DETAILED ACTION
This office action is in response to the communication received on 12/08/2025 concerning application no. 17/204,352 filed on 03/17/2021.
Claims 16-18, 21, and 24 are pending.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/08/2025 has been entered.
Claims 16-18, 21, and 24 are pending.
Response to Arguments
Applicant’s arguments with respect to claim 16 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.
Drawings
The drawings were received on 12/08/2025. These drawings are acceptable.
Claim Objections
Claims 17, 18, 21, and 24 are objected to because of the following informalities:
Claim 17, recite “The monitor”. This claim element should be amended to “The cuffless blood pressure monitor”. This amendment will proper consistency.
Claim 18, recite “The monitor”. This claim element should be amended to “The cuffless blood pressure monitor”. This amendment will proper consistency.
Claim 21, recite “The monitor”. This claim element should be amended to “The cuffless blood pressure monitor”. This amendment will proper consistency.
Claim 24, recite “The device”. This claim element should be amended to “The cuffless blood pressure monitor”. This amendment will proper consistency.
Claim 24, recite “a (4) a machine learning”. This claim element should be amended to “(4) a machine learning”. This amendment will ensure proper grammar.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 16-18, 21, and 24 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 16 recites “an empirical algorithm”. This is understood to be a computer-implemented functional limitation which requires disclosure of the underlying algorithm(s) for obtaining the result in order to comply with the written description requirement. See MPEP § 2161.01(I). While paragraph 0043 of the specification establishes “This may be done with an empirical algorithm, for example using regression or machine learning” and paragraph 0069 of the specification establishes “We derive an updated model of BP by substituting the latest theoretical and empirical expressions into the equations for conservation of mass and momentum to develop an updated model for BP that is dependent on PWV and relevant covariates”, the specification fails to provide a disclose of what the algorithm is, how it is implemented, and what inputs, outputs, considerations, and mathematical/logic manipulations are performed. With paragraph 0069 specifically, the specification does not disclose an algorithm. Rather, it states “empirical expressions” that are substituted into the equations of conversation of mass and momentum. Assuming that these expressions could be equated to algorithms, it still would be inadequate disclosure as the specification does not disclose that those expressions actually are. Therefore, the claim contains subject matter which is not described in the specification in such a way as to reasonably convey to one with ordinary skill in the art that the inventor had possession of the claim invention at the time of filing.
Claim 16 recites “an empirical algorithm obtained using empirical data which is processed using regression or machine learning”. However, the instant specification fails to explain the steps/procedure for performing of processing an empirical algorithm via machine learning, i.e. computer function, in sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. More specifically paragraphs 0043, 0045, 0055, 0063, 0066-70, 0129, 0137, 0153, and 0170 of the specification merely disclose the machine learning without any details regarding of the actual machine learning algorithm. For example, type of machine learning {e.g. supervised machine learning, unsupervised machine learning, self-supervised machine learning, reinforcement learning, semi-supervised learning, etc.} and/or architecture {e.g. layers, weights, feedback, feedforward, kernel, etc.} of the machine learning in sufficient details to inform one of ordinary skill the intended function of the classification device. The specification does not describe how the data is input or out, how it is analyzed or manipulated, what elements of the data are considered, what weighting considerations are given, or what parameters are being used in the algorithm. In conclusion, the specification provides no written support on what the algorithm is, how it is utilized, or how it functions with the information fed into it. Due to this, the claim contains subject matter that is not described in the specification in such a way as to reasonably convey to one with ordinary skill in the art that the inventor or joint inventor had possession of the claim invention at the time of filing.
Claim 18 recites “an analytical algorithm”. This is understood to be a computer-implemented functional limitation which requires disclosure of the underlying algorithm(s) for obtaining the result in order to comply with the written description requirement. See MPEP § 2161.01(I). The specification fails to disclose the analytical algorithm. Rather, the paragraphs 0076-86 disclose the derivation of a common analytical blood pressure tracking algorithm. The specification does not disclose an analytical algorithm that provides for the “relationship between blood pressure and the plethysmographic output” as required by the claim. Therefore, the claim contains subject matter which is not described in the specification in such a way as to reasonably convey to one with ordinary skill in the art that the inventor had possession of the claim invention at the time of filing.
