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 4/14/2026.
The amendments filed on 4/14/2026 have been entered. Accordingly claims 1-20 remain pending. Claims 19-20 have been newly added.
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 1-20 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 “detect”, “generate a blood pressure output signal… by executing”, 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, “detect”, “generate a blood pressure output signal… by executing” 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 12-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hwa (KR20200069545A) in view of Noh (US20190200932A1) and Lee et al (US20190059750A1).
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 (“blood pressure calculating unit for calculating the blood pressure from the altitude difference between the two points where the blood flow measurement is made…blood pressure monitor that measures blood pressure using a change in blood flow parameter caused by an altitude difference (height difference) of a measurement position” abst),
detect, for each of the measured height levels, a difference between the measured height level and a level representing a heart height level (see fig. 3 and “(△H is the altitude difference between any two points where blood flow measurement is made… P1 is the blood pressure at a high position (H1) among any two points where blood flow is measured, PM 1 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. 2),
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 variations of the bio-signals measured at the height levels (“Calculating the amount of change in the blood flow parameter (PM) caused by the altitude difference between any two points where blood flow measurement is made, and calculating the blood pressure from the altitude difference between the two points where the blood flow” pg. 2); and
generate a blood pressure output signal representing a blood pressure of the user (“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) based on the measured bio-signals by executing a state estimation algorithm based on a hemodynamic state space model (see flow diagrams of fig. 1 [block diagram showing the configuration of a blood pressure monitor] and fig. 5 [flow chart schematically showing a blood pressure measuring method] along with the associated sections of the disclosure; particularly, calculations and estimations flowchart algorithm);
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 detect, for each of the measured height levels, a difference between the measured height level and a reference level representing a heart height level; and hydrostatic pressure differences, based on a machine learning 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).
The combination does not point out the specifics of detect, for each of the measured height levels, a difference between the measured height level and a reference level representing a heart height level.
However, in the same field of endeavor, Lee teaches
[0035] FIG. 5 is an example flowchart showing a blood pressure measuring method in consideration of a height difference.
[0036] Firstly, a reference optical arterial pulse generated during a calibration process which is an initial setup process is matched in the program stored in the memory 121 and a reference blood pressure value input via measurement by an external manometer is matched with the reference optical arterial pulse to set a position of the baseline (S1). Through this process, a baseline indicating a value 0 on a Y-axis for an optical arterial pulse signal to be input later can be specified.
A distance H0 from the first point to the heart is measured (S2′). A height difference between the lowest position and a position of the heart can be measured using various sensors included in the portable blood pressure measuring device 10 [0054].
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 detect, for each of the measured height levels, a difference between the measured height level and a reference level representing a heart height level as taught by Lee because blood pressure variation caused by the height of measurement and thus possible to calculate an accurate blood pressure value as compared with a conventional measuring method ([0012] of Lee).
Regarding claims 2 and 13, the combination above teaches all the claimed limitations except to determine the reference level based on a first height level measured while the user raises a hand upward and a second height level measured while the user lowers the hand downward information of a user based on the height levels.
However, in the same field of endeavor, Lee teaches blood pressure measuring device performs: a process of calculating a difference in blood pressure between a first point and a second point on the basis of a difference between a height of the portable blood pressure measuring device at the first point and a height of the portable blood pressure measuring device at the second point; a process of checking a magnitude of blood pressure per unit length by dividing a difference in signal magnitude between a first optical arterial pulse sensed at the first point and a second optical arterial pulse sensed at the second point by the calculated difference in blood pressure [0009].
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[0054] Referring to FIG. 6, a distance H0 from the first point to the heart is measured (S2′). A height difference between the lowest position and a position of the heart can be measured using various sensors included in the portable blood pressure measuring device 10. However, the user may choose to measure the height difference using a tapeline and input and store the height difference in the portable blood pressure measuring device 10.
[0055] Then, a distance ΔH1 from the first point to a current position of blood pressure measurement (hereinafter, referred to as “second point”) is measured (S3′). Herein, the current position of blood pressure measurement refers to a certain position between the position of the user's heart and the lowest position.
[0056] Then, a distance H0-ΔH1 from the second point to the heart is calculated by deducting the distance from the first point to the second point ΔH1 from the distance H0 from the first point to the heart is calculated (S4′).
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 determine the reference level based on a first height level measured while the user raises a hand upward and a second height level measured while the user lowers the hand downward information of a user based on the height levels as taught by Lee because blood pressure variation caused by the height of measurement and thus possible to calculate an accurate blood pressure value as compared with a conventional measuring method ([0012] of Lee).
Regarding claims 3 and 15, Hwa teaches wherein the hemodynamic parameter estimating circuit is configured to determine the blood pressure from a first difference value, which is a difference between the reference level and a first height level, and a second difference value, which is a difference between the reference level and a 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 generate the blood pressure output signal based on 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 the user’s biosignal by executing a machine learning algorithm or a state estimation algorithm based on the 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 based on the user’s bio-signals; and generate the blood pressure output signal from the estimated hemodynamic parameters (“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 the user’s bio-signals by executing 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 based on 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 by executing the machine learning algorithm or a state estimation algorithm based on the 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, the combination noted above teaches all the claimed limitations. Specifically, Hwa teaches wherein the generating the blood pressure output signal includes determining (“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).
The combination does not teach determining the blood pressure from a first difference value, which is a difference between the reference level and a first height level, and a second difference value, which is a difference between the reference level and a second height level.
