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 .
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 7-09-2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-5, 8-13 and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hyde et al. (U.S. 2017/0164876) in view of Forsland et al. (U.S. 2021/0223864).
With regard to claim 1, Hyde teaches a system comprising:
an electrode array assembly ([0069] Cells 120 may also contain interaction devices such as electrodes; [0084] interaction device 780 may be an electrode for delivering an applied voltage to the attachment surface; [0251] the effector 1008 includes the electric stimulator 3902 having at least one electrode (e.g., electrode 3502)) comprising an electrode array ([0251] the effector 1008 includes the electric stimulator 3902 having at least one electrode (e.g., electrode 3502)) and an adhesive substrate ([abstract] a deformable substrate; [0064] substrate layer 105; [0137] The substrate 1002 is a deformable (e.g., flexible, stretchable) substrate configured to interface with a skin surface of a subject…The substrate 1002 can be positioned in proximity with the skin surface according to various mechanisms including, but not limited to, affixed to the skin via an adhesive material, and held in place by an external pressure), configured to attach to a forearm of a person ([0109] epidermal electronics devices 100 are placed on various body parts of user 680. For example, epidermal electronics devices may be placed on fingers, hands, forearms, upper arms, feet, legs, the head, etc.; [0110] the attachment surface parameters of a forearm may be measured relative to the attachment surface parameters of an upper arm. This allows epidermal electronics devices 100 and data acquisition and processing device 510 to determine the orientation or movement of the forearm relative to the upper arm), wherein the electrode array is formed by one or more flexible, conformable electrodes ([0191] the electrophysiological sensor 3300 can include a measurement electrode, a reference electrode capacitively coupled with the measurement electrode, and a ground electrode, whereby displacement currents induced in the electrodes provide data associated with ECG, EMG, EOC, etc.); and
a controller ([0220] includes a power controller) having:
a processor ([0105] processing device 510 may include one or more of processors); and
a memory having instructions stored thereon ([0083] control circuit 760 further includes memory 761), wherein execution of the instructions causes the processor to:
receive, by the processor, measured electromyographical (EMG) signals from the electrode array assembly ([0141] the physiological sensor 1012 includes an electromyograph (EMG) (FIG. 13 shows electromyograph 1408), such as sensor electrodes configured to monitor the electrophysiological activity of muscle tissue proximate to the body portion on which the system 1000; [0191] the electromyograph (EMG) 3310 (e.g., for measuring electrical activity of muscle)) at the forearm while the person is making one or more hand gestures (Fig. 6; Fig. 10; [0109] epidermal electronics devices 100 are placed on various body parts of user 680. For example, epidermal electronics devices may be placed on fingers, hands, forearms, upper arms, feet, legs, the head, etc.; [0110] the attachment surface parameters of a forearm may be measured relative to the attachment surface parameters of an upper arm. This allows epidermal electronics devices 100 and data acquisition and processing device 510 to determine the orientation or movement of the forearm relative to the upper arm);
determine, to a pre-defined hand gesture among a plurality of hand gestures (Fig. 6; Fig. 10; [0109] epidermal electronics devices 100 are placed on various body parts of user 680. For example, epidermal electronics devices may be placed on fingers, hands, forearms, upper arms, feet, legs, the head, etc.; [0113] epidermal electronics devices 100 placed on the fingers, hands, and arms may be used to detect gestures made using those body parts; [0139] the system 1000 can be positioned on a wrist of a subject and the motion sensor 1010 can include a proximity sensor configured to detect one or more of a presence, a position, an angle, and a movement of another body portion proximate the wrist, such as a hand, a palm, an arm, a finger, a shoulder, and so forth), using a plurality of EMG signals acquired at a set of forearms (Fig. 6; Fig. 10; [0109] epidermal electronics devices 100 are placed on various body parts of user 680. For example, epidermal electronics devices may be placed on fingers, hands, forearms, upper arms, feet, legs, the head, etc.; [0110] the attachment surface parameters of a forearm may be measured relative to the attachment surface parameters of an upper arm. This allows epidermal electronics devices 100 and data acquisition and processing device 510 to determine the orientation or movement of the forearm relative to the upper arm) and labels corresponding to hand gestures made by a set of people (Fig. 6; Fig. 10; [0109] epidermal electronics devices 100 are placed on various body parts of user 680. For example, epidermal electronics devices may be placed on fingers, hands, forearms, upper arms, feet, legs, the head, etc.…allow data to be collected from multiple networks (e.g., one network per user, with multiple users) by a single data acquisition and processing device 510; [0113] epidermal electronics devices 100 placed on the fingers, hands, and arms may be used to detect gestures made using those body parts; [0139] the system 1000 can be positioned on a wrist of a subject and the motion sensor 1010 can include a proximity sensor configured to detect one or more of a presence, a position, an angle, and a movement of another body portion proximate the wrist, such as a hand, a palm, an arm, a finger, a shoulder, and so forth); and