Claim 18 recites “wherein a relationship between blood pressure and the plethysmographic output signals from the sensors is obtained by… machine learning”. However, the instant specification fails to explain the steps/procedure for performing of processing an empirical algorithm via machine learning, i.e. computer function, in sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. More specifically paragraphs 0043, 0045, 0055, 0063, 0066-70, 0129, 0137, 0153, and 0170 of the specification merely disclose the machine learning without any details regarding of the actual machine learning algorithm. For example, type of machine learning {e.g. supervised machine learning, unsupervised machine learning, self-supervised machine learning, reinforcement learning, semi-supervised learning, etc.} and/or architecture {e.g. layers, weights, feedback, feedforward, kernel, etc.} of the machine learning in sufficient details to inform one of ordinary skill the intended function of the system. In particular, the specification does not disclose its application for the assessment of the relationship of the blood pressure and the PPG output. The specification does not describe how the data is input or out, how it is analyzed or manipulated, what elements of the data are considered, what weighting considerations are given, or what parameters are being used in the algorithm. In conclusion, the specification provides no written support on what the algorithm is, how it is utilized, or how it functions with the information fed into it. Due to this, the claim contains subject matter that is not described in the specification in such a way as to reasonably convey to one with ordinary skill in the art that the inventor or joint inventor had possession of the claim invention at the time of filing.
Claim 24 recites “internal calibration algorithm”. This is understood to be a computer-implemented functional limitation which requires disclosure of the underlying algorithm(s) for obtaining the result in order to comply with the written description requirement. See MPEP § 2161.01(I). While paragraph 0063 discloses “The embodiments may further include a processing unit that is used to detect periods of constant blood pressure to be used by the internal calibration algorithm”, the specification fails to disclose what the internal calibration algorithm is, how it is implemented, and what inputs, outputs, considerations, and mathematical/logic manipulations are performed. Therefore, the claim contains subject matter which is not described in the specification in such a way as to reasonably convey to one with ordinary skill in the art that the inventor had possession of the claim invention at the time of filing.
Claim 24 recites “estimates when blood pressure is remaining relatively constant within a pre-determined error bound via (1) logistic regression classification”. While paragraph 0063 discloses “To accomplish this, various techniques can be used including, but not limited to, (1) logistic regression classification, (2) support vector machines, (3) neural networks, (4) other machine learning classifiers, or (5) a combination of methods”, the specification fails to disclose or elaborate on the logistic regression classification. There is no further disclosure in the specification on what the logistic regression classification is or how it is performed with respect to the estimation when the blood pressure is relatively constant. However, the specification does not describe how the data is input or out, how it is analyzed or manipulated, what elements of the data are considered, what weighting considerations are given, or what parameters are being used in the algorithm. In conclusion, the specification provides no written support on what the algorithm is, how it is utilized, or how it functions with the information fed into it. Due to this, the claim contains subject matter that is not described in the specification in such a way as to reasonably convey to one with ordinary skill in the art that the inventor or joint inventor had possession of the claim invention at the time of filing.
Claim 24 recites “estimates when blood pressure is remaining relatively constant within a pre-determined error bound via…(2) a support vector machine”. While paragraph 0063 discloses “To accomplish this, various techniques can be used including, but not limited to, (1) logistic regression classification, (2) support vector machines, (3) neural networks, (4) other machine learning classifiers, or (5) a combination of methods”, the specification fails to disclose or elaborate the SVM. There is no further disclosure in the specification on what the SVM is or how it is performed with respect to the estimation when the blood pressure is relatively constant. The specification does not describe how the data is input or out, how it is analyzed or manipulated, what elements of the data are considered, what weighting considerations are given, or what parameters are being used in the algorithm. In conclusion, the specification provides no written support on what the algorithm is, how it is utilized, or how it functions with the information fed into it. Due to this, the claim contains subject matter that is not described in the specification in such a way as to reasonably convey to one with ordinary skill in the art that the inventor or joint inventor had possession of the claim invention at the time of filing.
