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 .
Drawings
The drawings filed on April 12, 2023 are accepted.
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 1-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 of the subject matter eligibility test (see MPEP 2106.03).
Claim 1 is directed to “a method” which describes one of the four statutory categories of patentable subject matter, i.e. a process.
Claim 18 is directed to “a system” which describes one of the four statutory categories of patentable subject matter, i.e. a machine or manufacture.
Each of Claims 1-26 has been analyzed to determine whether it is directed to any judicial exceptions.
Step 2A of the subject matter eligibility test (see MPEP 2106.04).
Prong One:
Claims 1 and 18 recite (“sets forth” or “describes”) the abstract idea of “a mental process” (MPEP 2106.04(a)(2).III.), substantially as follows: “determining a period of time during which the human being is stationary; determining, during the period of time, a plurality of measurements providing pulse-related data values from the human being; determining, from the plurality of measurements, a representative pulse-related data value representative of the pulse-related data values determined during the period of time; and determining, from the representative pulse-related data value, a blood pressure value for the human being during the period of time.”
In claims 1 and 18, the above recited steps can be practically performed in the human mind, with the aid of a pen and paper or with a generic computer, in a computer environment, or merely using the generic computer as a tool to perform the steps. If a person were to visually examine, i.e., perform an observation, the pulse data, either in a printout or an electronic format, he/she would be able to perform find a representative pulse data and determine a blood pressure via pen and paper. There is nothing recited in the claim to suggest an undue level of complexity in how the pulse data and the blood pressure is to be identified. Therefore, a person would be able to perform the identification of peaks mentally or with a generic computer.
Prong Two: Claims 1 and 18 do not include additional elements that integrate the mental process into a practical application.
This judicial exception is not integrated into a practical application. In particular, the claims recites (1) “a first sensor configured to determine a period of time during which the human being is stationary; at least one pulse-related sensor configured to determine a plurality of measurements providing pulse-related data values of the human being during the period of time; and”
(2) “a processor”.
The steps in (1) represent merely data gathering or pre-solution activities that are necessary for use of the recited judicial exception and are recited at a high level of generality with conventionally used tools (see below Step IIB for further details).
The steps in (2) merely recite generic computer components used to implement the abstract idea on, as tools.
As a whole, the additional elements merely serve to gather and feed information to the abstract idea and to output a notification based on the abstract idea, while generically implementing it on conventionally used tools. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way. No improvement to the technology is evident, and the estimated bio-information is not outputted in any way such that a practical benefit is realized. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application.
Step 2B of the subject matter eligibility test (see MPEP 2106.05).
Claims 1 and 18 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the claims recite additional steps of (1) “a first sensor configured to determine a period of time during which the human being is stationary; at least one pulse-related sensor configured to determine a plurality of measurements providing pulse-related data values of the human being during the period of time; and”
(2) “a processor”.
These steps represents mere data gathering, data outputting or pre/post/extra-solution activities that are necessary for use of the recited judicial exception and are recited at a high level of generality.
The motion and pulse information is obtained from a motion sensor and a pulse sensor. These additional limitations merely represent insignificant, conventional pre-solution activities well-understood in the industry of bio-information estimation, as the sensors recited are well understood, routine and conventional, as evidenced by Moyer et al. (US 2016/0302674 A1) (“Moyer”). Moyer [0116] describes the sensors as conventional.
Note that the pulse transit time (PTT) is known in the field of art as a mathematical variant of the pulse wave signal (or vibration waveforms), i.e., it can be derived from pulse wave signals via mathematical operations routinely practiced in the field of art. Accordingly, these additional steps and tools for measuring a pulse wave signal, and outputting a notification amount to no more than insignificant conventional extra-solution activity. Mere insignificant conventional extra-solution activity cannot provide an inventive concept.
The recited processors and computer-readable storage medium are generic computer elements (i.d. para. [0053] describing generic computers).
Therefore, none of the Claims 1 and 18 amounts to significantly more than the abstract idea itself.