output the subsequently employed for controls or analysis ([0130] control circuit 760 outputs data using communications device 750 and communications connection 753 after the data has been processed; [0266] the one or more target values can provide a guideline for an expected level of effort to be output by the individual subject during execution of the motion regimen). However, Hyde does not specifically teach:
- determine, via a trained ML model, a classification value using the measured EMG signals, wherein the classification value has a correspondence
- wherein the trained ML model was trained
- output the classification value, wherein the classification value is subsequently employed for controls or analysis
Forsland teaches a system and method for having a headset including an augmented reality display, one or more sensors, a processing module, at least one biofeedback device, and a battery [abstract]. Forsland also teaches determining, via a trained ML model ([0115] Machine Learning (ML) training is applied to create an individualized Recognizer-Categorizer (RC). Derived outputs of the ML training are stored into an Expert system (ES) knowledgebase in the cloud), a classification value ([0204] The AI machine learning may act as one or more of an auto-tuning dynamic noise reducer, a feature extractor, and a recognizer-categorizer-classifier; [0217] The collector subsystem 2020 communicates with a recognizer 2024 for EEG data and a classifier 2026 for EMG) using the measured EMG signals ([0036] there may also be EMG sensors attached to an arm or other body part wired to the circuit board for processing data from multiple sources; [0217] The collector subsystem 2020 communicates with a recognizer 2024 for EEG data and a classifier 2026 for EMG), wherein the classification value has a correspondence ([0204] The AI machine learning may act as one or more of an auto-tuning dynamic noise reducer, a feature extractor, and a recognizer-categorizer-classifier. AI machine learning training may be applied when the fully self-contained BCI is connected to the network 1516 to create an individualized recognizer-categorizer-classifier). Wherein the trained ML model was trained ([0115] Machine Learning (ML) training is applied to create an individualized Recognizer-Categorizer (RC). Derived outputs of the ML training are stored into an Expert system (ES) knowledgebase in the cloud), output the classification value ([0204] The AI machine learning may act as one or more of an auto-tuning dynamic noise reducer, a feature extractor, and a recognizer-categorizer-classifier; [0217] The collector subsystem 2020 communicates with a recognizer 2024 for EEG data and a classifier 2026 for EMG), wherein the classification value is subsequently employed for controls or analysis ([0204] The AI machine learning may act as one or more of an auto-tuning dynamic noise reducer, a feature extractor, and a recognizer-categorizer-classifier. AI machine learning training may be applied when the fully self-contained BCI is connected to the network 1516 to create an individualized recognizer-categorizer-classifier). Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to have modified the system for monitoring an individual and facilitating a motion of an individual as taught by Hyde, with the machine learning (ML) training stored into an Expert system as taught by Forsland, to have achieved a system and method of having a computer interface and headset, which includes an augmented reality display, one or more sensors, a processing module, at least one biofeedback device, and a battery.
With regard to claim 2, the limitations are addressed above and Hyde teaches wherein the classification value is associated with (i) a hand gesture defined by a combination of finger and wrist positions and orientation or (ii) a hand gesture defined by one or more finger positions and configurations (Fig. 11; Fig. 15A; [0138] in FIG. 10, the system 1000 is positioned on a wrist 1100 of the subject for monitoring, preventing, and treating a medical condition associated with a repetitive stress injury, arthritis, or other medical condition associated with the wrist or other body portion in close proximity to the wrist, including, but not limited to, the hand, one or more fingers, and the arm; [0139] the system 1000 can be positioned on a wrist of a subject and the motion sensor 1010 can include a proximity sensor configured to detect one or more of a presence, a position, an angle, and a movement of another body portion proximate the wrist, such as a hand, a palm, an arm, a finger, a shoulder, and so forth).
With regard to claim 3, the limitations are addressed above and Hyde teaches wherein the classification value is employed for a control system, wherein the control system is configured to transmit real-time video stream to an augmented reality device ([0149] The reporter 1500 can provide information associated with risks for repetitive stress injuries for one or more individuals interfacing with the system 1000 based on measured movements, positions, and physiological conditions in order for the program 612 to make real-time personnel scheduling assignments, such as to make substantially instantaneous or real-time determinations of personnel assignments to minimize the risk of repetitive stress injuries on an individual basis, an organizational basis, and so forth; [0218] augmented or virtual reality (VR) systems (e.g., VR headsets, VR immersive experience systems, etc.); [0262] the external device (e.g., external device 3406, external object 3800, etc.) includes at least one of a virtual reality display device or an augmented reality display device. For example, the virtual reality display device or the augmented reality display device can display an image responsive to receiving one or more communication signals from the system 1000).