Claim 24 recites “estimates when blood pressure is remaining relatively constant within a pre-determined error bound via… (3) neural networks”. While paragraph 0063 discloses “To accomplish this, various techniques can be used including, but not limited to, (1) logistic regression classification, (2) support vector machines, (3) neural networks, (4) other machine learning classifiers, or (5) a combination of methods”, the specification fails to disclose or elaborate on the neural networks. There is no further disclosure in the specification on what the neural networks are or how it is performed with respect to the estimation when the blood pressure is relatively constant. For example, type of neural network {e.g. convolution, recurrent, perceptron, Long Short-Term Memory, Generative Adversarial Network, etc.} and/or architecture {e.g. layers, weights, feedback, feedforward, kernel, etc.} of the neural network in sufficient details to inform one of ordinary skill the intended function of the system. The specification does not describe how the data is input or out, how it is analyzed or manipulated, what elements of the data are considered, what weighting considerations are given, or what parameters are being used in the algorithm. In conclusion, the specification provides no written support on what the algorithm is, how it is utilized, or how it functions with the information fed into it. Due to this, the claim contains subject matter that is not described in the specification in such a way as to reasonably convey to one with ordinary skill in the art that the inventor or joint inventor had possession of the claim invention at the time of filing.
Claim 24 recites “estimates when blood pressure is remaining relatively constant within a pre-determined error bound via… a (4) a machine learning classifier”. While paragraph 0063 discloses “To accomplish this, various techniques can be used including, but not limited to, (1) logistic regression classification, (2) support vector machines, (3) neural networks, (4) other machine learning classifiers, or (5) a combination of methods”, the specification fails to disclose or elaborate on the machine learning classifier. There is no further disclosure in the specification on what the machine learning classifier is or how it is performed with respect to the estimation when the blood pressure is relatively constant. For example, type of machine learning {e.g. supervised machine learning, unsupervised machine learning, self-supervised machine learning, reinforcement learning, semi-supervised learning, etc.} and/or architecture {e.g. layers, weights, feedback, feedforward, kernel, etc.} of the machine learning in sufficient details to inform one of ordinary skill the intended function of the system. However, the specification does not describe how the data is input or out, how it is analyzed or manipulated, what elements of the data are considered, what weighting considerations are given, or what parameters are being used in the algorithm. In conclusion, the specification provides no written support on what the algorithm is, how it is utilized, or how it functions with the information fed into it. Due to this, the claim contains subject matter that is not described in the specification in such a way as to reasonably convey to one with ordinary skill in the art that the inventor or joint inventor had possession of the claim invention at the time of filing.
Claims that are not discussed above but are cited to be rejected under 35 U.S.C. 112(a) are also rejected because they inherit the deficiencies of the claims they respectively depend upon.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 16-18, 21, and 24 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 16 is indefinite for the following reasons:
Recites “an empirical algorithm obtained using empirical data which is processed using regression or machine learning”. This claim element is indefinite. It would be unclear to one with ordinary skill in the art if the claim is establishing the empirical algorithm to be the regression or the machine learning or if it is utilizing the regression or machine learning or if it the algorithm is acquired according to the regression or machine learning.
Applicant is encouraged to provide consistent and clear language.
Recites “including a processor to estimate external pressure from a contact pressure sensor for measuring contact pressure when applied to a user, or a hydrostatic pressure sensor, or both”. This claim element is indefinite. It would be unclear to one with ordinary skill in the art what is assessing the contract pressure as the claim establishes that the processor is estimating the contract pressure and that the contract pressure sensor is measuring the contact pressure. Furthermore, it is unclear what the processor’s estimation of the contract pressure is with respect to the hydrostatic pressure sensor.
Applicant is encouraged to provide consistent and clear language.
Recites “processor to estimate external pressure from a contact pressure sensor for measuring contact pressure when applied to a user”. This claim element is indefinite. It would be unclear to one with ordinary skill in the art if the contact pressure is being actively claimed and requiring the contact pressure application and measure with respect to the user or is an intended use limitation with respect to the processor.