Accordingly, Claims 1 and 18 are not patent eligible and rejected under 35 U.S.C. 101 as being directed to abstract ideas implemented on a generic computer in view of the Supreme Court Decision in Alice Corporation Pty. Ltd. v. CLS Bank International, et al. and 2019 PEG.
Dependent Claims
The following dependent claims merely further define the abstract idea and are, therefore, directed to an abstract idea for similar reasons:
Claims 2, 9, 11, 13-17, 20-21, 23-24 and 26 recitations further limits the abstract idea above, removing noise and further mathematical modeling further defines the mental process or mathematical equations discussed above.
The following dependent claims merely further describe the extra-solution activities and therefore, do not amount to significantly more than the judicial exception or integrate the abstract idea into a practical application for similar reasons:
Claims 3-6, 8, 10, 12, 19, 22 and 25 further define the sensors used for insignificant extra-solution activity (data collection). The sensors recited are well understood, routine and conventional, as evidenced by as evidenced by Moyer et al. (US 2016/0302674 A1) (“Moyer”).
Claims 7 recitations merely recite data transmission to the output device discussed above as extra-solution activity (data output).
Taken alone and in combination, the additional elements do not integrate the judicial exception into a practical application at least because the abstract idea is not applied, relied on, or used in a meaningful way. They also do not add anything significantly more than the abstract idea. Their collective functions merely provide computer/electronic implementation and processing, and no additional elements beyond those of the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. There is no indication that the combination of elements improves the functioning of a computer, output device, improves technology other than the technical field of the claimed invention, etc. Therefore, the claims are rejected as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s 1-6, 9-10, 12-23 and 25-26 rejected under 35 U.S.C. 102(a)(1) as being anticipated by Pantelopoulos et al. (US 2017/0209053 A1”) (“Pantelopoulos”).
Regarding claim 1, Pantelopoulos discloses A method for determining a blood pressure value of a human being, comprising (Abstract and entire document):
determining a period of time during which the human being is stationary ([0130], “In some implementations, the system utilizes inertial sensors to determine that the user has been stationary during the calibration process and that changes in PPG amplitude are not due to hand motions and changing hydrostatic pressure. When these conditions are met, the process automatically proceeds to take a calibration blood pressure measurement.”);
determining, during the period of time, a plurality of measurements providing pulse-related data values from the human being ([0137 – 0138], “Process 400 then obtain at least one calibration data point corresponding to the calibration blood pressure measurement and the at least one pulse transit time. See block 408.”);
determining, from the plurality of measurements, a representative pulse-related data value representative of the pulse-related data values determined during the period of time ([0138], “When enough calibration data points have been collected, the process proceeds to fit the model to the calibration data points. See block 412. The model relates blood pressure to PTT. In some implementations, the model includes a linear mathematical relationship, a general linear model, a non-linear model, or a neural network model.” A calibration pulse data vale is obtained from the pulse data as the representative data value); and
determining, from the representative pulse-related data value, a blood pressure value for the human being during the period of time ([0138], “The model relates blood pressure to PTT.” Blood pressure is determined from the representative data value).
Regarding claim 2, Pantelopoulos discloses The method of claim 1, further comprising determining if one or more of the plurality of measurements providing pulse-related data values is below a threshold quality and discarding any of the one or more of the measurements providing pulse-related data determined to be below the threshold quality ([0185] discussing filters, discards data using thresholds and see [0271 – 0272] SNR).
Regarding claim 3, Pantelopoulos discloses The method of claim 1, wherein the plurality of measurements providing pulse-related data values of the human being are taken from a location on the human being other than a fingertip, wrist, or arm of the human being ([0083], “For instance, a proximal pulse wave may be represented by PPG data collected from the chest of the upper arm, and a distal pulse wave may be represented by PPG data collected at the wrist or the foot.”).
Regarding claim 4, Pantelopoulos discloses The method of claim 1, wherein the plurality of measurements providing pulse-related data values of the human being are taken from a wrist or chest of the human being ([0083], “For instance, a proximal pulse wave may be represented by PPG data collected from the chest of the upper arm, and a distal pulse wave may be represented by PPG data collected at the wrist or the foot.”).
