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 .
Response to Amendment
This action is in response to the remarks filed on 12/15/2025.
The amendments filed on 12/15/2025 have been entered. Accordingly claims 1-18 remain pending.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims 1 and 12 recite “estimate a blood pressure from the height levels and the user’s bio-signals based on a machine learning algorithm or a state estimation algorithm with a hemodynamic state space model”
The limitation of “estimate”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of “parameter estimating circuit” (i.e., generic computer components). That is, other than reciting by “parameter estimating circuit” (i.e., a processor), nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the by “parameter estimating circuit” language, “estimate” in the context of this claim encompasses the user manually estimating/calculating the values encompasses the user thinking and/or with the help of simple pen or paper of these mathematical process that the If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element that is “parameter estimating circuit” (i.e., generic computer components) to perform the limitation of “estimation”. The processor in the step is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of “estimation” such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
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 using a processor to perform “estimation” step amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
The depending claims also recite similar abstract ideas (e.g., determine a reference level, estimate, extract, etc.) without 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.
Therefore, the claims are not patent eligible.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-10 and 12-18 are rejected under 35 U.S.C. 103 as being unpatentable over Hwa (KR20200069545A) in view of Noh (US20190200932A1) and Colburn et al (US 20210219852 A1).
Regarding claims 1 and 12, Hwa teaches a cuffless blood pressure estimating device implemented as a hardware circuitry and a method (“digitized blood pressure measuring device” pg. 3; “a blood wrist pressure monitor that measures blood pressure by contacting a finger with a light measurement sensor of the smart phone” pg. 6) comprising:
a hemodynamic parameter estimating circuit configured to measure at least two height levels based on position information output from at least one position detection sensor,
measure user’s bio-signals respectively at the height levels (“blood pressure monitor and a blood pressure acquisition method for measuring blood pressure using a change in blood flow parameters according to a height difference (height difference) of a blood flow measurement position” pg. 3), and
estimate a blood pressure from the height levels and the user’s bio-signals (“calculating unit for calculating a change in blood flow parameter(PM) caused by an altitude difference between two arbitrary points where blood flow measurement is performed; And it provides a blood pressure monitor including a blood pressure calculating unit for calculating the blood pressure from the altitude difference between the two points where the blood flow measurement is made, the amount of change in the blood flow parameter and the blood flow parameter.” pg. 4); and
at least one processor configured to control the hemodynamic parameter estimating circuit (“10: parameter change amount calculation unit 20: blood pressure calculation unit 30: blood pressure difference calculation unit 40: parameter measurement unit 41: PTT detection unit 41a: ECG measurement unit 41b: pulse wave measurement unit 50: altitude difference detection unit 60: Sphygmomanometer setting unit 100, 100A:”).
Hwa does not point out the specifics of hydrostatic pressure differences, based on a machine learning algorithm or a state estimation algorithm with a hemodynamic state space model, such that the estimation is performed without requiring a reference cuff-based calibration, wherein the hydrostatic pressure differences are determined from changes in the user's bio-signals measured with respect to the height levels.
However, in the same field of endeavor, Noh teaches by analyzing a shape of the PPG signal, various types of hemodynamic information may be estimated, and bio-information may be measured based on the hemodynamic information [0005]. In the case where the examination point is located at a higher position than the reference point, the magnitude of the AC component of the pulse wave signal may increase [0071]. The processor 120 may adjust the calculation reference of reliability to a higher level [0111]. In the hydrostatic pressure condition, if an examination point is located at a higher position than a reference point, hydrostatic pressure is decreased. In contrast, if an examination point is located at a lower position than a reference point, hydrostatic pressure is increased. Accordingly, the processor 120 may generate guide information for guiding a user to a measurement posture at which the reference point and the examination point are located at an equal position [0119]. The bio-information measuring apparatus 100 may generate the bio-information estimation model by machine learning based on a correlation between the features, extracted from a pulse wave signal, and bio-information to be measured, and may also receive a pre-generated bio-information estimation model [0165].
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with based on a machine learning algorithm or a state estimation algorithm with a hemodynamic state space model as taught by Noh because it helps to measure bio-information accurately even when an object moves, or distortion of the PPG signal occurs. ([0007] of Noh).
