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 .
DETAILED ACTION
This communication is a Final office action in merits. Claims 2 and 13 are canceled. Claims 1, 3-12, 14-20, after amendment, are presently pending and have been elected and considered below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 8/23/2024 and 3/28/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 1, 12, and 20 are objected to because of the following informalities:
Claim 1 recites: “determining an error between a frequency distribution of the frequency domain signal and the second frequency distribution based on a squared Wassertein distance between the frequency distribution of the frequency domain signal and the second frequency distribution” in which Wassertein distance appears to be Wasserstein distance. Claims 12 and 20 are objected to with the same reason.
Appropriate correction is required.
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 of this title, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or
nonobviousness.
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.
Claims 1, 3-4, 10-12, 14-15, 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 2019/0347476 A1, Jo et al. (hereinafter Jo) in view of US 2018/ 0049669 A1, Vu et al. (hereinafter Vu), and further in view of US 2024/0201996 A1, STANLEY-MARBELL et al. (hereinafter STANLEY-MARBELL).
As to claim 1, Jo discloses a method performed by at least one processor, the method comprising:
obtaining an image of a subject (pars 0018, 0024, 0026, receiving a sequence of images that captures physiological signal including brain wave and heartbeat signals); preprocessing the image of the subject (pars 0026, 0030, 0033, extracting a physiological feature from the image sequence);
inputting the preprocessed image into a machine learning model (Fig 1; pars 0021, 0026, 0030, 0035, 0062, 0064, extracted features being input to a machine learning model) trained in accordance with a first frequency distribution corresponding to a first ground truth obtained from one or more sensors performing a vital measurement on one or more test subjects (Fig 1; pars 0026-0027, 0030-0031, 0035-0036, 0064, 0103, 0110);
obtaining, from the machine learning model, an estimate of a signal corresponding to the vital measurement of the subject (pars 0016, 0018, 0023, 0029-0030, 0032, 0038-0039, estimating vital/dynamic signals using the machine learning model).
Jo does not expressly disclose obtaining, from one or more sensors performing the vital measurement on the subject, a second frequency distribution corresponding to a second ground truth of the vital measurement; converting the estimate of the signal corresponding to the vital measurement of the subject to a frequency domain signal; and updating the machine learning model based on the determined error.
Vu, in the same or similar field of endeavor, further teaches obtaining a second frequency distribution corresponding to a second ground truth of the vital measurement (pars 0021, 0119-0126, obtaining power distribution within frequency range of the measured signal indicating a vital sign); determining an error between the frequency domain signal and the second frequency distribution (Figs 5, 21-22; pars 0028, 0044-0045, 0139. 0212, 0216-0217); and updating the machine learning model based on the determined error (pars 0020, 0043-0044, 0052, 0054, 0059, 0061, 0067, training/updating machine or neural network model).
Vu does not teach an error between a frequency distribution of the frequency domain signal and the second frequency distribution being determined based on a squared Wassertein distance between the frequency distribution of the frequency domain signal and the second frequency distribution.
STANLEY-MARBELL, in the same or similar field of endeavor, additionally teaches obtaining, from one or more sensors performing the vital measurement on the subject, a second frequency distribution corresponding to a second ground truth of the vital measurement Figs 2, 4); converting the estimate of the signal corresponding to the vital measurement of the subject to a frequency domain signal (Fig 15; pars 0012, 0131, 0199, 0312, compute probability distribution in frequency domain); determining an error between a frequency distribution of the frequency domain signal and the second frequency distribution based on a squared Wassertein distance between the frequency distribution of the frequency domain signal and the second frequency distribution; and updating the machine learning model based on the determined error (pars 0312, 0394-0395, 0418, note a squared Wasserstein distance, absolute value of the distance, or square root of the distance are commonly used cost function for the machine learning model update).
Therefore, consider Jo, Vu, and STANLEY-MARBELL’s teachings as a whole, it would have been obvious to one of skill in the art before the filing date of invention to incorporate Vu and STANLEY-MARBELL’s teachings in Jo’s method to train a neural network model utilizing a filtered/denoised data signal.
2.(Canceled)
As to claim 3, Jo as modified discloses the method according to claim 1, wherein the first frequency distribution is a Gaussian distribution centered at the first ground truth and having a first standard deviation that is a function of a N frames sampled a f frames per second, wherein N and f are positive integers (Vu: pars 0058, 0060, 0151, 0158, frequency distribution being a Gaussian distribution). Note Gaussian distribution is a normal distribution which is characterized by its means and standard deviation with the means represents center of the distribution (at peak of distribution in a bell shape distribution) and standard deviation represents spread of the distribution. Therefore, N frames sampled a f frames per second, wherein N and f are positive integers, as recited herein, merely indicates or reflects N frames of image captured with certain number of time samples being used to adequately obtain the frequency distribution statistically.