With regard to claim 4, the limitations are addressed above and Hyde teaches wherein the electrode array ([0069] Cells 120 may also contain interaction devices such as electrodes; [0084] interaction device 780 may be an electrode for delivering an applied voltage to the attachment surface; [0251] the effector 1008 includes the electric stimulator 3902 having at least one electrode (e.g., electrode 3502)) is embedded into the adhesive substrate ([abstract] a deformable substrate; [0064] substrate layer 105; [0137] The substrate 1002 is a deformable (e.g., flexible, stretchable) substrate configured to interface with a skin surface of a subject…The substrate 1002 can be positioned in proximity with the skin surface according to various mechanisms including, but not limited to, affixed to the skin via an adhesive material, and held in place by an external pressure).
With regard to claim 5, the limitations are addressed above and Hyde teaches wherein the controller is disposed on a surface of the adhesive substrate ([abstract] a deformable substrate; [0064] substrate layer 105; [0137] The substrate 1002 is a deformable (e.g., flexible, stretchable) substrate configured to interface with a skin surface of a subject…The substrate 1002 can be positioned in proximity with the skin surface according to various mechanisms including, but not limited to, affixed to the skin via an adhesive material, and held in place by an external pressure) of the electrode array assembly ([0069] Cells 120 may also contain interaction devices such as electrodes; [0084] interaction device 780 may be an electrode for delivering an applied voltage to the attachment surface; [0251] the effector 1008 includes the electric stimulator 3902 having at least one electrode (e.g., electrode 3502)).
With regard to claim 8, the limitations are addressed above and Hyde teaches wherein the classification value is employed as an actuatable control output to a control system ([0281] control systems including feedback loops and control motors (e.g., feedback for sensing lens position and/or velocity; control motors for moving/distorting lenses to give desired focuses). An image processing system can be implemented utilizing suitable commercially available components, such as those typically found in digital still systems and/or digital motion systems).
With regard to claim 9, the limitations are addressed above and Hyde teaches wherein the classification value is employed for an analysis system ([0245] provide analysis of the one or more sense signals and to provide control of the components of the system 1000 (e.g., control of the effector 1008). For example, the processor 1006 includes circuitry configured to identify a physiological state (e.g., a pain state, a motion state, etc.) of the individual subject based on analysis of the one or more sense signals).
With regard to claim 10, the limitations are addressed above and Hyde teaches wherein the classification value is employed as a prompt input for a computer operating system ([0225] a request for user input regarding an operation state of the effector 1008… the processor 1006 can direct the user interface 3600 to display a request for user input regarding whether the effector 1008 should activate…the processor 1006 can direct the user interface 3600 to display a request for user input regarding whether the effector 1008 should activate).
With regard to claim 11, the method claim corresponds to the system claim 1, respectively, and therefore is rejected with the same rationale.
With regard to claim 12, the limitations are addressed above and Hyde teaches wherein the electrode array assembly ([0069] Cells 120 may also contain interaction devices such as electrodes; [0084] interaction device 780 may be an electrode for delivering an applied voltage to the attachment surface; [0251] the effector 1008 includes the electric stimulator 3902 having at least one electrode (e.g., electrode 3502)) comprises an electrode array and an adhesive substrate ([abstract] a deformable substrate; [0064] substrate layer 105; [0137] The substrate 1002 is a deformable (e.g., flexible, stretchable) substrate configured to interface with a skin surface of a subject…The substrate 1002 can be positioned in proximity with the skin surface according to various mechanisms including, but not limited to, affixed to the skin via an adhesive material, and held in place by an external pressure), configured to attach to the forearm of the person, wherein the electrode array is formed by one or more flexible, conformable electrodes ([0191] the electrophysiological sensor 3300 can include a measurement electrode, a reference electrode capacitively coupled with the measurement electrode, and a ground electrode, whereby displacement currents induced in the electrodes provide data associated with ECG, EMG, EOC, etc.).
With regard to claim 13, the method claim corresponds to the system claim 4, respectively, and therefore is rejected with the same rationale.
With regard to claim 16, the method claim corresponds to the system claim 2, respectively, and therefore is rejected with the same rationale.
With regard to claim 17, the method claim corresponds to the system claim 3, respectively, and therefore is rejected with the same rationale.
With regard to claim 18, the method claim corresponds to the system claim 8, respectively, and therefore is rejected with the same rationale.
With regard to claim 19, the method claim corresponds to the system claim 9, respectively, and therefore is rejected with the same rationale.