Regarding claim 5, Pantelopoulos discloses The method of claim 1, wherein determining the plurality of measurements providing pulse-related data values of the human being includes measuring pulse-related values of the human being with one or more of at least one electrocardiogram (ECG) sensor, at least one photoplethysmogram (PPG) sensor, at least one accelerometer, and at least one acoustic sensor ([0083], “For instance, a pulse arrival time may be estimated from photoplethysmography (PPG), phonocardiography (PCG), ballistocardiography (BCG), ultrasound, or bioimpedance data. Also, as further explained below, proximal arrival time may be estimated or approximated from electrical signal such as ECG and impedance cardiography (ICG).” And [0020], “In some implementations, the inertial sensor is selected from the group consisting of an accelerometer, a gyroscope, a magnetometer, and any combinations thereof.” And [0165], “The plurality of the biometric sensors in the example illustrated in FIG. 8B includes a PPG sensor, an ECG sensor, an inertial or motion sensor, a temperature sensor, and an altimeter.”).
Regarding claim 6, Pantelopoulos discloses The method of claim 5, further comprising transmitting at least a portion of the plurality of measurements providing pulse-related data values of the human being from one or more of the at least one electrocardiogram (ECG) sensor, the at least one photoplethysmogram (PPG) sensor, the at least one accelerometer, and the at least one acoustic sensor to a server ([0164], “An example of a wearable biometric monitoring device is shown in FIG. 8; the example portable monitoring device may have a user interface, processor, biometric sensor(s), memory, environmental sensor(s) and/or a wireless transceiver which may communicate with a client and/or server.”).
Regarding claim 9, Pantelopoulos discloses The method of claim 5, wherein determining the plurality of measurements providing pulse-related data values of the human being includes determining ventricular contraction of the heart based on measurements taken by one or more of the at least one electrocardiogram (ECG) sensor, the at least one photoplethysmogram (PPG) sensor, the at least one accelerometer, and the at least one acoustic sensor ([0083], “For instance, a pulse arrival time may be estimated from photoplethysmography (PPG), phonocardiography (PCG), ballistocardiography (BCG), ultrasound, or bioimpedance data. Also, as further explained below, proximal arrival time may be estimated or approximated from electrical signal such as ECG and impedance cardiography (ICG).” And [0020], “In some implementations, the inertial sensor is selected from the group consisting of an accelerometer, a gyroscope, a magnetometer, and any combinations thereof.” And [0165], “The plurality of the biometric sensors in the example illustrated in FIG. 8B includes a PPG sensor, an ECG sensor, an inertial or motion sensor, a temperature sensor, and an altimeter.” ECG includes QRS complex which includes the ventricular contraction of the heart).
Regarding claim 10, Pantelopoulos discloses The method of claim 1, wherein determining the period of time during which the human being is stationary includes at least one of measuring motion of the human being and determining positioning of the human being ([0130], “In some implementations, the system utilizes inertial sensors to determine that the user has been stationary during the calibration process and that changes in PPG amplitude are not due to hand motions and changing hydrostatic pressure. When these conditions are met, the process automatically proceeds to take a calibration blood pressure measurement.”).
Regarding claim 12, Pantelopoulos discloses The method of claim 1, wherein the plurality of measurements providing pulse-related data values from the human being include a number of multi-channel and multi-beat measurements from a photoplethysmogram (PPG) sensor over a certain period of time, and the number of multi- channel and multi-beat measurements from the PPG sensor over the certain period of time are integrated ([0202], “Similarly, the optical detectors may sample, measure and/or detect one or more wavelengths that are also specific or directed to a type of physiological data to be collected and/or a physiological parameter (of the user) to be assessed or determined. For instance, in one embodiment, a light source emitting light having a wavelength in the green spectrum (for example, an LED that emits light having wavelengths corresponding to the green spectrum) and a photodiode positioned to sample, measure, and/or detect a response or reflection corresponding with such light may provide data that may be used to determine or detect heart rate. In contrast, a light source emitting light having a wavelength in the red spectrum”).