Further, Colburn, also in the same field of endeavor, teaches cuffless blood pressure monitoring that does not require external per-person calibration, such as with a cuff-based measurement device (abst). Tracking of hydrostatic pressure is relevant because a difference in elevation of 5 cm between the measurement site and the heart can contribute an error. the pressure sensor and hydrostatic pressure compensator enable monitoring of the external pressure applied to the arteries and enable more accurate blood pressure measurement [0051]. calibration procedure may potentially be performed with or without user interaction. In one implementation, the calibration procedure is automatically performed when the device detects a period of changing external pressure and the conditions for assuming constant blood pressure are met. The change in external pressure can be due to changes in contact pressure, hydrostatic pressure, muscle contraction or a combination of these [0060]. The calibration procedure would be performed with user assistance. When the device detects that the conditions for assuming constant blood pressure are met, the user may choose to calibrate the device. The user will then be instructed to perform a series of procedures to perturb the external pressure and thus allow the device to calibrate. For example, if the device is applied to the user's wrist, they may be instructed to slowly raise and lower their arm to alter hydrostatic pressure [0061]. calculate blood pressure using PAT while compensating for the effects of hydrostatic pressure under the given assumptions [0152]. In an alternative implementation, Eqn. 13B can be recasted as a machine learning problem. With machine learning, external pressure compensated blood pressure can be found through Eqn. 21A where the function ƒ is approximated using machine learning techniques and is a function of the signals from the various sensors and PAT [0153].
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with hydrostatic pressure differences, based on a machine learning algorithm or a state estimation algorithm with a hemodynamic state space model, such that the estimation is performed without requiring a reference cuff-based calibration, wherein the hydrostatic pressure differences are determined from changes in the user's bio-signals measured with respect to the height levels as taught by Colburn because cuffless monitoring is a desirable type of BP monitoring ([0003] of Colburn).
Regarding claims 2 and 13, Hwa teaches wherein the hemodynamic parameter estimating circuit is configured to determine a reference level that represents a heart height level information of a user based on the height levels (“measures a blood pressure by using a bloodstream parameter change caused by an elevation difference (height difference) of measurement positions” abst).
Regarding claims 3 and 15, Hwa teaches wherein the hemodynamic parameter estimating circuit is configured to estimate the blood pressure from a first difference value, which is a difference between the reference level and the first height level, and a second difference value, which is a difference between the reference level and the second height level (“measures a blood pressure by using a bloodstream parameter change caused by an elevation difference (height difference) of measurement positions” abst; “measuring blood pressure using a change in blood flow parameter caused by an altitude difference (height difference) of a measurement position, and more specifically, calculating a blood pressure with a blood flow in the sphygmomanometer and a rapid change in blood vessel state having a large influence on blood pressure measurement.” pg. 5).
Regarding claim 4, Hwa teaches wherein the hemodynamic parameter estimating circuit includes:
a position measurer including the at least one position detection sensor and configured to measure the height levels (“(P1 is the blood pressure at a high position (H1) among any two points where blood flow is measured, PM1 is the value of blood flow parameter measured at the height of H1, B is the vascular resistance, and K is the adjustment constant)” pg.5);
a bio-signal measurer configured to measure the user’s bio-signals (“an arterial pressure measurement unit for detecting the arterial pressure.” Pg 4); and
a hemodynamic parameter estimator configured to estimate the blood pressure from the height levels and the user’s bio-signals (“10: parameter change amount calculation unit 20: blood pressure calculation unit 30: blood pressure difference calculation unit 40: parameter measurement unit 41: PTT detection unit 41a: ECG measurement unit 41b: pulse wave measurement unit 50: altitude difference detection unit 60: Sphygmomanometer setting unit 100, 100A:”).
Hwa does not point out the specifics of based on a machine learning algorithm or a state estimation algorithm with a hemodynamic state space model.
However, in the same field of endeavor, Noh teaches by analyzing a shape of the PPG signal, various types of hemodynamic information may be estimated, and bio-information may be measured based on the hemodynamic information [0005]. In the case where the examination point is located at a higher position than the reference point, the magnitude of the AC component of the pulse wave signal may increase [0071]. The processor 120 may adjust the calculation reference of reliability to a higher level [0111]. In the hydrostatic pressure condition, if an examination point is located at a higher position than a reference point, hydrostatic pressure is decreased. In contrast, if an examination point is located at a lower position than a reference point, hydrostatic pressure is increased. Accordingly, the processor 120 may generate guide information for guiding a user to a measurement posture at which the reference point and the examination point are located at an equal position [0119]. The bio-information measuring apparatus 100 may generate the bio-information estimation model by machine learning based on a correlation between the features, extracted from a pulse wave signal, and bio-information to be measured, and may also receive a pre-generated bio-information estimation model [0165].
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with based on a machine learning algorithm or a state estimation algorithm with a hemodynamic state space model as taught by Noh because it helps to measure bio-information accurately even when an object moves, or distortion of the PPG signal occurs. ([0007] of Noh).
