DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. 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
2. According to the Amendment, filed 24 June 2026, the status of the claims is as follows:
Claims 1-20 are as originally filed.
Election/Restrictions
3. Applicant’s election without traverse of Group I-A, claims 2-7, and Group II-A, claim 14, in the reply filed on 24 June 2026 is acknowledged. Claims 1, 10-13, and 16-20 were indicated to be generic in the Restriction Requirement, p. 3, mailed 20 May 2026.
Claims 8, 9, and 15 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected species.
Claim Rejections - 35 USC § 102
4. 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.
5. 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.
6. Claims 1-4, 6, 7, 10, 11, 13, and 15-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Fan et al., “APG: Audioplethysmography for Cardiac Monitoring in Hearables”, Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization, ACMPUB27, New York, NY, USA, 02 October 2023, pages 1-15 (“Fan”).
As to Claim 1, Fan teaches the following:
A method (see “This paper presents Audioplethysmography (APG), a novel cardiac monitoring modality for active noise cancellation (ANC) headphones.” in Abstract, p. 1, col. 1) comprising:
transmitting and receiving, during a first time period, an acoustic signal that propagates within at least a portion of an ear canal of a user, the received acoustic signal representing a version of the transmitted acoustic signal with one or more characteristics modified based on the propagation within the ear canal (see “Figure signals 3(a) demonstrates how an ANC earbud sends acoustic signals and receives the echoes in the ear canal. Both eardrum and tissues surrounding the ear canal will slightly squeeze the ear canal cavity due to the blood vessel deformation. This volumetric change in the ear canal inherently modulates the acoustic probing signal in the ear canal. Note that the blood vessels deforms according to the cardiac activities [51]. Therefore, we can retrieve the heart rate pattern by examining the echo signals.” in p. 4, col. 1-2), the received acoustic signal comprising at least one motion artifact associated with the user moving during at least a portion of the first time period (see “As described by the physical model (Figure 3), AFG essentially measures the volumetric change in the ear canal. However, in practice, both head and body motions will squeeze the ear canals, altering their structures intermittently. Accordingly, these motion artifacts will be superimposed with the heartbeat signals, disturbing the cardiac signal detection. Since these body motion artifacts are orders of magnitude stronger than the cardiac activities, they will easily dominate the received signal, setting a strong barrier to the cardiac activity detection. The bottom left sub-figure in Figure 8 shows the head movement dominates the received signal.” in p. 8, col. 1);
accepting motion-sensing data generated by a motion sensor (“feedback microphone”) during the first time period (see “The received signal Sr(t) at the feedback microphone is the superposition of Se(t) and Sr(t), which can be represented as: … where N is the noise term.” in p. 4, col. 2);
processing a version of the received acoustic signal based on the motion-sensing data (see “Blind source separation (BSS) aims to recover each source signal from their mixtures, without the aid of information about the mixing process. BSS is equivalent to identifying the factorization of the N dimensional observations X into a mixing channel A and M dimensional source signals S:
X = AS (8)
The matrices X and S represent the received signal mixture and the source signals.
…
Specifically, the frequency bands of probing signals are statistically decorrelated due to the channel frequency diversity [49]. For instance, two separate frequency bands may capture totally different signal sources, as shown in Figure 8. Accordingly, the use of multi-frequency probing signals could significantly boost the number of independent observations and further make <Rx a determined covariance matrix. In our experiments, we found five frequencies are usually adequate to make 'Rx an over-determined covariance matrix (i.e., M < N).
