DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/07/2025 and 08/21/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 7, 8, 10-12, 15, and 18-21 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Tzvieli et al. (Pub. No.: US 2022/0151504).
Consider claim 1, Tzvieli discloses a method of obtaining a physiological signal representing a patient metric (paragraph [0041], Fig. 1, capturing of imaging photoplethysmogram signals (iPPG signals)), the method comprising:
acquiring patient physiological data from a sensor operating in ambient conditions (paragraph [0042], Fig. 1, head-mounted camera 552 captures images 554 of a region that includes skin on a user's head utilizing an image sensor);
generating a Photoplethysmography (PPG) signal or a pseudo PPG signal from the acquired patient physiological data (paragraph [0047], extract the iPPG signals from the images 554), the PPG signal or the pseudo PPG signal having a signal quality characteristic associated with a quality indicator of the PPG signal or the pseudo PPG signal (paragraph [0052], a PPG signal has a fundamental frequency of oscillation equal to the pulse rate, and the spectral power of the PPG signal is concentrated in a small frequency band around the pulse rate);
comparing the signal quality characteristic of the PPG signal or the pseudo PPG signal to a correlating signal quality characteristic or parameter of a deep learning-based model PPG signal (paragraph [0052], the quality scores for the iPPG signals can include a factor estimated as a ratio of (i) the power of the recorded signal around the pulse rate, to (ii) the power of the noise in the passband of the bandpass filter); and
determining that the comparison of the signal quality characteristic of the PPG signal or the pseudo PPG signal to the signal quality characteristic or parameter of the deep-learning based model PPG signal meets a signal quality criterion (paragraph [0053], the computer 556 selects a proper subset of the iPPG signals whose quality scores reach a threshold).
Consider claim 2, Tzvieli discloses wherein the patient physiological data comprises metadata measured by an inertial measurement unit (IMU) of a mobile device (paragraph [0133], Fig. 3, inertial measurement unit (IMU) 581, which measures movement signal 585).
Consider claim 3, Tzvieli discloses wherein acquiring the patient physiological data from the sensor further comprises capturing image frames using a camera of a mobile device (paragraph [0042], Fig. 1, head-mounted camera 552 captures images 554).
Consider claim 7, Tzvieli discloses wherein generating the PPG signal or the pseudo PPG signal from the acquired patient physiological data comprises using a deep-learning model applied to one or more individual frames of the image frames (paragraph [0050], iPPG signals may involve utilization of machine learning approaches to include deep learning, see paragraph [0029]).
Consider claim 8, Tzvieli discloses wherein generating the PPG signal or the pseudo PPG signal from the acquired patient physiological data comprises using a deep-learning model applied simultaneously to at least two of the image frames (paragraph [0180], computer 608 extracts one or more iPPG signals based on the interlaced images 606 wherein iPPG signals may involve utilization of machine learning approaches to include deep learning, see paragraph [0029]).
Consider claim 10, Tzvieli discloses wherein the image frames comprise a time-series of image frames that are partitioned into video chunks that each have a predetermined time span (paragraph [0120], a times-series signals obtained from video of a user can be filtered and processed to separate an underlying pulsing signal by, for example, using an ICA algorithm).
Consider claim 11, Tzvieli discloses wherein two contiguous video chunks are combined into a video segment having a portion that comprises an overlap between the two contiguous video chunks (paragraph [0090], a second set of images that are interlaced between the images 569 wherein the second set of images are interlaced between the advantageous timings (see paragraph [0091]) and times-series signals obtained from video of a user (see paragraph [0120])).
Consider claim 12, Tzvieli discloses wherein the deep-learning based model PPG signal is based, at least in part, on a neural network (paragraph [0029], artificial neural network) that is trained using transfer learning on pulse oximeter PPG data (paragraph [0023], contact PPG device, such as a pulse oximeter wherein a model trained using one or more machine learning approaches, see paragraph [0029]) and information collected or produced by the mobile device, wherein the information includes the video chunks or blood pressure data previously determined by the mobile device.
Consider claim 15, Tzvieli discloses wherein the determining that the comparison of the signal quality characteristic of the PPG signal or the pseudo PPG signal to the correlating signal quality characteristic or parameter of the model PPG signal meets the quality criterion is performed by a process based on a trained blood pressure deep-learning model (paragraph [0006], quality scores for the iPPG signals to include pulsatile arterial blood are calculated using a machine learning-based approach wherein machine learning approaches to include deep learning, see paragraph [0029].
Consider claim 18, Tzvieli discloses inputting the PPG signal or the pseudo PPG signal to a trained disease state or biomarker model (paragraph [0149], comparison to a baseline that is based on previously taken measurements);
determining a disease state or biomarker based on the input PPG signal or the pseudo PPG signal to the trained disease state or biomarker model (paragraph [0149], detect an abnormal medical event).
Consider claim 19, Tzvieli discloses outputting an alert that includes the determined disease state or biomarker or a recommendation to engage in an activity or undertake a change in behavior based on the determined disease state or biomarker (paragraph [0149], alert about an abnormal medical event).
Consider claim 20, Tzvieli discloses wherein the trained disease state or biomarker model is a trained blood pressure model (paragraph [0149], alculates one or more current differences between systolic blood pressure values).
