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
In response to amendments filed March 6, 2026, claims 1 and 14 have been amended. No claims have been cancelled. No claims have been added. Claims 1-20 are pending.
Response to Arguments
Applicant’s arguments, see Remarks, filed March 6, 2026, with respect to the prior art rejection(s) of claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on the same reference combination applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. A new ground(s) of rejection is made in view of the combinations of Salehizadeh (US 20160361021 A1)/Von Badinski (US 20150220109 A1)/Diab (US 20030032873 A1)/Nadeau (US 20210330209 A1).
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) 1, 3-7, 9-11, and 14-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Salehizadeh (US 20160361021 A1) in view of Von Badinski (US 20150220109 A1) and Diab (US 20030032873 A1).
Regarding claim 1, Salehizadeh teaches a method for measuring heart rate for a user ([Abstract] method and corresponding apparatus employ a time-varying spectral analysis approach for reconstructing a heart-related signal that includes motion artifacts), comprising: acquiring first physiological data from a user via using one or more sensors (biomedical sensor 108).
However, Salehizadeh fails to explicitly disclose a ring or adjusting sampling rate based on activity. Von Badinski teaches a wearable computing device (WCD) in the form of a ring that can be worn on the finger of a human user. Von Badinski discloses:
of a wearable ring device configured to be worn on a finger of the user (WCD 110, Fig. 1A);
determining, using one or more processors of the wearable ring device (processor module 210), that the user is performing an activity based at least in part on the first physiological data; causing, by the one or more processors of the wearable ring device, the one or more sensors of the wearable ring device (sensor modules 220, Fig. 2) to acquire second physiological data associated with the user throughout a time interval in accordance with a sampling rate and based at least in part on determining that the user is performing the activity, wherein the sampling rate is based at least in part on the user performing the activity ([0052] determining an activity level of a wearer of a wearable computing device based at least in part on data from at least one sensor disposed onboard the wearable computing device; comparing the activity level to a predetermined activity threshold; and increasing a first sensor sampling rate if the activity level is above a predetermined activity threshold.).
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 Salehizadeh to include a wearable ring device that adjusts sampling rate based on activity as disclosed in Von Badinski in order to monitor user physiological characteristics over long periods of time with little to no discomfort or interference (Von Badinski [0253]).
The combination of Salehizadeh/Von Badinski further discloses:
the second physiological data comprising photoplethysmogram (PPG) data and motion data collected throughout the time interval (Salehizadeh: [0028] The biomedical sensor may be at least one of: a photoplethysmogram [PPG] sensor, piezoelectric sensor, Light Emitting Diode [LED] based sensor, camera sensor, and pulse oximeter sensor, and the motion sensor may be an accelerometer; [0067] a biomedical sensor 108 that produces a heart-related signal 110; Fig. 1; Fig. 3A; Von Badinski: sensor modules 220, Fig. 2; [0253] if a person's heart rate deviates from a resting heart rate, but the accelerometer indicates that the user is exercising and/or engaging in strenuous activity that provides an equivalent workout, then the WCD may not issue an alert in this circumstance.).
determining a set of candidate heart rate measurements within the time interval based at least in part on the PPG data (Salehizadeh: [0088] The method of FIG. 3B may start (331) and retain (332) up to a pre-determined number N of candidate spectral peaks located at a first point in time in the first TFS; Fig. 3B, 332 PPG Filtered Spectrum);
selecting a first heart rate measurement from the set of candidate heart rate measurements based at least in part on the motion data (Salehizadeh: 352 selected candidate spectral peak, Fig. 3B; [0030] the method may further comprise employing the reconstructed representation to determine a heart rate estimate; [0106] employ the reconstructed representation of the heart-related signal 514 to determine a heart rate estimate).
However, the combination of Salehizadeh/Von Badinski fails to disclose a threshold accuracy metric.
