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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 05/14/2026 and 04/20/2026 have been considered and are in compliance with the provisions of 37 CFR 1.97.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Oleynik (US 2019/0291277; hereinafter Oleynik).
Regarding Claim 1:
Oleynik discloses a wearable data collection device comprising: a hand element configured to receive a hand of a user (Oleynik, Para. [0036], Oleynik discloses an instrumented glove);
a plurality of finger elements extending from the hand element (Oleynik, Fig. 8B, Oleynik discloses the instrumented glove includes a plurality of finger elements);
at least one piezoelectric microphone mounted on the wearable data collection device (Oleynik, Para. [0121], Oleynik discloses a multimodal sensing unit which includes a plurality of sensors mounted on the instrumented glove, with such motion collecting sensors including, but not limited to, touch sensors, microphones, haptic gloves, cameras, and other forms of user input),
wherein the at least one piezoelectric microphone is configured to: detect vibrations caused by contact between the wearable data collection device and an object (Oleynik, Para. [0479-0480], Oleynik discloses the instrumented glove with sensors detect movement of the operator and tools/objects touched by the gloves); and
convert the vibrations into electrical signals representing contact sound data (Oleynik, Para. [0453], [0630], [0637], Oleynik discloses converting and filtering the received sensory input data); and
a processing circuit operatively coupled to the at least one piezoelectric microphone configured to collect and transmit the contact sound data (Oleynik, Para. [1280], Oleynik discloses a processor coupled to the multi modal sensing system configured to collect and process the collected sensory data).
Regarding Claim 2:
Oleynik discloses the wearable data collection device of claim 1.
Oleynik further discloses wherein the at least one piezoelectric microphone comprises a first piezoelectric microphone mounted on a back surface of a first finger element of the plurality of finger elements (Oleynik, Para. [0479], Fig. 8B, Oleynik discloses mounting a plurality of sensors on the back of each finger element, and the joints of the finger elements, with the sensors including, but not limited to, touch sensors, microphones, haptic gloves, cameras, and other forms of user input, see at least Para. [0121]).
Regarding Claim 3:
Oleynik discloses the wearable data collection device of claim 2.
Oleynik further discloses wherein the at least one piezoelectric microphone further comprises a second piezoelectric microphone mounted on a second finger element of the plurality of finger elements (Oleynik, Para. [0479], Fig. 8B, Oleynik discloses mounting a plurality of sensors on the back of each finger element, and the joints of the finger elements, with the sensors including, but not limited to, touch sensors, microphones, haptic gloves, cameras, and other forms of user input, see at least Para. [0121]).
Regarding Claim 4:
Oleynik discloses the wearable data collection device of claim 3.
Oleynik further discloses wherein the first finger element is an index finger element and the second finger element is a thumb element (Oleynik, Para. [0479], Fig. 8B, Oleynik discloses mounting a plurality of sensors on the back of each finger element, and the joints of the finger elements, with the sensors including, but not limited to, touch sensors, microphones, haptic gloves, cameras, and other forms of user input, see at least Para. [0121]).
Regarding Claim 5:
Oleynik discloses the wearable data collection device of claim 1.
Oleynik further discloses wherein the wearable data collection device comprises contact surfaces on the plurality of finger elements, and wherein the at least one piezoelectric microphone is positioned on a back surface of at least one finger element opposite from a contact surface of the at least one finger element (Oleynik, Para. [0479], Fig. 8B, Oleynik discloses mounting a plurality of sensors on the back of each finger element, and the joints of the finger elements, with the sensors including, but not limited to, touch sensors, microphones, haptic gloves, cameras, and other forms of user input, see at least Para. [0121]).
Regarding Claim 6:
Oleynik discloses the wearable data collection device of claim 1.
Oleynik further discloses wherein the processing circuit is further configured to transform the contact sound data into spectrograms for input to a neural network (Oleynik, Para. [0938], Oleynik discloses the transformed sensory input data is used to train a convolutional neural network).
Regarding Claim 7:
Oleynik discloses the wearable data collection device of claim 1.
