receive data streams from at least one optical sensor and at least one IMU, wherein the sensors are configured to measure a user (Berenzweig, ¶0026-¶0027 – processing output from IMU sensors and camera),
provide the received data streams to a gesture classifier, and determine, using the gesture classifier, gesture state by detecting and classifying gesture transitions based on transients within the sensor data of the received data streams, the gesture classifier comprising a neural network classifier trained to detect said gesture transitions (Berenzweig, ¶0140-¶0144 – providing sensor signals to a trained neural network classifier to identify handstate, movements, and gestures. ¶0119, ¶0135 – accumulating estimations of individual motor unit firing or combinations of motor units to determine a pattern comprising a gesture. Time-varying movement signals)
and
However, Berenzweig appears not to expressly disclose the limitations in strikethrough above. However, in the same field of endeavor Ding discloses identification of gestures, including non-contact gesetures (Ding, Abstract), including
and wherein an adaptive time window is used as part of the detecting, wherein a typical gesture duration is determined based on training data, where the adaptive time window is based on said typical gesture duration (Ding, ¶0034, ¶0062, ¶0065, ¶0080, ¶0083 – gesture duration is learned from training data obtained from user input. A gesture habit (typical) is calculated from the observed gesture inputs and used to calculate the period of time).
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 Berenzweig to include adapting based on learning from typical user gesture times based on the teachings of Ding. The motivation for doing so would have been to enable active adaptation to the operation habit of the user, improving gesture recognition accuracy and computational effort.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Berenzweig in view of Ding in further view of Forutanpour.
Regarding claim 16, Berenzweig as modified discloses the elements of claim 15 above, and further wherein the determined gesture state is provided to an interaction and/or state interpreter configured to apply corrections
However, Berenzweig appears not to expressly disclose the limitations in strikethrough above. However, in the same field of endeavor Forutanpour discloses multi-sensor gesture recognition (Forutanpour, Abstract), including
apply corrections based on application state and/or context (Forutanpour, ¶0037 – classifier retraining for improved gesture detection based on secondary gesture classification. ¶0038 – rejection of sensor signals based on user state/context of walking. ¶0027-¶0028, ¶0032 – incorporation/deactivation of secondary sensor signals (corrected sensor input) in reliable/unreliable contexts. Fig. 4 with ¶0054 – gesture error detection (gesture detection correction to an error state) and reporting in circumstances where sensors disagree and the environment provides for a reliable second sensor. ¶0022 – confidence table is corrected based on detected errors).
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 wrist-worn apparatus of Berenzweig to include corrections based on secondary sensors in certain states/contexts based on the teachings of Forutanpour. The motivation for doing so would have been to improve gesture and error detection and classification accuracy in certain environments (Forutanpour, ¶0005).
Conclusion
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/DANIEL W PARCHER/Primary Examiner, Art Unit 2174
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