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
Claim Status
Claims 1-27,29,31-35,37-40 and 42-51 are pending for this application.
Claims 28,30,36,41 and 52-58 are cancelled .
Response to Remarks
1. Based on the amendment to claims 1,4 and 46, the Examiner respectfully withdraws the 35 USC § 101 rejection for claims 1,4 and 46.
2. Based on the amendment to claims 1,4 and 46 , the Examiner respectfully withdraws the 35 USC § 102 rejection for claims 1,4,5,6,7,8, 46, 35,40,43,45, 49,50 and 51.
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 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.
3. Claims 1,4,5,6,7,8, 46, 35,40,43,45, 49,50 and 51 are rejected under 35 U.S.C 103 as being unpatentable over MCDUFF et al. (USPUB 20200121256) in view of Meng Rong et al. ( NPL Doc. : “A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning,” 21st November 2020, Biomedical Signal Processing and Control,Voluem 64,February 2021,102328, Pages 1-10.) .
As per claim 1 , MCDUFF et al. teaches A method of monitoring a patient, comprising: at one or more processors ( Paragraph [0005-0006]- “…a processor configured to execute the instructions to perform a method including: receiving a video fame sequence capturing one or more skin regions of a body; …”) : receiving one or more image signals corresponding to a skin of the patient (FIG.2 shows Video frames of subject face and FIG. 4 -402- “Receive a video frame sequence capturing one or more skin region of a body AND Paragraph [0012]) ;
processing the one or more image signals using a first machine learning model ( FIG. 4 -404-“ Providing frames of the video frame sequence to a first neural network” AND Paragraphs [0036-0038] ) ; and predicting a physiological parameter based on the processed one or more image signals using a second machine learning model ( FIG. 4 -408-410-“ Determine, using the second neural network, the physiological signal based on the frames of the video” AND Paragraph [0036-0039]) .
MCDUFF et al. does not explicitly teach by calculating one or more of a short time Fourier transform (STFT), a continuous wavelet transform (CWT), a synchro-squeezing transform (SSQ), and a PPGlet of the processed one or more image signals as input to the second machine learning model.
However, within analogous art, Meng Rong et al. teaches calculating one or more of a short time Fourier transform (STFT), a continuous wavelet transform (CWT), a synchro-squeezing transform (SSQ), and a PPGlet of the processed one or more image signals as input to the second machine learning model ( Page 3- Fig. 3 teaches the input of PPG signal for images and also signal processing input to the machine learning module for calculation of physiological parameter ( Blood pressure , Also within Page 4 -Col. 1- 3.2- Signal filtering the processing of wavelet transform signaling is taught ) .
One of ordinary skill in the art would have been motivated to combine the teaching of Meng Rong et al. within the modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. because the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. provides a system and method for implementing non contact blood pressure measurement from images of subject and machine learning model system for processing signals.
Therefore, it would have been obvious for one in the ordinary skills in the art before the effective filing date of the claimed invention to implement the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. within the modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. for implementation of a system and method for non-contact blood pressure measurement from images of subject and machine learning model system for processing signals.
As per claim 4, MCDUFF et al. teaches A method of monitoring a patient, comprising: at one or more processors(Paragraph [0005-0006]- “…a processor configured to execute the instructions to perform a method including: receiving a video fame sequence capturing one or more skin regions of a body; …”): receiving one or more image signals corresponding to a finger and face of the patient(FIG.2 shows Video frames of subject face and FIG. 4 -402- “Receive a video frame sequence capturing one or more skin region of a body AND Paragraph [0012], AND Paragraph [0017]- “…videos obtained from a plurality of subjects and (ii) corresponding test data obtained by measuring the blood volume pulse using a finger probe on each of the subjects….”); processing the one or more image signals using a first machine learning model ( FIG. 4 -404-“ Providing frames of the video frame sequence to a first neural network” AND Paragraphs [0036-0038] ); and predicting blood pressure based on the processed one or more image signals using a second machine learning model( FIG. 4 -408-410-“ Determine, using the second neural network, the physiological signal based on the frames of the video” AND Paragraph [0036-0039]) .
MCDUFF et al. does not explicitly teach by calculating one or more of a short time Fourier transform (STFT), a continuous wavelet transform (CWT), a synchro-squeezing transform (SSQ), and a PPGlet of the processed one or more image signals as input to the second machine learning model.
However, within analogous art, Meng Rong et al. teaches calculating one or more of a short time Fourier transform (STFT), a continuous wavelet transform (CWT), a synchro-squeezing transform (SSQ), and a PPGlet of the processed one or more image signals as input to the second machine learning model ( Page 3- Fig. 3 teaches the input of PPG signal for images and also signal processing input to the machine learning module for calculation of physiological parameter ( Blood pressure , Also within Page 4 -Col. 1- 3.2- Signal filtering the processing of wavelet transform signaling is taught ) .
One of ordinary skill in the art would have been motivated to combine the teaching of Meng Rong et al. within the modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. because the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. provides a system and method for implementing non-contact blood pressure measurement from images of subject and machine learning model system for processing signals.
Therefore, it would have been obvious for one in the ordinary skills in the art before the effective filing date of the claimed invention to implement the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. within the modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. for implementation of a system and method for non-contact blood pressure measurement from images of subject and machine learning model system for processing signals.
As per claim 5, Combination of MCDUFF et al. and Meng Rong et al. teach claim 1,
MCDUFF et al. teaches wherein the skin corresponds to one or more of a finger and a face of the patient ( Paragraphs [0012-0013]) .
As per claim 6, Combination of MCDUFF et al. and Meng Rong et al. teach claim 1,
MCDUFF et al. teaches wherein the one or more image signals are generated by an optical sensor ( Paragraph [0018]- “…dependent on the light source as well as the distance between the light source, the skin tissue being captured with the light source, and the camera…”) .
