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 .
Applicant’s Response
In Applicant’s Response dated 12/11/25, the Applicant amended Claims 1, 2, 5, 7, 8 and argued Claims previously rejection in the Office Action dated 9/15/25.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(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, 4-6, 8 and 10 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Lev et al., United States Patent Publication 2022/0007950 (hereinafter “Lev”).
Claim 1:
Lev discloses:
A physiological signal measuring method, comprising:
a training’s thermal image providing step comprising providing a plurality of training’s thermal images, which are a plurality of thermal images for training, wherein each of the training’s thermal images is an infrared thermal image, and each of the training’s thermal images has a mark of a position of a person's face portion, and a mark of a mask-wearing state or a mark of a non-mask-wearing state (see paragraphs [0019] and [0026]-[0028]). Lev teaches training a model on thermal images depicting a face, determining if the person is wearing a mask, determining target locations within the images and identifying fixed points on the head of the person;
a training step comprising training the training’s thermal images by a machine learning algorithm (see paragraph [0102]). Lev teaches training the thermal image by a machine learning algorithm;
a classification model generating step comprising generating a mask-wearing classification model after the training step by the machine learning algorithm, wherein a most accurate model weight obtained from the training step by the machine learning algorithm is used in the mask-wearing classification model (see paragraph [0089]). Lev teaches generating a classification of the mask based on the trained thermal images. Lev teaches determining if it is analyzing forehead and nostrils (non-mask wearing)/ or forehead and mask (mask-wearing);
a measurement’s thermal image providing step comprising providing a measurement’s thermal image, which is an infrared thermal video for measuring (see paragraph [0047]). Lev teaches a video for measuring physiological features;
a mask-wearing classifying step comprising identifying a person's face portion of a subject in the measurement’s thermal image by the mask-wearing classification model, and classifying the person's face portion as the mask-wearing state or the non-mask-wearing state (see paragraph [0094]-[0096]). Lev teaches identifying a portion of the persons face and classifying the person as wearing a mask;
a block identifying step comprising
identifying a forehead block and a mask block in the person's face portion when the person's face portion is classified as the mask-wearing state (see paragraph [0094]). Lev teaches identifying the forehead and the mask block of the face when wearing a mask, and
identifying a forehead block and a nasal cavity block in the person’s face portion when the person's face portion is classified as the non-mask-wearing state (see paragraph [0096]). Lev teaches identifying the forehead and nostrils block of the persons face and classifying as not wearing a mask; and
a ROI (region of interest) determining step comprising determining a plurality of ROIs of each of the forehead block, and the mask block or the nasal cavity block (see paragraphs [0094]-[0096]). Lev teaches identifying the forehead and the mask block of the face when wearing a mask or forehead and nostrils when not wearing a mask, and
wherein a number of the ROls of the nasal cavity block is two, and the two ROls of the nasal cavity block match a left nostril and a right nostril, respectively (see paragraphs [0019], [0020], [0025], [0096]). Lev discloses teaches regions of interest such as the nostrils of the face.
a measurement result generating step comprising generating a measurement result of at least one physiological parameter of the subject according to a plurality of signals of the forehead block, and the mask block or the nasal cavity block (see paragraphs [0101] and [0103]). Lev teaches generating a measurement result based on the physiological parameters from the person based on the forehead and mask/nostril.
Claim 4:
Lev discloses:
wherein a number of the at least one physiological parameter is at least three, and the physiological parameters comprise a body temperature, a heart rate and a respiration rate (see paragraphs [0010] and [0082]-[0087]). Lev discloses determining three parameters such as body temperature, heart rate and respiratory rate;
wherein the measurement result generating step further comprising
generating a measurement result of the heart rate from a change of a forehead temperature (see paragraphs [0038] and [0103]). Lev teaches generating a measurement based on the difference in the forehead temperature, and
generating a measurement result of the respiration rate from a change of a mask temperature or a change of a nasal cavity temperature (see paragraph [0019]). Lev teaches generating a measurement result of the respiratory/breathing based the difference between in the mask or nostrils.
