DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This action is in response to the amendments and remarks filed on 01/27/2026. The amendments filed on 01/27/2026 have been entered. Accordingly Claims 1-7 and 10-11 are pending. Claims 8-9 have been canceled. The previous rejections of claims 1-7 and 10-11 have been withdrawn in light of Applicant’s amendments and remarks in the claim set filed 01/27/2026.
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.
Claims 1-6 and 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Yoshioka et. al. (20160100766, April 14, 2016)(hereinafter, “Yoshioka”) in view of Leabman (U.S. 20220287649, September 15, 2022)(hereinafter, “Leabman”).
Regarding Claim 1,Yoshioka teaches: An apparatus, for determining blood pressure of a subject (Fig.1, non-contact blood-pressure measuring device, [0121]), comprising:
at least one processor (“…the non-contact blood-pressure measuring device 110 includes an image acquiring section 101, a pulse-wave timing calculating section 102, a millimeter-wave acquiring section 103, a heartbeat timing calculating section 104, a blood-pressure determining section 105, and a presenting section 106.” [0122]);
at least one memory comprising computer program code (“…a computer system including a microprocessor and a memory, the memory storing the computer program, and the microprocessor operating in accordance with the computer program.” [0334]);
a radio frequency (RF) wave device configured to detect pulse waveform signals from the subject (“… the millimeter-wave acquiring section 103 is constituted by transmitting and receiving circuits of a radar utilizing a millimeter wave band.” [0143]); and
with regards to limitation: wherein the at least one memory and the computer program code are configured to, with the at least one processor, using a blood pressure specific transfer function (BTF) derivation model, derive a measured BTF from the pulse waveform signals, wherein the measured BTF is causally related to the blood pressure and determine an estimation of the blood pressure corresponding to the pulse waveform signals based on the measured BTF, Yoshioka further teaches: “…the non-contact blood-pressure measuring device 110 determines blood pressure on the basis of a pulse wave propagation period, which is a time difference between the heartbeat and the pulsebeat, and the presenting section 106 presents the determined blood pressure.” [0126]; “ The model accumulating section 107 accumulates therein models concerning a pulse wave propagation period used to determine blood pressure.” [0195].
Yoshioka does not teach the model being a blood pressure specific transfer function (BTF) derivation model.
Leabman in the field of health monitoring systems teaches: “The feature extractor 4644 is configured to extract features from the filtered signal, or from a mathematical model of the filtered signal.” [0281]; “…the RF-based sensor system implements coherent combining that is tuned based on the periodic, or quasi-periodic, nature of a pulse pressure waveform (e.g., an arterial pulse pressure waveform measured at the radial artery at the wrist), the pulse wave signal is very responsive to conditions of the blood that is circulating through the body…” [0282]; “…the blood pressure monitoring module 4630 and the blood glucose monitoring module 4640 may operate simultaneously…to produce blood pressure and blood glucose values.” [0284]; “FIG. 46, the blood pressure ML engine 4636 may be used in an inference process to generate estimates of blood pressure in response to a pulse wave signal that is generated by the RF-based sensor system. In order to use the blood pressure ML engine in an inference process to generate estimates of blood pressure, a trained model is generated.” [0287]; “FIG. 46, machine learning techniques may be used to generate a value that is indicative of a health parameter such as blood pressure… The health monitoring system includes an RF front-end 5048, a pulse wave signal processor 5078, a feature extractor 5084, and a health parameter determination engine 5080. In an embodiment, the RF front-end, the pulse wave signal processor, and the feature extractor are configured to function as described above to generate electrical signals in response to reflected radio waves, to generate a pulse wave signal in response to the electrical signals, and to extract features from the pulse wave signal…” [0301].
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the model for blood pressure in Yoshioka to a blood pressure specific transfer function (BTF) derivation model as taught in Leabman to monitor health parameters (Leabman, [0003]).
Regarding Claim 2, the combination of Yoshioka and Leabman teach the claim limitations as noted above.