Regarding claim 13, Pantelopoulos discloses The method of claim 1, further comprising applying a machine-learning model or a linear regression model with an edge computing engine for at least one of determining the representative pulse-related data value and determining the blood pressure value for the human being during the period of time ([0138], “When enough calibration data points have been collected, the process proceeds to fit the model to the calibration data points. See block 412. The model relates blood pressure to PTT. In some implementations, the model includes a linear mathematical relationship, a general linear model, a non-linear model, or a neural network model.”).
Regarding claim 14, Pantelopoulos discloses The method of claim 13, further comprising applying a cloud-based machine-learning model or a linear regression model for at least one of determining the representative pulse-related data value and determining the blood pressure value for the human being during the period of time ([0138], “When enough calibration data points have been collected, the process proceeds to fit the model to the calibration data points. See block 412. The model relates blood pressure to PTT. In some implementations, the model includes a linear mathematical relationship, a general linear model, a non-linear model, or a neural network model.” And [0192]).
Regarding claim 15, Pantelopoulos discloses The method of claim 1, further comprising applying at least one of a cloud-based machine- learning model, a linear regression model and a statistical model for at least one of determining the representative pulse-related data value and determining the blood pressure value for the human being during the period of time ([0138], “When enough calibration data points have been collected, the process proceeds to fit the model to the calibration data points. See block 412. The model relates blood pressure to PTT. In some implementations, the model includes a linear mathematical relationship, a general linear model, a non-linear model, or a neural network model.” And [0192]).
Regarding claim 16, Pantelopoulos discloses The method of claim 1, further comprising applying a machine-learning model or a linear regression model with an edge computing engine for determining an estimated blood pressure value for the human being during the period of time ([0138], “When enough calibration data points have been collected, the process proceeds to fit the model to the calibration data points. See block 412. The model relates blood pressure to PTT. In some implementations, the model includes a linear mathematical relationship, a general linear model, a non-linear model, or a neural network model.” And [0192]).
Regarding claim 17, Pantelopoulos discloses The method of claim 16, further comprising providing the estimated blood pressure value for the human being during the period of time to a cloud-based determination module and determining the blood pressure value for the human being during the period of time with the cloud- based determination module ([0138], “When enough calibration data points have been collected, the process proceeds to fit the model to the calibration data points. See block 412. The model relates blood pressure to PTT. In some implementations, the model includes a linear mathematical relationship, a general linear model, a non-linear model, or a neural network model.” And [0192]).
Regarding claim 18, Pantelopoulos discloses A system for determining a blood pressure value of a human being, comprising (Abstract and entire document):
a first sensor configured to determine a period of time during which the human being is stationary ([0130], “In some implementations, the system utilizes inertial sensors to determine that the user has been stationary during the calibration process and that changes in PPG amplitude are not due to hand motions and changing hydrostatic pressure. When these conditions are met, the process automatically proceeds to take a calibration blood pressure measurement.”);
at least one pulse-related sensor configured to determine a plurality of measurements providing pulse-related data values of the human being during the period of time ([0137 – 0138], “Process 400 then obtain at least one calibration data point corresponding to the calibration blood pressure measurement and the at least one pulse transit time. See block 408.”); and
a processor configured to determine, from the plurality of measurements, a representative pulse-related data value representative of the plurality of pulse-related data values determined during the period of time ([0138], “When enough calibration data points have been collected, the process proceeds to fit the model to the calibration data points. See block 412. The model relates blood pressure to PTT. In some implementations, the model includes a linear mathematical relationship, a general linear model, a non-linear model, or a neural network model.” A calibration pulse data vale is obtained from the pulse data as the representative data value), and
to determine, from the representative pulse-related data value, a blood pressure value for the human being during the period of time ([0138], “The model relates blood pressure to PTT.” Blood pressure is determined from the representative data value).