Regarding claim 5, Hwa teaches wherein the at least one position detection sensor includes at least one of an accelerometer, a gyroscope, a magnetometer, a barometer, an altimeter, a variometer and a distance measurement sensor (“blood flow measurement position can be performed by the altitude difference detection unit 50,the altitude difference detection unit 50, the acceleration sensor” pg 7).
Regarding claims 6 and 16, Hwa teaches wherein each of the user’s bio-signals includes a photoplethysmogram (PPG) signal, and wherein the bio-signal measurer includes a PPG sensor (“An example of the pulse wave measuring unit 41b is a photoplethysmography (PPG), but is not limited thereto, and a sensor capable of measuring pulse wave, for example, a pressure sensor is also possible. Although not shown, a general photo blood flow meter includes a light emitting part and a light receiving part.” pg. 7).
Regarding claims 7 and 17, Hwa teaches wherein the hemodynamic parameter estimating circuit is configured to: estimate hemodynamic parameters from the height levels and the user’s bio-signals and extract information about the blood pressure from the hemodynamic parameters to estimate the blood pressure (“10: parameter change amount calculation unit 20: blood pressure calculation unit 30: blood pressure difference calculation unit 40: parameter measurement unit 41: PTT detection unit 41a: ECG measurement unit 41b: pulse wave measurement unit 50: altitude difference detection unit 60: Sphygmomanometer setting unit 100, 100A:”).
Hwa does not point out the specifics of based on a machine learning algorithm or a state estimation algorithm with a hemodynamic state space model.
However, in the same field of endeavor, Noh teaches by analyzing a shape of the PPG signal, various types of hemodynamic information may be estimated, and bio-information may be measured based on the hemodynamic information [0005]. In the case where the examination point is located at a higher position than the reference point, the magnitude of the AC component of the pulse wave signal may increase [0071]. The processor 120 may adjust the calculation reference of reliability to a higher level [0111]. In the hydrostatic pressure condition, if an examination point is located at a higher position than a reference point, hydrostatic pressure is decreased. In contrast, if an examination point is located at a lower position than a reference point, hydrostatic pressure is increased. Accordingly, the processor 120 may generate guide information for guiding a user to a measurement posture at which the reference point and the examination point are located at an equal position [0119]. The bio-information measuring apparatus 100 may generate the bio-information estimation model by machine learning based on a correlation between the features, extracted from a pulse wave signal, and bio-information to be measured, and may also receive a pre-generated bio-information estimation model [0165].
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with based on a machine learning algorithm or a state estimation algorithm with a hemodynamic state space model as taught by Noh because it helps to measure bio-information accurately even when an object moves, or distortion of the PPG signal occurs. ([0007] of Noh).
Regarding claims 8 and 18, Hwa teaches wherein the hemodynamic parameter estimating circuit is configured to:
extract a plurality of features from each of the user’s bio signals (“blood pressure calculating unit 20 may calculate blood pressure using the following [Equation 2] from the vascular resistivity and blood flow parameters.” Pg. 7); and
estimate the hemodynamic parameters from the height levels and the plurality of the features (“10: parameter change amount calculation unit 20: blood pressure calculation unit 30: blood pressure difference calculation unit 40: parameter measurement unit 41: PTT detection unit 41a: ECG measurement unit 41b: pulse wave measurement unit 50: altitude difference detection unit 60: Sphygmomanometer setting unit 100, 100A:”).
Hwa does not point out the specifics of based on a machine learning algorithm or a state estimation algorithm with a hemodynamic state space model.
However, in the same field of endeavor, Noh teaches by analyzing a shape of the PPG signal, various types of hemodynamic information may be estimated, and bio-information may be measured based on the hemodynamic information [0005]. In the case where the examination point is located at a higher position than the reference point, the magnitude of the AC component of the pulse wave signal may increase [0071]. The processor 120 may adjust the calculation reference of reliability to a higher level [0111]. In the hydrostatic pressure condition, if an examination point is located at a higher position than a reference point, hydrostatic pressure is decreased. In contrast, if an examination point is located at a lower position than a reference point, hydrostatic pressure is increased. Accordingly, the processor 120 may generate guide information for guiding a user to a measurement posture at which the reference point and the examination point are located at an equal position [0119]. The bio-information measuring apparatus 100 may generate the bio-information estimation model by machine learning based on a correlation between the features, extracted from a pulse wave signal, and bio-information to be measured, and may also receive a pre-generated bio-information estimation model [0165].
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with based on a machine learning algorithm or a state estimation algorithm with a hemodynamic state space model as taught by Noh because it helps to measure bio-information accurately even when an object moves, or distortion of the PPG signal occurs. ([0007] of Noh).
