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 Arguments
Applicant’s amendments to the pending claims appear to overcome the rejections under 35 U.S.C. § 101 and § 112 and the rejections are therefore withdrawn.
Applicant’s amendments further merit new grounds for rejection in view of Rahman et al. (U.S. Patent Application Publication No. 2021/0386318) hereinafter referred to as Rahman ‘318 as the amended scope is substantially different from that which was previously presented.
Applicant’s arguments with respect to claim(s) 15-32 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 15-19, 21-29, and 31-32 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rahman et al. (U.S. Patent Application Publication No. 2019/0298224) hereinafter referred to as Rahman; in view of Rahman et al. (U.S. Patent Application Publication No. 2021/0386318) hereinafter referred to as Rahman ‘318.
Regarding claim 15, Rahman teaches a wearable device (¶[0019]), wherein the wearable device comprises:
at least one first sensor, the at least one first sensor configured to obtain an audio signal of a user (¶[0028] microphone collecting breathing and/or voice data, ¶[0030]);
at least one second sensor, the at least one second sensor configured to obtain a physiological parameter signal of the user (¶[0120]),
wherein the at least one second sensor comprises a photoplethysmography (PPG) sensor, and the PPG sensor is configured to obtain a PPG signal of the user (¶[0120]); and
at least one processor (¶[0120] processor), the at least one processor configured to:
perform band-pass filtering, by using at least one first band-pass filter and at least one second band-pass filter (¶¶[00068-0069]), on the PPG signal (¶[0068] performed on biomarkers, PPG is a biomarker) to obtain a first respiratory rate of the user (¶¶[0044-0049] biomarkers).
Attention is drawn to the Rahman ‘318 reference, which teaches at least one first and second band-pass filter applied to biomarker signals to extract multiple frequency bands of respiratory signals for the purposes of determining respiratory rate (¶[0068] “Time domain features of such signals include zero crossing rate (ZCR), spectral features include spectral power, sub-band power, spectral roll-off, spectral flux, and cepstral coefficient (including Mel-frequency cepstral coefficients (MFCCs)). MFCCs can be extracted using the discrete cosine transform of log-scaled outputs of FFT coefficients filtered by a triangular band-pass filter bank. In one embodiment, signal filter 104 uses 20 filters and calculates the first 12 coefficients, which ARCA 100 (e.g., collectively with other adaptive respiratory condition assessors) can provide for training the machine learning model to detect breathing segments in determining a respiratory condition”) and the biomarker signals of Rahman ‘318 include PPG (¶[0043], ¶[0049], ¶[0120]).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify the wearable device of Rahman to include two or more band-pass filters applied to a PPG signal to obtain respiratory rate, as taught by Rahman ‘318, because the filtering methods of Rahman ‘318 obtain a mean absolute error of less than one breath per minute (Rahman ‘318, ¶[0036]).
Regarding claim 16, Rahman as modified teaches the wearable device according to claim 15.
Rahman further teaches wherein the at least one second sensor further comprises at least one accelerometer (ACC) sensor (¶[0035]), the at least one ACC sensor is configured to obtain an ACC signal of the user (¶[0094]), and the at least one processor is further configured to obtain a second respiratory rate of the user based on the ACC signal (Fig. 8, respiratory rate Est RR based on accelerometer).
Regarding claim 17, Rahman as modified teaches the wearable device according to claim 16.
Rahman further teaches wherein the at least one processor is further configured to perform filtering (¶[0068] processing performed on each sensor data) and fusion (¶¶[0096-0097]) on the first respiratory rate and the second respiratory rate to obtain a third respiratory rate of the user (Fig. 8, 3 sensor outputs are fused to obtain a final RR).
Regarding claim 18, Rahman as modified teaches the wearable device according to claim 16.
Rahman further teaches wherein the at least one processor is further configured to:
obtain a posture classification result of the user based on the ACC signal (¶[0076] categories of posture state), wherein the posture classification result comprises a first posture (Fig. 2).
Rahman does not teach obtaining a respiratory tract infection evaluation report based on the first respiratory rate and the audio signal when the user is in the first posture.