As per claim 7, Combination of MCDUFF et al. and Meng Rong et al. teach claim 1,
MCDUFF et al. teaches wherein the one or more image signals comprise a video ( FIG. 1- 108- Video Frame Sequence) .
As per claim 8, Combination of MCDUFF et al. and Meng Rong et al. teach claim 1,
MCDUFF et al. teaches wherein processing the one or more image signals selects one or more spatial and temporal portions of the one or more image signals( Paragraph [0041]- “… the detected spatial-temporal distribution of the physiological signal in the video frame sequence received at block 402. The spatial-temporal distribution of the physiological signal may serve as an additional tool in conjunction with the physiological signal and/or physiological parameters obtained based on the physiological signal 106 to assess health of the subject, for example….”).
As per claim 46, MCDUFF et al. teaches A system ( Paragraph [0012]) , comprising: an optical sensor configured to generate one or more image signals corresponding to a skin of the patient ( Paragraph [0018]- “… caused in the image by the hemoglobin and melanin absorption in blood capillaries near the surface of the skin captured by the camera,…” AND Paragraph [0015]) ; a memory ( Paragraph [0042]- “… the computing system 500 may include at least one processor 502 and at least one memory 504…”) ; a processor operatively coupled to the memory and the optical sensor ( Paragraph [0036]- “…The video frame sequence may be obtained by a remote camera, and may be transmitted, via a suitable network, from the remote camera. As just an example, the video frame sequence may be obtained by a laptop or a smart phone camera and may be transmitted over any suitable wireless or wired network coupled to the laptop or the smart phone camera….”) , the processor configured to: receive one or more image signals corresponding to a skin of the patient using the optical sensor ( Paragraph [0013]- “….The video frame sequence 108 may be obtained from a remote camera,…”) ; process the one or more image signals using a first machine learning model( FIG. 4 -404-“ Providing frames of the video frame sequence to a first neural network” AND Paragraphs [0036-0038] ); and predict a physiological parameter based on the processed one or more image signals using a second machine learning model( FIG. 4 -408-410-“ Determine, using the second neural network, the physiological signal based on the frames of the video” AND Paragraph [0036-0039]) .
Combination of MCDUFF et al. and WEI does not explicitly teach parameter comprising a blood pressure and by calculating one or more of a short time Fourier transform (STFT), a continuous wavelet transform (CWT), a synchro-squeezing transform (SSQ), and a PPGlet of the processed one or more image signals as input to the machine learning model.
However, within analogous art, Meng Rong et al. teaches parameter comprising a blood pressure and by calculating one or more of a short time Fourier transform (STFT), a continuous wavelet transform (CWT), a synchro-squeezing transform (SSQ), and a PPGlet of the processed one or more image signals as input to the machine learning model ( Page 3- Fig. 3 teaches the input of PPG signal for images and also signal processing input to the machine learning module for calculation of physiological parameter ( Blood pressure , Also within Page 4 -Col. 1- 3.2- Signal filtering the processing of wavelet transform signaling is taught ) .
One of ordinary skill in the art would have been motivated to combine the teaching of Meng Rong et al. within the modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. because the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. provides a system and method for implementing non-contact blood pressure measurement from images of subject and machine learning model system for processing signals.
Therefore, it would have been obvious for one in the ordinary skills in the art before the effective filing date of the claimed invention to implement the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. within the modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. for implementation of a system and method for non-contact blood pressure measurement from images of subject and machine learning model system for processing signals.
As per claim 35, Combination of MCDUFF et al. and Meng Rong et al. teach claim 1,
MCDUFF et al. teaches wherein the physiological parameter comprises respiratory rate ( Paragraph [0017]- “…in which the physiological signal 106 to be recovered is a respiratory signal for determining a breathing rate,…”) .
As per claim 40, Combination of MCDUFF et al. and Meng Rong et al. teach claim 1,
MCDUFF et al. teaches wherein the physiological parameter comprises heart rate Paragraph [0025]- “… physiological signal p′(t) is used as the labeled data, ensemble learning may be performed so that a more accurate physiological parameter (e.g., heart rate or breathing rate) based on a recovered physiological signal (e.g., blood volume pulse or respiratory signal) may subsequently be determined. …”) .
As per claim 43, Combination of MCDUFF et al. and Meng Rong et al. teach claim 1,
MCDUFF et al. teaches wherein the physiological parameter comprises heart rate variability ( Paragraph [0012]- “…physiological parameters, such as a heart rate, a breathing rate, etc., from frames of a video. The video-based physiological measurement system may include a first module that generates, …”) .
As per claim 45, Combination of MCDUFF et al. and Meng Rong et al. teach claim 43,
MCDUFF et al. teaches wherein predicting the physiological parameter comprises extracting color channels from the processed one or more image signals and identifying a set of peak locations ( Paragraph [0013]- “…The physiological signal measurement module 102 may recover the physiological signal 106 by detecting changes in intensity (e.g., color) of light reflected from the subject's skin as captured by the video. The changes in light intensity reflected from the skin of the subject may be representative of the movement of blood in skin capillaries,…”) .
As per claim 49, Combination of MCDUFF et al. and Meng Rong et al. teach claim 46,
MCDUFF et al. teaches comprising a handheld housing, wherein processing the one or more image signals and predicting the physiological parameter is performed within the handheld housing ( Handheld mobile platform taught within Paragraph [0046]- “… including telecommunications, mobile, and cellular phone networks) may perform instruction sequences to practice the present disclosure in coordination with one another…”) .
As per claim 50, Combination of MCDUFF et al. and Meng Rong et al. teach claim 46,
MCDUFF et al. teaches further comprising a communication device and a display operatively coupled to the processor, the processor configured to: establish a video conference using the communication device; and output the predicted physiological parameter using the display during the video conference ( Paragraph [0042]- “…Components may also include an output component, such as a display, 511 that may display, for example, results of operations performed by the at least one processor 502. A transceiver or network interface 506 may transmit and receive signals between computer system 500 and other devices, such as user devices that may utilize results of processes implemented by the computer system 500…” AND Paragraph [0046]) .