Regarding claim 19, Pantelopoulos discloses The system of claim 18, wherein the first sensor is an accelerometer, and the first sensor and the at least one pulse-related sensor are positioned in a common housing ([0165], “For example, the PPG sensor may be integrated into a wristband that is attached to a housing, providing a device with a form similar to a wrist-watch that can be worn on the wrist. See FIG. 13A. The plurality of the biometric sensors in the example illustrated in FIG. 8B includes a PPG sensor, an ECG sensor, an inertial or motion sensor, a temperature sensor, and an altimeter.”).
Regarding claim 20, Pantelopoulos discloses The system of claim 18, wherein the at least one pulse-related sensor is configured to determine the plurality of measurements providing pulse-related data values of the human being during the period of time in response to a determination by the first sensor that the human being is stationary ([0130], “In some implementations, the system utilizes inertial sensors to determine that the user has been stationary during the calibration process and that changes in PPG amplitude are not due to hand motions and changing hydrostatic pressure. When these conditions are met, the process automatically proceeds to take a calibration blood pressure measurement.”).
Regarding claim 21, Pantelopoulos discloses The system of claim 18, wherein each of the plurality of measurements providing pulse- related data values includes at least a portion of a pulse waveform (FIG. 1B, 2A).
Regarding claim 22, Pantelopoulos discloses The system of claim 18, wherein the at least one pulse-related sensor includes one or more of an electrocardiogram (ECG) sensor, a photoplethysmogram (PPG) sensor, an acoustic sensor, and an accelerometer ([0165], “For example, the PPG sensor may be integrated into a wristband that is attached to a housing, providing a device with a form similar to a wrist-watch that can be worn on the wrist. See FIG. 13A. The plurality of the biometric sensors in the example illustrated in FIG. 8B includes a PPG sensor, an ECG sensor, an inertial or motion sensor, a temperature sensor, and an altimeter.”).
Regarding claim 23, Pantelopoulos discloses The system of claim 18, wherein the at least one pulse-related sensor is configured to determine ventricular contraction of the heart based on measurements taken by an accelerometer and an acoustic sensor ([0083], “For instance, a pulse arrival time may be estimated from photoplethysmography (PPG), phonocardiography (PCG), ballistocardiography (BCG), ultrasound, or bioimpedance data. Also, as further explained below, proximal arrival time may be estimated or approximated from electrical signal such as ECG and impedance cardiography (ICG).” And [0020], “In some implementations, the inertial sensor is selected from the group consisting of an accelerometer, a gyroscope, a magnetometer, and any combinations thereof.” And [0165], “The plurality of the biometric sensors in the example illustrated in FIG. 8B includes a PPG sensor, an ECG sensor, an inertial or motion sensor, a temperature sensor, and an altimeter.” ECG includes QRS complex which includes the ventricular contraction of the heart).
Regarding claim 25, Pantelopoulos discloses The system of claim 18, wherein the plurality of measurements providing pulse-related data values from the human being include a number of multi-channel and multi-beat measurements from a photoplethysmogram (PPG) sensor over a certain period of time, and the processor is configured to integrate the number of multi-channel and multi-beat measurements from the PPG sensor over the certain period ([0202], “Similarly, the optical detectors may sample, measure and/or detect one or more wavelengths that are also specific or directed to a type of physiological data to be collected and/or a physiological parameter (of the user) to be assessed or determined. For instance, in one embodiment, a light source emitting light having a wavelength in the green spectrum (for example, an LED that emits light having wavelengths corresponding to the green spectrum) and a photodiode positioned to sample, measure, and/or detect a response or reflection corresponding with such light may provide data that may be used to determine or detect heart rate. In contrast, a light source emitting light having a wavelength in the red spectrum”).
Regarding claim 26, Pantelopoulos discloses The system of claim 18, wherein the processor is configured to apply a machine-learning model or a linear regression model to determine, from the plurality of measurements, the representative pulse-related data value representative of the plurality of pulse-related data values determined during the period of time, and to determine, from the representative pulse-related data value, the blood pressure value for the human being during the period of time ([0138], “When enough calibration data points have been collected, the process proceeds to fit the model to the calibration data points. See block 412. The model relates blood pressure to PTT. In some implementations, the model includes a linear mathematical relationship, a general linear model, a non-linear model, or a neural network model.” And [0192]).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Pantelopoulos et al. (US 2017/0209053 A1”) (“Pantelopoulos”) in view of Hu et al. (US 2015/0164430 A1) (“Hu”).