Diab teaches pulse recognition that involves applying a plethysmograph model to candidate pulses to decides which pulses satisfy the model. Diab discloses determining a first heart rate for the user within the time interval ([0065] FIG. 19, a pulse rate selection and comparison module 1900) based at least in part on the first heart rate corresponding to a first accuracy metric that satisfies a threshold accuracy metric and based at least in part on the selected first heart rate measurement, wherein the threshold accuracy metric is associated with a strength, a quantity, or both, of the set of candidate heart rate measurements ([0011] The physiological model portion of the processor has a series of components that discard potential pulses that do not compare to a physiologically acceptable pulse. The first component of the model portion extracts features of the potential pulses, including pulse starting point, pulse period, and pulse signal strength. These features are compared against various checks, including checks for pulses that have a period below a predetermined threshold, that are asymmetric, that have a descending trend that is generally slower that a subsequent ascending trend, that do not sufficiently comply with an empirical relationship between pulse rate and pulse signal strength, and that have a signal strength that differs from a short-term average signal strength by greater than a predetermined amount.).
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 combination of Salehizadeh/Von Badinski to include a threshold accuracy metric for determining heart rate as disclosed in Diab in order to determine pulse rate in the presence of motion artifact and other noise sources (Diab [0009]).
Regarding claim 3, the combination of Salehizadeh/Von Badinski/Diab discloses the method of claim 1, further comprising:
identifying one or more heart rate measurements from the set of candidate heart rate measurements as motion artifacts based at least in part on a comparison of data trends between the set of candidate heart rate measurements and the motion data (Salehizadeh: 338 Fig. 3B; [0090] the method may discard [338] each of the candidate spectral peaks retained, that is, each peak of the Nfirst highest peaks determined at [332], if the peak is associated with a same frequency as a dominant spectral peak located at a second point in time in the second TFS [314] computed as disclosed above with regard to FIG. 3 A);
and selecting the first heart rate measurement from a subset of the set of candidate heart rate measurements that does not include the one or more heart rate measurements that were identified as motion artifacts (Salehizadeh: [0094] Based on at least one last candidate spectral peak remaining from the second discarding 348, reconstructing the current representation may be based on a selected candidate spectral peak 352; Fig. 3B).
Regarding claim 4, the combination of Salehizadeh/Von Badinski/Diab discloses the method of claim 3, wherein identifying the one or more heart rate measurements as motion artifacts (Salehizadeh: Stage 4. Motion Artifact Detection, Fig. 3B) comprises: identifying the one or more heart rate measurements from the set of candidate heart rate measurements as motion artifacts based at least in part on the one or more heart rate measurements exhibiting a similar frequency pattern relative to the motion data (Salehizadeh: Table 2, Compare the frequencies of the three peaks in the PPG spectrum with the frequency of the largest peak in the accelerometers' spectra. If the first or second largest peaks in the PPG spectrum are similar to that of the accelerometers' peaks, then motion artifact is present in the PPG).
Regarding claim 5, the combination of Salehizadeh/Von Badinski/Diab discloses the method of claim 1, further comprising:
receiving additional physiological data associated with the user, the additional physiological data comprising additional PPG data and additional motion data collected throughout a second time interval via the wearable ring device (Salehizadeh: biomedical sensor 108; Von Badinski: WCD 110, sensor modules 220), the second time interval subsequent to the time interval (Salehizadeh: [0096] The method of FIG. 3C may start 362 and compute an average value of a number m of prior reconstructed representations outputted prior to the reconstructing 362 ... For example, if motion artifacts were detected for windows k-1, k-2, and k-3, m may be understood to be 3; [0111] take the frequency and power information of the first three peaks in the PSD at each window and signal segment);
determining an additional set of candidate heart rate measurements within the second time interval based at least in part on the additional PPG data; selecting a second heart rate measurement from the additional set of candidate heart rate measurements based at least in part on the additional motion data (Salehizadeh: [0097] The cutoff frequency F.sub.c may be based on the average value computed HR.sub.mavg. The method may filter the pre-processed heart-rate signal 306, disclosed in FIG. 3A, above, by applying the band pass filter computed 368 to produce a filtered, pre-processed heart-related signal);
and determining a second heart rate for the user within the second time interval based at least in part on the selected second heart rate measurement (Salehizadeh: [0097] The reconstructed representation HR(k) may be based on an average peak-to-peak interval value 372 of the filtered, pre-processed heart-rate signal).