As per claim 51, Combination of MCDUFF et al. and Meng Rong et al. teach claim 46,
MCDUFF et al. teaches further comprising a communication device operatively coupled to the processor, the processor configured to: transmit the predicted physiological parameter to a predetermined device using the communication device ( Communication link taught within Paragraphs [0042-0043]) .
4. Claims 2 and 47 are rejected under 35 U.S.C 103 as being unpatentable over MCDUFF et al. (USPUB 20200121256) in view of WEI (USPUB 20210113105) in further view of Meng Rong et al. ( NPL Doc. : “A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning,” 21st November 2020, Control, Volume 64,February 2021,102328, Pages 1-10.) .
As per claim 2, MCDUFF et al. teaches A method of monitoring a patient, comprising: at one or more processors( Paragraph [0005-0006]- “…a processor configured to execute the instructions to perform a method including: receiving a video fame sequence capturing one or more skin regions of a body; …”): receiving one or more image signals corresponding to a finger of the patient(FIG.2 shows Video frames of subject face and FIG. 4 -402- “Receive a video frame sequence capturing one or more skin region of a body AND Paragraph [0012], AND Paragraph [0017]- “…videos obtained from a plurality of subjects and (ii) corresponding test data obtained by measuring the blood volume pulse using a finger probe on each of the subjects….”); selecting one or more spatial and temporal portions of the one or more image signals ( Paragraph [0041]- “… the detected spatial-temporal distribution of the physiological signal in the video frame sequence received at block 402. The spatial-temporal distribution of the physiological signal may serve as an additional tool in conjunction with the physiological signal and/or physiological parameters obtained based on the physiological signal 106 to assess health of the subject, for example….”); and predicting a physiological parameter based on the selected one or more spatial and temporal portions using a machine learning model( FIG. 4 -408-410-“ Determine, using the second neural network, the physiological signal based on the frames of the video” AND Paragraph [0036-0039] AND Paragraph [0041-0042]) .
MCDUFF et al. does not explicitly teach based on contact pressure of the finger to an optical sensor;
Within analogous art, WEI teaches based on contact pressure of the finger to an optical sensor ( Paragraph [0176]- “… one or more photoelectric sensors 1330 are located on the finger of the subject and the photoelectric sensors are configured for detecting one or more PPG signals or pulse wave related signals….”) ;
One of ordinary skill in the art would have been motivated to combine the teaching of WEI within the modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. because the System and method for physiological parameter monitoring mentioned by WEI provides a system and method for implementing monitoring physiological parameters of health.
Therefore, it would have been obvious for one in the ordinary skills in the art before the effective filing date of the claimed invention to implement the System and method for physiological parameter monitoring mentioned by WEI within the modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. for implementation of a system and method for monitoring physiological parameters of health.
Combination of MCDUFF et al. and WEI does not explicitly teach parameter comprising a blood pressure and by calculating one or more of a short time Fourier transform (STFT), a continuous wavelet transform (CWT), a synchro-squeezing transform (SSQ), and a PPGlet of the one or more image signals as input to the machine learning model.
However, within analogous art, Meng Rong et al. teaches parameter comprising a blood pressure and by calculating one or more of a short time Fourier transform (STFT), a continuous wavelet transform (CWT), a synchro-squeezing transform (SSQ), and a PPGlet of the one or more image signals as input to the machine learning model ( Page 3- Fig. 3 teaches the input of PPG signal for images and also signal processing input to the machine learning module for calculation of physiological parameter ( Blood pressure , Also within Page 4 -Col. 1- 3.2- Signal filtering the processing of wavelet transform signaling is taught ) .
One of ordinary skill in the art would have been motivated to combine the teaching of Meng Rong et al. within the combined modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. and the System and method for physiological parameter monitoring mentioned by WEI because the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. provides a system and method for implementing non-contact blood pressure measurement from images of subject and machine learning model system for processing signals.
Therefore, it would have been obvious for one in the ordinary skills in the art before the effective filing date of the claimed invention to implement the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. within the combined modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. and the System and method for physiological parameter monitoring mentioned by WEI for implementation of a system and method for non-contact blood pressure measurement from images of subject and machine learning model system for processing signals.
As per claim 47, Combination of MCDUFF et al. and Meng Rong et al. teach claim 46,
Combination of MCDUFF et al. and Meng Rong et al. does not explicitly teach comprising a pressure sensor configured to measure finger pressure against the optical sensor.
Within analogous art, WEI teaches comprising a pressure sensor configured to measure finger pressure against the optical sensor ( Paragraph [0176]- “… one or more photoelectric sensors 1330 are located on the finger of the subject and the photoelectric sensors are configured for detecting one or more PPG signals or pulse wave related signals….”) .
One of ordinary skill in the art would have been motivated to combine the teaching of WEI within the combined modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. and the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. because the System and method for physiological parameter monitoring mentioned by WEI provides a system and method for implementing monitoring physiological parameters of health.
Therefore, it would have been obvious for one in the ordinary skills in the art before the effective filing date of the claimed invention to implement the System and method for physiological parameter monitoring mentioned by WEI within the combined modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. and the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. for implementation of a system and method for monitoring physiological parameters of health.
5. Claims 3 and 15 are rejected under 35 U.S.C 103 as being unpatentable over MCDUFF et al. (USPUB 20200121256) in view of Shen et al. (USPUB 20200051217) in further view of Meng Rong et al. ( NPL Doc. : “A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning,” 21st November 2020, Biomedical Signal Processing and Control,Voluem 64,February 2021,102328, Pages 1-10.) .
