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
Applicant’s remarks filed 2/27/2026 are acknowledged. Claims 1, 14, and 91 have been amended, claim 9 has been canceled and claims 93-95 have been added. Claims 1-4, 7, 10, 13-14, and 84-95 are pending.
Response to Arguments
Applicant’s arguments, see Remarks, filed 2/27/2026, with respect to the rejection(s) of claim(s) 1-4,7,10,13-14, and 84-92 under 35U.S.C 103 Elliot(US 20150374249) in view of Mukkamala et al (20140066793) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration and in view of the amendments to the claims and newly added claims, a new ground(s) of rejection is made in view of Elliot(US 20150374249) in view of Kang et al.( US 20190104997).
With respect to the reference to Mukkamala et al (20140066793) applicant argues Mukkamala determines a different set of parameters a, b, c, d, Ao, Ai, pi, and p2-to compute blood pressure. Mukkamala reference relies upon a parametric model that determines blood pressure using a series of model constants, the magnitude of sine wave component at fundamental frequency of pulse and first harmonic frequency of pulse, and the phase of first harmonic frequency of pulse. This is a wholly different set of parameters than those required and specified by independent Claim 14.
Although Mukkamala et al (20140066793) does set forth [0044] a parametric physical model approach may be used to determine blood pressure. The main idea of this approach is to determine the unknown parameters of a physical model by fitting the model to the oscillometric cuff pressure waveform and to then compute the blood pressure values along with the entire blood pressure waveform using the determined parameters. It is the interpretation of the examiner that since Mukkamala et al.(US20140066793) uses the entire blood pressure wave form this would implicitly include the parameters claimed including the rate of rise before systole, rate of fall after systole, as well as the dicrotic notch. Mukkamala et al.(US20140066793) also determines systolic and diastolic pressure as well as the arterial P-A relationship. And it is noted that there are a limited number of choices available to a person of ordinary skill in the art for selecting the desired parameters, relationships and constraints for which to define a parametric model’s geometry.
However, the examiner is persuaded that Mukkamala et al (20140066793) does not explicitly set forth the specific parameters of systolic pressure, diastolic pressure, rate of rise before systole, and rate of fall after systole as set forth in now amended claims 14 and new claims 92 and 93.
Applicant’s attention is directed to Carter et al. (US 20170181649) hereinafter Carter et al. as set forth in the rejection(s) below. Including, Paragraph [0045] and [0082], In some embodiments, the machine learning unit 46 is configured to receive one or more of mean beat shape data, one or more additional shape features 44, and one or more biometric features 36 ( interpreted as the parametric model in which these parameters, constraints, and relationships are used to define the model’s geometry), and Table 1: wherein biometric features 36 can include one or more of the features which includes systolic pressure, diastolic pressure, rate of rise before systole, and rate of fall after systole.
Election/Restrictions
Newly submitted claim 95 is directed to an invention that is independent or distinct from the invention originally claimed for the following reasons: The claims to the different species recite the mutually exclusive characteristics of such species: Species I. examined claims 1-4,7,10,13-14,84-94 are directed to a device for acquiring signals to measure a user’s blood pressure that include a pressure sensor, a blood flow sensor, a touch sensitive screen, and a processor. Species II. Newly added claim 95 is directed to a device for acquiring signals to measure a user’s blood pressure that include a pressure sensor, a blood flow sensor, a touch sensitive screen, a processor, and a pair of glasses where the pressure sensor hangs below an arm of the glasses. The examiner notes there does not appear to be an embodiment disclosed in the current specification that includes a pair of glasses with a pressure sensor and a touch sensitive screen, nor is it clear how touching the touch sensitive screen with one finger would correspond to a pressure measured by touching the pressure sensor incorporated into the glasses.
Since applicant has received an action on the merits for the originally presented invention, this invention has been constructively elected by original presentation for prosecution on the merits. Accordingly, claim 95 is withdrawn from consideration as being directed to a non-elected invention. See 37 CFR 1.142(b) and MPEP § 821.03.
To preserve a right to petition, the reply to this action must distinctly and specifically point out supposed errors in the restriction requirement. Otherwise, the election shall be treated as a final election without traverse. Traversal must be timely. Failure to timely traverse the requirement will result in the loss of right to petition under 37 CFR 1.144. If claims are subsequently added, applicant must indicate which of the subsequently added claims are readable upon the elected invention.
