CTNF 18/855,970 CTNF 99518 DETAILED ACTION Claims 1-20 are pending and hereby under examination. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Objections 07-29-01 AIA Claim 20 is objected to because of the following informalities: Claim 20, line 9, “the RF reflection signals” should read “the processed RF reflection signals” for consistent language used in the claim . Appropriate correction is required. Claim Rejections - 35 USC § 112 07-30-01 AIA 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. 07-31-01 Claims 4, 8, 15, and 19 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. The claims recite that the analyzing the RF reflection signals step is performed by one or more neural networks. However, there is little disclosure as to how the neural networks perform the analyzing step. For example, paragraphs 0004 and 0006 of Applicant’s specification repeats the claim language of claims 4 and 15 that “Analyzing the RF reflection signals at the first distance range and at the second distance range can be performed by one or more neural networks”. In paragraphs 0061-0066, Applicant discloses that a chest analysis engine 221, an extremity analysis engine 222, and an analysis engine 311 can be trained machine learning models, such as a trained neural network. Lastly, Applicant discloses that at blocks 930 and 935 of Fig. 9 and block 1030 of Fig. 10 may involve applying a trained machine learning model, such as a trained neural network (see paragraphs 0087-0095). There is no disclosure of a type of neural network the trained neural network could be; for example, a convolutional neural network. There is no disclosure of what type of algorithms or calculations are made by the neural network to determine blood pressure based on two RF reflection signals. There is also no disclosure of how the trained neural networks or machine learning models are trained. What data is used to train these models/networks? Additionally, there is no disclosure as to how the machine learning models are updated based on the calibration profile specific to the user as recited in claims 8 and 19. What data is updated? How does the machine learning model changed based on the updated calibration profile? Applicant merely discloses that the neural network or machine learning models may be applied to the method of determining blood pressure of the user, and that they may be updated based on a calibration profile. Applicant discloses what values may be input and what may be output (see paragraphs 0065-0067), but there is no disclosure as how the neural network or machine learning model makes the calculation or manipulates the data to determine the output. Additionally, there is no disclosure as to how the calibration profile updates the machine learning model, nor how the update changes the machine learning model to perform the analysis of blood pressure. As such, the claims contain subject matter which was not described in such a way to reasonably convey to one skilled in the art that the inventor(s) had possession of the claimed invention at the time the application was filed. 07-30-02 AIA 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. 07-34-01 Claims 1-20 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. 07-34-03 AIA The term “ distance away ” in claim s 1, 10, and 20 is a relative term which renders the claim indefinite. The term “ distance away ” 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. How far away from the user can the base plate be placed in order to emit and receive RF signals that are useable for determining blood pressure? Is there a limit as to how far away the housing can be? Applicant does not define a range or limit in the specification for the term “distance away”. For examination purposes, the claims will be interpreted such that the term “distance away” is defined as any distance that is not directly in contact with the user. Claims 2-9 and 11-19 are also rejected due to their dependence on claims 1 and 10 . R egarding claims 1, 10, and 20 , it is unclear what is required of processing the received RF reflection signals to obtain distance-binned frequency measurements. How are the signals processed, or what is taken from the signals to obtain these frequency measurements. Do these measurements represent some characteristic of the RF signals? Is the processing step merely converting or transforming the signals into a frequency domain for analysis? Further, it is unclear what is required to meet the claim with respect to the analyzing steps. Are the received RF reflection signals analyzed or are the distance binned frequency measurements analyzed? For examination purposes, the claims will be interpreted such that the signals are analyzed in the frequency domain or are analyzed based on frequency characteristics. Claims 2-9 and 11-19 are also rejected due to their dependence on claims 1 and 10. Regarding claims 4, 8, 15, and 19 , the claims recite that the analyzing the RF reflection signals step is performed by one or more neural networks and that a machine learning model is based on a calibration profile specific to the user. However, it is unclear as to how the neural networks perform the analyzing step. For example, paragraphs 0004 and 0006 of Applicant’s specification repeats the claim language of claims 4 and 15 that “Analyzing the RF reflection signals at the first distance range and at the second distance range can be performed by one or more neural networks”. In paragraphs 0061-0066, Applicant discloses that a chest analysis engine 221, an extremity analysis engine 222, and an analysis engine 311 can be trained machine learning models, such as a trained neural network. Lastly, Applicant discloses that at blocks 930 and 935 of Fig. 9 and block 1030 of Fig. 10 may involve applying a trained machine learning model, such as a trained neural network (see paragraphs 0087-0095). There is no disclosure of a