With regard to claim 20, the medium claim corresponds to the system claim 1, respectively, and therefore is rejected with the same rationale.
Claims 6-7 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Hyde et al. (U.S. 2017/0164876) in view of Forsland et al. (U.S. 2021/0223864) and further in view of Daniels et al. (U.S. 2021/0244941).
With regard to claim 6, the limitations are addressed above. However, Hyde does not specifically teach:
- wherein the one or more flexible, conformable electrodes are formed of (i) a serpentine-patterned structure at a first end and (ii) a terminal at a second end
Daniels teaches a wearable electronic digital therapeutic device having one or more biometric detectors for detecting one or more biometric parameters [abstract]. Daniels also teaches one or more flexible, conformable electrodes are formed of (i) a serpentine-patterned structure at a first end ([0119] FIG. 43 is a close-up view showing individually addressable dry electrode strips having a stretchable serpentine pattern ganged on a stretchable fabric substrate; [0120] FIG. 44 shows an assembled elastic wrap having integrally fixed individually addressable dry electrode strips having a stretchable serpentine pattern; [0300]) and (ii) a terminal at a second end ([0270] FIG. 3 shows an end the inventive elastic bandage; [0271] a buckle is provided at the electrode end to facilitate wrapping the elastic bandage…the distal end of the elastic bandage; [0689] the body part moves from the start position to a determined end position). Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to have modified the system for monitoring an individual and facilitating a motion of an individual as taught by Hyde and the machine learning (ML) training stored into an Expert system as taught by Forsland, with the stretchable serpentine pattern taught by Daniels, to have achieved a system and method of having a computer interface and headset, which includes an augmented reality display, one or more sensors, a processing module, at least one biofeedback device, and a battery.
With regard to claim 7, the limitations are addressed above. However, Hyde does not specifically teach:
- wherein each serpentine-patterned structure of the one or more flexible, conformable electrodes is formed of a first layer comprising a metal and a second layer comprising a polyimide
Daniels teaches a wearable electronic digital therapeutic device having one or more biometric detectors for detecting one or more biometric parameters [abstract]. Daniels also teaches wherein each serpentine-patterned structure of the one or more flexible, conformable electrodes ([0119] FIG. 43 is a close-up view showing individually addressable dry electrode strips having a stretchable serpentine pattern ganged on a stretchable fabric substrate; [0120] FIG. 44 shows an assembled elastic wrap having integrally fixed individually addressable dry electrode strips having a stretchable serpentine pattern; [0300]) is formed of a first layer comprising a metal ([0264] a glass tube with a metal coating inside the tube has been proposed; [0368] using band actuated by a shape memory metal or other mechanical actuator to apply a squeezing force to the muscles); [0680] A strain gauge wire or the like can also be used to detect muscle movement, and/or a memory metal used to contract and apply a squeezing force, acting as conductive pathways to the electrodes 14 or provided as separate components) and a second layer comprising a polyimide ([0012] The conductive gel is made from co-polymers derived from polymerization, e.g. of acrylic acid and N-vinylpyrrolidone; [0298] A super absorbent polymer, such as a hydrogel or other suitable material). Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to have modified the system for monitoring an individual and facilitating a motion of an individual as taught by Hyde and the machine learning (ML) training stored into an Expert system as taught by Forsland, with the stretchable serpentine pattern and polyimide layer taught by Daniels, to have achieved a system and method of having a computer interface and headset, which includes an augmented reality display, one or more sensors, a processing module, at least one biofeedback device, and a battery.
With regard to claim 14, the method claim corresponds to the system claim 6, respectively, and therefore is rejected with the same rationale.
With regard to claim 15, the method claim corresponds to the system claim 7, respectively, and therefore is rejected with the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tian et al. (US 2024/0081711) teaches a flexible electrode which includes a wearable device and voltage sensors comprising internal electrodes arranged between outer electrodes.
Kumar et al. (US 2019/0021671) teaches device features and design elements for long-term adhesion.
Raj et al. (US 2019/0365263) teaches a wearable sensor including an accelerometer sensor in contact with the skin of the patient to measure mechano-acoustic signals generated from a bodily function and generate an accelerometer waveform.
Furtwangler (US 2026/0104755) teaches a head-wearable device to display a first user interface (UI) overlaid on a portion of the artificial-reality environment while displaying an artificial-reality environment in response to receiving an indication that a first activation hand gesture has been performed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREA C. LEGGETT whose telephone number is (571)270-7700. The examiner can normally be reached M-F 9am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kieu Vu can be reached at 571-272-4057. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREA C LEGGETT/Primary Examiner, Art Unit 2171
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