Should applicant traverse on the ground that the inventions are not patentably distinct, applicant should submit evidence or identify such evidence now of record showing the inventions to be obvious variants or clearly admit on the record that this is the case. In either instance, if the examiner finds one of the inventions unpatentable over the prior art, the evidence or admission may be used in a rejection under 35 U.S.C. 103 or pre-AIA 35 U.S.C. 103(a) of the other invention.
Claim Rejections - 35 USC § 112
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 1-4,7,10,13-14, 84-95 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.
Regarding claims 1, 14, and 91, added language directed to “the pressure exerted when the user presses a finger or thumb against the touch-sensitive screen” lacks antecedent basis. The examiner suggests changing the language to -- a pressure exerted when the user presses a finger or thumb against the touch-sensitive screen --.
Regarding claim 90, line 2, “the blood flow sensor of light” lacks antecedent basis.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claim(s) 1-4, 7, 10, 13, and 84-89 is/are rejected under 35 U.S.C. 103 as being unpatentable over Elliot(US 20150374249) in view of Kang et al.( US 20190104997).
Regarding claim 1, Elliot teaches a device for acquiring signals which can be used to derive a measurement of a user's blood pressure (abstract, “This disclosure provides a personal hand-held monitor PHARMD which comprises a signal acquisition device for acquiring signals which can be used to derive a measurement of a subject's blood pressure (BP)”), the device comprising; a pressure sensor configured to measure a range of pressures that are applied by a fingertip, to the device and to create pressure signals related to the range of pressures (abstract, “a means for measuring the pressure applied by or to the body part”; [0126], “In carrying out BP monitoring, the user may vary the pressure applied by or to the blood flow occlusion means in a random manner.”); a blood flow sensor configured to detect flow of blood through the fingertip and to create blood flow signals related to the flow of blood (abstract, “The signal acquisition device comprises a blood flow occlusion means adapted to be pressed against one side only of a body part or to have one side only of a body part pressed against 1”; [0126], “However, the data from the blood flow sensor can be correlated with the signal from the pressure sensor of the blood flow occlusion means to fit the measured data to a known theoretical relationship between flow rate and pressure’”)); and a processor ([0002], “The PHIHM uses the processor of the PHHCD to control and analyze signals received from the signal acquisition device.”) adapted to predict luminal area of an artery of the fingertip using a parametric model, the pressure signals, and the blood flow signals ([0276]-[0278], “The waveforms of typical electrical signals received from optical, pressure and electrical sensors are shown in FIG. 12 in the attached drawings... The relationship between the luminal area and pressure is referred to as the Arterial Optical/Pressure Curve (AOPC.”; [0280], “The quantitative form of the AOPC is found by fitting the measured values of the optical signal to a parametric representation of the AOPC, such as that proposed by Langeworters et al. (loc. cit.). “). Elliott further teaches a device comprising: a pressure sensor configured to measure pressure applied by a body part, to the device; a blood flow sensor configured to detect flow of blood through the body part in contact with the device (abstract, “a means for measuring the pressure applied by or to the body part”); and a processor ([0002], “The PHHM uses the processor of the PHHCD to control and analyze signals received from the signal acquisition device.”), wherein the processor is adapted to determine a user's blood pressure throughout a pulse cycle ((0119], “The processor of the PHHM is preferably adapted to analyze the signals from the PPG sensor to provide a direct estimate of systolic and diastolic BP at the point of measurement”) wherein the processor is adapted to represent a relationship between area of an artery of the body part and a difference between pressure inside of the artery and pressure in tissue outside of the artery by a parametric equation ([0278], “in order to explain the form of the AOPC, if is necessary to consider how the artery behaves. The relationship between luminal area and pressure is as shown io FIG. 13 where TMP is the TransMural Pressure, which is the instantaneous pressure in the artery mins the External Applied Pressure (EAP), which is the pressure generated by the occlusion means and measured by the pressure sensor.) [0280], “The quantitative form of the AOPC is found by fitting the measured values of the optical signal to a parametric representation of the AOPC, such as that proposed by Langeworters et al. (loc. cit.). “).