type of neural network the trained neural network could be; for example, a convolutional neural network. There is no disclosure of what type of algorithms or calculations are made by the neural network to determine blood pressure based on two RF reflection signals. There is also no disclosure of how the trained neural networks or machine learning models are trained or how they may be modified based on a calibration profile. Thus, it becomes unclear how the analysis is performed, what type of machine learning model or neural network is used, what data and method is used to train the machine learning model or neural network, and what type of algorithm or calculation is used to perform the analysis step and/or the modifying step. Applicant merely discloses that the neural network or machine learning models may be applied to the method of determining blood pressure of the user. Applicant discloses what values may be input and what may be output (see paragraphs 0065-0067), but there is no disclosure as how the neural network or machine learning model makes the calculation or manipulates the data to determine the output. As such, the claims are unclear as to how the neural network or machine learning model performs the analysis step or how the neural network or machine learning model is updated based on a user calibration profile . Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Analysis of independent claims 1, 10, and 20: Step 1 of the subject matter eligibility test (see MPEP 2106.03). Claim 10 is directed to a system, which describes one of the four statutory categories of patentable subject matter, i.e., a machine. Claim 1 is directed to a computer implemented method, which describes one of the four statutory categories of patentable subject matter, i.e., a method. Claim 20 is directed to a non-transitory computer-program software product, which describes one of the four statutory categories of patentable subject matter, i.e., a machine. Therefore, further consideration is necessary regarding claims. Step 2A of the subject matter eligibility test (see MPEP 2106.04). Prong One: Claims 1, 10, and 20 recite an abstract idea. In particular, the claims generally recite the following: processing, by a processing system, the received RF reflection signals to obtain distance- binned frequency measurements (claims 1, 10, and 20); analyzing, by the processing system, the processed received RF reflection signals at a first distance range corresponding to a first distance bin to identify a first time of a pulse pressure wave at an aortic valve of the user (claims 1, 10, and 20); analyzing, by the processing system, the processed received RF reflection signals at a second distance corresponding to a second distance bin to identify a second time of the pulse pressure wave at an extremity of the user, wherein the second distance bin corresponds to a shorter distance to the radar sensor than the first distance bin (claims 1, 10, and 20); determining, by the processing system, a pulse transmit time (PTT) of the pulse pressure wave from the aortic valve of the user to the extremity of the user using the first time and the second time (claims 1, 10, and 20); and determining, by the processing system, a blood pressure of the user based on the determined PTT (claims 1, 10, and 20). These elements recited in claims 1, 10, and 20 are drawn to an abstract idea since they are directed towards mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III). “processing, by a processing system, the received RF reflection signals to obtain distance- binned frequency measurements” is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, with the aid of pen and paper or a generic computer. A person of ordinary skill in the art could reasonably take a signal and transform the signal into frequency components, for example, by using a Fourier transform. There is nothing to suggest an undue level of complexity in “processing, by a processing system, the received RF reflection signals to obtain distance- binned frequency measurements”. “analyzing, by the processing system, the processed received RF reflection signals at a first distance range corresponding to a first distance bin to identify a first time of a pulse pressure wave at an aortic valve of the user” is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, with the aid of pen and paper or a generic computer. A person of ordinary skill in the art could reasonably analyze the frequency components of a radar signal to identify a time of a pulse pressure wave. There is nothing to suggest an undue level of complexity in “analyzing, by the processing system, the processed received RF reflection signals at a first distance range corresponding to a first distance bin to identify a first time of a pulse pressure wave at an aortic valve of the user”. “analyzing, by the processing system, the processed received RF reflection signals at a second distance corresponding to a second distance bin to identify a second time of the pulse pressure wave at an extremity of the user, wherein the second distance bin corresponds to a shorter distance to the radar sensor than the first distance bin” is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, with the aid of pen and paper or a generic computer. A person of ordinary skill in the art could reasonably analyze the frequency components of a radar signal to identify a time of a pulse pressure wave. There is nothing to suggest an undue level of complexity in “analyzing, by the processing system, the processed received RF reflection signals at a second distance corresponding to a second distance bin to identify a second time of the pulse pressure wave at an extremity of the user, wherein the second distance bin corresponds to a shorter distance to the radar sensor than the first distance bin”. “determining, by the processing system, a pulse transmit time (PTT) of the pulse