Regarding claim 9, Hwa teaches wherein each of the user’s bio-signals further includes an electrocardiogram (ECG) signal, and wherein the bio-signal measurer further includes an ECG sensor (“A blood pressure monitor further comprising a Pulse Transit Time (PTT) detector for detecting the pulse transmission time. The method of claim 4, The PTT detection unit, characterized in that it comprises an electrocardiogram measuring unit for measuring the electrocardiogram (ECG) and a pulse wave measuring unit for measuring the pulse wave.” pg. 2).
Regarding claim 10, Hwa teaches wherein the hemodynamic parameters include information on one or more of the blood pressure, a vessel density, a vessel elastic modulus, a vessel wall thickness, a vessel radius, and/or a vessel length (“10: parameter change amount calculation unit 20: blood pressure calculation unit 30: blood pressure difference calculation unit 40: parameter measurement unit 41: PTT detection unit 41a: ECG measurement unit 41b: pulse wave measurement unit 50: altitude difference detection unit 60: Sphygmomanometer setting unit 100, 100A:” pg. 10).
Regarding claim 14, Hwa teaches wherein the hemodynamic parameter estimating circuit is configured to estimate the blood pressure from a first difference value, which is a difference between the reference level and the first height level, and a second difference value, which is a difference between the reference level and the second height level (“measures a blood pressure by using a bloodstream parameter change caused by an elevation difference (height difference) of measurement positions” abst; “measuring blood pressure using a change in blood flow parameter caused by an altitude difference (height difference) of a measurement position, and more specifically, calculating a blood pressure with a blood flow in the sphygmomanometer and a rapid change in blood vessel state having a large influence on blood pressure measurement.” pg. 5).
Hwa does not point out the specifics of based on a machine learning algorithm or a state estimation algorithm with a hemodynamic state space model.
However, in the same field of endeavor, Noh teaches by analyzing a shape of the PPG signal, various types of hemodynamic information may be estimated, and bio-information may be measured based on the hemodynamic information [0005]. In the case where the examination point is located at a higher position than the reference point, the magnitude of the AC component of the pulse wave signal may increase [0071]. The processor 120 may adjust the calculation reference of reliability to a higher level [0111]. In the hydrostatic pressure condition, if an examination point is located at a higher position than a reference point, hydrostatic pressure is decreased. In contrast, if an examination point is located at a lower position than a reference point, hydrostatic pressure is increased. Accordingly, the processor 120 may generate guide information for guiding a user to a measurement posture at which the reference point and the examination point are located at an equal position [0119]. The bio-information measuring apparatus 100 may generate the bio-information estimation model by machine learning based on a correlation between the features, extracted from a pulse wave signal, and bio-information to be measured, and may also receive a pre-generated bio-information estimation model [0165].
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with based on a machine learning algorithm or a state estimation algorithm with a hemodynamic state space model as taught by Noh because it helps to measure bio-information accurately even when an object moves, or distortion of the PPG signal occurs. ([0007] of Noh).
Response to Arguments
Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Regarding the rejection of claims under 35 USC 101, the applicant argues the following;
As clearly set forth in the claims; by structurally incorporating the hydrostatic pressure correction mechanism into the hardware, the claimed system performs physical signal measurement and model-based estimation - not mental or mathematical abstraction.
The claimed invention is rooted in physical measurement and physiological modeling, not in disembodied data manipulation. It integrates the underlying mathematical relationship (hydrostatic pressure) into a specific medical device that transforms real-world sensor inputs, such as height, barometric, and photoplethysmographic signals, into a tangible physiological output (blood pressure). Even if the "estimation" step includes mathematical modeling, the claim integrates it into a specific medical device that performs cuffless, calibration-free blood pressure estimation through hydrostatic pressure-based sensing.
The claimed invention provides a specific technological improvement in cuffless blood pressure estimation by eliminating or minimizing the need for external calibration (i.e., by enabling calibration-free or minimum-calibration measurement based on hydrostatic pressure differences).
Contrary to the applicant’s assertion, claims still recite abstract idea as “estimating” which is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. Other than the recitation of generic computer components (“hardware circuitry”) nothing in the claim element precludes the step from practically being performed in the mind.
Further, the applicant also argues that the structural components (i.e., “by structurally incorporating the hydrostatic pressure correction mechanism into the hardware” etc,) which are all generic component that is used for mere data gathering which are examples of activities that courts have found to be insignificant extra-solution activity. These components are widely practiced and commonly known with no specificity which courts have found to be insignificant extra-solution activity.
Therefore, under its broadest reasonable interpretation, claims cover performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Judicial exception is not integrated into a practical application since the claim only recites additional element generic computer components (“hardware circuitry”).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims are not patent eligible.
Conclusion
Claim 11 is free from prior art.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/SERKAN AKAR/ Primary Examiner, Art Unit 3797