Elliot et al. does teach incorporating the pressure sensors into a cell phone and a touch screen for data entry. However Elliot et al. does not specifically set forth wherein the processor is adapted to determine a value for the pressure exerted when the user presses a finger or thumb against the touch-sensitive screen, wherein the value of exerted pressure is used to complement or check a measurement made by the pressure sensor.
Kang et al. teaches in the same field of endeavor an apparatus for measuring bio-information includes a pulse wave measurer configured to measure a pulse wave signal from a first region of an object; a contact pressure extractor including a touch screen and configured to obtain a contact pressure signal, indicating a contact pressure between the first region and the pulse wave measurer, based on touch data that is generated based on a second region of the object being in contact with the touch screen; and a processor configured to measure bio-information of the object based on the pulse wave signal and the contact pressure signal. Paragraph [0019] The bio-information may include one or more of a blood pressure, a vascular age, an arterial stiffness, an aortic artery pressure waveform, a vascular elasticity, a peripheral resistance, a stress index, and a fatigue level. Note figs. 3C, 3D and paragraphs [0008] The contact pressure extractor may obtain the contact pressure signal based on at least one of a pixel intensity and a force value corresponding to the touch data, the touch data being generated based on the second region of the object that applies a pressure to the touch screen during measurement of the pulse wave signal from the first region. This includes pixel data and pressure force data. Paragraph [0011] The apparatus may further include an outputter configured to, based on the second region being in contact with the touch screen, output information including at least one of information regarding a reference pressure to be applied by the second region to the touch screen during measurement of the pulse wave signal from the first region and information about a contact pressure applied by the second region to the touch screen. Paragraph [0076] Referring to FIG. 3C, a first region of an object is an index finger 01 and a second region is a thumb 02. The user may change a pressure applied by the index finger 01 to the pulse wave sensor 32 by adjusting the strength of holding the main body 30 with the index finger 01 and the thumb 02 in a state in which the index finger 01 and the thumb 02 are in contact with the pulse wave sensor 32 and the touch screen 31, respectively, in order to measure bio-information. In this case, the forces exerted by the index finger 01 and the thumb 30 on the main body 30 may be the same or substantially correspond to each other due to action and reaction, and thus the contact pressure extractor 102 may acquire a contact pressure between the index finger 01 and the pulse wave sensor 32 by extracting a pressure exerted by the thumb 01 on the touch screen. Paragraph [0077] Referring to FIG. 3D, an outputter 210 may display information about an area 33 which the thumb 02, which is the second region of the object, is to be in contact with in a predetermined position of the touch screen panel 31. In addition, the outputter 210 may display reference pressure information SP in a predetermined area 34 of the touch screen panel 31, indicating a reference pressure which is to be applied by the thumb 02 to the touch screen while the pulse wave sensor 32 measures a pulse wave signal from the index finger 01. In addition, the outputter 201 may output actual contact pressure information AP extracted by the contact pressure extractor 120.
Therefor it would have been obvious to one of ordinary skill in the art at the time of the invention to modify Elliot et al. to include determining a value for the pressure exerted when the user presses a finger or thumb against the touch-sensitive screen, wherein the value of exerted pressure is used to complement or check a measurement made by the pressure sensor as taught by Kang et al. to measure the blood pressure.
Regarding claim 2, Elliott teaches, wherein the blood flow sensor comprises an optical sensor ([0174]-[0175], “an optical sensor associated with the open surface for providing an electrical signal related to the luminal area of the artery which, in use, is occluded by the open surface; and processing means for controlling the device and for receiving and analyzing electrical signals from the pressure sensor and the optical sensor to provide a measurement of the subject's SBP and/or DBP.”).
Regarding claim 3, Elliott teaches the device of claim 1, wherein the pressure sensor comprises a sensor immersed in a gel ((0232], “the pressure is sensed by means of a flexible and essentially incompressible gel“).
Regarding claim 4, Elliott teaches , which includes one or more of another blood flow sensor, another pressure sensor, an electrical sensor, and a temperature sensor ((0086], “The PHHM may include one or more of the temperature sensor, electrical sensor, blood flow occlusion means, blood photosensor for PPG, acoustic sensor, movement sensor,
Regarding claim 7, Elliott teaches, wherein the processor of the device is adapted to provide communications, computing and display capability ([0147], “The PHHCD is preferably further programmed to communicate the measured data directly to the user, for instance via a visual display or audibly. Preferably, the communication is via a visual display. Hf desired, the processor may be programmed so that the display shows not only the measured parameter(s) but also trends in the measured parameter(s}.”.
