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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on December 8, 2025 has been entered.
Response to Amendment
In response to amendments, filed December 8, 2025, claims 1, 6, 13, and 14 have been amended. Claims 5 and 20 have been cancelled. Claims 21-22 have been added. Claims 1-4, 6-19 and 21-22 are pending.
Response to Arguments
Applicant’s arguments, see Remarks, filed December 8, 2025, with respect to objections to the claims have been fully considered and are persuasive in view of the amendments. The objections to the claims have been withdrawn.
Applicant's arguments with respect to 35 U.S.C. 101 have been fully considered and are persuasive in view of being eligible as improvements to technology or computer functionality. The 35 U.S.C. 101 rejections have been withdrawn.
Applicant’s arguments with respect to prior art rejections have been considered but are moot because the new ground of rejection does not rely on the same reference combination applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. A new ground(s) of rejection is made in view of the combinations of Lu (US 20210153750 A1), Li (US 20180075209 A1), Ferber (US 20160242700 A1), LeBoeuf (US 20220313098 A1), Ibtehaz (N Ibtehaz, et al., "PPG2ABP: Translating Photoplethysmogram [PPG] Signals to Arterial Blood Pressure [ABP] Waveforms using Fully Convolutional Neural Networks," Electrical Engineering and Systems Science: Signal Processing, Published May 2020), and Li (US 20190313947 A1).
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“Calibrator” in claims 1 – Specification pg 12 [1] “PPG to BP estimator and calibrator 14 may be a computer or a circuit system.”
“CBP estimator” in claims 6 and 11 – Specification pg 14 [1] “A CBP estimator 16 further uses linear regression equations to estimate CBPs in clinic use. The linear regression equations are initially established as the method taught by U.S. Patent No. 201502725112, the teachings of which are incorporated herein by reference in their entirety, to fit correlation between waveform parameters of the approximated PVR waveforms, estimated BPs, and real CBPs measured from the plurality of subjects. In view of above, the PPG to CBP subsystem 102 may be a computer or a circuit system.”
“Analyzer” in claims 6, 9, 11, 14, 18, 19 – Specification pg 11 [1] “PPG signal receiver and analyzer 13 such as a notebook, computer or smart phone”; Specification pg 12 [1] “the PPG to BP subsystem 101 including the PPG signal receiver and analyzer 13 and the PPG to BP estimator and calibrator 14 may be a computer or a circuit system.”
“Transformer” in claims 6 and 11 – Specification pg 13 [1] “PPG to CBP subsystem 102 may be a computer or a circuit system.” Based on Fig. 1, the PPG to PVR transformer 15 is part of the PPG to CBP subsystem 102.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The term “approximate” in claims 13, 21, and 22 is a relative term which renders the claim indefinite. The term “approximate” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
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(s) 1-4 and 14-19 are rejected under 35 U.S.C. 103 as being unpatentable over Lu (US 20210153750 A1) in view of Li (US 20180075209 A1) and Ferber (US 20160242700 A1).
Regarding claim 1, Lu teaches an apparatus for improving accuracy in estimating and calibrating blood pressure (BPs) based on modeling-used photoplethysmography (PPG) waveform signals sensed by a PPG sensor included in an upper-arm wearable apparatus from a plurality of subjects, modeling-used personal information parameters derived from the plurality of subjects, and modeling-used characteristic parameters extracted from the modeling-used PPG waveform signals ([0004] “a system for determining blood pressure”; medical system 100; [0036] “PPG monitor… an armband 116”; [0062] “obtaining module 402 may be configured to obtain a signal indicative of heart activity of a first subject … a pulse-wave-related signal [e.g., photoplethysmogram (PPG)]”), the apparatus comprising:
an estimator (obtaining module 402, parameter determination module 404, model generation module 406) configured to:
divide age parameters of the modeling-used personal information parameters into at least first and second age groups by ranges ([0081] “The vital information may include but not limited to gender of the first subject, age of the subject”; [0119] “The specific condition may be associated with a group of second subjects classified by an age range, … the age range may include but not limited to 0-5 years old, 5-12 years old, 12-18 years old, 18-30 years old, 30-45 years old, 45-60 years old, 60-80 years old, or the like, or a combination thereof. The age range may include any other length of age separations.”).
However, Lu fails to disclose training first and second prediction models corresponding to respective age groups in parallel. Li teaches a method and apparatus for establishing blood pressure models.
Li discloses execute, by a circuit system, machine learning algorithms by a circuit system to train a first prediction model corresponding to the first age group and a second prediction model corresponding to the second age group in parallel based on the modeling-used characteristic parameters and the modeling-used personal information parameters ([0074] “Photoplethysmogram (PPG for short) Intensity Ratio (PIR for short) may reflect changes in diameter of blood vessels and movements of the blood vessels … a correlation between a diastolic blood pressure (DBP for short) and the PIR is high … the PIR is mainly considered for the DBP. When a model is established for a group, corresponding models are established for different groups, and ages, genders, heights, weights, etc. may be impact factors.” [0082] “wherein DBP is a diastolic blood pressure, PIR is a pulse wave intensity ratio, BMI is a body mass index, and b1, b2, b3, b4 and b5 are diastolic blood pressure regression parameters.” [0084] "For example, it is also possible to divide the plurality of subjects to be examined based on ages, for example, the plurality of subjects to be examined are grouped every 10 years old..." [0085] "Further, for each divided group, steps S250 and S260 are performed to generate a systolic blood pressure model and a diastolic blood pressure model for each group”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Lu to include training a first prediction model corresponding to the first age group and a second prediction model corresponding to the second age group in parallel as disclosed in Li so that blood pressure models can be established with higher accuracy by differentiating between different categories of people (Li [0098]).
However, the combination of Lu/Li fails to disclose calculating BPs from each model in parallel and calculating a minimal mean error to select an optimal prediction model. Ferber teaches systems and methods for non-invasive blood pressure measurement using PPG signals.
