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 .
Status of Claims
Claims 1-14 are rejected.
Response to Arguments
Claim Objections
Some of the previous claim objections have been withdrawn in view of the amendment.
Claim Interpretation
Regarding the amendment of “processing circuitry” and “measurement device,” Applicant is encouraged to change these back to the original limitations as these are new matter.
Claim Rejections - 35 USC § 112
The previous 112(b) rejections have been withdrawn in view of the amendment.
Claim Rejections - 35 USC § 101
Applicant's arguments filed 1/9/26 have been fully considered but they are not persuasive.
Applicant argues that the claims are simply not directed to the “mathematical concepts” category because the claims do not recite a mathematical relationship, formula, or calculation. However, the Examiner disagrees. MPEP 2106.04(a)(2) states:
A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation.
In this case, the limitations of “converting timestamps of said sample measurements into chosen temporal units, downsampling a sampling frequency of said recorded sample measurements to obtain at least one downsampled signal, and applying at least one window function to the at least one downsampled signal to obtain at least one windowed signal” are mathematical calculations of addition, subtraction, multiplication, and division in order to determine cardiac or respiratory activity of a subject.
Applicant argues that the claims cannot be directed to a “mental process” because the claimed features cannot practically be performed in the human mind. However, Applicant does provide arguments for why the claims cannot practically be performed in the human mind. Under the broadest reasonable standard, these limitations are nothing more than a medical professional selecting a windowed signal based on a print out of a windowed signal and predicting a quality through choosing peaks on the selected.
Applicant asserts that the claims are integrated into a practical application and recite significantly more. Applicant asserts that the method according the invention aims at providing with a solution to measure the cardiac and respiratory activity of a subject in a non-constraint, permanent or semi-permanent manner. However, the alleged improvement is directed to the abstract idea itself. An improvement to the abstract idea is still an abstract idea.
Claim Rejections - 35 USC § 103
Applicant's arguments filed 1/9/26 have been fully considered but they are not persuasive.
Applicant asserts that Hernandez does not teach or suggest converting timestamps of said sample measurements into chosen temporal units. However, the Examiner disagrees. Hernandez teaches converting timestamps of said sample measurements into chosen temporal units (page 5, left col., ¶2-the band of frequencies used for the pulse and respiration rates are the same ones considered in the previous section (i.e., [0.75-2.5] Hz for heart rate and [0.13-0.75] Hz for respiration rate). The final estimated heart rate and respiration rate corresponded to the maximum frequency multiplied by 60 (beats per minute)). By multiplying by a unit of beats per minute, the timestamps are now in beats per minute which is the chosen temporal unit as claimed.
Applicant asserts that the discussion in Hernandez is different from the downsampling of Claim 1. However, the Examiner disagrees. Hernandez teaches this limitation: downsampling a sampling frequency of said at least one recorded signal to obtain at least one downsampled signal (col. 3 left col., ¶3-cubic interpolation at a sampling rate of 256 Hz (the same as the FlexComp Infiniti sensor). Additionally, Applicant’s instant specification on page 8 discloses: “After the time conversion of sub-step S21, the recorded signals are downsampled in a sub-step S22 by interpolation, for instance by linear, cubic, or polynomial interpolation.” Therefore, Applicant has described that downsampling can be performed through cubic interpolation.
Applicant traverses that Clifton’s hamming window function shows the “applying” of Claim 1. However, Clifton teaches in ¶120 downsampling the signal and performing a Hamming window function in ¶74.
Applicant asserts that Claim 1 defines a time window and then estimates the signal quality by searching for peaks and Clifton proposes the opposite. However, Clifton does disclose this. ¶72 of Clifton states: the obtaining of the quality parameter for each extracted modulation in each time window. Therefore, it is clear that the time window is defined before the quality is estimated.
Specification
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required: “measurement device” and “processing circuitry” as claimed.
Claim Objections
Claims 10 and 12 are objected to because of the following informalities: following the term comprises in the preamble, Applicant is encouraged to add a colon after it.
Claim 4 is objected to because of the following informalities: regarding the limitation of “at least a position or a speed or a rotational speed or an acceleration of the accessory,” Applicant is encouraged to recite - at least a position, a speed, a rotational speed, or an acceleration of the accessory--.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 4-12 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The amendments of “measurement device” in claims 4, 6, 10, and 12 and “processing circuitry” in claims 4 and 6 are new matter as they are not disclosed in the originally filed drawings, specification, or claims. While the specification discloses “measurement unit” and “processing unit,” since there is no recitation of “measurement device” and “processing circuitry,” there is no recitation that these mean the same thing.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, specifically an abstract idea.
Step 1
The claimed invention in claims 1-14 are directed to statutory subject matter as the claims recite a method and a system for determining cardiac or respiratory activity of a subject.