Applicant is encouraged to provide consistent and clear language.
Recites “processor to estimate external pressure from a contact pressure sensor for measuring contact pressure when applied to a user, or a hydrostatic pressure sensor, or both and comprising the hydrostatic pressure sensor that includes an accelerometer, a gyroscope, or a barometer”. This claim element is indefinite. It would be unclear to one with ordinary skill in the art if the hydrostatic pressure sensor is required by the claim as the claims states that it comprises the element and it includes an accelerometer, a gyroscope, or a barometer of if the claim is establishing the hydrostatic pressure sensor to be an alternative and the contact pressure sensor alone to be used.
Applicant is encouraged to provide consistent and clear language.
Recites “wherein a calibration is automatically begun as external pressure changes”. This claim element is indefinite. It would be unclear to one with ordinary skill in the art if the establishing the calibration to occur in instances where the “processor to estimate external pressure from a contact pressure sensor for measuring contact pressure when applied to a user” is operating with the contact pressure sensor or is operating to calibrate independently. If it is the later, it is further unclear what external pressure is assessed for calibration in the case of the use of a hydrostatic pressure sensor.
Applicant is encouraged to provide consistent and clear language.
Claim 17 is indefinite for the following reasons:
Recites “an accelerometer”. This claim element is indefinite. It would be unclear to one with ordinary skill in the art if the “accelerometer” is the same as the “accelerometer” established in claim 16 or is a separate and distinct feature.
Applicant is encouraged to provide consistent and clear language.
Recites “a gyroscope”. This claim element is indefinite. It would be unclear to one with ordinary skill in the art if the “gyroscope” is the same as the “gyroscope” established in claim 16 or is a separate and distinct feature.
Applicant is encouraged to provide consistent and clear language.
Recites “a barometer”. This claim element is indefinite. It would be unclear to one with ordinary skill in the art if the “barometer” is the same as the “barometer” established in claim 16 or is a separate and distinct feature.
Applicant is encouraged to provide consistent and clear language.
Claim 18 is indefinite for the following reasons:
Recites “analytical algorithm”. This claim element is indefinite. An algorithm is “a procedure for solving a mathematical problem in a finite number of steps that frequently involves repetition of an operation”.1 An algorithm is itself an analytical approach in solving a problem via mathematics and logic. It would be unclear to one with ordinary skill in the art what algorithms are excluded by the adjective defining the instant element as “analytical”. It would be unclear to one with ordinary skill in the art what the scope of an “analytical algorithm” is and what algorithms are excluded as being non-analytical.
Applicant is encouraged to provide consistent and clear language.
Claim 24 is indefinite for the following reasons:
Recites “constant blood pressure”. This claim element is indefinite. It would be unclear to one with ordinary skill in the art if the constant blood pressure is referring to the stable blood pressure in claim 16 or is separate and distinct. If it is the later, it is further unclear what the distinction between stable blood pressure and constant blood pressure is.
Applicant is encouraged to provide consistent and clear language.
The term “blood pressure is remaining relatively constant” is a relative term which renders the claim indefinite. The term “relatively constant” 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. It would be unclear to one with ordinary skill in the art what is the criterion or standard for the blood pressure to be sufficient to be “relatively” constant. While the claim states “relatively constant within a pre-determined error bound”, the claim language appears to establish that the relative constancy is within the error bound rather than the error bound defining the relative constancy.
Claims that are not discussed above but are cited to be rejected under 35 U.S.C. 112(b) are also rejected because they inherit the indefiniteness of the claims they respectively depend upon.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 16-18 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Botsva et al. (PGPUB No. US 2019/0059752) in view of Tang et al. (PGPUB No. US 2020/0029838).
