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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process of estimating a heart and heart rate reliability) without significantly more.
Step 1
The claimed invention in claims 1-20 is directed to statutory subject matter as the claims recite systems and methods for estimating a heart and heart rate reliability.
Step 2A, Prong One
Regarding claims 1-20, the recited steps are directed to mental processes of performing concepts in a human mind or by a human using a pen and paper (See MPEP 2106.05(a)(2) subsection (III)).
Regarding claims 1 and 12-14, the limitations of “a first signal quality estimator that estimates a first signal quality…”, “a second signal quality estimator that estimates a second signal quality…”, “a heart rate estimator that estimates a heart rate…and estimates a reliability of the estimated heart rate…”, and “a trained estimation model that estimates a person's emotional state…” are a process, as drafted, that can be performed by a human mind (including an observation, evaluation, and judgment) under the broadest reasonable interpretation but for the recitation of generic computer components.
Step 2A, Prong Two
For claims 1-20, the judicial exception is not integrated into a practical application. For claims 1 and 12-14, the additional limitation of “a first signal quality estimator”, “a second signal quality estimator”, “a heart rate estimator”, “a processor”, “a program”, “a body motion analyzer”, and “a correction processor” are recited at a high level of generality and amount to nothing more than parts of a generic computer. Merely including instructions to implement an abstract idea on a computer does not integrate a judicial exception into a practical application.
Step 2B
The recitation of the above-identified additional limitations of “a first signal quality estimator”, “a second signal quality estimator”, “a heart rate estimator”, “a processor”, “a program”, “a body motion analyzer”, and “a correction processor” in claims 1 and 12-14 amount to mere instructions to implement the abstract idea on a computer. Simply using a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
Dependent claims 2-11 and 15-20 are further directed to the abstract idea. The above-mentioned claims do not introduce any additional elements which amount to significantly more under the Step 2A prong 2 and Step 2B analyses.
Regarding claims 6 and 15, the additional limitation of “a sensor” used to acquire body motion information is considered to be well-known, routine, and conventional in the art. For examples, see Ishikawa et al (US 2020/0305798) [0072] and Kusakabe et al (US 2016/0360972) [0040].
Regarding claim 11, the additional limitation of “a wearable housing” used to house a processing system is considered to be well-known, routine, and conventional in the art. For examples, see Ishikawa et al (US 2020/0305798) [0065] and Kusakabe et al (US 2016/0360972) [0033].
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-2, 4-6, 9-13, 15 and 17-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ishikawa et al (US 2020/0305798) hereinafter Ishikawa.
Regarding claim 1, Ishikawa discloses a signal processing device, comprising:
a first signal quality estimator (arm motion noise reduction processing unit 30) that estimates a first signal quality of an input biometric signal ([0087] outputs a first error signal which corresponds to the pulse wave signal from which a body motion noise (arm motion noise) is separated);
a second signal quality estimator (heart rate trend detection unit 70) that estimates a second signal quality (reliability) of an output signal from a noise reduction processor ([0107] based on the pulse wave signal output from the second noise reduction processing unit 40, the heart rate trend is detected by the heart rate trend detection unit 70; [0128] reliability is calculated based on the heart rate trend ); and
a heart rate estimator ([0107] heart rate variation detection unit 60; Fig. 2) that estimates a heart rate based on the output signal from the noise reduction processor and estimates a reliability of the estimated heart rate based on a result of the estimated first signal quality and a result of the estimated second signal quality ([0120] instantaneous heart rate/reliability calculation unit 63 calculates an instantaneous heart rate and a reliability; Fig. 2 shows the heart rate trend detection unit 70 and the finger and wrist noise reduction processing unit 40 feeding their respective outputs into the heart rate variation detection unit 60 containing the instantaneous heart rate/reliability calculation unit 63).
Regarding claim 2, Ishikawa discloses wherein the heart rate estimator ([0107] heart rate variation detection unit 60) detects a peak position of the output signal from the noise reduction processor and estimates the reliability of the estimated heart rate at the detected peak position ([0122] reliability is calculated on a basis of a level difference between a local maximum value and a local minimum value; Fig 10 shows the pulse wave signal ([0103] second error signal determined using the first error signal output by noise reduction processing unit 30) being used as input to the peak detection unit 62).