Step 2A, Prong One
Regarding claims 1 and 4, the recited steps are directed to mathematical concepts and a mental process of performing concepts in a human mind or by a human using a pen and paper (see MPEP 2106.04(a)(2) subsections (I) and (III)).
Regarding claims 1 and 4, the limitations of “converting timestamps of said sample measurements into chosen temporal units, downsampling a sampling frequency of said recorded sample measurements to obtain at least one downsampled signal, and applying at least one window function to the at least one downsampled signal to obtain at least one windowed signal” are mathematical calculations of addition, subtraction, multiplication, and division in order to determine cardiac or respiratory activity of a subject.
Regarding claims 1 and 4, the limitations of “choosing at least one selected windowed signal among the at least one windowed signal, and estimating quality of said at least one selected windowed signal by searching for peaks in said at least one selected windowed signal” are a process, as drafted, covers performance of the limitation that can be performed by a human mind (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard. For example, these limitations are nothing more than a medical professional selecting a windowed signal based on a print out of a windowed signal and predicting a quality through choosing peaks on the selected windowed signal.
Step 2A, Prong Two
For claims 1 and 4, the judicial exception is not integrated into a practical application. In particular, claims 1 and 4 recite “measuring and recording, over at least one period of time, sample measurements relating at least to a kinematic characteristic or a position of an accessory worn by the subject's head/an accessory which is configured to be worn by the subject's head and which is provided with a measurement device comprising at least one motion sensor and delivering at least one measuring signal reporting at least a position or a speed or a rotational speed or an acceleration of the accessory, and processing circuitry.” The measuring and recording step using a motion sensor on the head worn accessory amounts to pre-solution activity of data gathering. The processing circuitry is recited a high level of generality and amounts to nothing more than parts of a generic computer. Merely including instructions to implement an abstract idea on a computer does not integrate a judicial exception into practical application.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into
a practical application, the additional element of the measuring and recording step using a motion sensor on the head worn accessory amounts to nothing more than mere pre-solution activity of data gathering, which does not amount to an inventive concept. Moreover, the measuring and recording step using a motion sensor on the head worn accessory is well-understood, routine, and conventional activity as evidenced by US 20070015611 (¶34-a conventional orientation and motion-sensing device 110 attached to the head of a user), US 20150265161 as cited in the IDS (¶5-a head-mounted sensor module includes at least three sensors: a tri-axial gyroscope, a tri-axial accelerometer, and a camera), and US 20160007935 (¶66-(d) “head accelerometer” means a 3-axis accelerometer (e.g., 202) in a head-mounted sensor module 201; (e) “head gyroscope” means a 3-axis gyroscope (e.g., 203) in a head-mounted sensor module 201). Further, simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)).
Regarding dependent claims 2-3 and 5-14, the limitations of claims 1 and 4 further define the limitations already indicated as being directed to the abstract idea.
Regarding claim 2, the accessory being provided with at least one motion sensor amounts to pre-solution activity of data gathering and is well-understood, routine, and conventional activity as shown above.
Claim 3 is recited a high level of generality and amounts to nothing more than parts of a generic computer.
Regarding claim 5, wherein the accessory is chosen from the following: an eyewear frame, an eyewear add-on, an eyewear clip, an eyewear holder strap cord, headphones, an earphone, or a jewel further amounts to pre-solution activity of data gathering. Additionally, it is well-understood, routine, and conventional activity as evidenced by: US 20160148431 (¶9-in a conventional FPV device, the virtual reality pair of glasses is equipped with a gyroscope and an accelerometer so as to take into account the displacements of user's head), US 20100234741 (¶9-conventional drowsiness detection sensors are classified into an earring-type sensor and a glasses-type sensor configured such that an accelerometer is attached to an earring or glasses), and US 20160220105 (¶186-the central unit 3 is powerful and conventional, but the software is specific to the invention. The glasses used are the classic Google Glass 1, to which accelerometers and two cameras are attached).
Regarding claim 6, the limitations of “determining whether sample measurements from the measurement device satisfy a triggering condition, in case the triggering condition is satisfied, increasing a sampling rate, a sampling duration and sensitivity of the measurement device” are a process, as drafted, covers performance of the limitation that can be performed by a human mind (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard. For example, these limitations are nothing more than a medical professional analyzing print outs of sample measurements to determine if they satisfy a triggering condition, and increasing the sampling rate, a sampling duration and sensitivity if the condition is satisfied. The limitations of “triggering the recording of said at least one measuring signal, and wherein recording said at least one measuring signal is performed over a time slot included in the at least one period of time to obtain said sample measurements forming at least one recorded signal” amount to pre-solution activity of data gathering.
Regarding claim 7, the limitations of “wherein when peaks are found during the quality evaluating, said pre-processing phase further comprises reducing noise in the at least one selected windowed signal, to obtain at least one cleaned signal” are a process, as drafted, covers performance of the limitation that can be performed by a human mind (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard. For example, these limitations are nothing more than a medical professional analyzing print outs of data for peaks, and if peaks are found reducing noise in the at least one selected windowed signal, to obtain at least one cleaned signal.