Oleynik further discloses a plurality of sensors mounted on the wearable data collection device configured to capture sensor data during a recording session (Oleynik, Para. [0479], [0595], Fig. 8B, Oleynik discloses mounting a plurality of sensors on the back of each finger element, and the joints of the finger elements during a training session), wherein the plurality of sensors includes:
at least one pressure sensor positioned on each finger element of the plurality of finger elements (Oleynik, Para. [0514], Oleynik discloses a plurality of pressure sensors mounted on each finger element); and
at least one position sensor at each joint of a plurality of joints that couple the plurality of finger elements to the hand element (Oleynik, Para. [0479], [0595], Fig. 8B, Oleynik discloses mounting a plurality of sensors on the back of each finger element); and
wherein the contact sound data and the sensor data captured during the recording session are configured to be used to train a neural network that controls a robotic counterpart device having a joint and sensor configuration that matches the wearable data collection device (Oleynik, Para. [0595], Fig. 65A, Oleynik discloses the collected sensory data are configured to be used for training a neural network which operates a robotic counterpart, performing the trained operations).
Regarding Claim 8:
The claim recites analogous limitations to claim 1 above, and is therefore rejected on the same premise.
Regarding Claim 9:
The claim recites analogous limitations to claim 2 above, and is therefore rejected on the same premise.
Regarding Claim 10:
Oleynik discloses the wearable data collection device of claim 9.
Oleynik further discloses wherein the at least one piezoelectric microphone further comprises a second piezoelectric microphone mounted on a second finger element of the plurality of finger elements, and wherein detecting the vibrations comprises detecting vibrations at both the first piezoelectric microphone and the second piezoelectric microphone (Oleynik, Para. [0479], Fig. 8B, Oleynik discloses mounting a plurality of sensors on the back of each finger element, and the joints of the finger elements, with the sensors including, but not limited to, touch sensors, microphones, haptic gloves, cameras, and other forms of user input, see at least Para. [0121]).
Regarding Claim 11:
The claim recites analogous limitations to claim 4 above, and is therefore rejected on the same premise.
Regarding Claim 12:
The claim recites analogous limitations to claim 5 above, and is therefore rejected on the same premise.
Regarding Claim 13:
The claim recites analogous limitations to claim 6 above, and is therefore rejected on the same premise.
Regarding Claim 14:
The claim recites analogous limitations to claim 7 above, and is therefore rejected on the same premise.
Regarding Claim 15:
Oleynik discloses a method of training a robotic control model, the method comprising: receiving contact sound data captured during a recording session by at least one piezoelectric microphone mounted on a wearable data collection device (Oleynik, Para. [0479], [0595], Fig. 8B, Oleynik discloses mounting a plurality of sensors on the back of each finger element, and the joints of the finger elements during a training session), wherein:
the wearable data collection device comprises a hand element configured to receive a hand of a user and a plurality of finger elements extending from the hand element (Oleynik, Fig. 8B, Oleynik discloses the instrumented glove includes a plurality of finger elements); and
the at least one piezoelectric microphone is configured to detect vibrations caused by contact between the wearable data collection device and an object and convert the vibrations into electrical signals representing the contact sound data (Oleynik, Para. [0453], [0630], [0637], Oleynik discloses converting and filtering the received sensory input data);
processing the contact sound data to generate training data for a neural network (Oleynik, Para. [0938], Oleynik discloses the transformed sensory input data is used to train a convolutional neural network); and
training the neural network using the training data to generate a trained neural network model, wherein the trained neural network model is configured to control a robotic counterpart device having a sensor configuration that includes at least one piezoelectric microphone corresponding to the at least one piezoelectric microphone of the wearable data collection device (Oleynik, Para. [0595], Fig. 65A, Oleynik discloses the collected sensory data are configured to be used for training a neural network which operates a robotic counterpart, performing the trained operations).
Regarding Claim 16:
Oleynik discloses the method of claim 15.