Regarding claim 16, Botsva teaches the a cuffless blood pressure monitor (Abstract teaches the method and apparatus for cuffless measuring of blood pressure value from one limb of a subject), comprising: a device support configured to be placed or worn over an artery (Paragraph 0035 teaches the electronic device described in this invention is assembled in a housing and fixed to the user's hand by a strap. Paragraph 0048 teaches arterial measurement); the device support having
a pulse wave detection element (Paragraph 0037 teaches using as a PPG sensor such a LEDs kit with different wavelengths makes it possible to realize the measuring not only the pulse rate, but also the degree of oxygen saturation of the blood—SpO2, as an additional function of this device),
the pulse wave detection element having only a single photoplethysmographic (PPG) sensor, wherein the PPG sensor comprises multiple LEDs of different wavelengths, and whose output signal is characterized by a wave form, wherein the shape of the wave form is used to obtain pulse wave velocity, transmural pressure, or blood pressure using an empirical algorithm obtained using empirical data which is processed using regression or machine learning (Paragraph 0037 teaches using as a PPG sensor such a LEDs kit with different wavelengths makes it possible to realize the measuring not only the pulse rate, but also the degree of oxygen saturation of the blood—SpO2, as an additional function of this device. Paragraph 0064 teaches PWV, the velocity at which the arterial pulse propagates through the artery tree, can be used as a measure of arterial stiffness. It has a strong correlation with cardiovascular events and all-cause mortality, and was recognized by the European Society of Hypertension, as a useful additional test in the investigation of hypertension (thanks to correlation between PWV and blood pressure. Paragraph 0058 teaches when data scanning process reaches the ends of each recorded signals, algorithm of microcontroller goes right to systolic and diastolic blood pressure calculation 711 from average values of PTT1 and PTT2. Paragraph 0037 teaches each of the two photosensors includes a set of green, red and infrared LEDs 212, 213, as well as a photodiode of 214, 215. Using as a PPG sensor such a LEDs kit with different wavelengths makes it possible to realize the measuring not only the pulse rate, but also the degree of oxygen saturation of the blood—SpO2, as an additional function of this device);
an external-pressure processing element, the external-pressure processing element including a processor to estimate external pressure from a contact pressure sensor for measuring contact pressure when applied to a user, or a hydrostatic pressure sensor, or both and comprising the hydrostatic pressure sensor that includes an accelerometer, a gyroscope, or a barometer, wherein the external-pressure processing element is configured to combine signals from the accelerometer, a gyroscope, or barometer, to track altitude changes in real-time (Paragraph 0048 teaches steps of verifying the presence of movements 322 and recording data 323 are repeated cyclically. This is with consideration to the contact and of the device with the patient and the determination of the value. If the value is insufficient, there is a measurement flag. When the buffer is full, the program proceeds to the processing of the recorded data and the arterial pressure calculation 325, at the end of which the measurement flag is reset (measure Flag=0) 326, and the measurement results are output to the screen 327 and written to the flash memory of the device 330. Paragraph 0066 teaches the calibration process is carried out in the form of a gradual raising of the hand with short measurements every 5-10 cm. Such measurements are made in fixed definite distance, determined by means of an accelerometer, in order that in the future it would be possible to calculate the value of the hydrostatic blood pressure, since it has a contribution to the value of transmural pressure (see Equation 4). Paragraph 0012 teaches there is provided an electronic device comprising: a block of sensors, including at least a set (kit) of two ECG electrodes and two PPG photosensors representing the main information sensors, as well as a set of sensors necessary for calibration—an accelerometer and a barometer);
a blood pressure tracking processing element (Paragraph 0048 teaches steps of verifying the presence of movements 322 and recording data 323 are repeated cyclically. When the buffer is full, the program proceeds to the processing of the recorded data and the arterial pressure calculation 325, at the end of which the measurement flag is reset (measure Flag=0) 326, and the measurement results are output to the screen 327 and written to the flash memory of the device 330. Paragraph 0066 teaches the calibration process is carried out in the form of a gradual raising of the hand with short measurements every 5-10 cm. Such measurements are made in fixed definite distance, determined by means of an accelerometer, in order that in the future it would be possible to calculate the value of the hydrostatic blood pressure, since it has a contribution to the value of transmural pressure (see Equation 4). Paragraph 0012 teaches there is provided an electronic device comprising: a block of sensors, including at least a set (kit) of two ECG electrodes and two PPG photosensors representing the main information sensors, as well as a set of sensors necessary for calibration—an accelerometer and a barometer),