Regarding claim 6, the combination of Salehizadeh/Von Badinski/Diab discloses the method of claim 5, further comprising:
interpolating between the first heart rate measurement for the time interval and the second heart rate measurement for the second time interval, wherein determining the first heart rate for the user within the time interval, determining the second heart rate for the user within the second time interval, or both, is based at least in part on the interpolating (Salehizadeh: [0096] windows k-1, k-2, and k-3; [Table 5] [when] the HR frequency cannot be extracted from the spectrum and in this case the previous HR frequency is used or for offline implementation a cubic spline interpolation can be applied to fill in the missing HR information … The PPG signal is reconstructed by using the amplitude, frequency and phase information corresponding to the HR components [extracted in stage 4] that are calculated from the spectrum at each window … HR can be extracted).
Regarding claim 7, the combination of Salehizadeh/Von Badinski/Diab discloses the method of claim 5, wherein interpolating between the first heart rate measurement for the time interval and the second heart rate measurement for the second time interval based at least in part on an intensity of the motion data collected throughout the time interval, an intensity of the additional motion data collected throughout the second time interval, or both (Von Badinski: [00052] increasing a first sensor sampling rate if the activity level is above a predetermined activity threshold; Salehizadeh: [0096] The method of FIG. 3C may start (362) and compute an average value of a number m of prior reconstructed representations outputted prior to the reconstructing (362). The number m may be based on a duration of consecutive motion artifacts, detected prior to the reconstructing of the representation HR(k). For example, if motion artifacts were detected for windows k−1, k−2, and k−3, m may be understood to be 3; [Table 5] [when] the HR frequency cannot be extracted from the spectrum and in this case the previous HR frequency is used or for offline implementation a cubic spline interpolation can be applied to fill in the missing HR information. … The PPG signal is reconstructed by using the amplitude, frequency and phase information corresponding to the HR components (extracted in stage 4) that are calculated from the spectrum at each window … HR can be extracted).
Regarding claim 9, the combination of Salehizadeh/Von Badinski/Diab discloses the method of claim 1, wherein the motion data comprises first acceleration data relative to a first direction, second acceleration data relative to a second direction, and third acceleration data relative to a third direction (Salehizadeh: [0075] accelerometer may be a 3-axial type accelerometer), the method further comprising:
combining the first acceleration data, the second acceleration data, the third acceleration data, or any combination thereof, using one or more mathematical operations based at least in part on one or more characteristics of the first acceleration data, the second acceleration data, or the third acceleration data, wherein the selection of the first heart rate measurement from the set of candidate heart rate measurements is based at least in part on combining the first acceleration data, the second acceleration data, the third acceleration data, or any combination thereof (Salehizadeh: 310, 312, 314 in Fig. 3A).
Regarding claim 10, the combination of Salehizadeh/Von Badinski/Diab discloses the method of claim 1, wherein determining the set of candidate heart rate measurements comprises: determining the set of candidate heart rate measurements within a frequency range that corresponds to an expected range of human heart rates (Salehizadeh: [0014] The candidate spectral peaks retained may be based on having corresponding frequencies within a given frequency range … given frequency range may be 0.5 Hz to 3 Hz).
Regarding claim 11, the combination of Salehizadeh/Von Badinski/Diab discloses the method of claim 1, further comprising: causing a graphical user interface of a user device associated with the user to display an indication of the first heart rate (Salehizadeh: [0071] The method may output the reconstructed representation of the heart-related signal 206).
Regarding claim 14, Salehizadeh teaches an apparatus for measuring heart rate for a user, comprising:
one or more processors; one or more memories coupled with the one or more processors; and instructions stored in the one or more memories and executable by the one or more processors (central processor unit 2318, memory 2308, and computer software instructions 2310 in Fig. 23) to cause the apparatus to: acquire first physiological data from the user via using one or more sensors (biomedical sensor 108).
However, Salehizadeh fails to explicitly disclose a ring or adjusting sampling rate based on activity. Von Badinski teaches a wearable computing device (WCD) in the form of a ring that can be worn on the finger of a human user. Von Badinski discloses:
of a wearable ring device configured to be worn on a finger of the user (WCD 110, Fig. 1A);
determine, using one or more processors of the wearable ring device (processor module 210), that the user is performing an activity based at least in part on the first physiological data; cause, by the one or more processors of the wearable ring device, the one or more sensors of the wearable ring device (sensor modules 220, Fig. 2) to acquire second physiological data associated with the user throughout a time interval in accordance with a sampling rate and based at least in part on determining that the user is performing the activity, wherein the sampling rate is based at least in part on the user performing the activity ([0052] determining an activity level of a wearer of a wearable computing device based at least in part on data from at least one sensor disposed onboard the wearable computing device; comparing the activity level to a predetermined activity threshold; and increasing a first sensor sampling rate if the activity level is above a predetermined activity threshold.).