Regarding claim 4, Ishikawa discloses a periodical analyzer (periodical analyzer 64) that periodically performs an analysis of the output signal from the noise reduction processor ([0108] heart rate variation detection unit 60 contains resampling unit 64; Fig. 2 shows output from the second noise reduction processing unit 40 feeding into the heart rate variation detection unit 60),
wherein the heart rate estimator estimates the reliability of the estimated heart rate based on the result of estimating the first signal quality (Fig. 2 shows a signal reliability from the first noise reduction processing unit 30 being input into the second noise reduction processing unit 40), the result of estimating the second signal quality (Fig. 2 shows a signal reliability from the second noise reduction processing unit 40 being input into the heart rate variation detection unit), and a result of the periodical analysis of the output signal from the noise reduction processor (Fig. 10 shows the heart rate variation detection unit 60 perform resampling via the resampling unit 64 on the pulse wave signal output from the second noise reduction processing unit 40 before outputting heart rate and reliability information).
Regarding claim 5, Ishikawa discloses wherein the noise reduction processor performs a noise reduction process on the input biometric signal to obtain the output signal ([0103] An observation signal is the first error signal (pulse wave signal) output from the first noise reduction processing unit 30. The subtractor 43 subtracts an output value of the adaptive filter 41 from the first error signal, to output the second error signal).
Regarding claim 6, Ishikawa discloses a body motion state analyzer (body motion analysis unit 20) that determines body motion of a user, based on body motion information about the user, acquired by a sensor ([0081] the acceleration sensor 14 is a triaxial acceleration sensor, the norm value calculation unit 21 calculates an acceleration norm from a triaxial acceleration signal as a body motion signal).
Regarding claim 9, Ishikawa discloses a correction processor (integration processing unit 80) that corrects the estimated heart rate based on the reliability of the estimated heart rate ([0135] Integration processing unit 80 determines whether fallback is performed or not. For example, in a case where the reliability of the instantaneous the heart rate and the reliability of the heart rate based on the heart rate trend are lower than a predetermined threshold value, the fallback is performed).
Regarding claim 10, Ishikawa discloses wherein the input biometric signal includes a pulse wave signal ([0087] a pulse wave signal output from the first PPG sensor 12).
Regarding claim 11, Ishikawa discloses wherein the signal processing device is provided in a wearable housing ([0063] A heart rate measurement apparatus 100 is a wrist band type heart rate sensor with a PPG system, and is used by being worn around a wrist of a user; Fig. 1B).
Regarding claim 12, Ishikawa discloses a signal processing method, the method comprising:
estimating a first signal quality of an input biometric signal ([0087] The first error signal corresponds to the pulse wave signal from which a body motion noise (arm motion noise) is separated);
reducing a noise of the input biometric signal ([0102] second noise reduction processing unit 40 performs a reduction process of the finger and wrist motion noise);
estimating a second signal quality of the input biometric signal after reducing the noise of the input biometric signal ([0103] The second error signal corresponds to the pulse wave signal from which the body motion noise (finger and wrist motion noise) is separated); and
estimating a heart rate based on the input biometric signal after reducing the noise of the input biometric signal ([0120] instantaneous heart rate/reliability calculation unit 63 calculates an instantaneous heart rate and a reliability; Fig. 2 shows the second signal reliability (second error signal) output from the second noise reduction processing unit 40 being input into the heart rate variation detection unit 60 containing the instantaneous heart rate/reliability calculation unit 63), and
estimating a reliability of the estimated heart rate based on a result of estimating the first signal quality and a result of estimating the second signal quality ([0120] instantaneous heart rate/reliability calculation unit 63 calculates an instantaneous heart rate and a reliability; Fig. 2 shows the second reliability signal (second error signal determined based on the first error signal) output from the second noise reduction processing unit 40 being input into the heart rate variation detection unit 60 containing the instantaneous heart rate/reliability calculation unit 63).