As per claim 3, MCDUFF et al. teaches A method of monitoring a patient, comprising: at one or more processors (Paragraph [0005-0006]- “…a processor configured to execute the instructions to perform a method including: receiving a video fame sequence capturing one or more skin regions of a body; …”): receiving one or more image signals corresponding to a face of the patient (FIG.2 shows Video frames of subject face and FIG. 4 -402- “Receive a video frame sequence capturing one or more skin region of a body AND Paragraph [0012], AND Paragraph [0017]- “…videos obtained from a plurality of subjects and (ii) corresponding test data obtained by measuring the blood volume pulse using a finger probe on each of the subjects….”); and predicting a physiological parameter based on the processed one or more image signals using a machine learning model ( FIG. 4 -408-410-“ Determine, using the second neural network, the physiological signal based on the frames of the video” AND Paragraph [0036-0039] AND Paragraph [0041-0042]) .
MCDUFF et al. does not explicitly teach processing the one or more image signals based on a shutter speed and signal gain of an optical sensor associated with the one or more image signals;
However, within analogous art, Shen et al. teaches processing the one or more image signals based on a shutter speed and signal gain of an optical sensor associated with the one or more image signals( Paragraph [0116]- “…input image(s) may be captured using the ND filter for different settings of exposure time, ISO settings, shutter speed, and/or aperture of the imaging device. …” AND Paragraph [0172]) ;
One of ordinary skill in the art would have been motivated to combine the teaching of Shen et al. within the modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. because the Artificial intelligence techniques for image enhancement mentioned by Shen et al. provides a system and method for implementing artificial intelligence algorithm for enhancing input images .
Therefore, it would have been obvious for one in the ordinary skills in the art before the effective filing date of the claimed invention to implement the Artificial intelligence techniques for image enhancement mentioned by Shen et al. within the modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. for implementation of a system and method for artificial intelligence algorithm for enhancing input images .
Combination of MCDUFF et al. and WEI does not explicitly teach parameter comprising a blood pressure and by calculating one or more of a short time Fourier transform (STFT), a continuous wavelet transform (CWT), a synchro-squeezing transform (SSQ), and a PPGlet of the processed one or more image signals as input to the machine learning model.
However, Meng Rong et al. teaches parameter comprising a blood pressure and by calculating one or more of a short time Fourier transform (STFT), a continuous wavelet transform (CWT), a synchro-squeezing transform (SSQ), and a PPGlet of the processed one or more image signals as input to the machine learning model ( Page 3- Fig. 3 teaches the input of PPG signal for images and also signal processing input to the machine learning module for calculation of physiological parameter ( Blood pressure , Also within Page 4 -Col. 1- 3.2- Signal filtering the processing of wavelet transform signaling is taught ) .
One of ordinary skill in the art would have been motivated to combine the teaching of Meng Rong et al. within the combined modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. and the Artificial intelligence techniques for image enhancement mentioned by Shen et al. because the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. provides a system and method for implementing non-contact blood pressure measurement from images of subject and machine learning model system for processing signals.
Therefore, it would have been obvious for one in the ordinary skills in the art before the effective filing date of the claimed invention to implement the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. within the combined modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. and the Artificial intelligence techniques for image enhancement mentioned by Shen et al. for implementation of a system and method for non-contact blood pressure measurement from images of subject and machine learning model system for processing signals.
As per claim 15, Combination of MCDUFF et al. and Meng Rong et al. teach claim 1,
Combination of MCDUFF et al. and Meng Rong et al. does not explicitly teach wherein processing the one or more image signals comprises modifying the one or more image signals based on a shutter speed and signal gain of an optical sensor associated with the one or more image signals.
Within analogous art, Shen et al. teaches wherein processing the one or more image signals comprises modifying the one or more image signals based on a shutter speed and signal gain of an optical sensor associated with the one or more image signals( Paragraph [0116]- “…input image(s) may be captured using the ND filter for different settings of exposure time, ISO settings, shutter speed, and/or aperture of the imaging device. …” AND Paragraph [0172]).
6. Claims 9,10,11,12,24,2628,32,33 and 39 are rejected under 35 U.S.C 103 as being unpatentable over MCDUFF et al. (USPUB 20200121256) in view of Meng Rong et al. ( NPL Doc. : “A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning,” 21st November 2020, Biomedical Signal Processing and Control,Voluem 64,February 2021,102328, Pages 1-10.) in further view of DiMaio et al. (USPUB 2018031082).
As per claim 9, Combination of MCDUFF et al. and Meng Rong et al. teach claim 1,
Combination of MCDUFF et al. and Meng Rong et al. does note explicitly teach wherein the first machine learning model is trained using a first machine learning model training set of photoplethysmography (PPG) signals based on a set of physiological parameter values.
Within analogous art, DiMaio et al. teaches wherein the first machine learning model is trained using a first machine learning model training set of photoplethysmography (PPG) signals based on a set of physiological parameter values ( Paragraph [0488]- “…feature sets include photoplethysmography (PPG), multispectral imaging (MSI), real image (RI). Example methodology includes drawing ground truth, training a classification algorithm with all three feature sets both separately and also together, classifying images,…” AND Paragraph [0423]).
One of ordinary skill in the art would have been motivated to combine the teaching of DiMaio et al. within the combined modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. and the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. because the Reflective mode multi-spectral time-resolved optical imaging methods and apparatuses for tissue classification mentioned by DiMaio et al. provides a system and method for implementing medical image of human body classification with neural network model.
Therefore, it would have been obvious for one in the ordinary skills in the art before the effective filing date of the claimed invention to implement the Reflective mode multi-spectral time-resolved optical imaging methods and apparatuses for tissue classification mentioned by DiMaio et al. within the combined modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. and the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. for implementation of a system and method for medical image of human body classification with neural network model.
As per claim 10, Combination of MCDUFF et al. and Meng Rong et al. and DiMaio et al. teach claim 9,
Combination of MCDUFF et al. and Meng Rong et al. does note explicitly teach wherein the set of predetermined physiological parameter values corresponds to one or more of heart rate, heart rate variability, oxygen saturation, respiratory rate, and blood pressure.
