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 .
Election/Restrictions
Applicant's election with traverse of Species I, claims 1-19, 38 and 40 in the reply filed on 9/22/2025 is acknowledged. The traversal is on the ground(s) that Species I-III are all interrelated. This is not found persuasive because the three groups of claims are directed to patentably distinct species as outlined in the species election on 6/26/2025. The traversal is based on the three species are “interrelated” and they use “similar structure to extract a unique recognition vector”. However, applicant omits the fact that different sensors are being utilized for each species. These different sensors contribute to the complexities of searches and considerations and the potential of divergent focus for each of them.
The requirement is still deemed proper and is therefore made FINAL.
Claims 20-35, 36-37, 39 and 41 withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected Species II and III, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on 9/22/2025.
Claim Rejections - 35 USC § 102
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 13, 16, 18-19, 38 and 40 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kim (Two-Step Biometrics Using Electromyogram Signal Based on Convolutional Neural Network-Long Short-Term Memory Networks, Appl. Sci. 2021, 11, 6824. https://doi.org/ 10.3390/app11156824).
Regarding claim 1, Kim teaches a device to extract a unique recognition vector from a user comprising: a sensor (The surface electrode method measures an action potential by placing electrodes to the skin surface. Page 1 last paragraph) generating an electrical signal responsive to one of: muscle, skin or brain activity of the user (The EMG signal is in the form of a voltage value obtained by measuring microcurrent (which reads on “an electrical signal”) generated when a muscle contracts. Page 5 last paragraph); a spectral conversion mechanism converting the electrical signal into a spectral image (The time domain of the EMG signal represents information on muscle activity, and the frequency domain has the advantage of being immune to noise. Therefore, the one-dimensional EMG signal was converted into a two-dimensional spectrogram (i.e., a spectral image) to simultaneously analyze time–frequency domain information. The spectrogram was generated using a window function of a fixed length within the time domain of a given signal. In the generated spectrogram, the horizontal axis represented time, and the vertical axis represented frequency information. Page 8 1st paragraph); and a machine learning device converting the spectral image into a recognition vector associated with the user (To solve this problem, deep learning methods such as CNN and LSTM networks, which have been actively studied recently, can automatically extract optimal features (which reads on “a recognition vector”.) from data without extracting features via the handcrafted method. Page 2nd paragraph. Figure 2 “Personal Identification” reads on “associated with the user”.
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).
Regarding claim 2, Kim teaches the device of Claim 1, wherein the sensor is at least one of: a capacitive sensor, a contact sensor (Figure 1 EMG signal measurement using the surface electrode method.
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), a field strength sensor, a magnetic sensor, a radar sensor, an ultrasonic CMUT sensor, an acoustic MEMs sensor, a piezo sensor, a silver-silver chloride sensor, a skin impedance sensor and the like (Note: claim language is interpreted as disjunctive.).
Regarding claim 3, Kim teaches the device of Claim 1, wherein the sensor monitors at least one of: eye muscles, electrooculogram related muscles, jaw muscles, mouth muscle, brain activity, electroencephalogram (EEG) signals from brain, facial muscles, forehead muscles, ear area muscles, neck muscles, heart muscles, arm muscles (
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), hand muscles, finger muscles, stomach muscles, groin muscles, leg muscles, ankle muscles, foot muscles, toe muscles, galvanic skin response, and the like (Note: claim language is interpreted as disjunctive.).
Regarding claim 13, Kim teaches the device of Claim 1, comprising a battery powering the device (It is common knowledge that a battery can power the device.).
Regarding claim 16, Kim teaches the device of Claim 1, wherein the machine learning device is a convolutional neural network (Two-Step Biometrics Using Electromyogram Signal Based on Convolutional Neural Network-Long Short-Term Memory Networks, title).
Regarding claim 18, Kim teaches the device of Claim 1, further comprising a database storing recognition vectors associated with the user (In this paper, an experiment was performed using a public EMG database (DB), and EMG signal-based two-step biometrics were performed using gesture recognition information to improve FAR. Page last paragraph).
Regarding claim 19, Kim teaches the device of Claim 18, comprising a comparison device (Figure 2 Similarity Measurement
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) comparing a currently extracted recognition vector to recognition vectors stored in the database (Figure 2 DB Enrollment EMG) to authorize or identify the user (Figure 2 Personal Identification).