Claim 5:
Lev discloses:
A physiological signal measuring system, comprising:
a thermographic unit configured for providing a measurement’s thermal image, which is an infrared thermal video for measuring (see paragraph [0004]). Lev teaches thermographic unit used to measure thermal images;
a processor coupled to the thermographic unit (see paragraph [0004]). Lev teaches a processor for the thermographic unit; and
a storage medium coupled to the processor and configured to provide a mask-wearing classification model and a physiological signal calculation program (see paragraph [0050] and [0051]). Lev teaches a storage medium to provide the models and algorithms;
wherein based on the mask-wearing classification model, the processor is configured to:
identify a person's face portion of a subject in the measurement’s thermal image, and classify the person's face portion as a mask-wearing state or a non-mask-wearing state see paragraph [0094]-[0096]). Lev teaches identifying a portion of the persons face and classifying the person as wearing a mask;
wherein based on the physiological signal calculation program, the processor is configured to:
identify a forehead block and a mask block in the person's face portion when the person's face portion is classified as the mask-wearing state, and identify a forehead block and a nasal cavity block in the person’s face portion when the person's face portion is classified as the non-mask-wearing state (see paragraphs [0089] and [0094]-[0096]). Lev teaches analyzing the thermal images and identifying forehead and nostrils (non-mask wearing)/ or forehead and mask (mask-wearing) within the images; and
a ROI (region of interest) determining step comprising determining a plurality of ROIs of each of the forehead block, and the mask block or the nasal cavity block (see paragraphs [0094]-[0096]). Lev teaches identifying the forehead and the mask block of the face when wearing a mask or forehead and nostrils when not wearing a mask, and
wherein a number of the ROls of the nasal cavity block is two, and the two ROls of the nasal cavity block match a left nostril and a right nostril, respectively (see paragraphs [0019], [0020], [0025], [0096]). Lev discloses teaches regions of interest such as the nostrils of the face.
generate a measurement result of at least one physiological parameter of the subject according to a plurality of signals of the forehead block, and the mask block or the nasal cavity block (see paragraphs [0101] and [0103]). Lev teaches generating a measurement result based on the physiological parameters from the person based on the forehead and mask/nostril.
Claim 6:
Lev discloses:
wherein the mask-wearing classification model is generated from training a plurality of training’s thermal images, which are a plurality of thermal images for training, by a machine learning algorithm, and a most accurate model weight obtained from training by the machine learning algorithm is used in the mask-wearing classification model (see paragraph [0089]). Lev teaches generating a classification of the mask based on the trained thermal images. Lev teaches determining if it is analyzing forehead and nostrils (non-mask wearing)/ or forehead and mask (mask-wearing).
Claim 8:
Lev discloses:
wherein a number of the at least one physiological parameter is at least three (see paragraphs [0010], [0027], [0082]-[0089], [0117], [0118]). Lev discloses determining three parameters such as body temperature, heart rate and respiratory rate. Lev also teaches regions of interest such as the nostrils of the face.
Claim 10:
Lev discloses:
wherein a number of the at least one physiological parameter is at least three, and the physiological parameters comprise a body temperature, a heart rate and a respiration rate (see paragraphs [0010] and [0082]-[0087]). Lev discloses determining three parameters such as body temperature, heart rate and respiratory rate;
wherein based on the physiological signal calculation program, the processor is further configured to:
generate a measurement result of the heart rate from a change of a forehead temperature (see paragraphs [0038] and [0103]). Lev teaches generating a heart rate measurement based on the difference in the forehead temperature, and
generate a measurement result of the respiration rate from a change of a mask temperature or a change of a nasal cavity temperature (see paragraph [0019]). Lev teaches generating a measurement result of the respiratory/breathing based the difference between in the mask or nostrils.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2 and 3 are rejected under 35 U.S.C. 103 as being unpatentable over Lev, in view of Sakamoto et al., United States Patent Publication 20230112537 (hereinafter “Sakamoto”).
Claim 2:
Lev discloses:
a ROI (region of interest) determining step comprising determining a plurality of ROIs of each of the forehead block, and the mask block or the nasal cavity block (see paragraphs [0094]-[0096]). Lev teaches identifying the forehead and the mask block of the face when wearing a mask or forehead and nostrils when not wearing a mask, and
wherein the ROI determining step further comprises taking an average of a plurality of tracking signals of each of the ROls as an average tracking signal (see paragraph [0089]). Lev teaches taking an average of the signals/values being tracked,
wherein the signals in the mask block or the nasal cavity block are tracked and ones windows that have maximum changes after a variance calculating are defined as the ROIs of the mask block or the nasal cavity block (see paragraph [0089]). Lev teaches taking an average of the signals/values being tracked. The signals of the mask/nostril are tracked and the changes/differences are calculated as the ROI;
wherein the measurement result generating step further comprising generating the measurement result of the at least one physiological parameter of the subject according to the average tracking signals of the ROIs, respectively, of the forehead block, and the mask block or the nasal cavity block (see paragraphs [0067] and [0089]). Lev teaches generating a measurement result of a physiological parameter of a person received from multiple sensors based on an average of the signals/values.
Lev fails to expressly disclose tracking signals using a sliding window method.