pressure wave from the aortic valve of the user to the extremity of the user using the first time and the second time” is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, with the aid of pen and paper or a generic computer. A person of ordinary skill in the art could reasonably calculate the difference in time between the first and second times of the pulse pressure wave. There is nothing to suggest an undue level of complexity in “determining, by the processing system, a pulse transmit time (PTT) of the pulse pressure wave from the aortic valve of the user to the extremity of the user using the first time and the second time”. “determining, by the processing system, a blood pressure of the user based on the determined PTT” is drawn to an abstract idea since it is a mental process that can be practically performed in the human mind, with the aid of pen and paper or a generic computer. A person of ordinary skill in the art could reasonably calculate a blood pressure based on the pulse transit time. There is nothing to suggest an undue level of complexity in “determining, by the processing system, a blood pressure of the user based on the determined PTT”. Prong Two : Claims 1, 10, and 20 do not recite additional elements that integrate the exception into a practical application. Therefore, the claims are "directed to" the abstract idea. The additional elements merely: Recite the words "apply it" or an equivalent with the judicial exception, or include instructions to implement the abstract idea on a computer, or merely use the computer as a tool to perform the abstract idea (e.g., “a processing system, comprising one or more processors, in communication with the radar subsystem” ( claim 10 )) and Add insignificant extra-solution activity (the pre-solution activity of: using generic data gathering components (e.g., “emitting, by a radar sensor, radio frequency (RF) signals, wherein the radar sensor is housed by a housing comprising a base plate configured to be placed on a surface at a distance away from a user” ( claim 1 ), "receiving, by the radar sensor, RF reflection signals based on the emitted RF signals being reflected" ( claim 2 ), "a housing comprising a base plate configured to be placed on a surface at a distance away from a user; a radar subsystem housed by the housing” ( claim 10 ), and “a radio frequency (RF) emitter that emits RF signals; an RF receiver that receives RF reflection signals based on the emitted RF signals being reflected” ( claim 10 ); the post-solution activity of: (e.g. “outputting, by the processing system, an indication of the determined blood pressure” ( claim 1 ), “output an indication of the determined blood pressure” ( claim 10 ), and “output an indication of the determined blood pressure” ( claim 20 ))). As a whole, the additional elements merely serve to gather information to be used by the abstract idea, while generically implementing it on a computer. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way. The processing performed remains in the abstract realm, i.e., the result is not used for a treatment. No improvement to the technology is evident. 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, 10, and 20 do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception (i.e., an inventive concept) for the same reasons as described above. E.g., all elements are directed to implementing the abstract ideas on generic processing components, the pre-solution activity of using generic data-gathering components, and generic post-solution activities, which merely facilitate the abstract idea. Per the Berkheimer requirement, the additional elements are well-understood, routine, and conventional. For example, “a radar sensor” or “a radar subsystem” as disclosed in the Applicant’s specification, “Radar subsystem 205 may include RF emitter 206, RF receiver 207, and radar processing circuit 208. RF emitter 206 can emit radio waves, such as in the form of continuous-wave (CW) radar. RF emitter 206 may use frequency-modulated continuous-wave (FMCW) radar. The FMCW radar may operate in a burst mode or continuous sparse-sampling mode. In burst mode, a frame or burst of multiple chirps, with the chirps spaced by a relatively short period of time, may be output by RF emitter 206. Each frame may be followed by a relatively long amount of time until a subsequent frame. In a continuous sparse-sampling mode, frames or bursts of chirps are not output, rather chirps are output periodically.” (Paragraph 0039). Further, "a processing system" and "a housing comprising a base plate" do not qualify as significantly more because this limitation is simply appending well understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int'/, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well understood, routine and conventional activity previously known in the industry (see Electric PowerGroup, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int'/, 110 USPQ2d 1976 (2014); SAP Am. v. lnvestPic, 890 F.3d 1016 (Fed. Circ. 2018)). In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements include a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process. Analysis of the dependent claims: Claims 2-9 and 11-19 depend from the independent claims. Dependent claims 2-9 and 11-19 merely further define the abstract idea and are, therefore, directed to an abstract idea for similar reasons: they merely Further describe the abstract idea (“wherein analyzing the RF reflection signals at the first distance range and at the second distance range is performed by one or more neural networks” ( claims 4 and 15 ), “determining a heart rate based on analyzing the RF reflection signals, wherein determining the blood pressure of the user is further based on the heart rate” ( claims 5 and 16 ), “determining a derived pulse waveform amplitude (DPWA) at the extremity of the user based on analyzing the RF reflection