Regarding claim 7, Pantelopoulos discloses The method of claim 6, Pantelopoulos fails to disclose wherein the transmitting includes providing one or both of a compressed version of the portion of the plurality of measurements providing pulse-related data values of the human being and a raw version of the portion of the plurality of measurements providing pulse-related data values of the human being.
However, in the same field of endeavor, He teaches further comprising selecting a number of the plurality of measurements providing pulse-related data values satisfying a threshold quality over a segment of time and averaging the number of the plurality of measurements providing pulse-related data values for the segment of time ([0010], “Generally, the first method S100 enables a wearable device to compress raw motion data at various levels prior to transmission to a mobile computing device paired to the wearable device. For example, the first method S100 can handle transmission of raw motion data, compressed motion data (e.g., quaternions), extrapolated motion types, and/or extrapolated actions (e.g., including an identified action, a start time, an end time, a duration, and/or an intensity, etc.).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the method as taught by Pantelopoulos to include wherein the transmitting includes providing one or both of a compressed version of the portion of the plurality of measurements providing pulse-related data values of the human being and a raw version of the portion of the plurality of measurements providing pulse-related data values of the human being as taught by He to extend battery life ([0012], “Therefore, by dynamically processing raw motion data locally on the wearable device, such as into compressed motion data, a motion type, or an activity based on a degree of confidence in extrapolated data prior to transmitting data to a paired mobile computing device, the first method S100 can reduce the amount of transmitted data and thus extend the battery life of the wearable device.”).
Regarding claim 8, Pantelopoulos as modified discloses The method of claim 7, Pantelopoulos further discloses wherein at least one of the server and one or more of the at least one electrocardiogram (ECG) sensor, the at least one photoplethysmogram (PPG) sensor, the at least one accelerometer, and the at least one acoustic sensor is configured to discard one or more of the plurality of measurements providing pulse-related data values which fall below a threshold quality ([0185] discussing filters, discards data using thresholds and see [0271 – 0272] SNR).
Claims 11 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Pantelopoulos et al. (US 2017/0209053 A1”) (“Pantelopoulos”) in view of He et al. (US 2015/0164351 A1) (“He”).
Regarding claims 11 and 24, Pantelopoulos discloses The method/system of claim 1/18, Pantelopoulos fails to disclose further comprising selecting a number of the plurality of measurements providing pulse-related data values satisfying a threshold quality over a segment of time and averaging the number of the plurality of measurements providing pulse-related data values for the segment of time.
However, in the same field of endeavor, He teaches further comprising selecting a number of the plurality of measurements providing pulse-related data values satisfying a threshold quality over a segment of time and averaging the number of the plurality of measurements providing pulse-related data values for the segment of time ([0826], “The representative segment 904 in this example is calculated by averaging across multiple segments 906 of equal duration. The MoCG data is then analyzed using the representative segment (706) to calculate candidate MPTT values. The representative segment can be calculated, for example, by averaging across multiple segments of equal duration arranged on the same time grid as a representative PPG signal”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the method/system as taught by Pantelopoulos to include further comprising selecting a number of the plurality of measurements providing pulse-related data values satisfying a threshold quality over a segment of time and averaging the number of the plurality of measurements providing pulse-related data values for the segment of time as taught by He to avoid noise ([0826], “The length of the segment 904 and the corresponding MoCG data can be in the order of several seconds. In the example shown in FIG. 9, the length of the segment 904 is 2 seconds. However segments of other lengths (e.g. 1.5 seconds-5 second) can also be used. In some implementations, the representative segment is generated from data collected when a user is stationary, so that the data does not include a significant amount of unwanted noise.”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH A TOMBERS whose telephone number is (571)272-6851. The examiner can normally be reached on M-TH 7:00-16:00, F 7:00-11:00(Eastern).
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/JOSEPH A TOMBERS/Examiner, Art Unit 3791