Within analogous art, DiMaio et al. teaches wherein the set of predetermined physiological parameter values corresponds to one or more of heart rate, heart rate variability, oxygen saturation, respiratory rate, and blood pressure ( Paragraph [0022]- “ PPG imaging may use similar technology as that used in pulse oximetry to capture vital signs including: heart rate, respiratory rate, and SpO.sub.2. The PPG signal may be generated by measuring light's interaction with dynamic changes in the vascularized tissues….”) .
As per claim 11, Combination of MCDUFF et al. and Meng Rong et al. and DiMaio et al. teach claim 9,
Combination of MCDUFF et al. and Meng Rong et al. does note explicitly teach wherein the first machine learning model training set comprises PPG signals of a plurality of patients.
Within analogous art, DiMaio et al. teaches wherein the first machine learning model training set comprises PPG signals of a plurality of patients ( Paragraphs [0488] AND [0195]) .
As per claim 12, Combination of MCDUFF et al. and Meng Rong et al. and DiMaio et al. teach claim 9,
Combination of MCDUFF et al. and Meng Rong et al. does note explicitly teach wherein the first machine learning model training set comprises artificial photoplethysmography PPG signals comprising a set of predetermined physiological parameter values.
Within analogous art, DiMaio et al. teaches wherein the first machine learning model training set comprises artificial photoplethysmography PPG signals comprising a set of predetermined physiological parameter values ( Paragraph [0171]- “…photoplethysmography (PPG) has been used to detect blood volume changes in microvascular beds of tissue. In some instances, PPG alone does not fully classify tissue because it only makes volumetric measurements. Also, multispectral imaging (MSI) has been used to discern differences in skin tissue but this technique does not fully classify tissue…”) .
As per claim 24, Combination of MCDUFF et al. and Meng Rong et al. teach claim 1,
Combination of MCDUFF et al. and Meng Rong et al. does note explicitly teach wherein processing the one or more image signals comprises generating a virtual multispectral PPG signal.
Within analogous art, DiMaio et al. teaches wherein processing the one or more image signals comprises generating a virtual multispectral PPG signal ( Paragraphs [0092-0093]- “…graphical overview of two optical imaging techniques, photoplethysmography imaging (PPG Imaging) and multispectral imaging (MSI) that can be combined with patient health metrics to generate prognostic information….”) .
As per claim 26, Combination of MCDUFF et al. and Meng Rong et al. teach claim 1,
Combination of MCDUFF et al. and Meng Rong et al. does note explicitly teach wherein the physiological parameter comprises one or more of oxygen saturation and blood glucose.
Within analogous art, DiMaio et al. teaches wherein the physiological parameter comprises one or more of oxygen saturation and blood glucose ( Paragraph [0168]- “…. a patient may wear a pulse oximeter in order to measure oxygen saturation and pulse. However, the pulse oximeter may also act as a PPG device as well, measuring blood volume…” AND Paragraph [0486]- “…control as measured by the HbA1c and blood-glucose testing…”) .
As per claim 32, Combination of MCDUFF et al. and Meng Rong et al. teach claim 1,
Combination of MCDUFF et al. and Meng Rong et al. does note explicitly teach wherein the blood pressure comprises a continuous arterial blood pressure.
Within analogous art, DiMaio et al. teaches wherein the blood pressure comprises a continuous arterial blood pressure ( Paragraph [0223]- “…assessment of the arterial blood pressure values and waveforms gives valuable information about the physiologic interaction between the LVAD and the cardiovascular system….” AND Paragraph [0142]- “…biometric readers for measuring heart rate, temperature, body composition, body mass index, body shape, blood pressure…”) .
As per claim 33, Combination of MCDUFF et al. and Meng Rong et al. and DiMaio et al. teach claim 1,
Combination of MCDUFF et al. and Meng Rong et al. does note explicitly teach wherein predicting the physiological parameter comprises calculating for the processed one or more image signals an upper envelope corresponding to systolic blood pressure and a lower envelope corresponding to diastolic blood pressure.
Within analogous art, DiMaio et al. teaches wherein predicting the physiological parameter comprises calculating for the processed one or more image signals an upper envelope corresponding to systolic blood pressure and a lower envelope corresponding to diastolic blood pressure ( Paragraph [0167]- “… measure phase shifts in the systolic and diastolic activities of the heartbeat waveform. These shifts can be used to find systolic and diastolic pressures, which in some circumstances can be used to estimate the pulse pressures for the right and left ventricle. An external cuff may also be used to measure systolic and diastolic pressure as an addition or an alternative. …” AND Paragraph [0223]) .
As per claim 39, Combination of MCDUFF et al. and Meng Rong et al. teach claim 26,
Combination of MCDUFF et al. and Meng Rong et al. does not explicitly teach wherein processing the one or more image signals comprises extracting one or more of frequency modulation, amplitude modulation, and baseline wander of one or more color channels of the PPG signal.
Within analogous art, DiMaio et al. teaches wherein processing the one or more image signals comprises extracting one or more of frequency modulation, amplitude modulation, and baseline wander of one or more color channels of the PPG signal ( Modulation in intensity and the PPG signal taught within Paragraph [0241] and [0257]) .
7. Claim 13 is rejected under 35 U.S.C 103 as being unpatentable over MCDUFF et al. (USPUB 20200121256) in view of DiMaio et al. (USPUB 2018031082) in further view of Meng Rong et al. ( NPL Doc. : “A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning,” 21st November 2020, Biomedical Signal Processing and Control,Voluem 64,February 2021,102328, Pages 1-10.) and Martinez (USPUB 20190114824).
As per claim 13, Combination of MCDUFF et al. and Meng Rong et al. and DiMaio et al. teach claim 9,
Combination of MCDUFF et al. and Meng Rong et al. and DiMaio et al. does not explicitly teach wherein the first machine learning model training set comprises artificial noise comprising one or more of Gaussian noise, white noise, stretching, sloping, saturation, replacement, scaling, and baseline wander.