signals, wherein determining the blood pressure of the user is further based on the DPWA” ( claims 6 and 17 ), and “comparing, by the processing system, the external blood pressure measurement and the determined blood pressure measurement” ( claims 7 and 18 )), Further describe the pre-solution activity (“wherein the extremity of the user is one or more hands of the user” ( claims 2 and 13 ), “wherein the extremity of the user is one or more feet of the user” ( claims 3 and 14 ), “receiving, by the processing system, an external blood pressure measurement made using a blood pressure device separate from a device comprising the radar sensor” ( claims 7 and 18 ), “wherein the radar sensor and the processing system are integrated as part of a home assistant hub device that further comprises a display screen and speaker, wherein the indication of the determined blood pressure is output using the display screen, the speaker, or both” ( claim 9 ), “a housing, wherein the housing houses the radar subsystem and the processing system” ( claim 11 ) and “wherein the housing further houses: a microphone, a speaker, and an electronic display, wherein the indication of the determined blood pressure is output via the electronic display” ( claim 12 )), and Further describe the post-solution activity (“creating, by the processing system, a calibration profile for use in modifying a future determined blood pressure measurement” ( claims 7 and 18 ) and “wherein a machine learning model is modified based on the calibration profile specific to the user” ( claims 7 and 18 )). Taken alone or 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. The additional elements do not add anything significantly more than the abstract idea. The collective functions of the additional elements merely provide computer/electronic implementation and processing, and no additional elements beyond those of the abstract idea. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. 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. The result of the abstract idea does not cause the computing device and/or application to perform different. Therefore, claims 1-20 are rejected as being directed to non-statutory subject matter. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA The factual inquiries 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. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim s 1-3, 6-7, 10, 13-14, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Sakamoto (“Signal Separation Using a Mathematical Model of Physiological Signals for the Measurement of Heart Pulse Wave Propagation With Array Radar”) and Ravid (US 20170296093) as evidenced by Winter (US 20210315467) . Regarding claims 1-3 and 6-7 , Sakamoto discloses a method for measuring blood pressure, comprising: emitting, by a radar sensor, radio frequency (RF) signals, wherein the radar sensor is housed by a housing comprising a base plate configured to be placed on a surface at a distance away from a user (Page 175922, left column, third paragraph, wherein the measurement is performed using a single radar system with array antennas; See Fig. 1 below, wherein the radar is placed away from the user; While Sakamoto does not explicitly disclose a housing comprising a base plate, one or ordinary skill would recognize that radar systems are typically packaged in a housing and would have a plate to stand upright or be placed on a surface, as evidenced by Winter (see Figs. 3A-B of Winter, housing 310 with radar sensor 212 standing upright on a base) ); PNG media_image1.png 265 426 media_image1.png Greyscale receiving, by the radar sensor, RF reflection signals based on the emitted RF signals being reflected (Page 175923, left column, paragraph 1, “A radar system with an antenna array is used to measure physiological signals. In particular, we use a multiple-input multiple-output (MIMO) array comprising M1 and M2 elements for transmitting and receiving , respectively”); processing, by a processing system, the received RF reflection signals to obtain distance-binned frequency measurements (While Sakamoto does not explicitly disclose a processing system, Sakamoto discusses performing signal processing of the received signals (see page 175922, left column, last paragraph and right column, last paragraph). As such, Sakamoto inherently discloses a processing system; Page 175925, wherein the relationship between displacements at multiple body parts is used in a simplified model of pulse wave propagation. Equation 22 represent a discrete Fourier transform matrix, which is interpreted such that frequency measurements are obtained); analyzing, by the processing system, the processed received RF reflection signals at a first distance range corresponding to a first distance bin to identify a first time of a pulse pressure wave at an aortic valve of the user; analyzing, by the processing system, the processed received RF reflection signals at a second distance corresponding to a second distance bin to identify a second time of the pulse pressure wave at an extremity of the user, wherein the second distance bin corresponds to a shorter distance to the radar sensor than the first distance bin (Page 175922, right column, last paragraph, “If the skin displacement at two body parts on the pulse wave propagation path can be measured at the same time, the PTT can be measured from the time difference between the pulse wave signals”; Page 175925, right column under equation 24, wherein PTT is estimated from the echo and displacement frequency measurements representing the two body parts from Fig. 1; Per Fig. 1 above, the distance between the radar system and body part 1 is larger than the distance between the radar system and body part 2); determining, by the processing system, a pulse transmit time (PTT) of the pulse pressure wave from the aortic valve of the user to the extremity of the user using the first time and