The combination of Lu/Li/Ferber discloses:
and calculate a first estimated BP and a second estimated BP for a user respectively from the first prediction model and the second prediction model (Li: [0084] "divide the plurality of subjects to be examined based on ages, for example, the plurality of subjects to be examined are grouped every 10 years old..." [0085] "Further, for each divided group, steps S250 and S260 are performed to generate a systolic blood pressure model and a diastolic blood pressure model for each group.” Lu: [0116] “For each set of data in the testing sample data, the model generation module 406 may determine a first blood pressure value;” Ferber: [0310] "Error metrics between the ground truth and estimated blood pressure values in test dataset are averaged k-times… the model giving optimal (or, the lowest) error values may be selected and used by the blood pressure calculation system 1206 to calculate arterial blood pressure values.”);
a calibrator (Lu: model generation module 406) configured to:
use a calibration model which approximately fits relationship between each of the first and second estimated BP and a real BP of the user measured with a cuff-based BP measuring apparatus (Lu: [0121] “In step 608, the model generation module 406 may generate a preliminary second model for predicting a plurality of residuals between the plurality of historical blood pressure measurements and the first blood pressure values predicted by the used preliminary first model based on the historical first data and the historical information associated with the plurality of second subjects.” [0129] “the historical blood pressure measurements may be measured by a sphygmomanometer [e.g., an aneroid sphygmomanometer, a mercurial sphygmomanometer, an automatic sphygmomanometer, an electronic sphygmomanometer, etc.]” Li: [0085] “a systolic blood pressure model and a diastolic blood pressure model for each group.”);
and compute, by the circuit system, a minimal mean error between each of the first and second estimated BP and a real BP of the user through the calibration model and select the first prediction model among the first and second prediction models as an optimal prediction model (Ferber: [0310] "Error metrics between the ground truth and estimated blood pressure values in test dataset are averaged k-times. Those metrics may include, and are not limited to, mean square error (MSE, Example Equation 1, shown below), root mean square error (RMSE, Example Equation 2, shown below), median absolute deviation (MAD, Example Equation 3, shown below), and/or coefficient of determination (R.sup.2, Example Equation 4, shown below). In some embodiments, the model giving optimal (or, the lowest) error values may be selected and used by the blood pressure calculation system 1206 to calculate arterial blood pressure values.”) when an age parameter of the user is within the second age group, the age parameter of the user is not within the first age group (Li: [0084] "divide the plurality of subjects to be examined based on ages, for example, the plurality of subjects to be examined are grouped every 10 years old..." [0085] "a systolic blood pressure model and a diastolic blood pressure model for each group.”), and modeling-used characteristic parameters of the first prediction model are more similar to PPG characteristic parameters of the user so as to predict the BPs more accurately (Ferber: [0125] " a blood metrics measurement apparatus may generate multi-channel signals (e.g., PPG signals) which may be provided to a blood pressure calculation system to calculate arterial blood pressure values (e.g., systolic blood pressure value and/or diastolic blood pressure value). More specifically, the blood pressure calculation system (or the blood pressure measurement apparatus) may … select (or, “extract”) sets of features from each of the high quality waves, and generate sets of feature vectors based on the selected sets of features. In some embodiments, an empirical blood pressure model is used to calculate arterial blood pressure values based on the sets of feature vectors." -- as BP estimation is based on PPG signals and the model is selected based on minimizing the error, the selected model has more similar modeling-used characteristic parameters to the PPG characteristics of the user).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Lu/Li to include calculating BPs from each model in parallel and calculating a minimal mean error to select an optimal prediction model as disclosed in Ferber to decrease the bias of the BP results to the selected test and training sets (Ferber [0310]).
The combination of Lu/Li/Ferber further discloses a display unit for displaying a calibrated-estimated SBP (systolic BP) and a calibrated-estimated DBP (diastolic BP) predicted by the first prediction model (Lu: [0037] “the wearable or portable device may ... display a result including the physiological parameter of interest in the form of, e.g., an image;” Ferber: [0167] “the user interface module 1202 may display one or more graphical user interfaces (GUIs) to present a calculated blood pressure to a user.” [0189] The blood pressure calculation system 1206 may be configured to calculate blood pressure values (e.g., systolic, diastolic), and generate messages or alerts based on those values.”).
Regarding claim 2, the combination of Lu/Li/Ferber discloses the apparatus according to claim 1 (Lu: medical system 100), wherein the modeling-used characteristic parameters are derived by performing feature extraction on the modeling-used PPG waveform signals (Lu: [0062] “obtaining module 402 may be configured to obtain a signal indicative of heart activity of a first subject … a pulse-wave-related signal [e.g., photoplethysmogram (PPG)”; [0063] “the parameter determination module 404 may determine the data based on a characteristic point of a beat of the signal. In some embodiments, the parameter determination module 404 may transform the signal from the time domain to the frequency domain and determine a representation of the signal in the frequency domain. The parameter determination module 404 may determine the data based on the representation.”).
Regarding claim 3, the combination of Lu/Li/Ferber discloses the apparatus according to claim 1 (Lu: medical system 100), wherein the modeling-used personal information parameters includes gender parameters of the plurality of subjects (Lu: [0081] “The vital information may include but not limited to gender of the first subject, age of the subject”; Li: [0074] “When a model is established for a group, corresponding models are established for different groups, and ages, genders, heights, weights, etc. may be impact factors.”).
Regarding claim 4, the combination of Lu/Li/Ferber discloses the apparatus according to claim 3 (Lu: medical system 100), wherein the estimator further divides the first and second age groups into subgroups by the gender parameters (Lu: [0081] “The vital information may include but not limited to gender of the first subject, age of the subject”; Li: [0074] “When a model is established for a group, corresponding models are established for different groups, and ages, genders, heights, weights, etc. may be impact factors.” [0084] “the plurality of subjects to be examined may be divided by combining ages and genders, for example, the plurality of subjects to be examined are classified based on genders and then are grouped every 5 years old”).