For each earbud, we get an observation matrix X from the mix-down signals across all probing frequencies and apply an eigenvalue decomposition on <Rx to calculate the channel matrix A. The source signals are recovered by S = A-1 X.” in p. 8, col. 2, to p. 9, col. 1); and
generating, based on the processing, data associated with at least one of the following:
the one or more characteristics associated with the propagation of the acoustic signal within the ear canal (see “Figure 12 further shows the spectrogram of APG signals before and after applying blind source separation. The data is collected during a running session. Figure 13 (a) shows the ground truth heart rate measurement in the same running experiment from a Polar H10 ECG chest strap [6]. We can clearly see the running cadence (around 3.3 Hz) in Figure 12 (a) as well as two dimmer lines (around 2 Hz and 4 Hz) that indicate the user's heart rate frequency and its harmonics. The heart rate frequencies are significantly enhanced in SNR after the blind source separation, which align with the ground truth heart rate frequencies in Figure 13 (a). Figure 13 (b) shows the calculated heart rate and running cadence from APG and ECG. We can see that APG tracks the growth of heart rate during the running session accurately.” in p. 9, col. 2) or
the at least one motion artifact associated with the user moving (see “Figure 11 gives an exemplary motion separation result. In this experiment, we send probing signals on six frequencies spanning from 30 kHz to 39 kHz. The user is asked to move her body intensively to introduce a large motion interference. Before applying the separation algorithm (Figure 11 (a)), we observe that the cardiac activities are overwhelmed by the body motions across all six frequency bands. In contrast, as we applying the blind source separation algorithm, we can see the cardiac signal and body motion signal are disaggregated (Figure 11 (b)). Moreover, we observe that the body motion artifacts are about lOX stronger than the cardiac signals, which explains the result that the cardiac signal is not observable on the raw mix-down signals across all frequency bands. The resolved cardiac signal also has a significantly higher PAR than other sources.” in p. 9, col. 1-2).
As to Claim 2, Fan teaches the following:
wherein:
the processing of the version of the received acoustic signal comprises generating a denoised signal by filtering the version of the received acoustic signal based on the motion-sensing data, the denoised signal having motion artifacts that are attenuated relative to the motion artifacts within the version of the received acoustic signal, the denoised signal comprising the one or more characteristics (see “Specifically, the frequency bands of probing signals are statistically decorrelated due to the channel frequency diversity [49]. For instance, two separate frequency bands may capture totally different signal sources, as shown in Figure 8. Accordingly, the use of multi-frequency probing signals could significantly boost the number of independent observations and further make <Rx a determined covariance matrix. In our experiments, we found five frequencies are usually adequate to make 'Rx an over-determined covariance matrix (i.e., M < N).” in p. 9, col. 1); and
the generating of the data comprises generating the data based on the denoised signal (see “For each earbud, we get an observation matrix X from the mix-down signals across all probing frequencies and apply an eigenvalue decomposition on <Rx to calculate the channel matrix A. The source signals are recovered by S = A-1 X.” in p. 9, col. 1).
As to Claim 3, Fan teaches the following:
wherein the generating of the denoised signal comprises:
performing informed filtering to attenuate the motion artifacts within the version of the received acoustic signal that are also present in the motion-sensing data (see “Specifically, the frequency bands of probing signals are statistically decorrelated due to the channel frequency diversity [49]. For instance, two separate frequency bands may capture totally different signal sources, as shown in Figure 8. Accordingly, the use of multi-frequency probing signals could significantly boost the number of independent observations and further make <Rx a determined covariance matrix. In our experiments, we found five frequencies are usually adequate to make 'Rx an over-determined covariance matrix (i.e., M < N).” in p. 9, col. 1); or
performing adaptive filtering with the received acoustic signal representing a primary reference and the motion-sensing data representing a noise reference (see “The received signal Sr(t) at the feedback microphone is the superposition of Se(t) and Sr(t), which can be represented as: … where N is the noise term.” in p. 4, col. 2).
As to Claim 4, Fan teaches the following:
wherein:
the transmitting and the receiving of the acoustic signal during the first time period comprises:
transmitting and receiving a first acoustic signal using a first hearable (see “We expand the number of transmissions to 11 frequencies and get 44 signal profiles from the signals collect by both left- and right-headphone.” in p. 7, col. 1-2); and
transmitting and receiving a second acoustic signal using a second hearable (see “We expand the number of transmissions to 11 frequencies and get 44 signal profiles from the signals collect by both left- and right-headphone.” in p. 7, col. 1-2); and
the generating of the denoised signal comprises performing adaptive filtering or blind-source separation based on a version of the received first acoustic signal, a version of the received second acoustic signal, and the motion-sensing data to generate the denoised signal and a motion signal comprising the at least one motion artifact (see “Blind source separation with multi-tone APG. We take advantage of the multi-frequency nature of APG probing signals to address this challenge. Specifically, the frequency bands of probing signals are statistically decorrelated due to the channel frequency diversity [49]. For instance, two separate frequency bands may capture totally different signal sources, as shown in Figure 8. Accordingly, the use of multi-frequency probing signals could significantly boost the number of independent observations and further make Rx a determined covariance matrix. In our experiments, we found five frequencies are usually adequate to make Rx an over-determined covariance matrix (i.e., M < N).” in p. 9, col. 1).