Regarding claim 38, Kim teaches the device of Claim 1, wherein the device is coupled to a wireless device to communicate one of an output of the sensor, the spectral image or an output of the machine learning device result to an external device (EMG signal measurements were collected at twelve channels using Delsys Trigno Wireless EMG equipment and measured at a 2000 Hz sampling rate. Page 9 1st paragraph).
Regarding claim 40, Kim teaches the device of Claim 1, wherein converting the electrical signal into a spectral image and the converting the spectral image into a recognition vector is done on an external device (EMG signal measurements were collected at twelve channels using Delsys Trigno Wireless EMG equipment and measured at a 2000 Hz sampling rate. Page 9 1st paragraph).
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.
Claims 4-6, 8 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Kim (Two-Step Biometrics Using Electromyogram Signal Based on Convolutional Neural Network-Long Short-Term Memory Networks, Appl. Sci. 2021, 11, 6824. https://doi.org/ 10.3390/app11156824), hereinafter Kim, in view of Zhang (Personalized health monitoring via vital sign measurements leveraging motion sensors on AR/VR headsets, MobiSys ’22, June 25-July 1, 2022, Portland, OR, USA), hereinafter Zhang.
Regarding claim 4, Kim teaches all of the elements of the claimed invention as stated in claim 1 except for the following limitations as further recited. However, Zhang teaches wherein the device is coupled to eyewear (Poster: Personalized Health Monitoring via Vital Sign Measurements Leveraging Motion Sensors on AR/VR Headsets. Title).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kim to incorporate the teachings of Zhang to couple the device to eyewear in order to design and implement a user identification model leveraging respiratory and cardiac features from both time domain and
frequency domain.
Regarding claim 5, Zhang in the combination teaches the device of Claim 4, wherein the eyewear is one of: eyeglasses, an AR/VR headset (Poster: Personalized Health Monitoring via Vital Sign Measurements Leveraging Motion Sensors on AR/VR Headsets. Title), goggles, eye mask or the like.
Regarding claim 6, Zhang in the combination teaches the device of Claim 1, wherein the device is coupled to a headphone device (The key insight is that the conductive vibrations induced by chest and heart movements can propagate through the user’s cranial bones, thereby vibrating the AR/VR headset mounted on the user’s head. Abstract).
Regarding claim 8, Zhang in the combination teaches the device of Claim 1, wherein the device is coupled to headwear (The key insight is that the conductive vibrations induced by chest and heart movements can propagate through the user’s cranial bones, thereby vibrating the AR/VR headset mounted on the user’s head. Abstract).
Regarding claim 15, Zhang in the combination teaches the device in accordance with Claim 1, wherein the spectral conversion mechanism is an fft based time frequency analysis (Fast Fourier Transform (FFT) with Hann Window is applied to reveal the period of the signal, and then a peak selection algorithm will be utilized to determine the breathing/heartbeat rate measurement. Page 530 left column 3rd paragraph).
Claims 7, 10 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Kim (Two-Step Biometrics Using Electromyogram Signal Based on Convolutional Neural Network-Long Short-Term Memory Networks, Appl. Sci. 2021, 11, 6824. https://doi.org/ 10.3390/app11156824), hereinafter Kim, in view of Heinrich (US Patent Pub. No.: US 2018/0229674 A1), hereinafter Heinrich.
Regarding claim 7, Kim teaches all of the elements of the claimed invention as stated in claim 1 except for the following limitations as further recited. However, Heinrich teaches wherein the device is coupled to an article of clothing (In some embodiments, the driver wearable 22 may include a chest strap, arm band, ear piece, necklace, belt, clothing, headband, or another type of wearable form factor comprising one or more physiological sensors. [0023]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kim to incorporate the teachings of Heinrich to couple the device to an article of clothing in order to identify the user based on the sensed one or more physiological parameters.
Regarding claim 10, Heinrich in the combination teaches the device of Claim 1, wherein the device is coupled to an article of jewelry (In some embodiments, the driver wearable 22 may include a chest strap, arm band, ear piece, necklace, belt, clothing, headband, or another type of wearable form factor comprising one or more physiological sensors. [0023]).