Regarding claim 8, the limitations of “applying a first bandpass filter, a Hilbert transform, an envelope analysis and either a chirp z-transform or a numerical Fourier transform to the at least one cleaned signal to obtain at least one first power spectral density function, identifying first spectral peaks in the at least one first power spectral density function, and evaluating heart rate of said subject from said first spectral peaks” are a process, as drafted, covers performance of the limitation that can be performed by a human mind (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard. For example, these limitations are nothing more than a medical professional using pen and paper to apply a first bandpass filter, a Hilbert transform, an envelope analysis and either a chirp z-transform or a numerical Fourier transform to the cleaned signal on paper to obtain at least one first power spectral density function, and further identifying first spectral peaks in the at least one first power spectral density function, and evaluating heart rate of said subject from said first spectral peaks.
Regarding claim 9, the limitations of “wherein b) comprises after the pre-processing phase: applying a second bandpass filter and either a chirp z-transform or a digital Fourier transform to the at least one cleaned signal to obtain at least one second power spectral density function, identifying second spectral peaks in the at least one second power spectral density function, and evaluating respiratory rate of said subject from said second spectral peaks” are a process, as drafted, covers performance of the limitation that can be performed by a human mind (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard. For example, these limitations are nothing more than a medical professional using pen and paper to apply a second bandpass filter and either a chirp z-transform or a digital Fourier transform to the cleaned signal on paper to obtain at least one second power spectral density function, and further identifying second spectral peaks in the at least one second power spectral density function, and evaluating respiratory rate of said subject from said second spectral peaks.
Regarding claim 10, wherein the measurement device comprises at least one accelerometer and at least one gyroscope to receive at least one first spectral density function amounts to pre-solution activity of data gathering. Additionally, it is well-understood, routine, and conventional activity as evidenced by: US 10139631 (col. 5 and lines 6-8- conventional techniques of head tracking, including but not limited to external cameras, accelerometers, gyroscopes, magnetometers, and combinations thereof), US 20210183343 (¶26-a conventional head-mounted device may include gyroscopes and accelerometers), and US 20160148431 (¶9-in a conventional FPV device, the virtual reality pair of glasses is equipped with a gyroscope and an accelerometer so as to take into account the displacements of user's head).
Regarding claim 11, the limitations of “identifying first spectral peaks comprises identifying accelerometer spectral peaks in the first accelerometer power spectral density function and gyroscope spectral peaks in the first gyroscope power spectral density function, and evaluating said heart rate comprises comparing a first relative band power of one chosen accelerometer spectral peak with a second relative band power of one chosen gyroscope peak” are a process, as drafted, covers performance of the limitation that can be performed by a human mind (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard. For example, these limitations are nothing more than a medical professional using pen and paper to identify first spectral peaks comprises identifying accelerometer spectral peaks in the first accelerometer power spectral density function and gyroscope spectral peaks in the first gyroscope power spectral density function, evaluating said heart rate comprises comparing a first relative band power of one chosen accelerometer spectral peak with a second relative band power of one chosen gyroscope peak.
Regarding claim 12, wherein the measurement device comprises at least one accelerometer and at least one gyroscope amounts to pre-solution activity of data gathering. Additionally, it is well-understood, routine, and conventional activity as shown above in claim 10. The limitations of “after the pre-processing phase, implementing a principal component analysis on the temporal acceleration signals ax(t), ay(t) and az(t) and on the temporal angular rotational speed signals gx(t), gy(t) and gz(t), in order to evaluating heart rate of said subject and respiratory rate of said subject” are a process, as drafted, covers performance of the limitation that can be performed by a human mind (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard. For example, these limitations are nothing more than a medical professional using pen and paper to implement a principal component analysis on the temporal acceleration signals and temporal angular rotational speed signals in order to evaluate heart rate of said subject and respiratory rate of said subject.
Regarding claim 13, the limitations of “wherein b) further comprises: processing the at least one first power spectral density signal to obtain at least one seismocardiographic signal, processing said at least one seismocardiographic signal to obtain at least one windowed aortic valve opening peak signal, extracting local minima and maxima of the at least one windowed aortic valve opening peak signal, and annotating said local minima and maxima with fiducial points” are a process, as drafted, covers performance of the limitation that can be performed by a human mind (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard. For example, these limitations are nothing more than a medical professional using pen and paper to process the at least one first power spectral density signal to obtain at least one windowed aortic valve opening peak signal, and further extracting local minima and maxima of the at least one windowed aortic valve opening peak signal, and annotating said local minima and maxima with fiducial points.