As to claim 4, Jo as modified discloses the method according to claim 1, wherein the first frequency distribution is a Gaussian distribution centered at the first ground truth and having a second standard deviation that is a function of a N frames sampled a f frames per second, wherein N and f are positive integers (see rejection in claims 2-3).
As to claim 10, Jo as modified discloses the method of claim 1, wherein the vital measurement is one of a pulse rate, blood pressure, oxygen saturation level (Jo: par 0003, 0016, 0107, heart rate, pulse, oximetry, blood volume, etc.).
As to claim 11, Jo as modified discloses the method of claim 1 wherein the machine learning model is a three dimensional (3D) Convolutional Neural Network (CNN) (Jo: pars 0017, 0064, three-dimensional data being input to the ConvLSTM (e.g. a three-dimensional Convolutional network)).
As to claim 12, it is an apparatus claim encompassed claim 1. Rejection of claim 1 is therefore incorporated herein.
13.(Canceled)
As to claims 14-15, they are rejected with the same reason as set forth in claims 3-4, respectively.
As to claim 20, it recites a non-transitory CRM having instructions executed to perform functions and features recited in claim 1. Rejection of claim 1 is therefore incorporated herein.
Claims 5-9, 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Jo in view of Vu and further in view of STANLEY-MARBELL and US 2019/0325621 A1, Wang et al. (hereinafter Wang).
As to claim 5, Jo as modified discloses the method according to claim 3, but does not expressly disclose wherein the determining the error comprises: determining a mean squared error (MSE) loss between the frequency domain signal and the second frequency distribution.
Wang, in the same or similar field of endeavor, further teaches determining a mean square error (MSE) loss between the two (pars 0016, 0116).
Therefore, consider Jo as modified and Wang’s teachings as a whole, it would have been obvious to one of skill in the art before the filing date of invention to incorporate Wang’s teachings in Jo as modified’s method to adopt commonly used MSE for measuring the performance of machine learning modeling process.
As to claim 6, Jo as modified discloses the method according to claim 5, wherein the determining the error further comprises: determining a signal-to-noise ratio (SNR) based on a proportion of a power centered at a peak frequency of the frequency domain signal compared to a sum of power between a lower cutoff frequency and an upper cutoff frequency of the frequency domain signal (Vu: pars 0019, 0109, 0151, 0210, a bandpass filter with low and high cutoff frequencies to filter out the out off band noise; Wang: Fig 20; pars 0025, 0133, PSNR, obtaining peak signal to noise ratio with defined bandwidth).
As to claim 7, Jo as modified discloses the method according to claim 6, wherein the determining the error further comprises: determining an irrelevant power ratio (IPR) based on a proportion of a power between the lower cutoff frequency and the upper cutoff frequency of the frequency domain signal compared to a total power of the frequency domain signal (see rejection in claim 6, IPR being the irrelevant power filtered out over the total power without applying the bandpass filter).
As to claim 8, Jo as modified discloses the method of claim 1, but does not expressly disclose the preprocessing the image of the subject comprises: detecting a region of interest of the image subject; and resizing the region of interest of the image subject.
Wang, in the same or similar field of endeavor, further teaches detecting a region of interest of the image subject (pars 0026-0027, 0117-0119, 0123, detecting an ROI with respect to the subject in an image); and resizing the region of interest of the image subject (pars 0117, 0137, 0143, adjustable ROI/patch size).
Therefore, consider Jo as modified and Wang’s teachings as a whole, it would have been obvious to one of skill in the art before the filing date of invention to incorporate Wang’s teachings in Jo’s method to setup flexible size of ROI based on applications.
As to claim 10, Jo as modified discloses the method of claim 8, wherein the region of interest is at least a portion of a face of the subject (Jo: pars 0007, 0018; Wang: pars 0033, 0040, ROI with facial recognition).
As to claims 16-18, they are rejected with the same reason as set forth in claims 5-7, respectively.
As to claim 19, it is rejected with the same reason as set forth in claim 8.
Response to Arguments
Applicant’s arguments have been considered but they are moot in light of new ground of rejection.
Conclusion
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.
Examiner’s Note
Examiner has cited particular column, line number, paragraphs and/or figure(s) in the reference(s) as applied to the claims for the convenience of the Applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the reference(s) in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to QUN SHEN whose telephone number is (571)270-7927. The examiner can normally be reached on Mon-Fri 8:30-5:50 PT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amandeep Saini can be reached on 571-272-3382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/QUN SHEN/
Primary Examiner, Art Unit 2662