Within analogous art, Martinez teaches wherein the first machine learning model training set comprises artificial noise comprising one or more of Gaussian noise, white noise, stretching, sloping, saturation, replacement, scaling, and baseline wander ( Gaussian noise and training within machine learning taught within Paragraphs [0074],[0077] and [0092] AND FIG. 7) .
One of ordinary skill in the art would have been motivated to combine the teaching of Martinez within the combined modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. and the Reflective mode multi-spectral time-resolved optical imaging methods and apparatuses for tissue classification mentioned by DiMaio et al. and the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. because the Fast and precise object alignment and 3Dshape reconstruction from a single 2D image mentioned by Martinez provides a system and method for implementing data augmentation of image with machine learning model.
Therefore, it would have been obvious for one in the ordinary skills in the art before the effective filing date of the claimed invention to implement the Fast and precise object alignment and 3d shape reconstruction from a single 2d image mentioned by Martinez within the combined modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. and the Reflective mode multi-spectral time-resolved optical imaging methods and apparatuses for tissue classification mentioned by DiMaio et al. and the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. for implementation of a system and method for data augmentation of image with machine learning model.
8. Claim 23 is rejected under 35 U.S.C 103 as being unpatentable over MCDUFF et al. (USPUB 20200121256) in view of Meng Rong et al. ( NPL Doc. : “A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning,” 21st November 2020, Biomedical Signal Processing and Control,Voluem 64,February 2021,102328, Pages 1-10.) in further view Martinez (USPUB 20190114824).
As per claim 23, Combination of MCDUFF et al. and Meng Rong et al. teach claim 1,
Within analogous art, Martinez teaches wherein processing the one or more image signals comprises generating a polygon mesh corresponding to a face and neck of the patient ( Paragraph [0042]- “… the area of the polygon envelope can be computed, i.e., a non-self-intersecting polygon contained by the t landmark points…This polygon may be computed as follows. First, a Delaunay triangulation of image (for example a face image) landmark points is computed. A polygon envelop is easily obtained by connecting the lines of the set of t landmark points in counter-clockwise order…”) .
One of ordinary skill in the art would have been motivated to combine the teaching of Martinez within the modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. and the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. because the Fast and precise object alignment and 3D shape reconstruction from a single 2D image mentioned by Martinez provides a system and method for implementing data augmentation of image with machine learning model.
Therefore, it would have been obvious for one in the ordinary skills in the art before the effective filing date of the claimed invention to implement the Fast and precise object alignment and 3D shape reconstruction from a single 2D image mentioned by Martinez within the modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. and the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. for implementation of a system and method for data augmentation of image with machine learning model.
9. Claims 16,17,18 and 48 are rejected under 35 U.S.C 103 as being unpatentable over MCDUFF et al. (USPUB 20200121256) in view of Meng Rong et al. ( NPL Doc. : “A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning,” 21st November 2020, Biomedical Signal Processing and Control,Voluem 64,February 2021,102328, Pages 1-10.) in further view of MCDUFF et al. (USPUB 20210398337).
As per claim 16, Combination of MCDUFF et al. and Meng Rong et al. teach claim 1,
Within analogous art, MCDUFF et al. teaches wherein processing the one or more image signals comprises generating one or more albedo signals corresponding to the one or more image signals ( Paragraph [0075]- “…here a base sub-surface skin color forming a base color under the skin may be modified based on the weighted physiological data to obtain a sub-surface skin color. At 920, an albedo may be selected. The albedo may correspond to a texture map transferred from a high-quality 3D face scan. The albedo may be chosen at random or chosen to represent a specific population. The albedo may be devoid of facial hair so that the skin properties can be easily manipulated. …” AND Paragraph [0121]) .
One of ordinary skill in the art would have been motivated to combine the teaching of MCDUFF et al. within the combined modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. and the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. because the Generating physio-realistic avatars for training non-contact models to recover physiological characteristics mentioned by MCDUFF et al. provides a system and method for implementing the training of machine learning model with plurality of video image segment.
Therefore, it would have been obvious for one in the ordinary skills in the art before the effective filing date of the claimed invention to implement the Generating physio-realistic avatars for training non-contact models to recover physiological characteristics mentioned by MCDUFF et al. within the combined modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. and the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. for implementation of a system and method for the training of machine learning model with plurality of video image segment.
As per claim 17, Combination of MCDUFF et al. and Meng Rong et al. teach claim 16,
Within analogous art, MCDUFF et al. teaches wherein the one or more albedo signals comprises diffuse reflection and is absent specular reflection ( Paragraph [0031]- “…kin tissue and camera; I(t) is modulated by two components in the DRM: specular (glossy) reflection v.sub.s(t), mirror-like light reflection from the skin surface, and diffuse reflection v.sub.d(t). The diffuse reflection in turn has two parts: the absorption v.sub.abs(t) and sub-surface scattering of light in skin-tissues v.sub.sub(t); v.sub.n(t) denotes the quantization noise of the camera sensor. I(t),…” AND Paragraphs [0037-0038]) .
One of ordinary skill in the art would have been motivated to combine the teaching of MCDUFF et al. within the combined modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. and the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. because the Generating physio-realistic avatars for training non-contact models to recover physiological characteristics mentioned by MCDUFF et al. provides a system and method for implementing the training of machine learning model with plurality of video image segment.
Therefore, it would have been obvious for one in the ordinary skills in the art before the effective filing date of the claimed invention to implement the Generating physio-realistic avatars for training non-contact models to recover physiological characteristics mentioned by MCDUFF et al. within the combined modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. and the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. for implementation of a system and method for the training of machine learning model with plurality of video image segment.
As per claim 18, Combination of MCDUFF et al. and Meng Rong et al. teach claim 1,
Within analogous art, MCDUFF et al. teaches wherein processing the one or more image signals comprises: selecting a face and a neck of the skin of the one or more image signals ( Figs 1 and 2 ) ; extracting a mean RGB signal of the selected skin as input to the first machine learning model ( Paragraphs [0031-0033]) .