As to Claim 6, Fan teaches the following:
wherein:
the transmitting and the receiving of the acoustic signal during the first time period comprises:
transmitting and receiving a first acoustic signal using a first hearable (see “We expand the number of transmissions to 11 frequencies and get 44 signal profiles from the signals collect by both left- and right-headphone.” in p. 7, col. 1-2); and
transmitting and receiving a second acoustic signal using a second hearable (see “We expand the number of transmissions to 11 frequencies and get 44 signal profiles from the signals collect by both left- and right-headphone.” in p. 7, col. 1-2); and
the generating of the denoised signal comprises providing a version of the received first acoustic signal, a version of the received second acoustic signal, and the motion-sensing data as inputs to a machine-learned model to generate the denoised signal and a motion signal (see “Blind source separation with multi-tone APG. We take advantage of the multi-frequency nature of APG probing signals to address this challenge. Specifically, the frequency bands of probing signals are statistically decorrelated due to the channel frequency diversity [ 49]. For instance, two separate frequency bands may capture totally different signal sources, as shown in Figure 8. Accordingly, the use of multi-frequency probing signals could significantly boost the number of independent observations and further make <Rx a determined covariance matrix. In our experiments, we found five frequencies are usually adequate to make 'Rx an over-determined covariance matrix (i.e., M < N).” in p. 9, col. 1).
As to Claim 7, Fan teaches the following:
wherein the generating of the data comprises measuring a biometric (“heart rate”) of the user based on the denoised signal (see “Figure 12 further shows the spectrogram of APG signals before and after applying blind source separation. The data is collected during a running session. Figure 13 (a) shows the ground truth heart rate measurement in the same running experiment from a Polar HlO ECG chest strap [ 6]. We can clearly see the running cadence (around 3.3 Hz) in Figure 12 (a) as well as two dimmer lines (around 2 Hz and 4 Hz) that indicate the user's heart rate frequency and its harmonics. The heart rate frequencies are significantly enhanced in SNR after the blind source separation, which align with the ground truth heart rate frequencies in Figure 13 (a). Figure 13 (b) shows the calculated heart rate and running cadence from APG and ECG. We can see that APG tracks the growth of heart rate during the running session accurately.” in p. 9, col. 2).
As to Claim 10, Fan teaches the following:
wherein the classifying of the type of activity comprises determining that the type of activity is relatively motionless or associated with a substantial amount of motion (see “Blind source separation with multi-tone APG. We take advantage of the multi-frequency nature of APG probing signals to address this challenge. Specifically, the frequency bands of probing signals are statistically decorrelated due to the channel frequency diversity [49]. For instance, two separate frequency bands may capture totally different signal sources, as shown in Figure 8. Accordingly, the use of multi-frequency probing signals could significantly boost the number of independent observations and further make Rx a determined covariance matrix. In our experiments, we found five frequencies are usually adequate to make Rx an over-determined covariance matrix (i.e., M < N).” in p. 9, col. 1).
As to Claim 11, Fan teaches the following:
controlling an operation of a hearable or an operation of a computing device that is coupled to the hearable based on at least one of the following:
a determination that the user moved (see “Figure 12 further shows the spectrogram of APG signals before and after applying blind source separation. The data is collected during a running session. Figure 13 (a) shows the ground truth heart rate measurement in the same running experiment from a Polar H10 ECG chest strap [6]. We can clearly see the running cadence (around 3.3 Hz) in Figure 12 (a) as well as two dimmer lines (around 2 Hz and 4 Hz) that indicate the user's heart rate frequency and its harmonics. The heart rate frequencies are significantly enhanced in SNR after the blind source separation, which align with the ground truth heart rate frequencies in Figure 13 (a). Figure 13 (b) shows the calculated heart rate and running cadence from APG and ECG. We can see that APG tracks the growth of heart rate during the running session accurately.” in p. 9, col. 2); or
a classification of the type of activity that the user performed.