Regarding claim 14, the limitations of “identifying respiratory cycles using said respiratory rate, and processing said respiratory cycles to obtain an inhalation volume of said subject, an estimation of a lung volume of said subject, an estimation of a lung capacity of said subject, an estimation of an inhalation phase and an exhalation phase of said subject” are a process, as drafted, covers performance of the limitation that can be performed by a human mind (including an observation, evaluation, judgment, opinion) under the broadest reasonable standard. For example, these limitations are nothing more than a medical professional using pen and paper to identify respiratory cycles using said respiratory rate on paper, and processing said respiratory cycles to obtain an inhalation volume of said subject, an estimation of a lung volume of said subject, an estimation of a lung capacity of said subject, an estimation of an inhalation phase and an exhalation phase of said subject.
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.
The factual inquiries 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.
Claims 1-5, 7, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Hernandez (NPL “Cardiac and Respiratory Parameter Estimation Using Head-mounted Motion-sensitive Sensors” published in 2015 as cited in the IDS and the copy relied upon is being furnished with this Office Action) in view of Clifton (US 20190069808 filed on 11/2/18).
Regarding claim 1, Hernandez teaches a method for determining cardiac or respiratory activity of a subject, comprising: measuring and recording, over at least one period of time (Figs. 2-3-measuring accelerometer and gyroscope data over a period of time), sample measurements relating at least to a kinematic characteristic or a position of an accessory worn by the subject's head (Abstract-a head-mounted gyroscope sensor; page 3, left col., ¶3-the 3-axis accelerometer captures the acceleration applied to the device (meters/second2) along the X, Y and Z axes (see Fig. 1); page 3, left col., last ¶-the 3-axis gyroscope captures the rate of rotation (radians/second) of the device along the X, Y and Z axis of the device (see Fig. 1)); and processing (page 3, right col., ¶3-processing steps) said sample measurements for determining said cardiac or respiratory activity (Fig. 2-gyroscope data used to measure a pulse wave; Fig. 3-accelereomter data used to measure a respiratory wave), wherein the processing comprises a pre-processing phase with the following (page 4, left col.,, last ¶-the middle graph shows the pulse wave obtained by BVP (blue) and the pulse wave obtained after applying the described methods on the gyroscope data (red line); page 5, left col., ¶1-Fig. 3 shows an example of respiratory wave frequency estimation from accelerometer data of a supine participant. As in Fig. 2, the two waves are closely aligned): converting timestamps of said sample measurements into chosen temporal units (page 5, left col., ¶2-the band of frequencies used for the pulse and respiration rates are the same ones considered in the previous section (i.e., [0.75-2.5] Hz for heart rate and [0.13-0.75] Hz for respiration rate). The final estimated heart rate and respiration rate corresponded to the maximum frequency multiplied by 60 (beats per minute)), and downsampling a sampling frequency of said recorded sample measurements to obtain at least one downsampled signal (col. 3 left col., ¶3-cubic interpolation at a sampling rate of 256 Hz (the same as the FlexComp Infiniti sensor); page 3, right col., ¶1). However, Hernandez does not teach applying at least one window function to the at least one downsampled signal to obtain at least one windowed signal, choosing at least one selected windowed signal among the at least one windowed signal, and estimating quality of said at least one selected windowed signal by searching for peaks in said at least one selected windowed signal.
Clifton relates to measuring the frequency of a periodic physiological process, particularly respiration rate or heart rate (¶2). Clifton further teaches the invention using the following steps:
applying at least one window function to the at least one downsampled signal to obtain at least one windowed signal (¶74-windowed using a Hamming window function; ¶120), choosing at least one selected windowed signal among the at least one windowed signal (¶25-selecting time windows; ¶62), and estimating quality of said at least one selected windowed signal by searching for peaks in said at least one selected windowed signal (¶72-obtaining of the quality parameter for each extracted modulation in each time window comprises Fourier transforming the extracted modulation in the time window and evaluating a property of the largest peak in the Fourier transform).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Hernandez to include applying at least one window function to the at least one downsampled signal to obtain at least one windowed signal, choosing at least one selected windowed signal among the at least one windowed signal, and estimating quality of said at least one selected windowed signal by searching for peaks in said at least one selected windowed signal of Clifton in order to increase the stability and/or accuracy of the frequency of the periodic physiological process output (Clifton, ¶25).
Regarding claim 2, the combination of Hernandez and Clifton teaches the method according to claim 1, wherein said accessory is provided with at least one motion sensor (Hernandez, Abstract-a head-mounted gyroscope sensor; page 3, left col., ¶3-the 3-axis accelerometer captures the acceleration applied to the device (meters/second2) along the X, Y and Z axes (see Fig. 1); page 3, left col., last ¶-the 3-axis gyroscope captures the rate of rotation (radians/second) of the device along the X, Y and Z axis of the device (see Fig. 1)).