Yoshioka further teaches: wherein the RF wave device comprises a sensor and a transmitter, wherein the transmitter is configured to emit RF beams, and the sensor is configured to receive reflected RF beams from which the pulse waveform signals can be detected (“…a non-contact blood-pressure measuring device that measures blood pressure in a non-contact manner by using a change of image information such as luminance of a skin portion such as a face or a hand and a change of signal information of a chest obtained by using a millimeter wave sensor.” [0073]; “… the millimeter-wave acquiring section 103 receives a millimeter wave, the millimeter-wave acquiring section 103 is constituted by transmitting and receiving circuits of a radar utilizing a millimeter wave band. Specifically, the millimeter-wave acquiring section 103 has the transmitting section 113A (transmitting circuit) that transmits a millimeter wave and the receiving section 113B (receiving circuit) that receives a millimeter wave. The millimeter-wave acquiring section 103 detects a distance to a target on the basis of a time difference between a time of transmission of a transmission wave and a time of reception of a reflected wave, which is the transmission wave reflected by the target. Furthermore, the millimeter-wave acquiring section 103 detects movement or speed of the target on the basis of a phase or frequency difference between the transmission wave and the reflected wave. Furthermore, the millimeter-wave acquiring section 103 detects a distance to an object on the basis of a difference in arrival direction obtained by using an array antenna.” [0143]).
Regarding Claim 3, the combination of Yoshioka and Leabman teach the claim limitations as noted above.
Yoshioka further teaches: wherein the RF wave device further comprises at least one antenna array configured to steer the RF beams (“… the millimeter-wave acquiring section 103 receives a millimeter wave, the millimeter-wave acquiring section 103 is constituted by transmitting and receiving circuits of a radar utilizing a millimeter wave band. Specifically, the millimeter-wave acquiring section 103 has the transmitting section 113A (transmitting circuit) that transmits a millimeter wave and the receiving section 113B (receiving circuit) that receives a millimeter wave. The millimeter-wave acquiring section 103 detects a distance to a target on the basis of a time difference between a time of transmission of a transmission wave and a time of reception of a reflected wave, which is the transmission wave reflected by the target. Furthermore, the millimeter-wave acquiring section 103 detects movement or speed of the target on the basis of a phase or frequency difference between the transmission wave and the reflected wave. Furthermore, the millimeter-wave acquiring section 103 detects a distance to an object on the basis of a difference in arrival direction obtained by using an array antenna.” [0143]).
Regarding Claim 4, the combination of Yoshioka and Leabman teach the claim limitations as noted above.
Yoshioka further teaches: wherein the RF wave device is further configured to scan an arm of the subject (“… the non-contact blood-pressure measuring device calculates a pulse-wave timing on the basis of a skin image of a portion, such as a hand, anterior to an arm of the user at which cuff-type blood pressure measurement is performed. The parameter included in the relational expression can be more accurately determined by using blood pressure obtained by the cuff-type blood pressure measurement from the arm of the user at which cuff-type blood pressure measurement is performed and blood pressure obtained on the basis of a time difference between the pulse-wave timing and the heartbeat timing.” [0090]).
Regarding Claim 5, the combination of Yoshioka and Leabman teach the claim limitations as noted above.
Yoshioka further teaches: wherein a field of view of the RF wave device is equal or more than a length of the arm of the subject, or the distance from the RF wave device to the arm is the same during scanning (“…a heartbeat timing calculator that calculates a temporal change of a distance between the user and the reception antenna by using the signal of the radio wave acquired by the radio wave acquirer and calculates, as a heartbeat timing, time information indicative of a time at which the distance reaches a peak; and a blood-pressure determiner that determines blood pressure of the user on the basis of a time difference between the pulse-wave timing and the heartbeat timing.” [0074]); “… the non-contact blood-pressure measuring device calculates a pulse-wave timing on the basis of a skin image of a portion, such as a hand, anterior to an arm of the user at which cuff-type blood pressure measurement is performed. The parameter included in the relational expression can be more accurately determined by using blood pressure obtained by the cuff-type blood pressure measurement from the arm of the user at which cuff-type blood pressure measurement is performed and blood pressure obtained on the basis of a time difference between the pulse-wave timing and the heartbeat timing.” [0090]. See Figs. 2B, 20 and 23).
Regarding Claim 6, the combination of Yoshioka and Leabman teach the claim limitations as noted above.