Consider claim 21, Tzvieli discloses generating a quality score based on the comparison of the signal quality characteristic of the PPG signal or the pseudo PPG signal to the correlating signal quality characteristic or parameter of the deep-learning based model PPG signal (paragraph [0052], the quality scores for the iPPG signals can include a factor estimated as a ratio of (i) the power of the recorded signal around the pulse rate, to (ii) the power of the noise in the passband of the bandpass filter); and
determining the quality score exceeds a threshold value (paragraph [0053], the computer 556 selects a proper subset of the iPPG signals whose quality scores reach a threshold).
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.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Tzvieli in view of Frank et al. (Pub. No.: US 2021/0259557).
Consider claim 4, Tzvieli discloses that analysis of the feature values (iPPG images) extracted from the PPG signal are well known in the art, and can be found in references to include Elgendi, Mohamed, “On the analysis of fingertip photoplethysmogram signals” (paragraph [0027]).
Tzvieli does not specifically wherein the image frames comprise images of at least a portion of a fingertip of a person.
Frank discloses wherein the image frames comprise images of at least a portion of a fingertip of a person (paragraph [1273], computer 828 may utilize the various approaches described in Elgendi, M. (2012), “On the analysis of fingertip photoplethysmogram signals” for generating feature values bases on the iPPG signal (“iPPG signal” when specifically referring to a PPG signal obtained from a camera, see paragraph [0152]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to replace the camera as disclosed by Tzvieli with the camera as taught by Frank in order to generate at least some of the feature values bases on the iPPG signal (Frank, paragraph [1273]).
Claims 5 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Tzvieli and Frank in view of Sinha et al. (Pub. No.: US 2016/0360980).
Consider claim 5, the combination of Tzvieli and Frank does not specifically disclose wherein acquiring the patient physiological data from the sensor further comprises: monitoring for problematic issues, in real-time, during the acquisition of the patient physiological data, wherein the problematic issues are based, at least in part, on relative motion between the fingertip and the camera.
Sinha discloses wherein acquiring the patient physiological data from the sensor further comprises: monitoring for problematic issues, in real-time, during the acquisition of the patient physiological data (paragraph [0036], Fi. 1, During operation, the data processing module 120 can additionally or alternatively correct for one or more of: body region placement errors with respect to captured image data by the camera module 112), wherein the problematic issues are based, at least in part, on relative motion between the fingertip and the camera (paragraph [0036], a user who shakes the mobile computing device during video capture of a finger).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to replace the processor as disclosed by the combination of Tzvieli and Frank with the processor as taught by Sinha to correct for body region placement errors with respect to captured image data by the camera module (Sinha, paragraph [0036]).
Consider claim 6, the combination of Tzvieli and Frank does not specifically disclose wherein the relative motion between the fingertip and the camera is determined by an accelerometer of the mobile device or is based, at least in part, on intensity measurements of pixel data of the image frames.
Sinha discloses wherein the relative motion between the fingertip and the camera is determined by an accelerometer of the mobile device or is based, at least in part, on intensity measurements of pixel data of the image frames (paragraph [0062], Fig. 2, identifying placement error of a body region of a user, based on image intensity S236, which functions to identify and correct for user error in capturing a body region of the user with a camera module wherein the body region is a finger of the user, see paragraph [0063]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to replace the processor as disclosed by the combination of Tzvieli and Frank with the processor as taught by Sinha to identify placement error of a body region of a user (Sinha, paragraph [0062]).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Tzvieli in view of Plans et al. (Pub. No.: US 2015/0093729).
Consider claim 9, Tzvieli does not specifically disclose wherein the image frames comprise red, green, blue (RGB) pixel data, and wherein the patient physiological data is based, at least in part, on the red pixel data.
Plan discloses wherein the image frames comprise red, green, blue (RGB) pixel data (paragraph [0051], red, green and blue (RGB) values of every pixel in images continuously taken from the cell phone camera's stream).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to replace the camera as disclosed by Tzvieli with the camera as taught by Plan to detect a biometric signal from the received images, and a biometric parameter associated with the biometric signal (Plan, paragraph [0051]).
Claims 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Tzvieli in view of Newberry (Pub. No.: US 2018/0214088).
Consider claims 13, 14, Tzvieli does not specifically discloses wherein the sensor is a camera of a mobile device (paragraph [0042], Fig. 1, head-mounted camera 552),
Tzvieli does not specifically disclose wherein acquiring the patient physiological data from the sensor further comprises controlling a flash of the mobile device.
Newberry discloses wherein acquiring the patient physiological data from the sensor further comprises controlling a flash of the mobile device (paragraph [0209], Fig. 17, camera may illuminate the body part using a flash).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to replace the camera as disclosed by Tzvieli with camera as taught by Newberry to obtained images of video or frames at a high frame rate (Newberry, paragraph [0209]).
Allowable Subject Matter
Claims 16 and 17 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.
Regarding claim 16, the prior art of references fails to disclose determining that the shape of the PPG signal or the pseudo PPG signal has a PPG-like shape based on the comparison of the shape to a correlating shape of a deep learning-based model PPG signal.
Claim 17 is objected to due dependency on objected claim 16.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GERALD JOHNSON whose telephone number is (571)270-7685. The examiner can normally be reached Monday-Friday 8am-5pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Carey Michael can be reached at (571)270-7235. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Gerald Johnson/
Primary Examiner, Art Unit 3797
Prosecution Insights