Regarding claim 14, Lu teaches a system for improving accuracy in estimating blood pressures (BPs) using a photoplethysmography (PPG) signal analysis (medical system 100) comprising:
a cuff-based BP measuring apparatus measuring a real BP from a user by a pressure sensor included in the cuff-based BP measuring apparatus ([0096] “the historical blood pressure measurements may be measured by a sphygmomanometer [e.g., an aneroid sphygmomanometer, a mercurial sphygmomanometer, an automatic sphygmomanometer, an electronic sphygmomanometer, etc.]”);
an upper-arm wearable apparatus including a PPG sensor adapted to sense modeling-used PPG waveform signals from a plurality of subjects wearing the upper-arm wearable apparatus ([0036] “PPG monitor… an armband 116”; [0062] “obtaining module 402 may be configured to obtain a signal indicative of heart activity of a first subject … a pulse-wave-related signal [e.g., photoplethysmogram (PPG)]”);
and a PPG to BP subsystem (obtaining module 402, parameter determination module 404, model generation module 406, blood pressure determination module 408) including:
a PPG signal receiver and analyzer (obtaining module 402 and parameter determination module 404) configured to:
process the modeling-used PPG waveform signals and derive modeling-used characteristic parameters from the modeling-used PPG waveform signals ([0063] “the parameter determination module 404 may determine the data based on a characteristic point of a beat of the signal. In some embodiments, the parameter determination module 404 may transform the signal from the time domain to the frequency domain and determine a representation of the signal in the frequency domain. The parameter determination module 404 may determine the data based on the representation.”);
and have modeling-used personal information parameters from the plurality of subjects, wherein the modeling-used personal information parameters includes an age parameter of the plurality of subjects ([0081] “obtaining module 402 may obtain information related to the first subject. The information may include vital information associated with the first subject. The vital information may include but not limited to gender of the first subject, age of the subject”; [0119] the model generation module 406 may further determine a plurality of sub-models. Each sub-model may be associated with a specific condition. The specific condition may be associated with a group of second subjects classified by an age range);
and a PPG to BP estimator and calibrator (model generation module 406) configured to:
divide the age parameter of the modeling-used personal information parameters of the plurality of subjects into at least first and second age groups ([0119] “The specific condition may be associated with a group of second subjects classified by an age range, … the age range may include but not limited to 0-5 years old, 5-12 years old, 12-18 years old, 18-30 years old, 30-45 years old, 45-60 years old, 60-80 years old, or the like, or a combination thereof. The age range may include any other length of age separations”).
However, Lu fails to disclose training first and second prediction models corresponding to respective age groups in parallel. Li teaches a method and apparatus for establishing blood pressure models.
Li discloses execute, by a circuit system, machine learning algorithms to train a first prediction model corresponding to the first age group and a second prediction model corresponding to the second age group in parallel based on the modeling-used characteristic parameters and the modeling-used personal information parameters of the plurality of subjects ([0074] “Photoplethysmogram (PPG for short) Intensity Ratio (PIR for short) may reflect changes in diameter of blood vessels and movements of the blood vessels … a correlation between a diastolic blood pressure (DBP for short) and the PIR is high … the PIR is mainly considered for the DBP. When a model is established for a group, corresponding models are established for different groups, and ages, genders, heights, weights, etc. may be impact factors.” [0082] “wherein DBP is a diastolic blood pressure, PIR is a pulse wave intensity ratio, BMI is a body mass index, and b1, b2, b3, b4 and b5 are diastolic blood pressure regression parameters.” [0084] "For example, it is also possible to divide the plurality of subjects to be examined based on ages, for example, the plurality of subjects to be examined are grouped every 10 years old..." [0085] "Further, for each divided group, steps S250 and S260 are performed to generate a systolic blood pressure model and a diastolic blood pressure model for each group”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Lu to include training a first prediction model corresponding to the first age group and a second prediction model corresponding to the second age group in parallel as disclosed in Li so that blood pressure models can be established with higher accuracy by differentiating between different categories of people (Li [0098]). However, the combination of Lu/Li fails to disclose calculating BPs from each model in parallel and calculating a minimal mean error to select an optimal prediction model.
The combination of Lu/Li/Ferber discloses:
calculate a first estimated BP and a second estimated BP for the user respectively from the first prediction model and the second prediction model (Li: [0084] "divide the plurality of subjects to be examined based on ages, for example, the plurality of subjects to be examined are grouped every 10 years old..." [0085] "Further, for each divided group, steps S250 and S260 are performed to generate a systolic blood pressure model and a diastolic blood pressure model for each group.” Lu: [0116] “For each set of data in the testing sample data, the model generation module 406 may determine a first blood pressure value;” Ferber: [0310] "Error metrics between the ground truth and estimated blood pressure values in test dataset are averaged k-times… the model giving optimal (or, the lowest) error values may be selected and used by the blood pressure calculation system 1206 to calculate arterial blood pressure values.”);
store a calibration model which approximately fits relationship between each of the first and second estimated BPs and the user’s real BPs (Lu: [0121] “In step 608, the model generation module 406 may generate a preliminary second model for predicting a plurality of residuals between the plurality of historical blood pressure measurements and the first blood pressure values predicted by the used preliminary first model based on the historical first data and the historical information associated with the plurality of second subjects.” [0129] “the historical blood pressure measurements may be measured by a sphygmomanometer [e.g., an aneroid sphygmomanometer, a mercurial sphygmomanometer, an automatic sphygmomanometer, an electronic sphygmomanometer, etc.]” Li: [0085] “a systolic blood pressure model and a diastolic blood pressure model for each group.”);
and calculate a minimal mean error between each of the first and second estimated BPs and the user’s real BP through the calibration model and select the first prediction model among the first and second prediction models as an optimal prediction model (Ferber: [0310] "Error metrics between the ground truth and estimated blood pressure values in test dataset are averaged k-times. Those metrics may include, and are not limited to, mean square error (MSE, Example Equation 1, shown below), root mean square error (RMSE, Example Equation 2, shown below), median absolute deviation (MAD, Example Equation 3, shown below), and/or coefficient of determination (R.sup.2, Example Equation 4, shown below). In some embodiments, the model giving optimal (or, the lowest) error values may be selected and used by the blood pressure calculation system 1206 to calculate arterial blood pressure values.”) when an age parameter of the user is within the second age group, the age parameter of the user is not within the first age group (Li: [0084] "divide the plurality of subjects to be examined based on ages, for example, the plurality of subjects to be examined are grouped every 10 years old..." [0085] "a systolic blood pressure model and a diastolic blood pressure model for each group.”), and the modeling-used characteristic parameters of the first prediction model are more similar to PPG characteristic parameters of the user so as to predict the BPs more accurately (Ferber: [0125] " a blood metrics measurement apparatus may generate multi-channel signals (e.g., PPG signals) which may be provided to a blood pressure calculation system to calculate arterial blood pressure values (e.g., systolic blood pressure value and/or diastolic blood pressure value). More specifically, the blood pressure calculation system (or the blood pressure measurement apparatus) may … select (or, “extract”) sets of features from each of the high quality waves, and generate sets of feature vectors based on the selected sets of features. In some embodiments, an empirical blood pressure model is used to calculate arterial blood pressure values based on the sets of feature vectors." -- as BP estimation is based on PPG signals and the model is selected based on minimizing the error, the selected model has more similar modeling-used characteristic parameters to the PPG characteristics of the user).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Lu/Li to include calculating BPs from each model in parallel and calculating a minimal mean error to select an optimal prediction model as disclosed in Ferber to decrease the bias of the BP results to the selected test and training sets (Ferber [0310]).