Regarding claim 10, Elliott teaches , wherein a touch-sensitive screen of the cell phone indicates a position for the user to place a finger over the pressure sensor and the blood flow sensor ([0200], “The PHHM is adapted such that, if they are not, the PHHM will issue visible and/or audible signals instructing the user to reposition the body part and try again.”.
Regarding claim 13, Elliott teaches collecting personal health data for user, wherein the personal health data is related to a health parameter selected from the group consisting of pulse rate, blood oxygen level (SpO2), body temperature, respiration rate, ECG, cardiac output, heart function timing, arterial stiffness, tissue stiffness, hydration, concentration of a constituent of the blood, and identity of the user ([0045], “Preferably, the one or more sensors is/are for acquiring signals related to BP, pulse wave velocity, BP waveform, temperature, blood oxygen partial pressure, electrocardiogram, heart rate and/or respiratory rate. The signal acquisition device may include sensors for acquiring signals from which measurements of more than one of the above-mentioned parameters can be derived. The signal acquisition device preferably includes one or more sensor(s) for acquiring signals from which measurements of BP, using, for instance, one or more of sphygmomanometer, photoplethysmography and measurement of pulse wave velocity, can he derived.”; [0084], “a sensor adapted to acquire signals from which the identity of the user can be derived”; [0087], “the medical indicators that are less well-known but which are recognized by medical specialists, such as arterial wall stiffness and pulse arrhythmia, may also be extracted.”).
Regarding claim 84, Elliott teaches wherein the processor of the device is further adapted to find a quasi-static contribution to the blood flow signals due to light that has passed through tissue surrounding the artery without being absorbed by blood in the artery ((0251], “infra-red light is preferentially absorbed by oxygenated hemoglobin so the amount of absorption is approximately proportional to the amount of arterial blood through which the light passes, for a given length of artery, the amount of arterial blood is proportional to the luminal area of the artery so the absorption signal is also approximately proportional to the luminal area.”.
Regarding claim 85, Elliott teaches the device of claim 1, wherein the processor is further adapted to estimate stiffness of tissue in the fingertip from an estimate of a relative position of the artery with respect to the pressure sensor, the blood flow sensor, or both ([0087], “Algorithms relating the combination of signals from any or all of the sensors and means contained in the PHIM of WO201 3/001 265 and from other sensors that may be part of the PHHCD may be used to convert the acquired signals to the relevant health-related data or improve the accuracy of the deduced medical indicators (vital signs”), such as systolic and diastolic BP. Other medical indicators that are less well-known but which are recognized by medical specialists, such as arterial wall stiffness and pulse arrhythmia, may also be extracted.”.
Regarding claim 86, Elliott teaches wherein the processor is further adapted to estimate hydration, stiffness, or both for tissue in the fingertip by combining measurements of pulse wave velocity, the blood pressure, and arterial stiffness ([0087], “Algorithms relating the combination of signals from any or all of the sensors and means contained in the PHHM of WO20 13/001 265 and from other sensors that may be part of the PHHCD may be used io convert the acquired signals to the relevant health -related data or improve the accuracy of the deduced medical indicators (‘vital signs”), such as systolic and diastolic BP. Other medical indicators that are less well-known but which are recognized by medical specialists, such as arterial wall stiffness and pulse arrhythmia, may also be extracted.”’).
Regarding claim 87, Elliott teaches wherein the device uses machine learning to find the parameters that represent instantaneous pressure in the artery through a pulse cycle ({0283]-[0285], “The pressure deficit method exploits the instantaneous balance between the pressure within the artery and the sum of the pressure applied by the occlusion means (EAP) and the pressure caused by the tension in the artery wall (TMP). Measured values of the optical signal are used to find the corresponding TMP from the AOPC. The instantaneous arterial pressure is then found by adding the TMP to the measured instantaneous EAP. The curve in FIG. 17 shows the result of such a calculation.... The pressure applied by the occlusion means at the time of these events allows the instantaneous pressure to be mapped through the pulse cycle...Preferably, the instantaneous pressure wave derived from either or both of these methods is then used to model the effect of the reflection of the pulse wave from the body part.”; [0087], “Any or all of these models may be coded as software and can be loaded onto the PHHM or onto a remote computer for processing of the signals.”; [0102], “There are thus five separate measurements and several pieces of data that may be combined using an optimizing mathematical algorithm such as a Bayesian estimator to obtain the most reliable estimate of BP.”; Bayesian estimation is machine learning).