a calibration processing element (Paragraph 0049 teaches to start the calibration process, it is necessary to select the appropriate menu item 342 and confirm action 343 (by pressing two buttons 228,229 at the same time), then the microcontroller will begin the calibration process 344), and, optionally,
a stability processing element configured to detect periods of stable blood pressure (Paragraph 0064 teaches immediately after differentiating the PPG signal, the P′-peak and PP intervals detector starts 800. Paragraph 0060 teaches PWV, the velocity at which the arterial pulse propagates through the artery tree, can be used as a measure of arterial stiffness. It has a strong correlation with cardiovascular events and all-cause mortality, and was recognized by the European Society of Hypertension, as a useful additional test in the investigation of hypertension (thanks to correlation between PWV and blood pressure).);
wherein a calibration is automatically begun as external pressure changes and wherein the calibration processing element calibrates the cuffless blood pressure monitor via the external pressure processing element monitoring the change in external pressure over a predefined period of time (Paragraph 0066 teaches the atmospheric pressure value is monitored with a barometer to ensure that the external pressure is not changed during the measurement. In such a case, reaching the point at which the transmural pressure is zero is possible due to a change in the magnitude and “sign” (+ or −) of the hydrostatic blood pressure… the calibration process is carried out in the form of a gradual raising of the hand with short measurements every 5-10 cm. Such measurements are made in fixed definite distance, determined by means of an accelerometer. Paragraph 0057 teaches after filtering, each of the two PPG signals are differentiated with respect to time 702. Paragraph 0065 teaches all parameters in right part of this equation do not change fast for the same human, but different from person to person. That is why calibration measurement is necessary to take in account influencing of individual users' parameters on calculation of blood pressure value);
the device support optionally connected to a controller being configured to output a signal indicating an estimate of blood pressure (Paragraph 0012 teaches there is provided an electronic device comprising: a block of sensors, including at least a set (kit) of two ECG electrodes and two PPG photosensors representing the main information sensors, as well as a set of sensors necessary for calibration—an accelerometer and a barometer. Paragraph 0039 teaches the display of the measurements).
However, Botsva is silent regarding a cuffless blood pressure monitor, which calibrates without user interaction;
the pulse wave detection element having only a single photoplethysmographic (PPG) sensor.
In an analogous imaging field of endeavor, regarding cuffless physiological monitoring, Tang teaches a cuffless blood pressure monitor, which calibrates without user interaction (Paragraph 0073 teaches a cuffless blood pressure monitoring system. The calibration is fully automated. I.e. without user input);
the pulse wave detection element having only a single photoplethysmographic (PPG) sensor (Paragraphs 0011-13 teaches the use of a sensor with PPG capability. See Fig. 3).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Botsva with Tang’s teaching of calibration without human intervention and a single PPG sensor. This modified apparatus would allow the user to improve the monitoring of patients in hospitals, clinics, and the home via non-invasive and continuously monitoring means (Paragraph 0005 of Tang). Furthermore, the modification is comfortable and discrete (Paragraph 0010 of Tang).
Regarding claim 17, modified Botsva teaches the monitor in claim 16, as discussed above.
Botsva further teaches a monitor, wherein the hydrostatic pressure sensor includes all three of an accelerometer, a gyroscope, and a barometer (Paragraph 0012 teaches there is provided an electronic device comprising: a block of sensors, including at least a set (kit) of two ECG electrodes and two PPG photosensors representing the main information sensors, as well as a set of sensors necessary for calibration—an accelerometer and a barometer. Paragraph 0038 teaches the device has a number of additional sensors, specifically a digital barometer 218 and a block of accelerometer, gyroscope and magnetometer sensors 219).
Regarding claim 18, modified Botsva teaches the monitor in claim 16, as discussed above.