Yoshioka further teaches: wherein the RF wave device is configured to: scan for pulse waveform signals at one or more measurement sites along the arm of the subject, or scan in successive directions (“…a heartbeat timing calculator that calculates a temporal change of a distance between the user and the reception antenna by using the signal of the radio wave acquired by the radio wave acquirer and calculates, as a heartbeat timing, time information indicative of a time at which the distance reaches a peak; and a blood-pressure determiner that determines blood pressure of the user on the basis of a time difference between the pulse-wave timing and the heartbeat timing.” [0074]); “… the non-contact blood-pressure measuring device calculates a pulse-wave timing on the basis of a skin image of a portion, such as a hand, anterior to an arm of the user at which cuff-type blood pressure measurement is performed. The parameter included in the relational expression can be more accurately determined by using blood pressure obtained by the cuff-type blood pressure measurement from the arm of the user at which cuff-type blood pressure measurement is performed and blood pressure obtained on the basis of a time difference between the pulse-wave timing and the heartbeat timing.” [0090]. See Figs. 2B, 20 and 23).
Regarding Claim 10, the combination of Yoshioka and Leabman teach the claim limitations as noted above.
Yoshioka does not teach: wherein the at least one memory and the computer program code are configured to, with the at least one processor, train the BTF derivation model using a blood pressure training set, wherein the blood pressure training set comprises pulse waveform signals and a corresponding true blood pressure.
Leabman in the field of health monitoring systems teaches: “The feature extractor 4644 is configured to extract features from the filtered signal, or from a mathematical model of the filtered signal.” [0281]; “…the RF-based sensor system implements coherent combining that is tuned based on the periodic, or quasi-periodic, nature of a pulse pressure waveform (e.g., an arterial pulse pressure waveform measured at the radial artery at the wrist), the pulse wave signal is very responsive to conditions of the blood that is circulating through the body…” [0282]; “…the blood pressure monitoring module 4630 and the blood glucose monitoring module 4640 may operate simultaneously…to produce blood pressure and blood glucose values.” [0284]; “FIG. 46, the blood pressure ML engine 4636 may be used in an inference process to generate estimates of blood pressure in response to a pulse wave signal that is generated by the RF-based sensor system. In order to use the blood pressure ML engine in an inference process to generate estimates of blood pressure, a trained model is generated.” [0287]; “FIG. 46, machine learning techniques may be used to generate a value that is indicative of a health parameter such as blood pressure… The health monitoring system includes an RF front-end 5048, a pulse wave signal processor 5078, a feature extractor 5084, and a health parameter determination engine 5080. In an embodiment, the RF front-end, the pulse wave signal processor, and the feature extractor are configured to function as described above to generate electrical signals in response to reflected radio waves, to generate a pulse wave signal in response to the electrical signals, and to extract features from the pulse wave signal…” [0301].
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the at least one memory and the computer program code in the combination of references to train the BTF derivation model using a blood pressure training set, wherein the blood pressure training set comprises pulse waveform signals and a corresponding true blood pressure.as taught in Leabman to monitor health parameters (Leabman, [0003]).
Regarding Claim 11, the combination of Yoshioka and Leabman teach the claim limitations as noted above.
With regards to limitations: wherein the at least one memory and the computer program code are configured to, with the at least one processor: generate a generated BTF from the estimated blood pressure and other BTF model parameters using a BTF generator and compare the generated BTF with a measured BTF of the corresponding pulse waveform signals detected by the RF wave device, wherein the measured BTF is derived using the BTF derivation model, or compare the true blood pressure with the estimated blood pressure of the corresponding pulse waveform signals, wherein the estimated blood pressure is inferred using the BTF derivation model based on the measured BTF.