Attention is drawn to the Rahman ‘318 reference, which teaches obtaining a respiratory tract infection evaluation report based on the first respiratory rate and the audio signal (¶[0073]) when the user is in the first posture (¶[0069]).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify the wearable device of Rahman to obtain a respiratory tract infection evaluation report, as taught by Rahman ‘318, because regular respiratory assessment of humans is key to both identifying and treating respiratory conditions, a cause of human suffering and a significant public health concern (Rahman ‘318, ¶[0003]).
Regarding claim 19, Rahman as modified teaches the wearable device according to claim 17.
Rahman further teaches wherein the at least one processor is further configured to obtain a posture classification result of the user based on the ACC signal (¶[0076] categories of posture state), the posture classification result comprises a second posture of the user (¶[0076] categories of posture state, plurality of postures).
Rahman does not teach obtaining a respiratory tract infection evaluation report based on the first respiratory rate and the audio signal when the user is in the first posture.
Attention is drawn to the Rahman ‘318 reference, which teaches obtaining a respiratory tract infection evaluation report based on the first respiratory rate and the audio signal (¶[0073]) when the user is in the first posture (¶[0069]).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify the wearable device of Rahman to obtain a respiratory tract infection evaluation report, as taught by Rahman ‘318, because regular respiratory assessment of humans is key to both identifying and treating respiratory conditions, a cause of human suffering and a significant public health concern (Rahman ‘318, ¶[0003]).
Regarding claim 21, Rahman as modified teaches the wearable device according to claim 19.
Rahman further teaches wherein the at least one processor is further configured to: prompt the user to switch to the second posture when the user is in a first posture (¶[0078] switch to supine) and the first respiratory rate is beyond a preset range (¶[0054] quality metrics including preset range of respiratory rate signal); and in response to an operation performed by the user for switching to the second posture (¶[0078] re-attempt measurement process after postural change), obtain the third respiratory rate based on the PPG signal and the ACC signal (Fig. 6, 8)
Attention is drawn to the Rahman ‘318 reference, which teaches obtaining a respiratory tract infection evaluation report based on the first respiratory rate and the audio signal (¶[0073]).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify the wearable device of Rahman to obtain a respiratory tract infection evaluation report, as taught by Rahman ‘318, because regular respiratory assessment of humans is key to both identifying and treating respiratory conditions, a cause of human suffering and a significant public health concern (Rahman ‘318, ¶[0003]).
Regarding claim 22, Rahman as modified teaches the wearable device according to claim 15.
Rahman further teaches wherein the wearable device is a wearable watch, a wearable bracelet, or a wearable monitor (¶[0031]).
Regarding claim 23, Rahman as modified teaches the wearable device according to claim 15.
Rahman ‘318 teaches wherein the wearable device further comprises the at least one first band-pass filter and the at least one second band-pass filter (¶[0068]);
the at least one first band-pass filter is configured to perform band-pass filtering on the PPG signal to obtain positions of a peak point and a valley point of the PPG signal (¶[0035], ¶[0037]);
the at least one second band-pass filter is configured to perform band-pass filtering on the PPG signal to obtain amplitudes of the peak point and the valley point of the PPG signal (¶[0035], ¶[0037]); and
the at least one processor is further configured to obtain the first respiratory rate of the user based on the positions and the amplitudes of the peak point and the valley point of the PPG signal (¶¶[0038-0039]).
Regarding claim 24, Rahman as modified teaches the wearable device according to claim 23.
Rahman ‘318 further teaches wherein a frequency band of signals that are allowed to pass through the first band-pass filter comprises 0.5 Hz to 10 Hz (¶[0066]), and a frequency band of signals that are allowed to pass through the second band-pass filter comprises 0.1 Hz to 10 Hz (¶[0069]).
Regarding claim 25, Rahman as modified teaches the wearable device according to claim 23.
Rahman further teaches a baseline-removed ACC signal (¶[0060]) and the at least one processor is further configured to obtain a second respiratory rate of the user based on a filtered ACC signal (Fig. 8).