Regarding claim 13, Ishikawa discloses a program executed by a processor ([0074] CPU loads a program stored in the ROM or the like into the RAM and executes the program) that causes the processor to:
estimate a first signal quality of an input biometric signal ([0087] The first error signal corresponds to the pulse wave signal from which a body motion noise (arm motion noise) is separated);
estimate a second signal quality of the input biometric signal after a noise reduction process ([0103] The second error signal corresponds to the pulse wave signal from which the body motion noise (finger and wrist motion noise) is separated);
estimate a heart rate based on the input biometric signal after the noise reduction process ([0120] instantaneous heart rate/reliability calculation unit 63 calculates an instantaneous heart rate and a reliability; Fig. 2 shows the second signal reliability (second error signal) output from the second noise reduction processing unit 40 being input into the heart rate variation detection unit 60 containing the instantaneous heart rate/reliability calculation unit 63); and
estimate a reliability of the estimated heart rate based on a result of estimating the first signal quality and a result of estimating the second signal quality ([0120] instantaneous heart rate/reliability calculation unit 63 calculates an instantaneous heart rate and a reliability; Fig. 2 shows the second signal reliability (second error signal determined based on the first error signal) output from the second noise reduction processing unit 40 being input into the heart rate variation detection unit 60 containing the instantaneous heart rate/reliability calculation unit 63).
Regarding claim 15, Ishikawa discloses determining body motion of a user, based on body motion information about the user, acquired by a sensor ([0081] In a case where the acceleration sensor 14 is a triaxial acceleration sensor, the norm value calculation unit 21 calculates an acceleration norm from a triaxial acceleration signal as a body motion signal).
Regarding claim 17, Ishikawa discloses periodically performing an analysis of the input biometric signal after reducing the noise of the input biometric signal ([0123] resampling unit 64 performs resampling to 1 Hz; Fig. 2 shows the second error signal (pulse wave signal) output from the second noise reduction processing unit 40 being input into the heart rate variation detection unit 60 containing resampling unit 64),
wherein the reliability of the estimated heart rate is based on the result of estimating the first signal quality (Fig. 2 shows a signal reliability from the first noise reduction processing unit 30 being input into the second noise reduction processing unit 40), the result of estimating the second signal quality (Fig. 2 shows a signal reliability from the second noise reduction processing unit 40 being input into the heart rate variation detection unit), and a result of the periodical analysis of the input biometric signal after reducing the noise of the input biometric signal (Fig. 10 shows the heart rate variation detection unit 60 perform resampling via the resampling unit 64 on the pulse wave signal output from the second noise reduction processing unit 40 before outputting heart rate and reliability information).
Regarding claim 18, Ishikawa discloses wherein reducing the noise of the input biometric signal comprises a noise reduction processor that performs a noise reduction process on the input biometric signal ([0102] second noise reduction processing unit 40 performs a reduction process of the finger and wrist motion noise; [0103] subtractor 43 subtracts an output value of the adaptive filter 41 from the first error signal, to output the second error signal. The second error signal corresponds to the pulse wave signal from which the body motion noise (finger and wrist motion noise) is separated).
Regarding claim 19, Ishikawa discloses correcting the estimated heart rate based on the reliability of the estimated heart rate ([0135] Integration processing unit 80 determines whether fallback is performed or not. For example, in a case where the reliability of the instantaneous the heart rate and the reliability of the heart rate based on the heart rate trend are lower than a predetermined threshold value, the fallback is performed).
Regarding claim 20, Ishikawa discloses wherein the input biometric signal includes a pulse wave signal ([0087] a pulse wave signal output from the first PPG sensor 12).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 3 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Ishikawa (US 2020/0305798).
Regarding claims 3 and 16, Ishikawa discloses the system of claim 1 and the method of claim 12 as discussed above, but fails to expressly disclose wherein the heart rate estimator estimates a high reliability of the estimated heart rate when the result of the estimated first signal quality is high and the result of the estimated second signal quality is high.
However, Ishikawa teaches that if the reliabilities calculated in the first and second noise reduction processing units 30 and 40 are high, even if the reliabilities calculated by the two heart rate estimators are low, the fallback is not performed, and the heart rate candidate information with the higher reliability is output [1036]. The output signal reliabilities (error signals) from the noise reduction processing units 30 and 40 are used as input for the heart rate variation detection unit 60 (Fig. 2; [0107]), so a high signal reliability (low noise) output from the noise reduction processing units 30 and 40 would necessarily result in a high heart rate estimation reliability output by the heart rate variation detection unit (Fig. 10). It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the system as taught by Ishikawa with the heart rate estimator estimates a high reliability of the estimated heart rate when the result of the estimated first signal quality is high and the result of the estimated second signal quality is high since there is a direct relationship between the signal reliability output by the noise reduction processing units 30 and 40 and the estimated heart rate reliability.