Regarding claim 3, the combination of Hernandez and Clifton teaches the method according to claim 1. However, Hernandez does not teach wherein said processing is performed in an embedded manner or on a remote host.
Clifton teaches wherein said processing is performed in an embedded manner or on a remote host (Clifton, ¶34-a processing station which can be remote from the subject).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Hernandez to include wherein said processing is performed in an embedded manner or on a remote host of Clifton in order for higher power or processing capacity (Clifton, ¶34).
Regarding claim 4, Hernandez teaches a system for determining cardiac or respiratory activity of a subject, said system comprising an accessory configured to be worn by the subject's head and which is provided with a measurement device comprising at least one motion sensor (Abstract-a head-mounted gyroscope sensor) and delivering at least one measuring signal reporting at least a position or a speed or a rotational speed or an acceleration of the accessory (page 3, left col., ¶3-the 3-axis accelerometer captures the acceleration applied to the device (meters/second2) along the X, Y and Z axes (see Fig. 1); page 3, left col., last ¶-the 3-axis gyroscope captures the rate of rotation (radians/second) of the device along the X, Y and Z axis of the device (see Fig. 1)), and provided with processing (page 3, right col., ¶3-processing steps) programmed to: a) receive and record said at least one measuring signal from the measurement device for obtaining sample measurements over at least one period of time (Figs. 2-3-measuring accelerometer and gyroscope data over a period of time), and b) process said sample measurements forming at least one recorded signal for determining said cardiac or respiratory activity (Fig. 2-gyroscope data used to measure a pulse wave; Fig. 3-accelereomter data used to measure a respiratory wave), wherein b) comprises a pre-processing phase with the following (page 4, left col.,, last ¶-the middle graph shows the pulse wave obtained by BVP (blue) and the pulse wave obtained after applying the described methods on the gyroscope data (red line); page 5, left col., ¶1-Fig. 3 shows an example of respiratory wave frequency estimation from accelerometer data of a supine participant. As in Fig. 2, the two waves are closely aligned): converting timestamps of said sample measurements into chosen temporal units (page 5, left col., ¶2-the band of frequencies used for the pulse and respiration rates are the same ones considered in the previous section (i.e., [0.75-2.5] Hz for heart rate and [0.13-0.75] Hz for respiration rate). The final estimated heart rate and respiration rate corresponded to the maximum frequency multiplied by 60 (beats per minute)), and downsampling a sampling frequency of said recorded sample measurements to obtain at least one downsampled signal (col. 3 left col., ¶3-cubic interpolation at a sampling rate of 256 Hz (the same as the FlexComp Infiniti sensor); page 3, right col., ¶1). However, Hernandez does not teach a processing circuitry; applying at least one window function to the at least one downsampled signal to obtain at least one windowed signal, choosing at least one selected windowed signal among the at least one windowed signal, and estimating quality of said at least one selected windowed signal by searching for peaks in said at least one selected windowed signal.
Clifton teaches a processing circuitry (¶32-a data processing unit); applying at least one window function to the at least one downsampled signal to obtain at least one windowed signal (¶74-windowed using a Hamming window function; ¶120), choosing at least one selected windowed signal among the at least one windowed signal (¶25-selecting time windows; ¶62), and estimating quality of said at least one selected windowed signal by searching for peaks in said at least one selected windowed signal (¶72-obtaining of the quality parameter for each extracted modulation in each time window comprises Fourier transforming the extracted modulation in the time window and evaluating a property of the largest peak in the Fourier transform).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Hernandez to include a processing circuitry; applying at least one window function to the at least one downsampled signal to obtain at least one windowed signal, choosing at least one selected windowed signal among the at least one windowed signal, and estimating quality of said at least one selected windowed signal by searching for peaks in said at least one selected windowed signal of Clifton in order to increase the stability and/or accuracy of the frequency of the periodic physiological process output (Clifton, ¶25).
Regarding claim 5, the combination of Hernandez and Clifton teaches the system according to claim 4, wherein the accessory is chosen from the following: an eyewear frame, an eyewear add-on, an eyewear clip, an eyewear holder strap cord, headphones, an earphone, or a jewel (Hernandez, Figure 1-Google Glass).
Regarding claim 7, the combination of Hernandez and Clifton teaches the system according to claim 4, wherein when peaks are found during the quality evaluating (Clifton, ¶72-the obtaining of the quality parameter for each extracted modulation in each time window comprises Fourier transforming the extracted modulation in the time window and evaluating a property of the largest peak in the Fourier transform), said pre-processing phase further comprises reducing noise in the at least one selected windowed signal, to obtain at least one cleaned signal (Clifton, ¶25-filter data prior to input to steps to calculate the frequency of the periodic physiological process (e.g. by selecting time windows and/or modulation modes using the quality parameter); ¶67).