Rahman as modified does not teach wherein the wearable device further comprises at least one third band-pass filter; the at least one third band-pass filter is configured to perform band-pass filtering on a ACC signal.
Rahman ‘318 further teaches wherein the wearable device further comprises at least one third band-pass filter; the at least one third band-pass filter is configured to perform band-pass filtering on a ACC signal (¶[0034]).
Regarding claim 26, Rahman as modified teaches the wearable device according to claim 25.
Rahman ‘318 further teaches teaches wherein a frequency band of signals that are allowed to pass through the third band-pass filter comprises 0.1 Hz to 0.5 Hz (¶[0034])
Regarding claims 27-29, 31/32, the claims are directed to a method/wearable device comprising substantially the same subject matter as the device of claims 15-16, 18-19, and 21 and are rejected under substantially the same sections of Rahman and Rahman ‘318.
Claim(s) 20 and 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rahman and Rahman ‘318 as applied to claims 15 and 27 above, and further in view of Coyle et al. (U.S. Patent Application Publication No. 2005/0119586) hereinafter referred to as Coyle; in view of Etemad et al. (U.S. Patent Application Publication No. 2015/0272483) hereinafter referred to as Etemad.
Regarding claims 20 and 30, Rahman as modified teaches the wearable device according to claim 19/28.
Rahman further teaches wherein the wearable device further comprises at least one low-pass filter (¶[0094]), the at least one low-pass filter is configured to perform low-pass filtering on the ACC signal (¶¶[0094-0095]).
Rahman as modified does not teach a cut-off frequency of the low-pass filter is 1 Hz;
the at least one processor configured to:
calculate a mean value and a standard deviation of the ACC signal, and
calculate a power spectrum based on a low-pass filtered ACC signal, to obtain a position and an amplitude of a peak point of the power spectrum; and
the at least one processor is further configured to input the mean value and the standard deviation of the ACC signal and the position and the amplitude of the peak point of the power spectrum into a classification model, to obtain the posture classification result.
Attention is drawn to the Coyle Reference which teaches a cut-off frequency of a low-pass filter applied to accelerometer signals is 1 Hz for detecting posture classification (¶[0052]).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify the low-pass filter of Rahman as modified to use a cut-off frequency of 1Hz, as taught by Coyle, because characteristic postural change signals are below 1Hz (Coyle ¶[0052]).
Rahman as modified does not teach the at least one processor configured to:
calculate a mean value and a standard deviation of the ACC signal, and
calculate a power spectrum based on a low-pass filtered ACC signal, to obtain a position and an amplitude of a peak point of the power spectrum; and
the at least one processor is further configured to input the mean value and the standard deviation of the ACC signal and the position and the amplitude of the peak point of the power spectrum into a classification model, to obtain the posture classification result.
Attention is drawn to the Etemad reference, which teaches at least one processor configured to:
calculate a mean value and a standard deviation of the ACC signal (¶[0222]), and
calculate a power spectrum based on a low-pass filtered ACC signal, to obtain a position and an amplitude of a peak point of the power spectrum (¶[0260] maximum within a window, ¶[0261] FFT); and
the at least one processor is further configured to input the mean value and the standard deviation of the ACC signal and the position and the amplitude of the peak point of the power spectrum into a classification model (¶[0265]), to obtain the posture classification result (¶[0245]).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify the postural classification of Rahman as modified to use a combination of statistics and spectral analysis, as taught by Etemad, to improve tracking accuracy (¶[0300]).
Conclusion
The prior art made of record and not relied upon is considered remaining pertinent to applicant's disclosure.
U.S. Patent Application Publication No. 2010/0004552 to Zhang et al. teaches respiratory rate from PPG using a band-pass filter
U.S. Patent Application Publication No. 2019/0209022 to Sobol et al. teaches detecting the presence of a respiratory illness from auditory and accelerometer, PPG data.
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 AMANDA L STEINBERG whose telephone number is (303)297-4783. The examiner can normally be reached Mon-Fri 8-4.
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/AMANDA L STEINBERG/ Examiner, Art Unit 3792
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