As per claim 48, Combination of MCDUFF et al. and Meng Rong et al. teach claim 46,
Within analogous art, MCDUFF et al. teaches comprising an audio sensor configured to measure patient audio ( audio sensor/interface taught within Paragraphs [0101]) .
One of ordinary skill in the art would have been motivated to combine the teaching of MCDUFF et al. within the combined modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. and the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. because the Generating physio-realistic avatars for training non-contact models to recover physiological characteristics mentioned by MCDUFF et al. provides a system and method for implementing the training of machine learning model with plurality of video image segment.
Therefore, it would have been obvious for one in the ordinary skills in the art before the effective filing date of the claimed invention to implement the Generating physio-realistic avatars for training non-contact models to recover physiological characteristics mentioned by MCDUFF et al. within the combined modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. and the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. for implementation of a system and method for the training of machine learning model with plurality of video image segment.
10. Claims 21,22, 27 and 29 are rejected under 35 U.S.C 103 as being unpatentable over MCDUFF et al. (USPUB 20200121256) in view of Meng Rong et al. ( NPL Doc. : “A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning,” 21st November 2020, Biomedical Signal Processing and Control,Voluem 64,February 2021,102328, Pages 1-10.) in further view of LEE et al. (USPUB 20220138493).
As per claim 21, Combination of MCDUFF et al. and Meng Rong et al. teach claim 1,
Within analogous art , LEE et al. teaches wherein the first machine learning model comprises one or more of a residual neural network (ResNet), U-Net, variational autoencoder, denoising autoencoder neural network, autoencoder neural network with residual connections, vector quantized autoencoder, graph convolutional network, graph attention network, multi-head attention transformer, U-Net model, and combinations thereof ( Neural network models taught within Paragraphs [0047-0048]) .
One of ordinary skill in the art would have been motivated to combine the teaching of LEE et al. within the combined modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. and the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. because the Method and apparatus with adaptive object tracking mentioned by LEE et al. provides a system and method for implementing the tracking of object within image frame with neural network models.
Therefore, it would have been obvious for one in the ordinary skills in the art before the effective filing date of the claimed invention to implement the Method and apparatus with adaptive object tracking mentioned by LEE et al. within the combined modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. and the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. for implementation of a system and method for the tracking of object within image frame with neural network models.
As per claim 22, Combination of MCDUFF et al. and Meng Rong et al. teach claim 1,
Within analogous art , LEE et al. teaches wherein the first and second machine learning models comprise one or more of self-supervised learning, semi-supervised learning, weakly-supervised learning, and federated learning ( Paragraph [0051]- “Through supervised or unsupervised learning of deep learning, a structure of the neural network or weights corresponding to a model may be obtained, and the input data and the output data may be mapped to each other by the weights….”) .
11. Claims 27 and 29 are rejected under 35 U.S.C 103 as being unpatentable over MCDUFF et al. (USPUB 20200121256) in view of DiMaio et al. (USPUB 2018031082) in further view of Meng Rong et al. ( NPL Doc. : “A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning,” 21st November 2020, Biomedical Signal Processing and Control,Voluem 64,February 2021,102328, Pages 1-10.) and LEE et al. (USPUB 20220138493).
As per claim 27, Combination of MCDUFF et al. and Meng Rong et al. and DiMaio et al. teach claim 26,
Within analogous art , LEE et al. teaches wherein the second machine learning model comprises one or more of a long short-term memory network (LSTM), a bi-directional long short-term memory network (bi-LSTM), convolutional neural network (CNN), deep neural network, a gradient boosting model, transformers, and combinations thereof ( ( Neural network models taught within Paragraphs [0047-0048]).
One of ordinary skill in the art would have been motivated to combine the teaching of LEE et al. within the combined modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. and the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. and the Reflective mode multi-spectral time-resolved optical imaging methods and apparatuses for tissue classification mentioned by DiMaio et al. because the Method and apparatus with adaptive object tracking mentioned by LEE et al. provides a system and method for implementing the tracking of object within image frame with neural network models.
Therefore, it would have been obvious for one in the ordinary skills in the art before the effective filing date of the claimed invention to implement the Method and apparatus with adaptive object tracking mentioned by LEE et al. within the combined modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. and the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. and the Reflective mode multi-spectral time-resolved optical imaging methods and apparatuses for tissue classification mentioned by DiMaio et al. for implementation of a system and method for the tracking of object within image frame with neural network models.
As per claim 29, Combination of MCDUFF et al. and Meng Rong et al. and DiMaio et al. teach claim 28,
Within analogous art , LEE et al. teaches wherein the second machine learning model comprises one or more of a Bayesian network, a long short-term memory network (LSTM), a bi-directional long short-term memory network (bi-LSTM), a convolutional neural network (CNN), a random forest, a gradient boosting model, a Wave net model, a residual neural network (ResNet) model, a WaveResNet model, a support vector machine (SVM), autoencoder, and combinations thereof( Neural network models taught within Paragraphs [0047-0048]).
12. Claim 25 is rejected under 35 U.S.C 103 as being unpatentable over MCDUFF et al. (USPUB 20200121256) in view of Meng Rong et al. ( NPL Doc. : “A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning,” 21st November 2020, Biomedical Signal Processing and Control,Voluem 64,February 2021,102328, Pages 1-10.) in further view of Sullivan et al. (USPUB 20160135706).
As per claim 25, Combination of MCDUFF et al. and Meng Rong et al. teach claim 1,
Within analogous art, Sullivan et al. teaches wherein processing the one or more image signals comprises applying one or more of a Kalman filter, principal component analysis, independent component analysis, and blind source separation ( Paragraphs [0448-0449] and image signal taught within Paragraph [0516]- “… image of the subject's face is captured. The control unit 120 may perform facial recognition analysis on the image to analyze the emotional…”) .