Yoshioka further teaches: “…the blood-pressure determiner that determines the blood pressure on the basis of a time difference between the pulse-wave timing calculated on the basis of the skin image acquired by the image acquirer during a period in which the amount of movement of the skin portion or the chest portion measured by the posture measurer is equal to or lower than a predetermined threshold value and the heartbeat timing calculated on the basis of the radio wave acquired by the radio wave acquirer during the period.” [0079]; “…the non-contact blood-pressure measuring device 110 determines blood pressure on the basis of a pulse wave propagation period, which is a time difference between the heartbeat and the pulsebeat, and the presenting section 106 presents the determined blood pressure.” [0126]; “ The model accumulating section 107 accumulates therein models concerning a pulse wave propagation period used to determine blood pressure.” [0195]; “Since there are differences among individuals in terms of determination of blood pressure based on a pulse wave propagation period, it is possible to more accurately determine blood pressure by adjusting parameters or models on the basis of usual blood pressure. Furthermore, since a pulse wave propagation period can be measured in a time-series manner, not only momentary blood pressure, but also a time-serial fluctuation of blood pressure can be easily measured. This makes it possible to easily measure continuously changing blood pressure.” [0199].
Yoshioka does not teach the model being a blood pressure specific transfer function (BTF) derivation model.
Leabman in the field of health monitoring systems teaches: “the blood pressure monitoring module 4630 includes a bandpass filter 4632, a feature extractor 4634, and a blood pressure machine learning (ML) engine 4636…the feature extractor is configured to extract features from the filtered pulse wave signal, or from a mathematical model of the pulse wave signal…the bandpass filter is implemented to pass components of the pulse wave signal that include the frequency of the pulse wave signal, e.g., 1 cycle per second (Hz) while blocking components of the pulse wave signal that are outside of the pass band. Features extracted from the pulse wave signal may include timing based features, magnitude based features, and/or area based features. In an embodiment, the blood pressure monitoring module does not include a bandpass filter and the pulse wave signal is fed directly to the feature extractor.” [0272] ;“The feature extractor 4644 is configured to extract features from the filtered signal, or from a mathematical model of the filtered signal.” [0281]; “…the RF-based sensor system implements coherent combining that is tuned based on the periodic, or quasi-periodic, nature of a pulse pressure waveform (e.g., an arterial pulse pressure waveform measured at the radial artery at the wrist), the pulse wave signal is very responsive to conditions of the blood that is circulating through the body…” [0282]; “…the blood pressure monitoring module 4630 and the blood glucose monitoring module 4640 may operate simultaneously…to produce blood pressure and blood glucose values.” [0284]; “FIG. 46, the blood pressure ML engine 4636 may be used in an inference process to generate estimates of blood pressure in response to a pulse wave signal that is generated by the RF-based sensor system. In order to use the blood pressure ML engine in an inference process to generate estimates of blood pressure, a trained model is generated.” [0287]; “FIG. 46, machine learning techniques may be used to generate a value that is indicative of a health parameter such as blood pressure… The health monitoring system includes an RF front-end 5048, a pulse wave signal processor 5078, a feature extractor 5084, and a health parameter determination engine 5080. In an embodiment, the RF front-end, the pulse wave signal processor, and the feature extractor are configured to function as described above to generate electrical signals in response to reflected radio waves, to generate a pulse wave signal in response to the electrical signals, and to extract features from the pulse wave signal…” [0301].
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the model for blood pressure in Yoshioka to a blood pressure specific transfer function (BTF) derivation model as taught in Leabman to monitor health parameters (Leabman, [0003]).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Yoshioka and Leabman as applied to claim 1 above, and further in view of Rincon (U.S. 9523768, December 20, 2016)(hereinafter, “Rincon”).
Regarding Claim 7, the combination of Yoshioka and Leabman teach the claim limitations as noted above.
The combination of references does not teach: wherein the pulse waveform signals are separated by null-steering.
Rincon in the field of pulse waveform radar systems teaches: “The images collected with each of the antenna beams provide the interferograms that carry three dimensional information of what was scanned by the radar…an increase in the measurement swath without reducing received antenna gain and the suppression of ambiguities or localized interference in the receiver signal by appropriate null-steering of the antenna pattern.” (column 6, lines 18-35).
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the pulse waveform signals in the combination of references to be separated by null-steering as taught in Rincon “…to minimize noise or interference of a particular received or transmitted signal or in a particular channel of the radar system…” (Rincon, column 9, lines 30-32).
Response to Arguments
Applicant’s arguments regarding amended claim 1 limitations are moot in view of the new grounds of rejections that rely on new art Leabman and prior art Gao not being relied on for any of the rejections in this office action.
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.
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/AMAL ALY FARAG/Primary Examiner, Art Unit 3798
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