As to Claim 13, Fan teaches the following:
transmitting and receiving, prior to the first time period, another acoustic signal that propagates within at least the portion of the ear canal of the user, the other acoustic signal having multiple tones, the received other acoustic signal representing a version of the transmitted other acoustic signal with one or more characteristics modified due to the propagation within the ear canal (see “Next we examine the PAR under different ultrasound intensity settings. In this experiment, we fix the frequency of the probing signal to 35 kHz. We then vary the intensity of the probing signal from 30 dB SPL to 75 dB SPL at DRP and measure the PAR in different intensity settings. The results are shown in Figure 16. We observe that the PAR grows gradually with increasing loudness (intensity) of the probing signal. Nevertheless, we can see the probing signal at 30 dB SPL intensity still achieves a decent PAR (around 4). Suggested hy this result, we adopt 45 dl3 SPL as our default setting in the following experiments.” in p. 10, col. 2); and
selecting a subset of the multiple tones based on the received other acoustic signal (see “Next we examine the PAR under different ultrasound intensity settings. In this experiment, we fix the frequency of the probing signal to 35 kHz. We then vary the intensity of the probing signal from 30 dB SPL to 75 dB SPL at DRP and measure the PAR in different intensity settings. The results are shown in Figure 16. We observe that the PAR grows gradually with increasing loudness (intensity) of the probing signal. Nevertheless, we can see the probing signal at 30 dB SPL intensity still achieves a decent PAR (around 4). Suggested hy this result, we adopt 45 dl3 SPL as our default setting in the following experiments.” in p. 10, col. 2),
wherein the transmitting and the receiving of the acoustic signal during the first time period comprises transmitting and receiving the acoustic signal having the subset of the multiple tones (see “Next we examine the PAR under different ultrasound intensity settings. In this experiment, we fix the frequency of the probing signal to 35 kHz. We then vary the intensity of the probing signal from 30 dB SPL to 75 dB SPL at DRP and measure the PAR in different intensity settings. The results are shown in Figure 16. We observe that the PAR grows gradually with increasing loudness (intensity) of the probing signal. Nevertheless, we can see the probing signal at 30 dB SPL intensity still achieves a decent PAR (around 4). Suggested hy this result, we adopt 45 dl3 SPL as our default setting in the following experiments.” in p. 10, col. 2).
As to Claim 14, Fan teaches the following:
wherein the generated data is used to control an operation of a hearable and/or a computing device (see “To determine a proper setup of the frequency spacing in our multifrequency APG design, we send a pair of probing signals, with one of their frequency fixed to 35 kHz, and another changes from 35.1 kHz to 35.9 kHz. We then measure the correlation of the received signal at each frequency using Pearson Correlation Coefficient (PCC [28]). A lower PCC value indicates the higher likelihood that these two signals are de-correlated. The experiment is repeated five times in each setting. As shown in Figure 17, the PCC value declines with growing frequency gap between two probing signals. It reaches to the minimum value at 700 Hz (average PCC=0.23). On the other hand, given a fixed resource band (e.g., 32 kHz to 38 kHz) and the total available power, the power and SNR of each APG tone declines with decreasing frequency spacing because the number of tones grows with decreasing frequency spacing. By jointly considering the de-correlation coefficient and the power of each APG tone, we set the frequency spacing between adjacent APG signals to 1 KHz.” in p. 10, col. 2, to p. 11, col. 1).
As to Claim 16, Fan teaches the following:
16. (Original) A computer-readable storage medium comprising instructions that, responsive to execution by a processor, cause a hearable to:
transmit and receive, during a first time period, an acoustic signal that propagates within at least a portion of an ear canal of a user, the received acoustic signal representing a version of the transmitted acoustic signal with one or more characteristics modified based on the propagation within the ear canal, the received acoustic signal comprising at least one motion artifact associated with the user moving during at least a portion of the first time period;
accept motion-sensing data generated by a motion sensor during the first time period;
process a version of the received acoustic signal based on the motion-sensing data; and
generate, based on the processing, data associated with at least one of the following:
the one or more characteristics associated with the propagation of the acoustic signal within the ear canal or the at least one motion artifact associated with the user moving.