the second time (Page 175925, right column under equation 24, wherein PTT is estimated from the echo and displacement frequency measurements representing the two body parts from Fig. 1; See Fig. 2 below, representing the body displacement waveforms used to calculate the PTT based on the difference of the two peaks. The red waveform representing body part 1 and the black waveform representing body part 2); PNG media_image2.png 172 327 media_image2.png Greyscale While Sakamoto measures pulse wave velocity (PWV) and pulse transit time (PTT) using a radar system, as described above, and relates blood pressure to a pulse wave velocity (see page 175921, first paragraph), Sakamoto fails to explicitly disclose measuring blood pressure based on PTT and outputting the result of blood pressure by a radar system with a housing and base plate. Regarding the limitations of claims 2-3 and 6-7, while Sakamoto discloses measuring at the calf, Sakamoto fails to disclose wherein the extremity of the user is one or more hands/feet of the user. Sakamoto also fails to disclose comparing the measurements to an external blood pressure measurement to create a calibration profile of a user and determining blood pressure based on an amplitude of the pulse. Ravid is in the same field as Sakamoto as they both take radar measurements for pulse measurements. Ravid teaches a radar measurement system for determining blood pressure (Abstract). Ravid teaches where the PTT of the pulse is determined between two different location (Paragraph 0028; Paragraph 0021, wherein the arteries for one location may be at the wrist, i.e., the hand; Paragraph 0044, wherein the arteries for one location may be the anterior tibial or popliteal arteries, i.e., the foot) and the blood pressure can be determined based on the PTT and amplitude (Paragraph 0023, “obtaining PTT, or conversely PWV of a pulse in an artery, may lead to the determination of blood pressure levels in the artery”; Paragraph 0027, “the sensor can analyze changes in amplitude from isolated signal reflected from an artery to determine the arterial pulse wave and thenceforth calculate the subject's heart rate, blood pressure, and/or the like”). Further, Ravid teaches that an accelerometer may accompany the RF device to calibrate the radar system based on the reading of the accelerometer (Paragraph 0021). The accelerometer measurements may indicate whether the subject is active/inactive or a mobility state of the subject, which Examiner interprets to be a “calibration profile”. Based on the state, the RF measurements may be conditioned or calibrated (Paragraph 0042). Ravid discusses this is useful to condition measurements for low acceleration or may not take measurements at all if they may not be accurate or reliable. Lastly, the results are output via a display (Paragraph 0032). As Sakamoto is concerned with measuring PTT and PWV based on radar measurements at two different body location and suggests that these measurements are indicative of blood pressure, Ravid introduces a method of measuring blood pressure based on PTT and PWV from two different body locations and outputting the result. A method of enhancing a particular class of devices, such as measuring blood pressure based on dual-site radar measurements, is made part of the ordinary capabilities of one skilled in the art based upon the teaching of Ravid. One of ordinary skill in the art would be motivated in applying this known blood pressure measurement method to the method of Sakamoto, and the results of determining a blood pressure from dual-site radar measurements would have been predictable to one of ordinary skill in the art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Sakamoto to incorporate the teaching of using PTT and PWV to measure blood pressure as taught by Ravid, and the results of outputting a blood pressure measurement would have been predictable to one of ordinary skill in the art. Regarding claims 10, 13-14, and 17-18 , Sakamoto discloses a blood pressure measurement system, comprising: a housing comprising a base plate configured to be placed on a surface at a distance away from the user; a radar subsystem housed by the housing (See Fig. 1 below, wherein the radar is placed away from the user; While Sakamoto does not explicitly disclose a housing comprising a base plate, one or ordinary skill would recognize that radar systems are typically packaged in a housing and would have a plate to stand upright or be placed on a surface, as evidenced by Winter (see Figs. 3A-B of Winter, housing 310 with radar sensor 212 standing upright on a base) ), comprising: a radio frequency emitter that emits RF signals; an RF receiver that receives RF reflection signals based on the emitted RF signals being reflected (Page 175922, left column, third paragraph, wherein the measurement is performed using a single radar system with array antennas; Page 175923, left column, paragraph 1, “A radar system with an antenna array is used to measure physiological signals. In particular, we use a multiple-input multiple-output (MIMO) array comprising M1 and M2 elements for transmitting and receiving , respectively”); a processing system, comprising one or more processors, in communication with the radar subsystem (While Sakamoto does not explicitly disclose a processing system, Sakamoto discusses performing signal processing of the received signals (see page 175922, left column, last paragraph and right column, last paragraph). As such, Sakamoto inherently discloses a processing system), wherein the processing system is configured to: process the received RF reflection signals to obtain distance-binned frequency (Page 175925, wherein the relationship between displacements at multiple body parts is used in a simplified model of pulse wave propagation. Equation 22 represent a discrete Fourier transform matrix, which is interpreted such that frequency measurements