One of ordinary skill in the art would have been motivated to combine the teaching of Sullivan et al. within the combined modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. and the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. because the Medical Premonitory Event Estimation mentioned by Sullivan et al. provides a system and method for implementing for analyzing risk factor from machine learning classification model.
Therefore, it would have been obvious for one in the ordinary skills in the art before the effective filing date of the claimed invention to implement the Medical Premonitory Event Estimation mentioned by Sullivan et al. within the combined modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. and the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. for implementation of a system and method for analyzing risk factor from machine learning classification model.
13. Claim 34 is rejected under 35 U.S.C 103 as being unpatentable over MCDUFF et al. (USPUB 20200121256) in view of DiMaio et al. (USPUB 2018031082) in further view of Meng Rong et al. ( NPL Doc. : “A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning,” 21st November 2020, Biomedical Signal Processing and Control,Voluem 64,February 2021,102328, Pages 1-10.) and Dangdang Shao et al. ( NPL Doc:"Noncontact Monitoring Breathing Pattern, Exhalation Flow Rate and Pulse Transit Time," 16th October 2014,IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 61, NO. 11, NOVEMBER 2014,Pages 2760-2765.).
As per claim 34, Combination of MCDUFF et al. and Meng Rong et al. teach claim 4,
Within analogous art, Dangdang Shao et al. teaches wherein predicting the blood pressure comprises calculating for the processed one or more image signals one or more of a pulse transit time (PTT) based on a plurality of portions of the face of the patient, a PTT between the face and the finger, and a modified Normalized Pulse Volume (mNPV) and a photoplethysmography (PPG) signal based on the finger or the face ( Page 2764-Col. 2- D. Pulse Transit Time- lines 1-15) .
One of ordinary skill in the art would have been motivated to combine the teaching of Dangdang Shao et al. within the combined modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. and the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. and the Reflective mode multi-spectral time-resolved optical imaging methods and apparatuses for tissue classification mentioned by DiMaio et al. because the Noncontact Monitoring Breathing Pattern, Exhalation Flow Rate and Pulse Transit Time mentioned by Dangdang Shao et al. provides a system and method for implementing the vital physiological signals such as pulse transit time measurement from optical images.
Therefore, it would have been obvious for one in the ordinary skills in the art before the effective filing date of the claimed invention to implement the Noncontact Monitoring Breathing Pattern, Exhalation Flow Rate and Pulse Transit Time mentioned by Dangdang Shao et al. within the combined modified teaching of the Video-based physiological measurement using neural networks mentioned by MCDUFF et al. and the A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning mentioned by Meng Rong et al. and the Reflective mode multi-spectral time-resolved optical imaging methods and apparatuses for tissue classification mentioned by DiMaio et al. for implementation of a system and method for the vital physiological signals such as pulse transit time measurement from optical images.
It is noted that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
Allowable Subject Matter
14. Claims 14,19,20,31, 37,38, 42 and 44 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
15. The following is an examiner’s statement of reasons for objecting the claims as allowable subject matter:
As to claim 14 , prior art of record does not teach or suggest the limitation mentioned within claim 14: “…image signals to select one or more portions of the one or more image signals is based on one or more of a dominant frequency, maximal variation, a correlation coefficient, contact pressure of a finger to an optical sensor, a cross-correlation among a set of cardiac cycles within a predetermined time period, cycle-by-cycle validation, bandpass filtering, smoothness, motion artifact removal, session filtering, and power spectrum. “
As to claim 19 , prior art of record does not teach or suggest the limitation mentioned within claim 19: “…wherein extracting the mean RGB signal comprises applying z- normalization separately to a plurality of sliding windows of the mean RGB signal, wherein the z- normalization comprises per temporal point normalization with respect to a local neighborhood.”
As to claim 20, prior art of record does not teach or suggest the limitation mentioned within claim20: Claim 20 depends on objected allowable claim 19. Therefore claim 20 is objected as allowable over prior art of record.
As to claim 31, prior art of record does not teach or suggest the limitation mentioned within claim 31: “…predicting the physiological parameter comprises calculating for the processed one or more image signals one or more of systolic amplitude, pulse area, pulse interval, heart rate, time between systolic peak and end of a cardiac cycle, ratio of time before and after a systolic peak in a cardiac cycle, pulse width, maximum upslope, absorbance, Kaiser-Teager energy, signal energy, magnitude, phase, crest time, pulse interval, pulse width at half height (PWHH), Dicrotic Notch time (T~), A2 time (A2T), diastolic time (DT), first derivative peak time (FDPT), pulse area (PA), area 1, area 2, pulse height (PH), ratio of b peak to a peak of a second derivative (b/a), ratio of e peak to a peak of the second derivative (e/a), modified Normalized Pulse Volume (mNPV), mean arterial pressure (MAP), cardiac output (CO), and total peripheral resistance (TPR).”
As to claims 37 and 38 , prior art of record does not teach or suggest the limitation mentioned within claims 37 and 38: Claims 37 and 38 depends on objected allowable claim 36. Therefore claims 37 and 38 are objected as allowable over prior art of record.
As to claim 42, prior art of record does not teach or suggest the limitation mentioned within claim 42: Claim 42 depends on objected allowable claim 41. Therefore claim 42 is objected as allowable over prior art of record.
As to claim 44, prior art of record does not teach or suggest the limitation mentioned within claim 44: “…wherein the heart rate variability comprises one or more of a standard deviation of NN intervals (SDNN), a mean of the NN (e.g., peak-to-peak distance) intervals, and a root mean square of successive differences between normal heartbeats (RMSSD). ”
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Refer to PTO-892, Notice of Reference Cited for a listing of analogous art.
16. Any inquiry concerning this communication or earlier communications from the examiner should be directed to OMAR S. ISMAIL whose telephone number is (571)272-9799 and Fax # (571)273-9799. The examiner can normally be reached on M-F: 9:00 AM - 6:00 PM.
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/OMAR S ISMAIL/Primary Examiner, Art Unit 2635
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