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 Salehizadeh to include a wearable ring device that adjusts sampling rate based on activity as disclosed in Von Badinski in order to monitor user physiological characteristics over long periods of time with little to no discomfort or interference (Von Badinski [0253]).
The combination of Salehizadeh/Von Badinski further discloses:
the second physiological data comprising photoplethysmogram (PPG) data and motion data collected throughout the time interval (Salehizadeh: [0028] The biomedical sensor may be at least one of: a photoplethysmogram [PPG] sensor, piezoelectric sensor, Light Emitting Diode [LED] based sensor, camera sensor, and pulse oximeter sensor, and the motion sensor may be an accelerometer; [0067] a biomedical sensor 108 that produces a heart-related signal 110; Fig. 1; Fig. 3A; Von Badinski: sensor modules 220, Fig. 2; [0253] if a person's heart rate deviates from a resting heart rate, but the accelerometer indicates that the user is exercising and/or engaging in strenuous activity that provides an equivalent workout, then the WCD may not issue an alert in this circumstance.);
determine a set of candidate heart rate measurements within the time interval based at least in part on the PPG data (Salehizadeh: [0088] The method of FIG. 3B may start (331) and retain (332) up to a pre-determined number N of candidate spectral peaks located at a first point in time in the first TFS; Fig. 3B, 332 PPG Filtered Spectrum);
select a first heart rate measurement from the set of candidate heart rate measurements based at least in part on the motion data (Salehizadeh: 352 selected candidate spectral peak, Fig. 3B; [0030] the method may further comprise employing the reconstructed representation to determine a heart rate estimate; [0106] employ the reconstructed representation of the heart-related signal 514 to determine a heart rate estimate).
However, the combination of Salehizadeh/Von Badinski fails to disclose a threshold accuracy metric.
Diab discloses and determine a first heart rate for the user within the time interval ([0065] FIG. 19, a pulse rate selection and comparison module 1900) based at least in part on the first heart rate corresponding to a first accuracy metric that satisfies a threshold accuracy metric and based at least in part on the selected first heart rate measurement, wherein the threshold accuracy metric is associated with a strength, a quantity, or both, of the set of candidate heart rate measurements ([0011] The physiological model portion of the processor has a series of components that discard potential pulses that do not compare to a physiologically acceptable pulse. The first component of the model portion extracts features of the potential pulses, including pulse starting point, pulse period, and pulse signal strength. These features are compared against various checks, including checks for pulses that have a period below a predetermined threshold, that are asymmetric, that have a descending trend that is generally slower that a subsequent ascending trend, that do not sufficiently comply with an empirical relationship between pulse rate and pulse signal strength, and that have a signal strength that differs from a short-term average signal strength by greater than a predetermined amount.).
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 combination of Salehizadeh/Von Badinski to include a threshold accuracy metric for determining heart rate as disclosed in Diab in order to determine pulse rate in the presence of motion artifact and other noise sources (Diab [0009]).
Regarding claim 15, the combination of Salehizadeh/Von Badinski/Diab discloses the apparatus of claim 14, wherein the instructions are further executable by the one or more processors (Salehizadeh: central processor unit 2318, and computer software instructions 2310 in Fig. 23) to cause the apparatus to:
identify one or more heart rate measurements from the set of candidate heart rate measurements as motion artifacts based at least in part on a comparison of data trends between the set of candidate heart rate measurements and the motion data (Salehizadeh: 338 Fig. 3B; [0090] the method may discard (338) each of the candidate spectral peaks retained, that is, each peak of the Nfirst highest peaks determined at (332), if the peak is associated with a same frequency as a dominant spectral peak located at a second point in time in the second TFS (314) [motion data] computed as disclosed above with regard to FIG. 3 A);
and select the first heart rate measurement from a subset of the set of candidate heart rate measurements that does not include the one or more heart rate measurements that were identified as motion artifacts (Salehizadeh: [0094] Based on at least one last candidate spectral peak remaining from the second discarding (348), reconstructing the current representation may be based on a selected candidate spectral peak (352); Fig. 3B).