Regarding claim 12, Heinrich in the combination teaches the device of Claim 1, wherein the device is coupled to a seat (A user, whether driver or passenger, once identified by the wearable device, causes adjustment in pre-defined settings for that user (e.g., as learned) including one or any combination of vehicle seat settings, mirror settings, interior climate settings, playback of one or a combination of video or audio playback, driving plan settings, or game console settings. [0006]).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Kim (Two-Step Biometrics Using Electromyogram Signal Based on Convolutional Neural Network-Long Short-Term Memory Networks, Appl. Sci. 2021, 11, 6824. https://doi.org/ 10.3390/app11156824), hereinafter Kim, in view of Tseng (Functional, RF-Trilayer Sensors for Tooth-Mounted, Wireless Monitoring of the Oral Cavity and Food Consumption, Advanced Materials, Volume 30, Issue 18, May 3, 2018), hereinafter Tseng.
Regarding claim 9, Kim teaches all of the elements of the claimed invention as stated in claim 1 except for the following limitations as further recited. However, Tseng teaches wherein the device is coupled to an oral appliance (Functional, RF-Trilayer Sensors for Tooth-Mounted, Wireless Monitoring of the Oral Cavity and Food Consumption. Title).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kim to incorporate the teachings of Tseng to couple the device to an oral appliance and utilize physiological responses to diet and nutrition in order to provide tools for personalized healthcare.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Kim (Two-Step Biometrics Using Electromyogram Signal Based on Convolutional Neural Network-Long Short-Term Memory Networks, Appl. Sci. 2021, 11, 6824. https://doi.org/ 10.3390/app11156824), hereinafter Kim, in view of Shouldice (US Patent Pub. No.: US 2022/0075050 A1), hereinafter Shouldice.
Regarding claim 11, Kim teaches all of the elements of the claimed invention as stated in claim 1 except for the following limitations as further recited. However, Shouldice teaches wherein the device is coupled to one of a CPAP mask or CPAP tubing (In some embodiments a sensor maybe integrated with or into a respiratory treatment apparatus such as a CPAP device such as a flow generator or similar (e.g., a Respiratory Pressure Therapy Device (RPT) or may be configured to communicate together. [0101]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kim to incorporate the teachings of Shouldice to couple the device to a CPAP device in order to detect the biometric of a person undergoing therapy and flag an alert to a monitoring centre and/or to a user if the expected person (e.g., a previously detected and identified user) cannot be identified.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Kim (Two-Step Biometrics Using Electromyogram Signal Based on Convolutional Neural Network-Long Short-Term Memory Networks, Appl. Sci. 2021, 11, 6824. https://doi.org/ 10.3390/app11156824), hereinafter Kim, in view of Lu (An EMG-Based Personal Identification Method Using Continuous Wavelet Transform and Convolutional Neural Networks, 2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)), hereinafter Lu.
Regarding claim 14, Kim teaches all of the elements of the claimed invention as stated in claim 1 except for the following limitations as further recited. However, Lu teaches wherein the spectral conversion mechanism is a wavelet image (In this work, the Mexican Hat wavelet function is selected as the mother wavelet function, since the shape of EMG signals shown in Fig.4 is similar to that of the Mexican Hat wavelet. Page 3 left column 2nd paragraph).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kim to incorporate the teachings of Lu to utilize a wavelet image in the spectral conversion mechanism in order to see more feature differences of EMG signals captured from different subjects.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Kim (Two-Step Biometrics Using Electromyogram Signal Based on Convolutional Neural Network-Long Short-Term Memory Networks, Appl. Sci. 2021, 11, 6824. https://doi.org/ 10.3390/app11156824), hereinafter Kim, in view of Sarkar (US Patent Pub. No.: US 2022/0384014 A1), hereinafter Sarkar.
Regarding claim 17, Kim teaches all of the elements of the claimed invention as stated in claim 1 except for expressly teaching the following limitations as further recited. However, Sarkar teaches wherein the machine learning device is a fully connected network (The external system may use multiple fully connected networks to create an ensemble network. [0030]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kim to incorporate the teachings of Sarkar to utilize machine learning model with a fully connected network in order to generate cardiac episode classification.
Conclusion
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/LEI ZHAO/Examiner, Art Unit 2668
/VU LE/Supervisory Patent Examiner, Art Unit 2668