Regarding claim 88, Elliot wherein the processor is adapted to use data from several pulse cycles ([0154], “The RSDS can offer a service to store many measurements from a PHHM and analyze trends and other derived information for the user. This may be linked to an automatic alert service in the event of any significant change in the data.”).
Regarding claim 89, Elliott teaches wherein the processor is further adapted to provide one or more independent estimates of systolic pressure and diastolic pressure ([0208], “The Pulse Wave Velocity may be used io make a direct independent estimate of BP as described in detail by Padilla Gec. cit.),”).
Claim(s) 14 and 90 is/are rejected under 35 U.S.C. 103 as being unpatentable over Elliot(US 20150374249) I hereinafter Elliot et al. in view of Kang et al.( US 20190104997) hereinafter Kang et al. and further in view of Carter et al. (US 20170181649) hereinafter Carter et al.
Elliot et al as modified by Kang et al. teaches the claimed invention as set forth above including wherein the processor is adapted to represent a relationship between area of an artery of the body part and a difference between pressure inside of the artery and pressure in tissue outside of the artery by a parametric equation ([0278], “in order to explain the form of the AOPC, if is necessary to consider how the artery behaves. The relationship between luminal area and pressure is as shown io FIG. 13 where TMP is the TransMural Pressure, which is the instantaneous pressure in the artery mins the External Applied Pressure (EAP), which is the pressure generated by the occlusion means and measured by the pressure sensor.) [0280], “The quantitative form of the AOPC is found by fitting the measured values of the optical signal to a parametric representation of the AOPC, such as that proposed by Langeworters et al. (loc. cit.). “).
Elliot et al as modified by Kang et al. does not specifically teach where the parametric model uses a set of parameters that comprises systolic pressure, diastolic pressure, rate of rise before systole, and rate of fall after systole, wherein the processor is adapted to optimize the parametric model using the pressure signals and the blood flow signals; and input systolic pressure, diastolic pressure, rate of rise before systole, that are obtained through real-time measurements to the parametric model after the parametric model is optimized, thereby obtaining the measurement of the user's blood pressure.
Carter et al. teaches in the same field of endeavor systems and methods include acquiring raw photoplethysmogram (PPG) data from a subject by measuring light reflected from or transmitted through a portion of the subject's body, determining a mean beat shape from the raw PPG data, and analyzing the mean beat shape to determine a blood pressure of the subject. The method can include analyzing a distinct shape of the mean beat shape to determine the blood pressure. The method can include identifying individual beats within the raw PPG data, collecting motion data and filtering the individual beats against the motion data, measuring biometric features of filtered beats, scaling individual beats in time and amplitude, and measuring additional shape features of scaled beats. Paragraph [0043] the signal processing unit 32 is configured to process raw data 20 to generate scaled beats 34 and biometric features 36. Biometric features 36 can include any suitable feature, characteristic, data, or similar value related to a subject's blood pressure. In some cases, biometric features 36 can include one or more of the features listed in Table 1. Table 1 includes systolic pressure, diastolic pressure, rate of rise before systole, and rate of fall after systole. Paragraph [0045] and [0082], In some embodiments, the machine learning unit 46 is configured to receive one or more of mean beat shape data, one or more additional shape features 44, and one or more biometric features 36 ( interpreted as the parametric model in which these parameters, constraints, and relationships are used to define the model’s geometry). In other embodiments, the machine learning unit 46 is configured to use machine learning to develop and/or implement a predictive model for blood pressure determination. In yet other embodiments, the machine learning unit 46 is configured to use machine learning to develop and/or implement the predictive model for blood pressure determination using one or more of mean beat shape data, one or more additional shape features 44, and one or more biometric features 36. The predictive model for blood pressure determination can be implemented to determine blood pressure results 50 (e.g., systolic pressure, diastolic pressure, and/or mean arterial pressure) of the subject. [0089] discusses some aspects of optimization of the predictive model and furthermore, the nature of solving a parametric model would include some degree of optimization of parameters to achieve desired results.