The combination of Lu/Li/Ferber further discloses:
wherein the PPG to BP subsystem obtains user’s characteristic parameters from the upper-arm wearable apparatus so as to have a user’s calibrated estimated BP from the first prediction model (Lu: [0036] “PPG monitor… an armband 116;” Ferber: [0125] " a blood metrics measurement apparatus may generate multi-channel signals (e.g., PPG signals) which may be provided to a blood pressure calculation system to calculate arterial blood pressure values (e.g., systolic blood pressure value and/or diastolic blood pressure value). More specifically, the blood pressure calculation system (or the blood pressure measurement apparatus) may … select (or, “extract”) sets of features from each of the high quality waves, and generate sets of feature vectors based on the selected sets of features. In some embodiments, an empirical blood pressure model is used to calculate arterial blood pressure values based on the sets of feature vectors.” [0310] “the model giving optimal (or, the lowest) error values may be selected and used by the blood pressure calculation system 1206 to calculate arterial blood pressure values.”);
a display unit for displaying a calibrated-estimated SBP (systolic BP) and a calibrated-estimated DBP (diastolic BP) predicted by the first prediction model (Lu: [0037] “the wearable or portable device may ... display a result including the physiological parameter of interest in the form of, e.g., an image;” Ferber: [0167] “the user interface module 1202 may display one or more graphical user interfaces (GUIs) to present a calculated blood pressure to a user.” [0189] The blood pressure calculation system 1206 may be configured to calculate blood pressure values (e.g., systolic, diastolic), and generate messages or alerts based on those values.”).
Regarding claim 15, the combination of Lu/Li/Ferber discloses the system for estimating BPs using a PPG signal analysis according to claim 14 (Lu: medical system 100), wherein the upper-arm wearable apparatus further includes a gravity sensor sensing a motion of the upper-arm of the subject when the subject wears the upper-arm wearable apparatus (Lu: an armband 116; [0039] the measuring devices 110 may incorporate various types of sensors, e.g., …a gravity sensor,…. The gravity sensor may detect the posture of the measured subject.).
Regarding claim 16, the combination of Lu/Li/Ferber discloses the system for estimating BPs using a PPG signal analysis according to claim 15 (Lu: medical system 100), wherein the upper-arm wearable apparatus further includes a reminder device to alert the subject when the gravity sensor is sensing the motion (Lu: an armband 116; [0039] the measuring devices 110 may incorporate various types of sensors, e.g., …a gravity sensor,…. The gravity sensor may detect the posture of the measured subject. [0037] the wearable or portable device may process at least some of the measured signals, estimate a physiological parameter of interest based on the measured signals, display a result including the physiological parameter of interest in the form of, e.g., an image, an audio alert).
Regarding claim 17, the combination of Lu/Li/Ferber discloses the system for estimating BPs using a PPG signal analysis according to claim 14 (Lu: medical system 100), wherein the modeling-used personal information parameters include gender parameters of the plurality of subjects, and the first and second age groups are further categorized into more subgroups by the gender parameters (Lu: [0081] “The vital information may include but not limited to gender of the first subject, age of the subject”; Li: [0074] “When a model is established for a group, corresponding models are established for different groups, and ages, genders, heights, weights, etc. may be impact factors.” [0084] “the plurality of subjects to be examined may be divided by combining ages and genders, for example, the plurality of subjects to be examined are classified based on genders and then are grouped every 5 years old”).
Regarding claim 18, the combination of Lu/Li/Ferber discloses the system for estimating BPs using a PPG signal analysis according to claim 14 (Lu: medical system 100), wherein the PPG signal receiver and analyzer is a computer or smart phone (Lu: [0043] the processing engine 122 may include one or more hardware processors, such as a central processing unit [CPU]; [0046] The terminal 140 may include a mobile device 140-1, a tablet computer 140-2, a laptop computer 140-3, … the mobile device 140-1 may include a smart home device, a wearable device, a smart mobile device).
Regarding claim 19, the combination of Lu/Li/Ferber discloses the system for estimating BPs using a PPG signal analysis according to claim 14 (Lu: medical system 100), wherein the upper-arm wearable apparatus wirelessly transmits the modeling-used PPG waveform signals to the PPG signal receiver and analyzer (Lu: Fig. 1; [0047] the medical system 100 [e.g., the measuring device 110, the server 120, the external data source 130, the terminal 140, and the storage device 160] may transmit information and/or data to other component[s] in the medical system 100 via the network 150… the network 150 may be any type of wired or wireless network).
Claim(s) 6-12 are rejected under 35 U.S.C. 103 as being unpatentable over Lu (US 20210153750 A1) in view of Li (US 20180075209 A1) and Ferber (US 20160242700 A1), and in further view of LeBoeuf (US 20220313098 A1) and Ibtehaz (N Ibtehaz, et al., "PPG2ABP: Translating Photoplethysmogram [PPG] Signals to Arterial Blood Pressure [ABP] Waveforms using Fully Convolutional Neural Networks," Electrical Engineering and Systems Science: Signal Processing, Published May 2020).