Claim(s) 7 is rejected under 35 U.S.C. 103 as being unpatentable over Ishikawa (US 2020/0305798) in view of Kusakabe et al (US 2016/0360972) hereinafter Kusakabe.
Regarding claim 7, Ishikawa discloses the system of claim 6 as discussed above, but fails to disclose wherein when the body motion of the user is greater than a first threshold value, at least one of the noise reduction processor, the second signal quality estimator, or the periodical analyzer stops a process.
However, Kusakabe discloses when the body motion of the user is greater than a first threshold value, an device stops a process ([0099] when the body motion signal is equal to or more than threshold value Tr in the determination of the autonomic nerve function, device main body 10 causes light emitter 22 of pulse wave detector 21 to stop the light emitting and causes light receiver 23 of pulse wave detector 21 to stop the light receiving) which would necessarily stop any subsequent signal processing or estimation. It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the system as taught by Ishikawa with when the body motion of the user is greater than a first threshold value, a device stops a process as taught by Kusakabe. Such a modification would provide the predictable results of suppressing body motion influence when analyzing a pulse wave (Kusakabe, [0198]).
Claim(s) 14 is rejected under 35 U.S.C. 103 as being unpatentable over Ishikawa (US 2020/0305798) in view of Li et al (US 2022/0280087) hereinafter Li.
Regarding claim 14, Ishikawa discloses a learning device comprising:
a first signal quality estimator (arm motion noise reduction processing unit 30) that estimates a first signal quality of an input biometric signal ([0087] The arm motion noise reduction processing unit 30 outputs first error signal that corresponds to the pulse wave signal from which a body motion noise (arm motion noise) is separated);
a second signal quality estimator (heart rate trend detection unit 70) that estimates a second signal quality (reliability) of an output signal from a noise reduction processor ([0107] based on the pulse wave signal output from the second noise reduction processing unit 40, the heart rate trend is detected by the heart rate trend detection unit 70; [0128] reliability is calculated based on the heart rate trend);
a heart rate estimator ([0107] heart rate variation detection unit 60; Fig. 2) that estimates a heart rate based on the output signal from the noise reduction processor and estimates a reliability of the estimated heart rate based on a result of the first signal quality estimation and a result of the second signal quality estimation ([0120] instantaneous heart rate/reliability calculation unit 63 calculates an instantaneous heart rate and a reliability; Fig. 2 shows the heart rate trend detection unit 70 and the second noise reduction processing unit 40 feeding their respective outputs into the heart rate variation detection unit 60 containing the instantaneous heart rate/reliability calculation unit 63); and
a correction processor (Integration processing unit 80) that corrects the estimated heart rate based on the estimated reliability of the heart rate ([0135] Integration processing unit 80 determines whether fallback is performed or not. For example, in a case where the reliability of the instantaneous the heart rate and the reliability of the heart rate based on the heart rate trend are lower than a predetermined threshold value, the fallback is performed).
Ishikawa fails to disclose a trained estimation model that estimates a person's emotional state using the estimated heart rate and the reliability of the estimated heart rate. However, Li discloses a trained estimation model that estimates a person's emotional state using an estimated heart rate and a reliability of the estimated heart rate ([0049] the physiological parameters including the heart rate and breathing waveforms, and HRV, reliable signal features are used for deep learning modeling after signal denoising and stabilization are performed, and on this basis, the VAD model is read to obtain the specific emotional representation). It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the system as taught by Ishikawa with a trained estimation model that estimates a person's emotional state using an estimated heart rate and a reliability of the estimated heart rate as taught by Li. Such a modification would provide the predictable results of recognizing specific emotions and emotional changes (Li, [0007]).
Examiner’s Note Regarding Prior Art
Examiner notes that while claim 8 is rejected under 35 USC 101, no prior art rejections have been made. Examiner has not found any prior art that teaches or suggests in combination “wherein when the result of the estimated first signal quality is high and the body motion of the user is lower than a second threshold value, at least one of the noise reduction processor, the second signal quality estimator, or the periodical analyzer stops the process.”
As discussed above, Kusakabe discloses when the body motion of the user is greater than a first threshold value, stopping a process [0099], but fails to disclose stopping a process when the result of the estimated first signal quality is high and the body motion of the user is lower than a second threshold value as required by the claim.
Conclusion
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/WILLOW GRACE WELCH/Examiner, Art Unit 3792
/Benjamin J Klein/Supervisory Patent Examiner, Art Unit 3792
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