Regarding claim 16, the combination of Salehizadeh/Von Badinski/Diab discloses the apparatus of claim 15, wherein the instructions to identify the one or more heart rate measurements as motion artifacts are executable by the one or more processors (central processor unit 2318 and computer software instructions 2310 in Fig. 23) to cause the apparatus to:
identify the one or more heart rate measurements from the set of candidate heart rate measurements as motion artifacts based at least in part on the one or more heart rate measurements exhibiting a similar frequency pattern relative to the motion data (Salehizadeh: Stage 4. Motion Artifact Detection; Fig. 3B; Table 2, Compare the frequencies of the three peaks in the PPG spectrum with the frequency of the largest peak in the accelerometers' spectra. If the first or second largest peaks in the PPG spectrum are similar to that of the accelerometers' peaks, then motion artifact is present in the PPG).
Regarding claim 17, the combination of Salehizadeh/Von Badinski/Diab discloses the apparatus of claim 14, wherein the instructions are further executable by the one or more processors (Salehizadeh: central processor unit 2318 and computer software instructions 2310 in Fig. 23) to cause the apparatus to:
receive additional physiological data associated with the user, the additional physiological data comprising additional PPG data and additional motion data collected throughout a second time interval via the wearable ring device (Salehizadeh: biomedical sensor 108; Von Badinski: WCD 110, sensor modules 220), the second time interval subsequent to the time interval (Salehizadeh: [0096] The method of FIG. 3C may start 362 and compute an average value of a number m of prior reconstructed representations outputted prior to the reconstructing 362 ... For example, if motion artifacts were detected for windows k-1, k-2, and k-3, m may be understood to be 3; [0111] take the frequency and power information of the first three peaks in the PSD at each window and signal segment);
determine an additional set of candidate heart rate measurements within the second time interval based at least in part on the additional PPG data; select a second heart rate measurement from the additional set of candidate heart rate measurements based at least in part on the additional motion data (Salehizadeh: [0097] The cutoff frequency F.sub.c may be based on the average value computed HR.sub.mavg. The method may filter the pre-processed heart-rate signal (306), disclosed in FIG. 3A, above, by applying the band pass filter computed (368) to produce a filtered, pre-processed heart-related signal);
and determine a second heart rate for the user within the second time interval based at least in part on the selected second heart rate measurement (Salehizadeh: [0097] The reconstructed representation HR(k) may be based on an average peak-to-peak interval value 372 of the filtered, pre-processed heart-rate signal).
Regarding claim 18, the combination of Salehizadeh/Von Badinski/Diab discloses the apparatus of claim 17, wherein the instructions are further executable by the one or more processors (Salehizadeh: central processor unit 2318 and computer software instructions 2310 in Fig. 23) to cause the apparatus to:
interpolate between the first heart rate measurement for the time interval and the second heart rate measurement for the second time interval, wherein determining the first heart rate for the user within the time interval, determining the second heart rate for the user within the second time interval, or both, is based at least in part on the interpolating (Salehizadeh: [0096] windows k-1, k-2, and k-3; [Table 5] [when] the HR frequency cannot be extracted from the spectrum and in this case the previous HR frequency is used or for offline implementation a cubic spline interpolation can be applied to fill in the missing HR information. … The PPG signal is reconstructed by using the amplitude, frequency and phase information corresponding to the HR components [extracted in stage 4] that are calculated from the spectrum at each window … HR can be extracted).