Sakamoto discloses:
wherein the signals of each of a plurality of windows in the blocks are tracked by a sliding window method (see paragraphs [0015], [0044], [0079] and [0083]). Sakamoto teaches the signals being tracked by different sliding window methods.
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Lev to include using a sliding window method to track signals and determine averages for the purpose of efficiently processing digital signals and visualizing the signals, as taught by Sakamoto.
Claim 3:
Lev fails to expressly disclose signal processing includes filtering and smoothing the signal.
Sakamoto discloses:
a signal processing step comprising:
a filtering step comprising processing each of the average tracking signals by at least one bandpass filtering algorithm and generating a filtered signal (see paragraphs [0079] and [0080]. Sakamoto teaches a filtering step that uses bandpass filtering to generate a filtered signal;
a signal integrating step comprising integrating the filtered signals of the ROls, respectively, of each of the block into a principal signal (see paragraphs [0009] and [0079]-[0081]. Sakamoto teaches integrating the signals into a principal signal at different ROI/positions; and
a signal smoothing step comprising smoothing each of the principal signals and generating a smoothed signal (see paragraphs [0009] and [0079]-[0081]. Sakamoto teaches generating a smoothed signal;
wherein the measurement result generating step further comprising
generating the measurement result of the at least one physiological parameter of the subject according to the smoothed signals (see paragraphs [0009] and [0079]-[0081]. Sakamoto teaches generating a heart rate/ respiratory rate based on the smoothed signal.
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Lev to include process the signal to determine a measurement for the purpose of efficiently tracking and visualizing signal data, as taught by Sakamoto.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Lev, in view of Sakamoto, in further view of Uchiyama et al., United States Patent Publication 20220146335 (hereinafter “Uchiyama”).
Claim 7:
Lev discloses:
take an average of a plurality of tracking signals of each of the ROls as an average tracking signal, wherein the signals of each of the mask block or the nasal cavity block are tracked, and ones of the windows that have maximum changes after a variance calculating are defined as the ROls of the mask block or the nasal cavity block; and (see paragraph [0089]). Lev teaches taking an average of the signals/values being tracked. The signals of the mask/nostril are tracked and the changes/differences are calculated as the ROI;
generate the measurement result of the at least one physiological parameter of the subject according to the average tracking signals of the ROls, respectively, of the forehead block, and the mask block or the nasal cavity block. (see paragraphs [0067] and [0089]). Lev teaches generating a measurement result of a physiological parameter of a person received from multiple sensors based on an average of the signals/values.
Lev fails to expressly disclose tracking signals using a sliding window method.
Sakamoto discloses:
wherein the signals of each of a plurality of windows in the mask block or the nasal cavity block are tracked by a sliding window method, and ones of the windows that have maximum changes after a variance calculating are defined as the Rols (see paragraphs [0015], [0044], [0079] and [0083]). Sakamoto teaches the signals being tracked by different sliding window methods; and
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Lev to include using a sliding window method to track signals and determine averages for the purpose of efficiently tracking and visualizing signal data, as taught by Sakamoto.
Lev and Sakamoto fail to expressly disclose coordinate system of the face.
Uchiyama discloses:
define a coordinate system of the person's face portion according to an upper left corner point (0, 0) and a lower right corner point (w, h), define the forehead block by two corner points (w/4, h/7) and (8w/4, 2h/7), define the mask block by two corner points (w/4, h/2) and (8w/4, 4h/5), and define the nasal cavity block by two corner points (w/3, 2h/5) and (2w/3, 3h/5) (see paragraph [0031]). Uchiyama teaches defining a coordinate system of a persons face that’s include the forehead, cheeks, nose and mouth;
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Lev and Sakamoto to include calculate a coordinate system for a persons face to determine temperature for the purpose of effectively calculating a face temperature at correct positions, as taught by Uchiyama.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Lev, in view of Sakamoto, in further view of Baru et al., United States Patent Publication 20160256692 (hereinafter “Baru”) and Denissen et al., United States Patent Publication 20240374150 (hereinafter “Denissen”).
Claim 9:
Lev fails to expressly disclose signal processing includes filtering and smoothing the signal.
Sakamoto discloses:
process the average tracking signal of each of the ROls by a bandpass filtering algorithm with a pass band and generate a filtered signal, and process the average tracking signal of each of the ROls of the block by a bandpass filtering algorithm with a pass and generate a filtered signal; (see paragraphs [0079] and [0080]. Sakamoto teaches a filtering step that uses bandpass filtering to generate a filtered signal;
integrate the filtered signals of the ROls, respectively, of each of the block into a principal signal (see paragraphs [0009] and [0079]-[0081]. Sakamoto teaches integrating the signals into a principal signal at different ROI/positions; and
smooth each of the principal signals and generating a smoothed signal (see paragraphs [0009] and [0079]-[0081]. Sakamoto teaches generating a smoothed signal;
wherein the measurement result generating step further comprising
generating the measurement result of the at least one physiological parameter of the subject according to the smoothed signals (see paragraphs [0009] and [0079]-[0081]. Sakamoto teaches generating a heart rate/ respiratory rate based on the smoothed signal.