Regarding claim 12, the combination of Hernandez and Clifton teaches the system according to claim 7, wherein the measurement device comprises at least one accelerometer and at least one gyroscope (Hernandez, page 3, left col., ¶2-we created a custom Android application to simultaneously log information from the accelerometer, the gyroscope and the camera), said at least one accelerometer being configured to measure temporal acceleration signals ax(t), ay(t) and az(t) along three orthogonal axes (Hernandez, page 3, left col., ¶4-the 3-axis accelerometer captures the acceleration applied to the device (meters/second2) along the X, Y and Z axes (see Fig. 1)), and said at least one gyroscope being configured to measure temporal angular rotational speed signals gx(t), gy(t) and gz(t) along three orthogonal axes (Hernandez, page 3, left col., last ¶-the 3-axis gyroscope captures the rate of rotation (radians/second) of the device along the X, Y and Z axis of the device (see Fig. 1)), wherein b) comprises, after the pre-processing phase, implementing a principal component analysis on the temporal acceleration signals ax(t), ay(t) and az(t) and on the temporal angular rotational speed signals gx(t), gy(t) and gz(t), in order to evaluating heart rate of said subject and respiratory rate of said subject (Hernandez, page 4, right col., last section-in order to estimate the respiratory wave from data of a specific sensor (same to what we used for pulse wave)…since different dimensions of the sensor reading (e.g., X and Y axis of accelerometer) may change in different directions depending on the body position, we applied Principal Component Analysis to reduce this influence).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Hernandez in view of Clifton as applied to claim 4 above, and further in view of Arnold (US 20160007934 filed on 9/18/15).
Regarding claim 6, the combination of Hernandez and Clifton teaches the system according to claim 4. However, the combination of Hernandez and Clifton does not teach wherein the processing circuitry is further programmed to implement, prior to recording said at least one measuring signal, the following: determining whether sample measurements from the measurement device satisfy a triggering condition, in case the triggering condition is satisfied, increasing a sampling rate, a sampling duration and sensitivity of the measurement device, and triggering the recording of said at least one measuring signal, and wherein recording said at least one measuring signal is performed over a time slot included in the at least one period of time to obtain said sample measurements forming at least one recorded signal.
Arnold teaches wherein the processing circuitry is further programmed to implement, prior to recording said at least one measuring signal, the following: determining whether sample measurements from the measurement device satisfy a triggering condition, in case the triggering condition is satisfied (¶166- automatically detection a user's state and/or a user's sleep stage, the information may be used to change power consumption of various sensors such as a photoplethysmographic sensor and a motion sensor… accurate measure of heart rate variability requires greater temporal precision in the signal compared to that required to measure heart rate), increasing a sampling rate, a sampling duration and sensitivity of the measurement device (¶316- wherein the increase of power consumption includes at least one of: entering a high precision state of the motion sensor, increase of sensitivity of the motion sensor, and increase of a sampling rate of the motion sensor; ¶57-characterize a user's movements over longer periods of time (e.g., 10 minutes); ¶166), and triggering the recording of said at least one measuring signal, and wherein recording said at least one measuring signal is performed over a time slot included in the at least one period of time to obtain said sample measurements forming at least one recorded signal (¶166-accurate measure of heart rate variability requires greater temporal precision in the signal compared to that required to measure heart rate, thus the light source of the photoplethysmographic sensor can be measured at a higher sampling rate and/or higher power for some fraction of the sleeping period (and/or an awake period) to achieve a better estimate of the heart rate variability. Similarly, accuracy of the measure of motion along the axes may be improved with a higher sampling rate, sensitivity, and/or power level, and this may be helpful for determining, for example, the user's sleep stage).
Arnold relates to the field of wearable electronic devices. Specifically, the embodiments are related to automatic movement detection utilizing a wearable electronic device (¶2).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Hernandez to include wherein the processing circuitry is further programmed to implement, prior to recording said at least one measuring signal, the following: determining whether sample measurements from the measurement device satisfy a triggering condition, in case the triggering condition is satisfied, increasing a sampling rate, a sampling duration and sensitivity of the measurement device, and triggering the recording of said at least one measuring signal, and wherein recording said at least one measuring signal is performed over a time slot included in the at least one period of time to obtain said sample measurements forming at least one recorded signal of Arnold in order to accurately measure heart rate variability which requires greater temporal precision in the signal compared to that required to measure heart rate (Arnold, ¶166).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Hernandez in view of Clifton as applied to claim 7 above, and further in view of Helong (NPL “Hilbert-Huang Transform for Analysis of Heart Rate Variability in Cardiac Health published in 2011).