Regarding claim 19, the combination of Salehizadeh/Von Badinski/Diab discloses the apparatus of claim 17, wherein the instructions to interpolate are executable by the one or more processors (Salehizadeh: central processor unit 2318 and computer software instructions 2310 in Fig. 23) to cause the apparatus to:
interpolate between the first heart rate measurement for the time interval and the second heart rate measurement for the second time interval based at least in part on an intensity of the motion data collected throughout the time interval, an intensity of the additional motion data collected throughout the second time interval, or both (Von Badinski: [00052] increasing a first sensor sampling rate if the activity level is above a predetermined activity threshold; Salehizadeh: [0096] The method of FIG. 3C may start (362) and compute an average value of a number m of prior reconstructed representations outputted prior to the reconstructing (362). The number m may be based on a duration of consecutive motion artifacts, detected prior to the reconstructing of the representation HR(k). For example, if motion artifacts were detected for windows k−1, k−2, and k−3, m may be understood to be 3; [Table 5] [when] the HR frequency cannot be extracted from the spectrum and in this case the previous HR frequency is used or for offline implementation a cubic spline interpolation can be applied to fill in the missing HR information. … The PPG signal is reconstructed by using the amplitude, frequency and phase information corresponding to the HR components (extracted in stage 4) that are calculated from the spectrum at each window … HR can be extracted).
Claim(s) 2, 8, 12-13, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Salehizadeh (US 20160361021 A1) in view of Von Badinski (US 20150220109 A1) and Diab (US 20030032873 A1), and in further view of Nadeau (US 20210330209 A1).
Regarding claim 2, the combination of Salehizadeh/Von Badinski/Diab discloses the method of claim 1. However, the combination of Salehizadeh/Von Badinski/Diab fails to disclose a machine learning model.
Nadeau teaches systems, devices, and methods for tracking one or more physiological metrics, including heart rate. Nadeau discloses further comprising: inputting the PPG data and the motion data into a machine learning model, wherein selecting the first heart rate measurement, determining the first heart rate, or both, is based at least in part on inputting the PPG data and the motion data into the machine learning model (Nadeau: [0099] In some embodiments, machine learning can be used to update or optimize the way that the processor 110 processes the multiple PPG signals to generate a value for the physiological parameters. For example, the processor 110 may access a set of PPG signals and determine, using a signal analysis engine stored in one or more memories of the wearable device (e.g., memory 112), whether the accessed set of PPG signals requires updating weight values corresponding to the individual signals in the set. The signal analysis engine can be trained, using unsupervised learning (e.g., in some cases, further based on motion data from the accelerometer and/or previously done supervised learning), to update the weight values based on one or more metrics (e.g., confidence values) associated with the individual signals in the set or other environmental factors (e.g., time, location on the user's wrist, weather, orientation of the device, how tightly the user is wearing the device, magnitude and/or direction of movement experienced by the device, activity being performed by the user, and the like). For example, the processor 110 may determine the final value to be outputted on the graphical user interface based on the PPG signal (or an estimated value based on the PPG signal) with the highest confidence value.).
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 combination of Salehizadeh/Von Badinski/Diab to include inputting the PPG data and the motion data into a machine learning model for heart rate selection as disclosed in Nadeau in order to optimize the way that the PPG signals are processed and produce an output based on the signals with the highest confidence value (Nadeau [0099]).
Regarding claim 8, the combination of Salehizadeh/Von Badinski/Diab discloses the method of claim 1. However, the combination of Salehizadeh/Von Badinski/Diab fails to disclose mathematical operations to generate a composite PPG signal from a plurality of pairs of PPG sensors.
Nadeau discloses wherein the PPG data comprises a plurality of PPG signals acquired from a plurality of pairs of PPG sensors, wherein each pair of PPG sensors comprises at least one light-emitting diode and at least one photodetector (Nadeau: [0025] PPG signals are extracted by each source-detector combination. For example, two light sources and two light detectors would result in four source-detector combinations; [0051] the light sources 102 comprise electronic semiconductor light sources, such as LEDs), the method further comprising:
combining the plurality of PPG signals to generate a composite PPG signal using one or more mathematical operations, the one or more mathematical operations comprising an averaging operation, a weighted averaging operation, or both, wherein determining the set of candidate heart rate measurements is based at least in part on the composite PPG signal (Nadeau: [0102] In some embodiments, at least some of the PPG signals are averaged together to produce a single PPG signal. For example, two of the three PPG signals corresponding to the three unique light paths are averaged together to produce a single PPG signal. Then, the PPG device 100 may generate HR, SpO2, and/or other physiological metrics based on the averaged PPG signal and the remaining one of the three PPG signal).