Lev and Sakamoto fail to expressly teach band pass filter with a pass band of .75-3 Hz, morphological filtering and converting the smoothed signal to a derivative signal.
Baru discloses:
process the average tracking signal of each of the ROls of the mask block or the nasal cavity block by a bandpass filtering algorithm with a pass band of 0.15 Hz to 0.5 Hz and generate a filtered signal (see paragraph [0062]). Baru teaches processing the tracking signal of each ROI by a bandpass filtering algorithm with a pass band of .1 Hz and .5 Hz;
smooth each of the principal signals by morphological filtering and generate a smoothed signal (see paragraph [0062]). Baru teaches smoothing the signals by morphological filtering and generating a smooth signal; and
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Lev and Sakamoto to include band pass filtering and smoothing signals for determine values for the purpose of efficiently measuring signal pulses from the heart rate, as taught by Baru.
Lev, Sakamoto and Baru fail to expressly disclose processing ROIs by a band pass filter of .75 Hz to 3 Hz and converting the smooth signal by a first derivative.
Denissen discloses:
process the average tracking signal by a bandpass filtering algorithm with a pass band of 0.75 Hz to 3.0 Hz and generate a filtered signal (see paragraph [0081]). Denissen teaches processing the signal by a bandpass filtering between .5-2Hz, and
convert each of the smoothed signals to a derivative signal by a first derivative method, calculate a plurality of time intervals formed by a plurality of intersection points intersected by each of the derivative signals and a zero-cross line, and generate the measurement result of the at least one physiological parameter of the subject (see paragraph [0081]). Denissen teaches converting the signal to a derivative signal, calculated time interval at intersection points according to the intersection of the derivative signa and zero cross line. Based on those calculations the resulting measurement values are determined.
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Lev, Sakamoto and Baru to include band pass filtering and smoothing signals for determine values for the purpose of efficiently estimating the energy expenditure of the person from the corrected heart rate signal, as taught by Denissen.
Response to Arguments
Applicant's arguments filed 12/11/25 have been fully considered but they are not persuasive.
Claim Rejections 35 USC 102 and 103
The Applicant argues that each of the cited references Lev, Sakamoto, Uchiyama, Baru and Denissen neither discloses nor teaches the features (a) and (b) together in each of the independent amended claims 1 and 5. Hence, the amended claims 1 and 5 should be novel and patentable over Lev, Sakamoto, Uchiyama, Baru, Denissen and even a combination thereof under 35 U.S.C. 102(a)(2) and 103.
The Examiner disagrees.
Lev teaches (a) identifying the forehead and the mask block of the face when wearing a mask (see paragraph [0094]). The analysis includes thermal images that are analyzed to measure a temperature of an identified mask (measured at 35.4 degrees Celsius) and/or measure a temperature of an identified forehead (measured at 36.1 degrees Celsius) of the respective depicted person. Lev also teaches identifying the forehead and nostrils block of the persons face and classifying as not wearing a mask (see paragraph [0096]). One or more sub-physiological parameters (e.g., breathing pattern) may be computed by analyzing changes in pixel intensity values of pixels corresponding to nostrils and/or face mask and/or open mouth of the person according to the identified facial features and/or mask features. For example, when the person is not wearing a mask, the breathing pattern is computed based on changes in pixel intensity values of the pixels corresponding to the nostrils and/or open mouth. In another example, when the person is wearing a mask, the breathing pattern is computed based on changes in pixel intensity values of regions of the face mask.
Lev discloses determining three parameters such as body temperature, heart rate and respiratory rate. Lev also teaches regions of interest such as the nostrils of the face (see paragraphs [0019], [0020], [0025], [0096]). Lev teaches segmenting the images to determine the nostrils on the face when the person is not wearing a wear. It is obvious to assume that when the nostrils are identified, the left and right nostril is also identified. The Examiner suggests further defining the process used to segment the images that differs from the art. Therefore, Lev teaches both (a) and (b) and the rejection is maintained.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIONNA M BURKE whose telephone number is (571)270-7259. The examiner can normally be reached M-F 8a-4p.
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, Stephen Hong can be reached at (571)272-4124. 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.
/TIONNA M BURKE/ Examiner, Art Unit 2178 2/16/26
/STEPHEN S HONG/ Supervisory Patent Examiner, Art Unit 2178