Therefor it would have been obvious to one of ordinary skill in the art at the time of the invention to modify Elliot et al. to include routine optimization of a parametric model using the one or more of mean beat shape data, one or more additional shape features 44, and one or more biometric features 36 which include (systolic pressure, diastolic pressure, rate of rise before systole, and rate of fall after systole) and interpreted as the parametric model in which these parameters, constraints, and relationships are used to define the model’s geometry) as taught by carter et al. Also, applicant’s specification provides no criticality to the particular parametric model used to determine a user’s blood pressure. Note: Paragraphs [45] – [46] of the applicant’s specification set forth the following: “ [0045] The present aspect is referred to as Model-Based Optimization (MBO). It applies the mathematical process of optimization to extract an accurate estimate of the parameters of a model of the waveform of the arterial pressure pulse from the values of A that are inferred from the optical signal. [0046] In order to illustrate the application of optimization, an example is described. The optimization process described here is one of many ways of solving for the pressure wave. Others are known to a person skilled in the art.” . Given a limited number of choices available for selecting, solving and optimizing a particular parametric equation to determine a user’s blood pressure as set forth in Elliot et al. as modified by Carter et al. it would have been obvious for one of ordinary skill in the art at the time of the invention to substitute one known parametric algorithm and method for optimization for another to achieve predictable solutions with a reasonable expectation of success.
Regarding claim 90, Elliott teaches wherein the pressure inside of the artery is found using measurements obtained from the blood flow sensor of light that has passed through the tissue outside of the artery and measurements of the pressure applied by the body part to the device ([0283], “The pressure deficit method exploits the instantaneous balance between the pressure within the artery and the sum of the pressure applied by the occlusion means (EAP) and the pressure caused by the tension in the artery wall (TMP). Measured values of the optical signal are used to find the corresponding PMP from the AOPC. The instantaneous arterial pressure is then found by adding the TMP to the measured instantaneous BAP.”.
Claim(s) 91 is/are rejected under 35 U.S.C. 103 as being unpatentable over Elliot(US 20150374249) I hereinafter Elliot et al. in view of Kang et al.( US 20190104997) hereinafter Kang et al. and further in view of Jones et al. (WO 9203967) hereinafter Jones et al.
Elliot et al as modified by Kang et al. teaches the claimed invention as set forth above. However,
Elliot et al as modified by Kang et al. does not explicitly teach wherein the processor of the device is further adapted to find a set of parameters that minimizes difference between the predicted luminal area of the artery and an area implied by the blood flow sensor.
Jones et al. teaches in the same field of endeavor a processor of the device is further adapted to find a set of parameters that minimizes difference between a predicted luminal area of the artery and an area implied by a blood flow sensor ([0129], “in a further analysis of the electrical signals, it is well known that respiration modulates the timing of the heartbeat, the amplitude of the ECG signal, the mean and pulse BP and possibly also the Pulse Wave Velocity. The analysis may exploit all of these to make several independent measurements using: the pulse period derived separately from the red and infrared channels of the optical sensor and from the electrical sensor; the phase difference between said optical and electrical signals; the amplitude and mean values as the PPG signal and the amplitude of the PPG signal, all of these may be subject to noise or inaccuracy. Rach may be independently analyzed to establish its quality, measured using parameters such as the repeatability of the periodicity and the signal/noise ratio.”).
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filling date to modify the device of Elliot et al as modified by Kang et al. to find a set of parameters to minimize the difference and noise between predicted and measured values as taught by Jones et al. Doing so would allow for more accurate data collection and prediction
Claim(s) 92-93 is/are rejected under 35 U.S.C. 103 as being unpatentable over Elliot(US 20150374249) I hereinafter Elliot et al. in view of Kang et al.( US 20190104997) hereinafter Kang et al. and further in view of Jones et al. (WO 9203967) hereinafter Jones et al. and further in view of Carter et al. (US 20170181649) hereinafter Carter et al.