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 combination of Salehizadeh/Von Badinski/Diab to include a plurality of pairs of PPG sensors and using mathematical operations to generate a composite PPG signal as disclosed in Nadeau in order to incorporate extra sensor data based on multiple light paths to improve HR or other physiological metric estimation accuracy of the PPG sensing device, especially when the user of the device is exercising or performing activities involving motion (Nadeau [0027, 0028]).
Regarding claim 12, the combination of Salehizadeh/Von Badinski/Diab discloses the method of claim 1, wherein the wearable device comprises a wearable ring device comprises one or more photodetectors, a first light-emitting diode, and a second light-emitting diode positioned along an inner curved surface of the wearable ring device (Von Badinski: Fig. 3B, red LED 320b, light sensor 320c, infrared LED 320d). However, the combination of Salehizadeh/Von Badinski/Diab fails to disclose sequentially activating and deactivating two LEDs such that one is off while the other is on and vice versa.
Nadeau discloses the method further comprising: sequentially activating and deactivating the first light-emitting diode and the second light-emitting diode such that the first light-emitting diode is in an activated state while the second light-emitting diode is in an inactive state, and vice versa, wherein the second physiological data is acquired based at least in part on sequentially activating and deactivating the first light-emitting diode and the second light-emitting diode (Nadeau: [0066] the PPG device 100 may activate (e.g., emit light via) light source A during a temporal window T1 (without emitting any light via light source B during T1) and activate light source B during a temporal window T2 (without emitting any light via light source A during T2). The light emitted by the light source A during T1 may be detected by the light detector during a temporal window T1′ and the light emitted by the light source B during T2 may be detected by the light detector during a temporal window T2′.).
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 combination of Salehizadeh/Von Badinski/Diab to include sequentially activating and deactivating two LEDs such that one is off while the other is on and vice versa as disclosed in Nadeau in order for the light detector to distinguish one PPG signal based on light emitted by light source A from another PPG signal based on light emitted by light source B by using time multiplexing (Nadeau [0066]).
Regarding claim 13, the combination of Salehizadeh/Von Badinski/Diab/Nadeau discloses the method of claim 12, wherein the one or more photodetectors are positioned along the inner curved surface of the wearable ring device at a first radial position, the first light-emitting diode is positioned along the inner curved surface of the wearable ring device at a second radial position, and the second light-emitting diode is positioned along the inner curved surface of the wearable ring device at a third radial position (Von Badinski: Fig. 3B, red LED 320b, light sensor 320c, infrared LED 320d).
Regarding claim 20, the combination of Salehizadeh/Von Badinski/Diab discloses the apparatus of claim 14. However, the combination of Salehizadeh/Von Badinski/Diab fails to disclose mathematical operations to generate a composite PPG signal from a plurality of pairs of PPG sensors.
Nadeau discloses wherein the PPG data comprises a plurality of PPG signals acquired from a plurality of pairs of PPG sensors (Nadeau: [0025] PPG signals are extracted by each source-detector combination. For example, two light sources and two light detectors would result in four source-detector combinations; [0051] the light sources 102 comprise electronic semiconductor light sources, such as LEDs), and the instructions are further executable by the one or more processors (processors 110) to cause the apparatus to:
combine the plurality of PPG signals to generate a composite PPG signal using one or more mathematical operations, the one or more mathematical operations comprising an averaging operation, a weighted averaging operation, or both, wherein determining the set of candidate heart rate measurements is based at least in part on the composite PPG signal (Nadeau: [0102] In some embodiments, at least some of the PPG signals are averaged together to produce a single PPG signal. For example, two of the three PPG signals corresponding to the three unique light paths are averaged together to produce a single PPG signal. Then, the PPG device 100 may generate HR, SpO2, and/or other physiological metrics based on the averaged PPG signal and the remaining one of the three PPG signal).
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 combination of Salehizadeh/Von Badinski/Diab to include a plurality of pairs of PPG sensors and using mathematical operations to generate a composite PPG signal as disclosed in Nadeau in order to incorporate extra sensor data based on multiple light paths to improve HR or other physiological metric estimation accuracy of the PPG sensing device, especially when the user of the device is exercising or performing activities involving motion (Nadeau [0027, 0028]).
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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/M.H./Examiner, Art Unit 3791
/DEVIN B HENSON/Primary Examiner, Art Unit 3791