Regarding claim 6, Lu teaches a system for improving accuracy in estimating central aortic blood pressures (CBPs) using a photoplethysmography (PPG) signal analysis (medical system 100) comprising:
a cuff-based blood pressure (BP) measuring apparatus measuring a user’s heart rate and a user’s real BP from a user by a pressure sensor included in the cuff-based BP measuring apparatus ([0096] the historical blood pressure measurements [and heart rate] may be measured by a sphygmomanometer [e.g., an aneroid sphygmomanometer, a mercurial sphygmomanometer, an automatic sphygmomanometer, an electronic sphygmomanometer, etc.]);
an upper-arm wearable apparatus including a PPG sensor adapted to sense modeling-used PPG waveform signals from a plurality of subjects wearing the upper-arm wearable apparatus ([0036] PPG monitor… an armband 116; ([0062] obtaining module 402 may be configured to obtain a signal indicative of heart activity of a first subject … a pulse-wave-related signal [e.g., photoplethysmogram (PPG)]);
and a PPG signal receiver and analyzer (obtaining module 402 and parameter determination module 404) configured to:
process the modeling-used PPG waveform signals and deriving modeling-based characteristic parameters from the modeling-used PPG waveform signals ([0063] the parameter determination module 404 may determine the data based on a characteristic point of a beat of the signal. In some embodiments, the parameter determination module 404 may transform the signal from the time domain to the frequency domain and determine a representation of the signal in the frequency domain. The parameter determination module 404 may determine the data based on the representation.);
have modeling-used personal information parameters from the plurality of subjects, wherein the modeling-used personal information parameters includes an age parameter of the plurality of subjects ([0081] obtaining module 402 may obtain information related to the first subject. The information may include vital information associated with the first subject. The vital information may include but not limited to gender of the first subject, age of the subject; [0119] The specific condition may be associated with a group of second subjects classified by an age range);
a PPG to BP estimator and calibrator (model generation module 406 and blood pressure determination module 408) configured to:
divide the age parameter of the modeling-used personal information parameters of the plurality of subjects into at least first and second age groups ([0119] The specific condition may be associated with a group of second subjects classified by an age range, … the age range may include but not limited to 0-5 years old, 5-12 years old, 12-18 years old, 18-30 years old, 30-45 years old, 45-60 years old, 60-80 years old, or the like, or a combination thereof. The age range may include any other length of age separations).
However, Lu fails to disclose training first and second prediction models corresponding to respective age groups in parallel. Li teaches a method and apparatus for establishing blood pressure models.
Li discloses train a first prediction model corresponding to the first age group and a second prediction model corresponding to the second age group in parallel by a circuit system based on the modeling-used characteristic parameters and the modeling-used personal information parameters of the plurality of subjects ([0074] “Photoplethysmogram (PPG for short) Intensity Ratio (PIR for short) may reflect changes in diameter of blood vessels and movements of the blood vessels … a correlation between a diastolic blood pressure (DBP for short) and the PIR is high … the PIR is mainly considered for the DBP. When a model is established for a group, corresponding models are established for different groups, and ages, genders, heights, weights, etc. may be impact factors.” [0082] “wherein DBP is a diastolic blood pressure, PIR is a pulse wave intensity ratio, BMI is a body mass index, and b1, b2, b3, b4 and b5 are diastolic blood pressure regression parameters.” [0084] "For example, it is also possible to divide the plurality of subjects to be examined based on ages, for example, the plurality of subjects to be examined are grouped every 10 years old..." [0085] "Further, for each divided group, steps S250 and S260 are performed to generate a systolic blood pressure model and a diastolic blood pressure model for each group”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Lu to include training a first prediction model corresponding to the first age group and a second prediction model corresponding to the second age group in parallel as disclosed in Li so that blood pressure models can be established with higher accuracy by differentiating between different categories of people (Li [0098]). However, the combination of Lu/Li fails to disclose calculating BPs from each model in parallel and calculating a minimal mean error to select an optimal prediction model.
The combination of Lu/Li/Ferber discloses:
calculate a first estimated BP and a second estimated BP respectively from the first prediction model and the second prediction model (Li: [0084] "divide the plurality of subjects to be examined based on ages, for example, the plurality of subjects to be examined are grouped every 10 years old..." [0085] "Further, for each divided group, steps S250 and S260 are performed to generate a systolic blood pressure model and a diastolic blood pressure model for each group.” Lu: [0116] “For each set of data in the testing sample data, the model generation module 406 may determine a first blood pressure value;” Ferber: [0310] "Error metrics between the ground truth and estimated blood pressure values in test dataset are averaged k-times… the model giving optimal (or, the lowest) error values may be selected and used by the blood pressure calculation system 1206 to calculate arterial blood pressure values.”);
store a calibration model which approximately fits relationship between each the first and the second estimated BPs and the user’s real BP (Lu: [0121] “In step 608, the model generation module 406 may generate a preliminary second model for predicting a plurality of residuals between the plurality of historical blood pressure measurements and the first blood pressure values predicted by the used preliminary first model based on the historical first data and the historical information associated with the plurality of second subjects.” [0129] “the historical blood pressure measurements may be measured by a sphygmomanometer [e.g., an aneroid sphygmomanometer, a mercurial sphygmomanometer, an automatic sphygmomanometer, an electronic sphygmomanometer, etc.]” Li: [0085] “a systolic blood pressure model and a diastolic blood pressure model for each group.”);
and designate the first prediction model as an optimal model after the calibration model calculates a minimal mean error between each the first and the second estimated BPs and the user’s real BP (Ferber: [0310] "Error metrics between the ground truth and estimated blood pressure values in test dataset are averaged k-times. Those metrics may include, and are not limited to, mean square error (MSE, Example Equation 1, shown below), root mean square error (RMSE, Example Equation 2, shown below), median absolute deviation (MAD, Example Equation 3, shown below), and/or coefficient of determination (R.sup.2, Example Equation 4, shown below). In some embodiments, the model giving optimal (or, the lowest) error values may be selected and used by the blood pressure calculation system 1206 to calculate arterial blood pressure values.”) when an age parameter of the user is within the second age group, the age parameter of the user is not within the first age group (Li: [0084] "divide the plurality of subjects to be examined based on ages, for example, the plurality of subjects to be examined are grouped every 10 years old..." [0085] "a systolic blood pressure model and a diastolic blood pressure model for each group.”), and the modeling-used characteristic parameters of the first prediction model are more similar to PPG characteristic parameters of the user so as to predict the BPs more accurately (Ferber: [0125] " a blood metrics measurement apparatus may generate multi-channel signals (e.g., PPG signals) which may be provided to a blood pressure calculation system to calculate arterial blood pressure values (e.g., systolic blood pressure value and/or diastolic blood pressure value). More specifically, the blood pressure calculation system (or the blood pressure measurement apparatus) may … select (or, “extract”) sets of features from each of the high quality waves, and generate sets of feature vectors based on the selected sets of features. In some embodiments, an empirical blood pressure model is used to calculate arterial blood pressure values based on the sets of feature vectors." -- as BP estimation is based on PPG signals and the model is selected based on minimizing the error, the selected model has more similar modeling-used characteristic parameters to the PPG characteristics of the user).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Lu/Li to include calculating BPs from each model in parallel and calculating a minimal mean error to select an optimal prediction model as disclosed in Ferber to decrease the bias of the BP results to the selected test and training sets (Ferber [0310]).