Elliot et al as modified by Kang et al. and Jones et al. teaches the claimed invention as set forth above including wherein the processor is adapted to represent a relationship between area of an artery of the body part and a difference between pressure inside of the artery and pressure in tissue outside of the artery by a parametric equation ([0278], “in order to explain the form of the AOPC, if is necessary to consider how the artery behaves. The relationship between luminal area and pressure is as shown io FIG. 13 where TMP is the TransMural Pressure, which is the instantaneous pressure in the artery mins the External Applied Pressure (EAP), which is the pressure generated by the occlusion means and measured by the pressure sensor.) [0280], “The quantitative form of the AOPC is found by fitting the measured values of the optical signal to a parametric representation of the AOPC, such as that proposed by Langeworters et al. (loc. cit.). “).
Elliot et al as modified by Kang et al. and Jones et al. does not specifically teach where the parametric model uses a set of parameters that comprises systolic pressure, diastolic pressure, rate of rise before systole, and rate of fall after systole, wherein the processor is adapted to optimize the parametric model using the pressure signals and the blood flow signals; and input systolic pressure, diastolic pressure, rate of rise before systole, that are obtained through real-time measurements to the parametric model after the parametric model is optimized, thereby obtaining the measurement of the user's blood pressure.
Carter et al. teaches in the same field of endeavor systems and methods include acquiring raw photoplethysmogram (PPG) data from a subject by measuring light reflected from or transmitted through a portion of the subject's body, determining a mean beat shape from the raw PPG data, and analyzing the mean beat shape to determine a blood pressure of the subject. The method can include analyzing a distinct shape of the mean beat shape to determine the blood pressure. The method can include identifying individual beats within the raw PPG data, collecting motion data and filtering the individual beats against the motion data, measuring biometric features of filtered beats, scaling individual beats in time and amplitude, and measuring additional shape features of scaled beats. Paragraph [0043] the signal processing unit 32 is configured to process raw data 20 to generate scaled beats 34 and biometric features 36. Biometric features 36 can include any suitable feature, characteristic, data, or similar value related to a subject's blood pressure. In some cases, biometric features 36 can include one or more of the features listed in Table 1. Table 1 includes systolic pressure, diastolic pressure, rate of rise before systole, and rate of fall after systole. Paragraph [0045] and [0082], In some embodiments, the machine learning unit 46 is configured to receive one or more of mean beat shape data, one or more additional shape features 44, and one or more biometric features 36 ( interpreted as the parametric model in which these parameters, constraints, and relationships are used to define the model’s geometry). In other embodiments, the machine learning unit 46 is configured to use machine learning to develop and/or implement a predictive model for blood pressure determination. In yet other embodiments, the machine learning unit 46 is configured to use machine learning to develop and/or implement the predictive model for blood pressure determination using one or more of mean beat shape data, one or more additional shape features 44, and one or more biometric features 36. The predictive model for blood pressure determination can be implemented to determine blood pressure results 50 (e.g., systolic pressure, diastolic pressure, and/or mean arterial pressure) of the subject. [0089] discusses some aspects of optimization of the predictive model and furthermore, the nature of solving a parametric model would include some degree of optimization of parameters to achieve desired results.
Therefor it would have been obvious to one of ordinary skill in the art at the time of the invention to modify Elliot et al. as modified by Kang et al. and Jones et al. to include routine optimization of a parametric model using the one or more of mean beat shape data, one or more additional shape features 44, and one or more biometric features 36 which include (systolic pressure, diastolic pressure, rate of rise before systole, and rate of fall after systole) and interpreted as the parametric model in which these parameters, constraints, and relationships are used to define the model’s geometry) as taught by carter et al. Also, applicant’s specification provides no criticality to the particular parametric model used to determine a user’s blood pressure. Note: Paragraphs [45] – [46] of the applicant’s specification set forth the following: “ [0045] The present aspect is referred to as Model-Based Optimization (MBO). It applies the mathematical process of optimization to extract an accurate estimate of the parameters of a model of the waveform of the arterial pressure pulse from the values of A that are inferred from the optical signal. [0046] In order to illustrate the application of optimization, an example is described. The optimization process described here is one of many ways of solving for the pressure wave. Others are known to a person skilled in the art.” . Given a limited number of choices available for selecting, solving and optimizing a particular parametric equation to determine a user’s blood pressure as set forth in Elliot et al. as modified by Carter et al. it would have been obvious for one of ordinary skill in the art at the time of the invention to substitute one known parametric algorithm and method for optimization for another to achieve predictable solutions with a reasonable expectation of success.