As to Claim 17, Fan teaches the following:
A device (see “This paper presents Audioplethysmography (APG), a novel cardiac monitoring modality for active noise cancellation (ANC) headphones.” in Abstract, p. 1, col. 1) comprising:
at least one transducer (“ANC earbud”) configured to transmit and receive, during a first time period, an acoustic signal that propagates within at least a portion of an ear canal of a user, the received acoustic signal representing a version of the transmitted acoustic signal with one or more characteristics modified based on the propagation within the ear canal (see “Figure signals 3(a) demonstrates how an ANC earbud sends acoustic signals and receives the echoes in the ear canal. Both eardrum and tissues surrounding the ear canal will slightly squeeze the ear canal cavity due to the blood vessel deformation. This volumetric change in the ear canal inherently modulates the acoustic probing signal in the ear canal. Note that the blood vessels deforms according to the cardiac activities [51]. Therefore, we can retrieve the heart rate pattern by examining the echo signals.” in p. 4, col. 1-2), the received acoustic signal comprising at least one motion artifact associated with the user moving during at least a portion of the first time period (see “As described by the physical model (Figure 3), AFG essentially measures the volumetric change in the ear canal. However, in practice, both head and body motions will squeeze the ear canals, altering their structures intermittently. Accordingly, these motion artifacts will be superimposed with the heartbeat signals, disturbing the cardiac signal detection. Since these body motion artifacts are orders of magnitude stronger than the cardiac activities, they will easily dominate the received signal, setting a strong barrier to the cardiac activity detection. The bottom left sub-figure in Figure 8 shows the head movement dominates the received signal.” in p. 8, col. 1); and
at least one processor configured to:
accept motion-sensing data generated by a motion sensor (“feedback microphone”) during the first time period (see “The received signal Sr(t) at the feedback microphone is the superposition of Se(t) and Sr(t), which can be represented as: … where N is the noise term.” in p. 4, col. 2);
process a version of the received acoustic signal based on the motion-sensing data (see “Blind source separation (BSS) aims to recover each source signal from their mixtures, without the aid of information about the mixing process. BSS is equivalent to identifying the factorization of the N dimensional observations X into a mixing channel A and M dimensional source signals S:
X = AS (8)
The matrices X and S represent the received signal mixture and the source signals.
…
Specifically, the frequency bands of probing signals are statistically decorrelated due to the channel frequency diversity [49]. For instance, two separate frequency bands may capture totally different signal sources, as shown in Figure 8. Accordingly, the use of multi-frequency probing signals could significantly boost the number of independent observations and further make <Rx a determined covariance matrix. In our experiments, we found five frequencies are usually adequate to make 'Rx an over-determined covariance matrix (i.e., M < N).
For each earbud, we get an observation matrix X from the mix-down signals across all probing frequencies and apply an eigenvalue decomposition on <Rx to calculate the channel matrix A. The source signals are recovered by S = A-1 X.” in p. 8, col. 2, to p. 9, col. 1); and
generate, based on the processing, data associated with at least one of the following:
the one or more characteristics associated with the propagation of the acoustic signal within the ear canal (see “Figure 12 further shows the spectrogram of APG signals before and after applying blind source separation. The data is collected during a running session. Figure 13 (a) shows the ground truth heart rate measurement in the same running experiment from a Polar H10 ECG chest strap [6]. We can clearly see the running cadence (around 3.3 Hz) in Figure 12 (a) as well as two dimmer lines (around 2 Hz and 4 Hz) that indicate the user's heart rate frequency and its harmonics. The heart rate frequencies are significantly enhanced in SNR after the blind source separation, which align with the ground truth heart rate frequencies in Figure 13 (a). Figure 13 (b) shows the calculated heart rate and running cadence from APG and ECG. We can see that APG tracks the growth of heart rate during the running session accurately.” in p. 9, col. 2) or
the at least one motion artifact associated with the user moving (see “Figure 11 gives an exemplary motion separation result. In this experiment, we send probing signals on six frequencies spanning from 30 kHz to 39 kHz. The user is asked to move her body intensively to introduce a large motion interference. Before applying the separation algorithm (Figure 11 (a)), we observe that the cardiac activities are overwhelmed by the body motions across all six frequency bands. In contrast, as we applying the blind source separation algorithm, we can see the cardiac signal and body motion signal are disaggregated (Figure 11 (b)). Moreover, we observe that the body motion artifacts are about lOX stronger than the cardiac signals, which explains the result that the cardiac signal is not observable on the raw mix-down signals across all frequency bands. The resolved cardiac signal also has a significantly higher PAR than other sources.” in p. 9, col. 1-2).