The combination of Lu/Li/Ferber further discloses:
wherein the PPG to BP estimator and calibrator obtains user’s characteristic parameters from the upper-arm wearable apparatus so as to have a user’s calibrated estimated BP from the optimal prediction model (Lu: [0036] “PPG monitor… an armband 116;” Ferber: [0125] " a blood metrics measurement apparatus may generate multi-channel signals (e.g., PPG signals) which may be provided to a blood pressure calculation system to calculate arterial blood pressure values (e.g., systolic blood pressure value and/or diastolic blood pressure value). More specifically, the blood pressure calculation system (or the blood pressure measurement apparatus) may … select (or, “extract”) sets of features from each of the high quality waves, and generate sets of feature vectors based on the selected sets of features. In some embodiments, an empirical blood pressure model is used to calculate arterial blood pressure values based on the sets of feature vectors.” [0310] “the model giving optimal (or, the lowest) error values may be selected and used by the blood pressure calculation system 1206 to calculate arterial blood pressure values.”).
However, the combination of Lu/Li/Ferber to disclose a PPG to PVR transformer.
LeBoeuf teaches a biometric waveform analysis system with waveform capture logic that receives the physiological data signal from the sensor system and separates the physiological data signal into a plurality of individual physiological waveforms. LeBoeuf discloses a PPG to pulse volume recording (PVR) transformer (Fig. 3, metric output generator 200, waveform analysis engine 300, and the relationship processor 500) configured to:
store a PVR prediction model which processes the modeling-used PPG waveform signals of the plurality of subjects ([0174] the high-rate PPG metrics generated by the metric output generator 200 may comprise heart rate information and/or pulse volume information … Generating a continuous biometric parameter of blood pulse volume may be generated by processing the PPG signal in real time to determine the real-time spectral amplitude at the heart rate frequency, which may be proportional to blood pulse volume);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Lu/Li/Ferber to include obtaining a pulse volume waveform from the PPG signal as disclosed in LeBoeuf to generate an assessment of heart health or heart disease for the patient (LeBoeuf [0153]).
However, the combination of Lu/Li/Ferber/LeBoeuf fails to disclose an Approximation Network and a Refinement Network.
Ibtehaz teaches a method to estimate the continuous arterial blood pressure (ABP) waveform through a non-invasive approach using Photoplethysmogram (PPG) signals. The combination of Lu/Li/Ferber/LeBoeuf/Ibtehaz discloses using an Approximation Network and a Refinement Network to have refined PVR waveforms based on the modeling-used PPG waveform signals; and obtain a user’s refined PVR waveform from a user’s PPG waveform signal using the PVR prediction model (Ibtehaz: Pgs 4-5, 2.2 Proposed Methodology [1] filtered signal is processed using an Approximation Network that approximates the ABP [arterial blood pressure] waveforms based on the input PPG signals. The preliminary rough estimate of ABP is further refined through a Refinement Network. LeBoeuf: [0174] generating a blood pressure assessment may comprise processing waveform features over a plurality of PPG waveforms using a machine learning model … Generating a relationship [i.e., via the relationship processor 500] between a high-rate metric [i.e., pulse rate and/or pulse volume] and the high-acuity assessment [blood pressure] may help reduce processing resources required to generate a continuous blood pressure assessment… the relationship processor 500 may be configured to update the relationship over time based on a fixed interval, random interval, or an interval smartly (autonomously) chosen by processing sensor signals in context of system confidence indicators.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Lu/Li/Ferber/LeBoeuf to include an Approximation Network and a Refinement Network as disclosed in Ibtehaz to preserve the spatial feature maps lost during pooling and upsampling and reduce the disparity between the feature maps of the corresponding levels of encoders and decoders (Ibtehaz pgs 5-6, 2.2.2 Approximation Network [1] and 2.2.3 Refinement Network [1]).
The combination of Lu/Li/Ferber/LeBoeuf/Ibtehaz discloses:
a CBP estimator (Li: blood pressure determination module 950) configured to: substitute the user’s calibrated estimated BP, the user’s heart rate and waveform parameters of the user’s refined PVR waveform into a linear regression equation to have an estimated CBP (Li: [0023] “the systolic blood pressure model establishment module or the diastolic blood pressure model establishment module is configured to determine the parameters by using a least square regression method.” [0104] “ the pre-established blood pressure model is, for example, the systolic blood pressure model and/or diastolic blood pressure model established in steps S340 and S350 disclosed above, such as equations 3 and 4 shown above. Obviously, the systolic blood pressure and the diastolic blood pressure of the subject to be examined can be easily calculated by using equations 3 and 4 based on the pulse wave transit time, the pulse wave intensity ratio, the body mass index and the age.” );
a display unit for displaying the estimated CBP (Lu [0037] the wearable or portable device may ... display a result including the physiological parameter of interest in the form of, e.g., an image).
Regarding claim 7, the combination of Lu/Li/Ferber/LeBoeuf/Ibtehaz discloses the system for estimating CBPs using a PPG signal analysis according to claim 6 (Lu: medical system 100), wherein the modeling-used characteristic parameters are derived by performing feature extraction on the PPG waveform signals (Lu: [0062] obtaining module 402 may be configured to obtain a signal indicative of heart activity of a first subject … a pulse-wave-related signal [e.g., photoplethysmogram (PPG); [0063] the parameter determination module 404 may determine the data based on a characteristic point of a beat of the signal. In some embodiments, the parameter determination module 404 may transform the signal from the time domain to the frequency domain and determine a representation of the signal in the frequency domain. The parameter determination module 404 may determine the data based on the representation.).