are obtained); analyze the processed received RF reflection signals at a first distance range corresponding to a first distance bin to identify a first time of a pulse pressure wave at an aortic valve of the user; analyze the processed received RF reflection signals at a second distance corresponding to a second distance bin to identify a second time of the pulse pressure wave at an extremity of the user, wherein the second distance bin corresponds to a shorter distance to the radar sensor than the first distance bin (Page 175922, right column, last paragraph, “If the skin displacement at two body parts on the pulse wave propagation path can be measured at the same time, the PTT can be measured from the time difference between the pulse wave signals”; Page 175925, right column under equation 24, wherein PTT is estimated from the echo and displacement frequency measurements representing the two body parts from Fig. 1; Per Fig. 1 above, the distance between the radar system and body part 1 is larger than the distance between the radar system and body part 2); determining a pulse transmit time (PTT) of the pulse pressure wave from the aortic valve of the user to the extremity of the user using the first time and the second time (Page 175925, right column under equation 24, wherein PTT is estimated from the echo and displacement frequency measurements representing the two body parts from Fig. 1; See Fig. 2 below, representing the body displacement waveforms used to calculate the PTT based on the difference of the two peaks. The red waveform representing body part 1 and the black waveform representing body part 2); PNG media_image2.png 172 327 media_image2.png Greyscale While Sakamoto measures pulse wave velocity (PWV) and pulse transit time (PTT), as described above, and relates blood pressure to a pulse wave velocity (see page 175921, first paragraph), Sakamoto fails to explicitly disclose measuring blood pressure based on PTT and outputting the result of blood pressure. Regarding the limitations of claims 13-14 and 17-18, while Sakamoto discloses measuring at the calf, Sakamoto fails to disclose wherein the extremity of the user is one or more hands/feet of the user. Sakamoto also fails to disclose comparing the measurements to an external blood pressure measurement to create a calibration profile of a user and determining blood pressure based on an amplitude of the pulse. Ravid is in the same field as Sakamoto as they both take radar measurements for pulse measurements. Ravid teaches a radar measurement system for determining blood pressure (Abstract). Ravid teaches where the PTT of the pulse is determined between two different location (Paragraph 0028; Paragraph 0021, wherein the arteries for one location may be at the wrist, i.e., the hand; Paragraph 0044, wherein the arteries for one location may be the anterior tibial or popliteal arteries, i.e., the foot) and the blood pressure can be determined based on the PTT and amplitude (Paragraph 0023, “obtaining PTT, or conversely PWV of a pulse in an artery, may lead to the determination of blood pressure levels in the artery”; Paragraph 0027, “the sensor can analyze changes in amplitude from isolated signal reflected from an artery to determine the arterial pulse wave and thenceforth calculate the subject's heart rate, blood pressure, and/or the like”). Further, Ravid teaches that an accelerometer may accompany the RF device to calibrate the radar system based on the reading of the accelerometer (Paragraph 0021). The accelerometer measurements may indicate whether the subject is active/inactive or a mobility state of the subject, which Examiner interprets to be a “calibration profile”. Based on the state, the RF measurements may be conditioned or calibrated (Paragraph 0042). Ravid discusses this is useful to condition measurements for low acceleration or may not take measurements at all if they may not be accurate or reliable. Lastly, the results are output via a display (Paragraph 0032). As Sakamoto is concerned with measuring PTT and PWV based on radar measurements at two different body location and suggests that these measurements are indicative of blood pressure, Ravid introduces a method of measuring blood pressure based on PTT and PWV from two different body locations and outputting the result. A method of enhancing a particular class of devices, such as measuring blood pressure based on dual-site radar measurements, is made part of the ordinary capabilities of one skilled in the art based upon the teaching of Ravid. One of ordinary skill in the art would be motivated in applying this known blood pressure measurement method to the method of Sakamoto, and the results of determining a blood pressure from dual-site radar measurements would have been predictable to one of ordinary skill in the art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Sakamoto to incorporate the teaching of using PTT and PWV to measure blood pressure as taught by Ravid, and the results of outputting a blood pressure measurement would have been predictable to one of ordinary skill in the art. Regarding claim 20 , Sakamoto discloses a non-transitory processor-readable medium, comprising processor-readable instructions configured to cause one or more processors to: process radio frequency (RF) reflection signals to obtain distance-binned frequency measurements (While Sakamoto does not explicitly disclose a processing system, Sakamoto discusses performing signal processing of the received signals (see page 175922, left column, last paragraph and right column, last paragraph). As such, Sakamoto inherently discloses a processing system; Page 175925, wherein the relationship between displacements at multiple body parts is used in a simplified model of pulse wave propagation. Equation 22 represent a discrete Fourier transform matrix, which is interpreted such that frequency measurements are obtained), wherein