Claim(s) 94 is/are rejected under 35 U.S.C. 103 as being unpatentable over Elliot(US 20150374249) I hereinafter Elliot et al. in view of Kang et al.( US 20190104997) hereinafter Kang et al. and further in view of Jones et al. (WO 9203967) hereinafter Jones et al. and Fonte et al. (US 20150245775) hereinafter Fonte et al.
Elliot et al as modified by Kang et al. and Jones et al. teaches the claimed invention as set forth above including [0102] BP may be estimated by combining the data from four different types of evidence: pulse wave velocity, pulse volume, sphygmomanometer and pulse rate. Sphygmomanometer is itself derived from two different measurements, from the high frequency signals from the pressure sensor and from the blood photosensor(s). External data, such as height, weight, age and sex of the user, may also be exploited. There are thus five separate measurements and several pieces of data that may be combined using an optimizing mathematical algorithm such as a Bayesian estimator to obtain the most reliable estimate of BP. [0139] The results of all of these measurements may be combined using an optimizing mathematical algorithm such as a Bayesian estimator to obtain the most reliable description of the amplitude and phase of the respiratory cycle. [0208] The estimate of BP may be further refined by the use of other measurements. The Pulse Wave Velocity may be used to make a direct independent estimate of BP as described in detail by Padilla (loc. cit.), which in turn references earlier work on a similar subject from 1995 and its specific use for estimating of BP in 2000. The technique is described in U.S. Pat. No. 5,865,755 dated 2 Feb., 1999.
Elliot et al as modified by Kang et al. and Jones et al. does not specifically teach wherein the processor is adapted to optimize the parametric model by: estimating values for the set of parameters;
finding a simulated blood flow signal based on the estimated values for the set of parameters; and refining the estimated values for the set of parameters to find a set of parameters that minimizes difference between the simulated blood flow signal and the blood flow signals.
Fonte et al. teaches in the same field of endeavor systems and methods are disclosed for determining individual-specific blood flow characteristics. One method includes acquiring, for each of a plurality of individuals, individual-specific anatomic data and blood flow characteristics of at least part of the individual's vascular system; executing a machine learning algorithm on the individual-specific anatomic data and blood flow characteristics for each of the plurality of individuals; relating, based on the executed machine learning algorithm, each individual's individual-specific anatomic data to functional estimates of blood flow characteristics; acquiring, for an individual and individual-specific anatomic data of at least part of the individual's vascular system; and for at least one point in the individual's individual-specific anatomic data, determining a blood flow characteristic of the individual, using relations from the step of relating individual-specific anatomic data to functional estimates of blood flow characteristics. [0017] The present disclosure describes certain principles and embodiments for providing advantages over physics-based simulation of blood flow to compute patient-specific blood flow characteristics and clinically relevant quantities of interest. Namely, the presently disclosed systems and methods may incorporate machine learning techniques to predict the results of a physics-based simulation. For example, the present disclosure describes an exemplary, less processing-intensive technique, which may involve modeling the fractional flow reserve (FFR) as a function of a patient's vascular cross-sectional area, diseased length, and boundary conditions. FFR can be predicted by optimizing the functions "f" and "g" such that the difference between the estimated FFR (FFR.sub.CT.sub.--.sub.ScalingLaw) and the measured FFR (mFFR) is minimized. Also note paragraphs [0025] –[0026], and [0034].
Therefore, It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the device of Elliot et al as modified by Kang et al. and Jones et al. finding a simulated blood flow signal based on the estimated values for the set of parameters; and refining the estimated values for the set of parameters to find a set of parameters that minimizes difference between the simulated blood flow signal and the blood flow signals as taught by Fonte et al. to obtain improved blood pressure measurements.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN L CASLER whose telephone number is (571)272-4956. The examiner can normally be reached M-Th 6:30 to 4:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Marmor can be reached at (571)272-4730. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/BRIAN L CASLER/Primary Examiner, Art Unit 3791
Prosecution Insights