Regarding claim 8, the combination of Hernandez and Clifton teaches the system according to claim 7, wherein step b) comprises after the pre-processing phase: applying a first bandpass filter (Hernandez, page 4, left col., (ii)-a band-pass Butterworth filter), an envelope analysis (Clifton, ¶120-the baseline wander (BW) is the envelope of the original signal) and either a chirp z-transform or a numerical Fourier transform to the at least one cleaned signal (Hernandez, page 5, ¶1 of Section 5.3-Fast Fourier Transform). However, the combination of Hernandez and Clifton does not teach a Hilbert transform, to obtain at least one first power spectral density function, identifying first spectral peaks in the at least one first power spectral density function, and evaluating heart rate of said subject from said first spectral peaks.
Helong teaches a Hilbert transform (page 1558- left col., 3rd to last line-Hilbert transform), to obtain at least one first power spectral density function (page 1562, right col., line 11-PSD for the application), identifying first spectral peaks in the at least one first power spectral density function, and evaluating heart rate of said subject from said first spectral peaks (page 1563, left col., last 3 lines-the peaks and mean shape of the signal at higher bands have lower amplitude, which is particularly important in the HRV analysis).
Helong relates to improving the spectrum estimates of heart rate variability (HRV) (Abstract).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Hernandez to include a Hilbert transform, to obtain at least one first power spectral density function, identifying first spectral peaks in the at least one first power spectral density function, and evaluating heart rate of said subject from said first spectral peaks of Helong in order to improve the spectrum estimates of heart rate variability (HRV) (Helong, Abstract).
Claims 9 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Hernandez in view of Clifton as applied to claim 7 above, and further in view of Park (US 20100152600 filed on 10/7/09).
Regarding claim 9, the combination of Hernandez and Clifton teaches the system according to claim 7. However, the combination of Hernandez and Clifton does not teach wherein b) comprises after the pre-processing phase: applying a second bandpass filter and either a chirp z-transform or a digital Fourier transform to the at least one cleaned signal to obtain at least one second power spectral density function, identifying second spectral peaks in the at least one second power spectral density function, and evaluating respiratory rate of said subject from said second spectral peaks.
Park teaches wherein b) comprises after the pre-processing phase: applying a second bandpass filter (¶306-the filtering is performed with bandpass filters) and either a chirp z-transform or a digital Fourier transform to the at least one cleaned signal to obtain at least one second power spectral density function (¶28-a Fourier transform of the samples included in the first subset to obtain a magnitude spectrum of the samples in the first subset), identifying second spectral peaks in the at least one second power spectral density function (¶60-determining whether there are significant peaks of the Fourier transform, the autocorrelation function, or the power spectral density of the waveform), and evaluating respiratory rate of said subject from said second spectral peaks (¶73-estimating the subject's respiratory rate).
Park relates to monitors that can assess the physiological and psychological state of a subject and, in particular, relates to non-contact and radar-based physiologic sensors and their method of use (¶4).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Hernandez to include wherein b) comprises after the pre-processing phase: applying a second bandpass filter and either a chirp z-transform or a digital Fourier transform to the at least one cleaned signal to obtain at least one second power spectral density function, identifying second spectral peaks in the at least one second power spectral density function, and evaluating respiratory rate of said subject from said second spectral peaks of Park in order for indication of abnormal breathing patterns (Park, ¶178).
Regarding claim 14, the combination of Hernandez, Clifton, and Park teaches the system according to claim 9, wherein b) further comprises: identifying respiratory cycles using said respiratory rate (Park, ¶59-estimation of the cycle length of periodic or Cheyne-Stokes breathing), and processing said respiratory cycles to obtain an inhalation volume of said subject (Park, ¶194-the tidal volume, or the amount of air inhaled and exhaled with each breath), an estimation of a lung volume of said subject (Park, ¶194-the tidal volume, or the amount of air inhaled and exhaled with each breath), an estimation of a lung capacity of said subject (Park, ¶227-predict the patient's vital capacity, such that if the patient performs a vital capacity maneuver by inhaling as deeply as possible and exhaling as fully as possible), an estimation of an inhalation phase (Park, ¶188-inhale time) and an exhalation phase of said subject (Park, ¶188-exhale time).
Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Hernandez in view of Clifton and further in view of Helong as applied to claim 8 above, and further in view of Tadi (NPL “Comprehensive Analysis of Cardiogenic Vibrations for Automated Detection of Atrial Fibrillation Using Smartphone Mechanocardiograms” published in 2019).
Regarding claim 10, the combination of Hernandez, Clifton, and Helong teaches the system according to claim 8, wherein the measurement device comprises at least one accelerometer and at least one gyroscope (Hernandez, page 3, left col., ¶2-we created a custom Android application to simultaneously log information from the accelerometer, the gyroscope and the camera). However, the combination of Hernandez, Clifton, and Helong does not teach wherein the at least one first spectral density function comprises a first accelerometer power spectral density function originating from a measuring signal delivered by the at least one accelerometer and a first gyroscope power spectral density function originating from measuring signal delivered by the at least one gyroscope.