Botsva further teaches a monitor, wherein a relationship between blood pressure and the plethysmographic output signals from the sensors is obtained by an analytical algorithm, a linear regression, a polynomial regression, machine learning, or a combination thereof (Paragraph 0057 teaches further in the pressure measurement algorithm, only the differentials of the PPG signals are involved, and not they themselves. Paragraph 0037 teaches using as a PPG sensor such a LEDs kit with different wavelengths makes it possible to realize the measuring not only the pulse rate, but also the degree of oxygen saturation of the blood—SpO2, as an additional function of this device. Paragraph 0064 teaches PWV, the velocity at which the arterial pulse propagates through the artery tree, can be used as a measure of arterial stiffness. It has a strong correlation with cardiovascular events and all-cause mortality, and was recognized by the European Society of Hypertension, as a useful additional test in the investigation of hypertension (thanks to correlation between PWV and blood pressure. Paragraph 0058 teaches when data scanning process reaches the ends of each recorded signals, algorithm of microcontroller goes right to systolic and diastolic blood pressure calculation 711 from average values of PTT1 and PTT2).
Regarding claim 21, modified Botsva teaches the monitor in claim 16, as discussed above.
However, Botsva is silent regarding a monitor, wherein the controller is part of a distributed system or cloud-based system.
In an analogous imaging field of endeavor, regarding cuffless physiological monitoring, Tang teaches a cuffless blood pressure monitor, wherein the controller is part of a distributed system or cloud-based system (Paragraph 0052 teaches the use of wireless transmission and a cloud based system for data processing and monitoring).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Botsva with Tang’s teaching of cloud based or distributed systems for processing. This modified apparatus would allow the user to improve the monitoring of patients in hospitals, clinics, and the home via non-invasive and continuously monitoring means (Paragraph 0005 of Tang). Furthermore, the modification is comfortable and discrete (Paragraph 0010 of Tang).
Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over Botsva et al. (PGPUB No. US 2019/0059752) in view of Tang et al. (PGPUB No. US 2020/0029838) further in view of Tu et al. ("Continuous blood pressure measurement based on a neural network scheme applied with a cuffless sensor", 2018).
Regarding claim 24, modified Botsva teaches the device in claim 16, as discussed above.
However, the combination of Botsva and Tang is silent regarding a device, comprising a processing unit that detects periods of constant blood pressure used by an internal calibration algorithm and which monitors the signals from the sensors and estimates when blood pressure is remaining relatively constant within a pre-determined error bound via (1) logistic regression classification, (2) a support vector machine, (3) neural networks, or a (4) a machine learning classifier.
In an analogous imaging field of endeavor, regarding cuffless physiological monitoring, Tu teaches a device, comprising a processing unit that detects periods of constant blood pressure used by an internal calibration algorithm and which monitors the signals from the sensors and estimates when blood pressure is remaining relatively constant within a pre-determined error bound via (1) logistic regression classification, (2) a support vector machine, (3) neural networks, or a (4) a machine learning classifier (Page 4548 teaches the use of a neural network model to assess the blood pressure and assess its variability within a margin of error defined by the standard deviation. This error distribution is within the BP distribution of the AAMI and the BHS. The parameters are assessed and identified from a calibration that is used in the blood pressure determination).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the combination of Botsva and Tang with Tu’s teaching of a constant PD detection and the assessment via a form of data processing model. This modified apparatus would allow the user to pass the AAMI and BHS criterion of blood pressure analysis (Abstract of Tu). Furthermore, the modification allows for continuous blood pressure monitoring in real time (Conclusion of Tu).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Kachuee et al. ("Cuffless Blood Pressure Estimation Algorithms for Continuous Health-Care Monitoring", 2017): Teaches the assessment of the blood pressure value with respect to an error based on a data processing model.
Zhuo et al. (PGPUB No. US 2017/0215749): Teaches the assessment of the blood pressure value with respect to an error based on a data processing model.
Qasem (PGPUB No. US 2018/0263513): Teaches the assessment of the blood pressure value with respect to an error based on a data processing model.
Tang et al. (PGPUB No. US 2020/0029826): Teaches automated calibration and use of a single PPG sensor.
Moon et al. (PGPUB No. US 2022/0015704): Teaches automated calibration and use of a single PPG sensor.
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/ADIL PARTAP S VIRK/Primary Examiner, Art Unit 3798
1 https://www.merriam-webster.com/dictionary/algorithm