Regarding 8, the combination of Lu/Li/Ferber/LeBoeuf/Ibtehaz discloses the system for estimating CBPs using a PPG signal analysis according to claim 6 (Lu: medical system 100), wherein the modeling-used personal information parameters include gender parameters of the plurality of subjects, and the PPG to BP estimator and calibrator further divides the first and second age groups into subgroups by the gender parameters (Lu: [0081] The vital information may include but not limited to gender of the first subject, age of the subject; [0119] In some embodiments, the model generation module 406 may further determine a plurality of sub-models. Each sub-model may be associated with a specific condition. The specific condition may be associated with a group of second subjects classified by an age range… different genders).
Regarding 9, the combination of Lu/Li/Ferber/LeBoeuf/Ibtehaz discloses the system for estimating CBPs using a PPG signal analysis according to claim 6 (Lu: medical system 100), wherein the upper-arm wearable apparatus wirelessly or by wire transmits the modeling-used PPG waveform signals to the PPG signal receiver and analyzer (Lu: Fig. 1; [0047] the medical system 100 [e.g., the measuring device 110, the server 120, the external data source 130, the terminal 140, and the storage device 160] may transmit information and/or data to other component[s] in the medical system 100 via the network 150… the network 150 may be any type of wired or wireless network.
Regarding 10, the combination of Lu/Li/Ferber/LeBoeuf/Ibtehaz discloses the system for estimating CBPs using a PPG signal analysis according to claim 9 (Lu: medical system 100), wherein the upper-arm wearable apparatus further includes a gravity sensor sensing a motion of the upper-arm of the subject when the subject wears the upper-arm wearable apparatus (Lu: an armband 116; [0039] the measuring devices 110 may incorporate various types of sensors, e.g., …a gravity sensor,…. The gravity sensor may detect the posture of the measured subject.).
Regarding 11, the combination of Lu/Li/Ferber/LeBoeuf/Ibtehaz discloses the system for estimating CBPs using a PPG signal analysis according to claim 6 (Lu: medical system 100), wherein the PPG signal receiver and analyzer (Lu: obtaining module 402 and parameter determination module 404), the PPG to BP estimator and calibrator (Lu: model generation module 406 and blood pressure determination module 408), the PPG to PVR transformer (LeBoeuf: Fig. 3, metric output generator 200, waveform analysis engine 300, and the relationship processor 500) and the CBP estimator (Li: blood pressure determination module 950) are integrated into the upper-arm wearable apparatus (Lu: armband 116; [0047] transmit information and/or data to other component[s] in the medical system 100 via the network 150… the network 150 may be any type of wired or wireless network).
Regarding claim 12, the combination of Lu/Li/Ferber/LeBoeuf/Ibtehaz discloses the system for estimating CBPs using a PPG signal analysis according to claim 6 (Lu: medical system 100), wherein the modeling-used PPG waveform signals is split into a plurality of episodes each with an identical interval (Lu: The sample data may correspond to a first predetermined times of measurements for each second subject. The first predetermined times of measurements may include the first three measurements, the first five measurements, the first ten measurements, etc), an initial episode is deleted, and a segment of a real PVR waveform with an initial interval is trimmed (LeBoeuf: [0174] generating a continuous biometric parameter of heart rate may be achieved by real-time pulse picking over peaks or troughs of a noise-filtered PPG signal. Generating a continuous biometric parameter of blood pulse volume may be generated by processing the PPG signal in real time to determine the real-time spectral amplitude at the heart rate frequency, which may be proportional to blood pulse volume … the relationship processor 500 may be configured to update the relationship over time based on a fixed interval, random interval, or an interval smartly [autonomously] chosen by processing sensor signals in context of system confidence indicators).
Claim(s) 13 are rejected under 35 U.S.C. 103 as being unpatentable Lu (US 20210153750 A1) in view of Li (US 20180075209 A1), Ferber (US 20160242700 A1), LeBoeuf (US 20220313098 A1) and Ibtehaz (N Ibtehaz, et al., "PPG2ABP: Translating Photoplethysmogram [PPG] Signals to Arterial Blood Pressure [ABP] Waveforms using Fully Convolutional Neural Networks," Electrical Engineering and Systems Science: Signal Processing, Published May 2020), and in further view of Li (US 20190313947 A1).
Regarding claim 13, the combination of Lu/Li/Ferber/LeBoeuf/Ibtehaz discloses the system for estimating CBPs using a PPG signal analysis according to claim 12 (Lu: medical system 100), wherein one of the modeling-used PPG waveform signals and the real PVR waveforms are synchronized with each other using a same peak number alignment (LeBoeuf: [0174] generating a continuous biometric parameter of heart rate may be achieved by real-time pulse picking over peaks or troughs of a noise-filtered PPG signal. Generating a continuous biometric parameter of blood pulse volume may be generated by processing the PPG signal in real time to determine the real-time spectral amplitude at the heart rate frequency, which may be proportional to blood pulse volume.).
However, the combination of Lu/Li/Ferber/LeBoeuf/Ibtehaz fails to disclose a dynamic time warping method.
Li-947 teaches a method for detecting arrhythmia, including determining heartbeat event locations from the generated segments and performing false alarm detection on the raw motion signals and heartbeat event locations to generated refined abnormal candidates. Li-947 discloses a dynamic time warping method ([0098] Various similarity matching techniques may be used for real time feature alignment, such as, for example, a correlation method and/or dynamic time warping [DTW] method. For example, DTW may be used to match temporal similarity patterns of two consecutive clipped segments.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Lu/Li/Ferber/LeBoeuf/Ibtehaz to include the dynamic time warping method as disclosed in Li-947 to provide an indicator of similarity for determining an optimal match between two given sequences and to improve reliability and accuracy of alignment performance (Li-947 [0098-0099]).
The combination of Lu/Li/Ferber/LeBoeuf/Ibtehaz/Li-947 discloses wherein the same peak-number alignment assigns an identical numerical label to two respective peaks of the modeling-used PPG waveform signal and the real PVR waveform that occur approximately at the same time along a time axis (LeBoeuf: [0174] generating a continuous biometric parameter of heart rate may be achieved by real-time pulse picking over peaks or troughs of a noise-filtered PPG signal. Generating a continuous biometric parameter of blood pulse volume may be generated by processing the PPG signal in real time to determine the real-time spectral amplitude at the heart rate frequency, which may be proportional to blood pulse volume).