As to Claim 18, Fan teaches the following:
a speaker (“speaker”) (see “The speaker and microphone are placed at this open end while the eardrum closes this tube at the other end.” in p. 4, col. 2); and
an active-noise-cancellation circuit (“analog high-pass filter”) comprising a feedback microphone (“microphone”) (see “To ensure the active noise canceling (avoiding saturated microphones), the CEA buds have an analog high-pass filter built into its feedback microphone whereas the CEB earbud filters the readings of the feedback microphone in its DSP.” in p. 3, col. 2), wherein the at least one transducer (“ANC earbud”) comprises the speaker (“speaker”) and the feedback microphone (“microphone”) (see “The speaker and microphone are placed at this open end while the eardrum closes this tube at the other end.” in p. 4, col. 2).
As to Claim 19, Fan teaches the following:
wherein:
the at least one transducer (“ANC earbud”) comprises a speaker (“speaker”) and a microphone (“microphone”) (see “The speaker and microphone are placed at this open end while the eardrum closes this tube at the other end.” in p. 4, col. 2);
the speaker (“speaker”) is configured to be positioned proximate to a first ear of a user (see “The speaker and microphone are placed at this open end while the eardrum closes this tube at the other end.” in p. 4, col. 2); and
the microphone (“microphone”) is configured to be positioned proximate to a second ear of the user (see “The speaker and microphone are placed at this open end while the eardrum closes this tube at the other end.” in p. 4, col. 2).
As to Claim 20, Fan teaches the following:
a motion sensor (“multi-tone APG”) (see “Although the multi-tone AFG design helps to find the frequencies that are hyper-sensitive to cardiac activities (e.g., many subfigures in Figure 8), we are still seeing cases that the intensive body motions ( e.g. walking) contaminate the echoes across all probing frequencies.” in p. 8, col. 1-2).
Allowable Subject Matter
7. Claims 5 and 12 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
8. The following is a statement of reasons for the indication of allowable subject matter:
As to Claim 5, Fan teaches the following:
wherein:
the transmitting and the receiving of the acoustic signal during the first time period comprises:
transmitting and receiving a first acoustic signal using a first hearable (see “We expand the number of transmissions to 11 frequencies and get 44 signal profiles from the signals collect by both left- and right-headphone.” in p. 7, col. 1-2); and
transmitting and receiving a second acoustic signal using a second hearable (see “We expand the number of transmissions to 11 frequencies and get 44 signal profiles from the signals collect by both left- and right-headphone.” in p. 7, col. 1-2); and …
However, neither Fan nor the prior art of record teaches the method of base claim 1, including the following, in combination with all other limitations of the base claim:
the accepting of the motion-sensing data comprises:
accepting first motion-sensing data generated by a first motion sensor of the first hearable during the first time period; and
accepting second motion-sensing data generated by a second motion sensor of the second hearable during the first time period;
the generating of the denoised signal comprises:
generating a first denoised signal based on the first acoustic signal and the first motion-sensing data; and
generating a second denoised signal based on the second acoustic signal and the second motion-sensing data; and
the generating of the data comprises generating the data based on the first denoised signal and the second denoised signal.
As to Claim 12, neither Fan nor the prior art of record teaches the method of base claim 1, including the following, in combination with all other limitations of the base claim:
wherein the motion-sensing data comprises at least one of the following:
linear accelerations associated with three orthogonal axes; or
rotational velocities associated with the three orthogonal axes.
Conclusion
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/NAVIN NATNITHITHADHA/Primary Examiner, Art Unit 3791 09/10/2026
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