Tadi teaches wherein the at least one first spectral density function comprises a first accelerometer power spectral density function originating from a measuring signal delivered by the at least one accelerometer (page 2235, left col., ¶1-a nonparametric estimate of the PSD – was used to calculate spectral contents (in total 5 features) of the signal segments; Fig. 3-spectral and temporal characteristics of SCG and GCG signals during SR (panel a) and AFib (panel b) episodes) and a first gyroscope power spectral density function originating from measuring signal delivered by the at least one gyroscope (page 2235, left col., ¶1-a nonparametric estimate of the PSD – was used to calculate spectral contents (in total 5 features) of the signal segments; Fig. 3-spectral and temporal characteristics of SCG and GCG signals during SR (panel a) and AFib (panel b) episodes).
Tadi relates to SCG-GCG based AFib detection (page 2240, left col., ¶3).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Hernandez to include wherein the at least one first spectral density function comprises a first accelerometer power spectral density function originating from a measuring signal delivered by the at least one accelerometer and a first gyroscope power spectral density function originating from measuring signal delivered by the at least one gyroscope of Tadi in order for AFib detection (page 2231, left col., last ¶).
Regarding claim 11, the combination of Hernandez, Clifton, Helong, and Tadi teaches the system according to claim 10, wherein: identifying first spectral peaks comprises identifying accelerometer spectral peaks in the first accelerometer power spectral density function (Tadi, page 2235, left col., ¶1-Spectral Peak features, we computed the total spectral power of the signal frame in different frequency bands as [0.5–1.5] Hz, [1.5–5] Hz, [5–10] Hz, [10–15] Hz, and [15–20] Hz in both accelerometer and gyroscope segmented signals independently for each sensor channel/axis) and gyroscope spectral peaks in the first gyroscope power spectral density function (Tadi, page 2235, left col., ¶1-Spectral Peak features, we computed the total spectral power of the signal frame in different frequency bands as [0.5–1.5] Hz, [1.5–5] Hz, [5–10] Hz, [10–15] Hz, and [15–20] Hz in both accelerometer and gyroscope segmented signals independently for each sensor channel/axis), and evaluating said heart rate (Tadi, page 2235, right col., last ¶-heart rate variability (HRV)) comprises comparing a first relative band power of one chosen accelerometer spectral peak with a second relative band power of one chosen gyroscope peak (Tadi, page 2235, left col., ¶1-a periodogram function – also known as a nonparametric estimate of the PSD – was used to calculate spectral contents (in total 5 features) of the signal segments; Fig. 3-spectral and temporal characteristics of SCG and GCG signals during SR (panel a) and AFib (panel b) episodes; Figs. 3 and 5).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Hernandez in view of Clifton and further in view of Helong as applied to claim 8 above, and further in view of Schmidt (US 20220296173 filed on 6/5/20).
Regarding claim 13, the combination of Hernandez, Clifton, and Helong teaches the system according to claim 8. However, the combination of Hernandez, Clifton, and Helong does not teach wherein b) further comprises: processing the at least one first power spectral density signal to obtain at least one seismocardiographic signal, processing said at least one seismocardiographic signal to obtain at least one windowed aortic valve opening peak signal, extracting local minima and maxima of the at least one windowed aortic valve opening peak signal, and annotating said local minima and maxima with fiducial points.
Schmidt teaches wherein b) further comprises: processing the at least one first power spectral density signal to obtain at least one seismocardiographic signal (¶39-power spectrum density, may be determined for each diastolic segments, or second window, of the seismocardiogram (SCG)), processing said at least one seismocardiographic signal to obtain at least one windowed aortic valve opening peak signal (¶69-each of the resulting systolic segments includes a second signal feature corresponding to the combined mitral valve closure (MC) and the aortic valve opening (AO) of a heart cycle; Fig. 6), extracting local minima and maxima of the at least one windowed aortic valve opening peak signal (¶24-the fiducial points may comprise: the local maxima (AC max) of the first signal feature, and/or the first local minima (AC min) immediately before to the local maxima (AC max); ¶51-aortic valve opening (AO); Fig. 6-AOmax and AOmin), and annotating said local minima and maxima with fiducial points (¶74-fiducial points are indicated in FIG. 6).
Schmidt relates generally to fitness applications, and particularly to methods and systems for determining an indication of cardiorespiratory fitness (¶1).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Hernandez to include wherein b) further comprises: processing the at least one first power spectral density signal to obtain at least one seismocardiographic signal, processing said at least one seismocardiographic signal to obtain at least one windowed aortic valve opening peak signal, extracting local minima and maxima of the at least one windowed aortic valve opening peak signal, and annotating said local minima and maxima with fiducial points of Schmidt in order for determining an indication of, cardiorespiratory fitness (VO2 max) (Schmidt, ¶11).
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.
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/L.N.H./Examiner, Art Unit 3792
/AMANDA L STEINBERG/Examiner, Art Unit 3792