Oleynik further discloses wherein controlling the robotic counterpart device with the trained neural network model comprises: receiving real-time contact sound data from at least one piezoelectric microphone on the robotic counterpart device (Oleynik, Para. [0479-0480], Oleynik discloses the instrumented glove with sensors detect movement of the operator and tools/objects touched by the gloves performed in real time, see at least Para. [0583]);
processing the real-time contact sound data using the trained neural network model to determine control signals (Oleynik, Para. [0595], Fig. 65A, Oleynik discloses the real time collected sensory data are configured to be used for training a neural network which operates a robotic counterpart, performing the trained operations); and
transmitting the control signals to the robotic counterpart device to control movement of the robotic counterpart device (Oleynik, Para. [0528], Oleynik discloses transmitting the control signals to the robotic counterpart, and the robot then performs the trained operations).
Regarding Claim 17:
Oleynik discloses the method of claim 15.
Oleynik further discloses receiving additional sensor data captured during the recording session by a plurality of sensors mounted on the wearable data collection device, wherein the plurality of sensors includes (Oleynik, Para. [0479-0480], Oleynik discloses the instrumented glove with sensors detect movement of the operator and tools/objects touched by the gloves performed in real time, see at least Para. [0583]):
at least one pressure sensor positioned on each finger element of the plurality of finger elements (Oleynik, Para. [0514], Oleynik discloses a plurality of pressure sensors mounted on each finger element); and
at least one position sensor at each of a plurality of joints that couple the plurality of finger elements to the hand element (Oleynik, Para. [0479], [0595], Fig. 8B, Oleynik discloses mounting a plurality of sensors on the back of each finger element); and
incorporating the additional sensor data with the contact sound data to generate the training data for the neural network (Oleynik, Para. [0595], Fig. 65A, Oleynik discloses the collected sensory data are configured to be used for training a neural network which operates a robotic counterpart, performing the trained operations).
Regarding Claim 18:
Oleynik discloses the method of claim 15.
Oleynik further discloses the at least one piezoelectric microphone comprises a first piezoelectric microphone mounted on a back surface of a first finger element of the plurality of finger elements and a second piezoelectric microphone mounted on a second finger element of the plurality of finger elements (Oleynik, Para. [0479], Fig. 8B, Oleynik discloses mounting a plurality of sensors on the back of each finger element, and the joints of the finger elements, with the sensors including, but not limited to, touch sensors, microphones, haptic gloves, cameras, and other forms of user input, see at least Para. [0121]); and
the contact sound data comprises data collected from both the first piezoelectric microphone and the second piezoelectric microphone (Oleynik, Para. [0479], Oleynik discloses the sensor elements collect vibration data).
Regarding Claim 19:
Oleynik discloses the method of claim 15.
Oleynik further discloses wherein processing the contact sound data comprises performing a Fourier transform on the contact sound data to generate spectrograms that are provided as input to the neural network (Oleynik, Para. [0938], Oleynik discloses the transformed sensory input data is used to train a convolutional neural network).
Regarding Claim 20:
Oleynik discloses the method of claim 15.
Oleynik further discloses receiving additional contact sound data from multiple recording sessions from the wearable data collection device, wherein the multiple recording sessions comprise recordings of different tasks performed with the wearable data collection device (Oleynik, Para. [0528, Oleynik discloses additional sensory information from a plurality of training sessions, where each session comprises a different recipe being performed by the training operator);
analyzing the additional contact sound data to identify one or more patterns associated with surface textures of objects being manipulated (Oleynik, Para. [0528], Oleynik discloses analyzing the collected sensory information to determine which objects/tools, ingredients, and workspaces are being manipulated to complete the recipe); and
refining the trained neural network model based on the one or more patterns to improve object identification capabilities of the robotic counterpart device (Oleynik, Para. [0685], Oleynik discloses adapting and refining the neural network model based on the collected sensory information to improve and adapt the robotic counterpart operations when performing the recipe).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZACHARY JOSEPH WALLACE whose telephone number is (469)295-9087. The examiner can normally be reached 7:00 am - 5:00 pm, Monday - Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Wade Miles can be reached at (571) 270-7777. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Z.J.W./Examiner, Art Unit 3656
/WADE MILES/Supervisory Patent Examiner, Art Unit 3656
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