the RF reflection signals are received from a radar sensor configured to be placed on a surface at a distance away from a user (See Fig. 1 below, wherein the radar is placed away from the user); analyze the processed RF reflection signals at a first distance range corresponding to a first distance bin to identify a first time of a pulse pressure wave at an aortic valve of the user; analyze the processed RF reflection signals at a second distance corresponding to a second distance bin to identify a second time of the pulse pressure wave at an extremity of the user, wherein the second distance bin corresponds to a shorter distance to the radar sensor than the first distance bin (Page 175922, right column, last paragraph, “If the skin displacement at two body parts on the pulse wave propagation path can be measured at the same time, the PTT can be measured from the time difference between the pulse wave signals”; Page 175925, right column under equation 24, wherein PTT is estimated from the echo and displacement frequency measurements representing the two body parts from Fig. 1; Per Fig. 1 above, the distance between the radar system and body part 1 is larger than the distance between the radar system and body part 2); determine a pulse transmit time (PTT) of the pulse pressure wave from the aortic valve of the user to the extremity of the user using the first time and the second time (Page 175925, right column under equation 24, wherein PTT is estimated from the echo and displacement frequency measurements representing the two body parts from Fig. 1; See Fig. 2 below, representing the body displacement waveforms used to calculate the PTT based on the difference of the two peaks. The red waveform representing body part 1 and the black waveform representing body part 2); PNG media_image2.png 172 327 media_image2.png Greyscale While Sakamoto measures pulse wave velocity (PWV) and pulse transit time (PTT), as described above, and relates blood pressure to a pulse wave velocity (see page 175921, first paragraph), Sakamoto fails to explicitly disclose measuring blood pressure based on PTT and outputting the result of blood pressure. Regarding the limitations of claims 2-3, while Sakamoto discloses measuring at the calf, Sakamoto fails to disclose wherein the extremity of the user is one or more hands/feet of the user. Ravid is in the same field as Sakamoto as they both take radar measurements for pulse measurements. Ravid teaches a radar measurement system for determining blood pressure (Abstract). Ravid teaches where the PTT of the pulse is determined between two different location (Paragraph 0028; Paragraph 0021, wherein the arteries for one location may be at the wrist, i.e., the hand; Paragraph 0044, wherein the arteries for one location may be the anterior tibial or popliteal arteries, i.e., the foot) and the blood pressure can be determined based on the PTT (Paragraph 0023, “obtaining PTT, or conversely PWV of a pulse in an artery, may lead to the determination of blood pressure levels in the artery”). Additionally, The results are output via a display (Paragraph 0032). As Sakamoto is concerned with measuring PTT and PWV based on radar measurements at two different body location and suggests that these measurements are indicative of blood pressure, Ravid introduces a method of measuring blood pressure based on PTT and PWV from two different body locations and outputting the result. A method of enhancing a particular class of devices, such as measuring blood pressure based on dual-site radar measurements, is made part of the ordinary capabilities of one skilled in the art based upon the teaching of Ravid. One of ordinary skill in the art would be motivated in applying this known blood pressure measurement method to the method of Sakamoto, and the results of determining a blood pressure from dual-site radar measurements would have been predictable to one of ordinary skill in the art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Sakamoto to incorporate the teaching of using PTT and PWV to measure blood pressure as taught by Ravid, and the results of outputting a blood pressure measurement would have been predictable to one of ordinary skill in the art . 07-22-aia AIA Claim s 4-5, 8, 15-16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Sakamoto (“Signal Separation Using a Mathematical Model of Physiological Signals for the Measurement of Heart Pulse Wave Propagation With Array Radar”) and Ravid (US 20170296093) as applied to claim s 1 and 10 above, and further in view of Leabman (US 20230355112) . Regarding claims 4-5 and 15-16, while Sakamoto as modified discloses using a mathematical model (see Section IV of Sakamoto), Sakamoto as modified fails to disclose performing the analysis of measuring blood pressure by one or more neural networks. Regarding the limitations of claims 5 and 16, while Sakamoto as modified by Ravid disclose measuring a heart rate (Ravid: Paragraphs 0027-0028), Sakamoto as modified fails to disclose using the heart rate to measure blood pressure. Leabman is in the same field of Sakamoto and Ravid as they take radar measurements for pulse measurements. Leabman teaches a blood pressure monitoring system using reflected electromagnetic energy to capture pulse wave data (Abstract). A machine learning technique is used to generate a value of blood pressure based on pulse wave signals measured from reflected radio waves (Paragraph 0308). Leabman discusses using these technique as they are useful to accurately and reliably determine these values (Paragraph 0192). The model may further have other inputs to determine a blood pressure, such as heart rate (Paragraph 0341). As Sakamoto as modified by Ravid disclose measuring blood pressure based on reflected radar measurements, Leabman teaches a method of implementing the method via machine learning techniques. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Sakamoto and Ravid to incorporate the machine learning technique of Leabman, the benefit being accurately and reliably determining a blood pressure measurement. Regarding claims 8 and 19, Sakamoto as modified by Ravid discloses calibrating the blood pressure measurements for each patient based on the external blood pressure measurement (Ravid: Paragraphs 0040 and 0042, wherein the calibration profile is the calibrated measurement based on the mobility state of the user). Sakamoto as modified fails to explicitly disclose modified a machine learning model based on the calibration. Leabman teaches a blood pressure monitoring system using reflected electromagnetic energy to capture pulse wave data (Abstract). The machine learning engine can be trained based on derived statistics from the raw data, such as the standard deviation of the amplitude and/or phase of the received RF energy. Additionally, it can be trained based on an acceptable correlation between raw data, derived data, and control data (Paragraph 0196). Examiner interprets this training based on the control data as modifying the machine learning model. As the combination of Sakamoto and Ravid disclose measuring blood pressure and calibrating blood pressure measurements based on external blood pressure measurements, Leabman teaches a machine learning model training technique that trains the model based on manipulating the data and comparing measured data to control data. One of ordinary skill would be motivated in training a machine learning model to automatically determine blood pressure for future measurements. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Sakamoto and Ravid to incorporate training the machine learning model as taught by Leabman in order to have a trained model for future measurements . 07-22-aia AIA Claim s 9 and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Sakamoto (“Signal Separation Using a Mathematical Model of Physiological Signals for the Measurement of Heart Pulse Wave Propagation With Array Radar”) and Ravid (US 20170296093) as applied to claim s 1 and 10 above, and further in view of Ravid (US 2016034584), hereinafter “Ravid 2016” . Regarding claims 9, 11, and 12 , Sakamoto as modified by Ravid teaches displaying the output of the blood pressure measurement on a display, as described above. Sakamoto as modified by Ravid fail to teach wherein the radar sensor and the processing system are integrated as part of a home assistant hub device with a display screen / electronic display, speaker, and a microphone. Ravid 2016 is in the same field of Sakamoto and Ravid as they take radar measurements for pulse measurements. Ravid 2016 teaches a blood pressure measurement apparatus utilizing radar sensors (Abstract). The apparatus includes one or more RF sensors 401a-n, a processor 404, an output 505, such as a printer, database, display, audio, and the like, and an input 405 all within the apparatus (see Fig. 5 and paragraph 0032; Examiner interprets the display to read on the “display screen” and “electronic display”, and the audio to read on the “speaker”). Ravid 2016 further discloses that, in some embodiments, the apparatus may be, for example, a smartphone, which Examiner interprets to include a microphone, a speaker, and a display. As Sakamoto modified by Ravid discloses a radar system for taking pulse measurements to determine blood pressure, Ravid 2016 introduces an analogous radar system wherein the sensors are located in the same structure as the processor and its peripherals. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the device of Sakamoto as modified by Ravid to incorporate the structure of the apparatus as taught by Ravid 2016, and the result of having all components in the same structure would have been predictable to one of ordinary skill in the art. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to NOAH MICHAEL HEALY whose telephone number is (703)756-5534. The examiner can normally be reached Monday - Friday 8:30am - 5:30pm ET. 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, Jason Sims can be reached at (571)272-7540. 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. /NOAH M HEALY/Examiner, Art Unit 3791 /JASON M SIMS/Supervisory Patent Examiner, Art Unit 3791 Application/Control Number: 18/855,970 Page 2 Art Unit: 3791 Application/Control Number: 18/855,970 Page 3 Art Unit: 3791 Application/Control Number: 18/855,970 Page 4 Art Unit: 3791 Application/Control Number: 18/855,970 Page 5 Art Unit: 3791 Application/Control Number: 18/855,970 Page 6 Art Unit: 3791 Application/Control Number: 18/855,970 Page 7 Art Unit: 3791 Application/Control Number: 18/855,970 Page 8 Art Unit: 3791 Application/Control Number: 18/855,970 Page 9 Art Unit: 3791 Application/Control Number: 18/855,970 Page 10 Art Unit: 3791 Application/Control Number: 18/855,970 Page 11 Art Unit: 3791 Application/Control Number: 18/855,970 Page 12 Art Unit: 3791 Application/Control Number: 18/855,970 Page 13 Art Unit: 3791 Application/Control Number: 18/855,970 Page 14 Art Unit: 3791 Application/Control Number: 18/855,970 Page 15 Art Unit: 3791 Application/Control Number: 18/855,970 Page 16 Art Unit: 3791 Application/Control Number: 18/855,970 Page 17 Art Unit: 3791 Application/Control Number: 18/855,970 Page 18 Art Unit: 3791 Application/Control Number: 18/855,970 Page 19 Art Unit: 3791 Application/Control Number: 18/855,970 Page 20 Art Unit: 3791 Application/Control Number: 18/855,970 Page 21 Art Unit: 3791 Application/Control Number: 18/855,970 Page 22 Art Unit: 3791 Application/Control Number: 18/855,970 Page 23 Art Unit: 3791 Application/Control Number: 18/855,970 Page 24 Art Unit: 3791 Application/Control Number: 18/855,970 Page 25 Art Unit: 3791 Application/Control Number: 18/855,970 Page 26 Art Unit: 3791 Application/Control Number: 18/855,970 Page 27 Art Unit: 3791
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