Claim(s) 21 and 22 are rejected under 35 U.S.C. 103 as being unpatentable Lu (US 20210153750 A1) in view of Li (US 20180075209 A1) and Ferber (US 20160242700 A1), and in further view of LeBoeuf (US 20220313098 A1) and Li (US 20190313947 A1).
Regarding claim 21, the combination of Lu/Li/Ferber discloses the apparatus according to claim 1, wherein one of the modeling-used PPG waveform signals (Lu: [0062] “obtaining module 402 may be configured to obtain a signal indicative of heart activity of a first subject … a pulse-wave-related signal [e.g., photoplethysmogram (PPG)”; [0063] “the parameter determination module 404 may determine the data based on a characteristic point of a beat of the signal. In some embodiments, the parameter determination module 404 may transform the signal from the time domain to the frequency domain and determine a representation of the signal in the frequency domain. The parameter determination module 404 may determine the data based on the representation.”).
However, the combination of Lu/Li/Ferber fails to disclose pulse volume waveforms.
LeBoeuf discloses and a real PVR waveforms are synchronized with each other using a same peak number alignment ([0174] the high-rate PPG metrics generated by the metric output generator 200 may comprise heart rate information and/or pulse volume information … Generating a continuous biometric parameter of blood pulse volume may be generated by processing the PPG signal in real time to determine the real-time spectral amplitude at the heart rate frequency, which may be proportional to blood pulse volume).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Lu/Li/Ferber to include obtaining a pulse volume waveform from the PPG signal as disclosed in LeBoeuf to generate an assessment of heart health or heart disease for the patient (LeBoeuf [0153]). However, the combination of Lu/Li/Ferber/LeBeouf fails to disclose a dynamic time warping method.
Li-947 teaches a method for detecting arrhythmia, including determining heartbeat event locations from the generated segments and performing false alarm detection on the raw motion signals and heartbeat event locations to generated refined abnormal candidates. Li-947 discloses and dynamic time warping method ([0098] Various similarity matching techniques may be used for real time feature alignment, such as, for example, a correlation method and/or dynamic time warping [DTW] method. For example, DTW may be used to match temporal similarity patterns of two consecutive clipped segments.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Lu/Li/Ferber/LeBeouf to include the dynamic time warping method as disclosed in Li-947 to provide an indicator of similarity for determining an optimal match between two given sequences and to improve reliability and accuracy of alignment performance (Li-947 [0098-0099]).
The combination of Lu/Li/Ferber/LeBeouf/Li-947 discloses wherein the same peak- number alignment assigns an identical numerical label to two respective peaks of the modeling-used PPG waveform signal and the real PVR waveform that occur approximately at the same time along a time axis (LeBoeuf: [0174] generating a continuous biometric parameter of heart rate may be achieved by real-time pulse picking over peaks or troughs of a noise-filtered PPG signal. Generating a continuous biometric parameter of blood pulse volume may be generated by processing the PPG signal in real time to determine the real-time spectral amplitude at the heart rate frequency, which may be proportional to blood pulse volume).
Regarding claim 22, the combination of Lu/Li/Ferber discloses the system for estimating BPs using a PPG signal analysis according to claim 14, wherein one of the modeling-used PPG waveform signals (Lu: [0062] “obtaining module 402 may be configured to obtain a signal indicative of heart activity of a first subject … a pulse-wave-related signal [e.g., photoplethysmogram (PPG)”; [0063] “the parameter determination module 404 may determine the data based on a characteristic point of a beat of the signal. In some embodiments, the parameter determination module 404 may transform the signal from the time domain to the frequency domain and determine a representation of the signal in the frequency domain. The parameter determination module 404 may determine the data based on the representation.”).
However, the combination of Lu/Li/Ferber fails to disclose pulse volume waveforms.
LeBoeuf discloses and the real PVR waveforms are synchronized with each other using a same peak number alignment ([0174] generating a continuous biometric parameter of heart rate may be achieved by real-time pulse picking over peaks or troughs of a noise-filtered PPG signal. Generating a continuous biometric parameter of blood pulse volume may be generated by processing the PPG signal in real time to determine the real-time spectral amplitude at the heart rate frequency, which may be proportional to blood pulse volume.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Lu/Li/Ferber to include obtaining a pulse volume waveform from the PPG signal as disclosed in LeBoeuf to generate an assessment of heart health or heart disease for the patient (LeBoeuf [0153]). However, the combination of Lu/Li/Ferber/LeBeouf fails to disclose a dynamic time warping method.
Li-947 teaches a method for detecting arrhythmia, including determining heartbeat event locations from the generated segments and performing false alarm detection on the raw motion signals and heartbeat event locations to generated refined abnormal candidates. Li-947 discloses and dynamic time warping method ([0098] Various similarity matching techniques may be used for real time feature alignment, such as, for example, a correlation method and/or dynamic time warping [DTW] method. For example, DTW may be used to match temporal similarity patterns of two consecutive clipped segments.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Lu/Li/Ferber/LeBeouf to include the dynamic time warping method as disclosed in Li-947 to provide an indicator of similarity for determining an optimal match between two given sequences and to improve reliability and accuracy of alignment performance (Li-947 [0098-0099]).
The combination of Lu/Li/Ferber/LeBeouf/Li-947 discloses wherein the same peak- number alignment assigns an identical numerical label to two respective peaks of the modeling-used PPG waveform signal and the real PVR waveform that occur approximately at the same time along a time axis (LeBoeuf: [0174] generating a continuous biometric parameter of heart rate may be achieved by real-time pulse picking over peaks or troughs of a noise-filtered PPG signal. Generating a continuous biometric parameter of blood pulse volume may be generated by processing the PPG signal in real time to determine the real-time spectral amplitude at the heart rate frequency, which may be proportional to blood pulse volume).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOLLY HALPRIN whose telephone number is (703)756-1520. The examiner can normally be reached 12PM-8PM ET.
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, Robert (Tse) Chen can be reached at (571) 272-3672. 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.
/M.H./Examiner, Art Unit 3791
/DEVIN B HENSON/Primary Examiner, Art Unit 3791