However, in the same field of endeavor, Lee teaches blood pressure measuring device performs: a process of calculating a difference in blood pressure between a first point and a second point on the basis of a difference between a height of the portable blood pressure measuring device at the first point and a height of the portable blood pressure measuring device at the second point; a process of checking a magnitude of blood pressure per unit length by dividing a difference in signal magnitude between a first optical arterial pulse sensed at the first point and a second optical arterial pulse sensed at the second point by the calculated difference in blood pressure [0009].
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[0054] Referring to FIG. 6, a distance H0 from the first point to the heart is measured (S2′). A height difference between the lowest position and a position of the heart can be measured using various sensors included in the portable blood pressure measuring device 10. However, the user may choose to measure the height difference using a tapeline and input and store the height difference in the portable blood pressure measuring device 10.
[0055] Then, a distance ΔH1 from the first point to a current position of blood pressure measurement (hereinafter, referred to as “second point”) is measured (S3′). Herein, the current position of blood pressure measurement refers to a certain position between the position of the user's heart and the lowest position.
[0056] Then, a distance H0-ΔH1 from the second point to the heart is calculated by deducting the distance from the first point to the second point ΔH1 from the distance H0 from the first point to the heart is calculated (S4′).
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 determine the reference level based on a first height level measured while the user raises a hand upward and a second height level measured while the user lowers the hand downward information of a user based on the height levels as taught by Lee because blood pressure variation caused by the height of measurement and thus possible to calculate an accurate blood pressure value as compared with a conventional measuring method ([0012] of Lee).
Regarding claim 19, the combination noted above teaches all the claimed limitations except for wherein the hemodynamic parameter estimating circuit is configured to detect the reference level based on a trajectory of a user's hand measured by the position detection sensor.
However, in the same field of endeavor, Lee teaches blood pressure measuring device performs: a process of calculating a difference in blood pressure between a first point and a second point on the basis of a difference between a height of the portable blood pressure measuring device at the first point and a height of the portable blood pressure measuring device at the second point; a process of checking a magnitude of blood pressure per unit length by dividing a difference in signal magnitude between a first optical arterial pulse sensed at the first point and a second optical arterial pulse sensed at the second point by the calculated difference in blood pressure [0009].
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[0054] Referring to FIG. 6, a distance H0 from the first point to the heart is measured (S2′). A height difference between the lowest position and a position of the heart can be measured using various sensors included in the portable blood pressure measuring device 10. However, the user may choose to measure the height difference using a tapeline and input and store the height difference in the portable blood pressure measuring device 10.
[0055] Then, a distance ΔH1 from the first point to a current position of blood pressure measurement (hereinafter, referred to as “second point”) is measured (S3′). Herein, the current position of blood pressure measurement refers to a certain position between the position of the user's heart and the lowest position.
[0056] Then, a distance H0-ΔH1 from the second point to the heart is calculated by deducting the distance from the first point to the second point ΔH1 from the distance H0 from the first point to the heart is calculated (S4′).
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 detect the reference level based on a trajectory of a user's hand measured by the position detection sensor as taught by Lee because blood pressure variation caused by the height of measurement and thus possible to calculate an accurate blood pressure value as compared with a conventional measuring method ([0012] of Lee).
Regarding claim 20, the combination noted above teaches all the claimed limitations except for wherein the reference level is detected based on a trajectory of a user's hand measured by the position detection sensor.
However, in the same field of endeavor, Lee teaches blood pressure measuring device performs: a process of calculating a difference in blood pressure between a first point and a second point on the basis of a difference between a height of the portable blood pressure measuring device at the first point and a height of the portable blood pressure measuring device at the second point; a process of checking a magnitude of blood pressure per unit length by dividing a difference in signal magnitude between a first optical arterial pulse sensed at the first point and a second optical arterial pulse sensed at the second point by the calculated difference in blood pressure [0009].
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[0054] Referring to FIG. 6, a distance H0 from the first point to the heart is measured (S2′). A height difference between the lowest position and a position of the heart can be measured using various sensors included in the portable blood pressure measuring device 10. However, the user may choose to measure the height difference using a tapeline and input and store the height difference in the portable blood pressure measuring device 10.
[0055] Then, a distance ΔH1 from the first point to a current position of blood pressure measurement (hereinafter, referred to as “second point”) is measured (S3′). Herein, the current position of blood pressure measurement refers to a certain position between the position of the user's heart and the lowest position.
[0056] Then, a distance H0-ΔH1 from the second point to the heart is calculated by deducting the distance from the first point to the second point ΔH1 from the distance H0 from the first point to the heart is calculated (S4′).
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 detect the reference level based on a trajectory of a user's hand measured by the position detection sensor as taught by Lee because blood pressure variation caused by the height of measurement and thus possible to calculate an accurate blood pressure value as compared with a conventional measuring method ([0012] of Lee).
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;
(ii) The claims do not recite a mental process.
The Office action maintains that the claims recite an abstract idea because the term "estimating" could theoretically be performed mentally. However, the amended claims no longer recite "estimating" as the operative term for blood pressure output generation. The amended claims instead recite "generating a blood pressure output signal" by executing a machine learning algorithm or a state estimation algorithm based on a hemodynamic state space model, which is a concrete computational operation implemented in hardware circuitry.
Although the claims are amended to no longer recite the “estimation” feature, claims now recite “detect”, “generate a blood pressure output signal… by executing”, 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, “detect”, “generate a blood pressure output signal… by executing” 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.
Accordingly, contrary to the applicant’s assertion, claims still recite abstract ideas as noted above which are processes that, under its broadest reasonable interpretation, covers performance of the limitation 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SERKAN AKAR whose telephone number is (571)270-5338. The examiner can normally be reached 9am-5pm M-F.
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/SERKAN AKAR/ Primary Examiner, Art Unit 3797
