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 .
Election/Restrictions
Applicant’s election with travers directed to the process (claims 1-8 elected by the applicant) in the reply filed on July 30, 2026, is acknowledged. The traversal is on the ground(s) that according to the applicant, because the examiner merely restated an inherent capability of the apparatus that is already expressly claimed in the process claims, meaning signals that include a noise component corresponding to “ a movement associated with the wearable ring device” and the examiner asserts requirement of a different field of search and has not identified which specific search queries.
These are not found persuasive because the “method of measuring motion of a is not limited to measuring motion for purposes of identifying a noise component in a PPG signal. Apparatus may be used to practice a materially different process, for example, a motion sensor could measure linear or angular body motion without performing the claimed PPG noise-filtering process and because the examiner and because the “method of measuring other physiological signals of a user” could be a measurement of a body temperature without performing the claimed process of emitting light, measuring a signal containing movement-associated coefficient sets, and filtering the noise (the applicant’s specification, Paragraph 0017: “the “motion artifacts” may be considered to be “noise” when performing heart rate measurements, but may be considered to be a “signal” when measuring movements of the user”). Therefore, these forementioned processes could use the apparatus for purposes materially different from the noise-filtering process recited in claims 1-17.
Regarding search burden, the method group (claims 1-17 but the applicant elected claims 1-8), the method claims require searching signal-processing and noise-filtering procedures including wavelet-transformation processing, whereas the apparatus claims require searching sensor, physiological-monitoring apparatus, and PPG sensor. A complete search of the method claims directed to the claimed noise-filtering methodology would not eliminate the additional search required for the apparatus including PPG sensors, and sensor configuration of the apparatus claims.
Therefore, both different classification areas and search strategies lead to a serious search burden if restrictions are not maintained.
The requirement is still deemed proper and is therefore made FINAL.
Applicant's election with traverse of Species A directed to the embodiment disclosed in Figure 3 in the reply filed on July 30, 2026, is acknowledged. The traversal is on the ground(s) that Species A and Species B are not patentably distinct because they share extensive common subject matters directed to noise filtering of physiological signals in a wearable ring device, that the DWT-based and ICA-based filtering techniques represent narrow variations on the shared core concept, and that a serious search and examination burden has not been established because a search directed to noise filtering in wearable PPG devices would encompass both DWT and ICA. Applicant’s reply filed 7/30/2026, p. 10. The applicant further argues that the identification of different subclasses for DWT and ICA is insufficient to establish a serious burden. Id.
These are not found persuasive because although the applicant responded that “Species A and B are not patentably distinct”, Species A (DWT) and Species B (ICA) share a general objective of filtering noise from physiological signals acquired from a wearable ring device, nevertheless, Species A employs DWT-based noise filtering quite different from Species B employing ICA-based noise filtering. As the applicant disclosed in claims and specification, DWT-based noise filtering requires decomposition of signal into sets of coefficients associated with respective frequency ranges and frequency/coefficient-based noise filtering, whereas Species B employs ICA-based noise filtering requires separation of measured signals into independent components and identification of components associated with noise. Therefore, DWT and ICA are distinct.
Responding to the applicant’s argument regarding search burden: two distinct subject matters of DWT and ICA could lead to different searching scopes, for example, DWT could require searching discrete wavelet-specific subject matter including DWT, wavelet decomposition, coefficients, frequencies, and wavelet reconstruction, whereas ICA could require searching ICA, independent component analysis, independent components, source separation, component identification, and motion-related processing. Therefore, a general search directed to wearable PPG noise filtering would not replace the separate searches required by the species-specific limitations of DWT and ICA. For example, the examiner should search for two query combinations of Wearable ring device + DWT and wearable device + ICA. Accordingly, examining species A and B together would impose serious search and examination
The requirement is still deemed proper and is therefore made FINAL.
Claim 9-20 (Applicant appears to misstate claims 1-9 but then corrected state claims 1-8 are elected in the same response. See Applicant’s reply, pp. 6 and 11. For this examination, claim 9 is considered withdrawn.) withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected invention and species, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on 7/30/2026.
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.
Claim 1-8 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process for noise filtering for a wearable ring device) without significantly more.
Step 1:
The claimed invention of claims 1-8 is directed to statutory subject matter as the claims recite a method for noise filtering for a wearable ring device.
Step 2A, Prong One:
Regarding claims 1-5, the recited steps are directed to mental process, i.e., concepts that can be performed in a mental process (see MPEP 2106.04(a)(2) subsection (III)) and mathematical concept, i.e., concepts that can be performed in a mathematical concept (see MPEP 2106.04(a)(2) subsection (I)). The courts have found that concepts performed in a human mind falls within the judicial exceptions, often labelled as “abstract ideas”.
Specifically, the following limitations recite mental process and mathematical concept;
Regarding claim 1, the limitation of “processing the first signal to filter one or more characteristics including the noise component from the first signal based at least in part on a filtering procedure wherein the filtering procedure comprises decomposing the first signal into a plurality of sets of coefficients, and wherein each set of coefficients is associated with a frequency range;” (mental process: mathematical calculation).
Regarding claim 1, the limitation of “calculating a second signal based at least in part on filtering the one or more characteristics including the noise component from the first signal” (mental process: calculation with pen and pencil).
Regarding claim 2, the limitation of “discrete wavelet transform procedure” (mathematical concepts: mathematical calculation).
Regarding claim 3, the limitation of “wherein processing the first signal to filter the one or more characteristics including the noise component further comprises: removing one or more sets of coefficients of the plurality of sets of coefficients, wherein the one or more sets of coefficients are associated with one or more respective frequency ranges that fall outside of a threshold frequency range” (mathematical concepts: mathematical calculation).
Regarding claim 4, the limitation of “calculating a noise reference based at least in part on the one or more sets of coefficients, wherein filtering the one or more characteristics including the noise component is based at least in part on the noise reference” (mathematical concepts: mathematical calculation and further mental process: calculation with pen and pencil).
Regarding claim 5, the limitation of “the threshold frequency range is predetermined” (mental process: judgement, opinion).
Furthermore, the “predetermine” step reasonably encompasses a user manually calculating mentally or using pen and paper.
Regarding claim 6, the limitation of “wherein the second signal is associated with a clean signal.” (further narrows abstract idea of mental process: calculation with pen and pencil).
Regarding claim 8, the limitation of “wherein the one or more physiological phenomena comprise blood oxygen levels, heart rate measurements, or both.” (further narrows abstract idea of mental process: calculation with pen and pencil).
Step 2A, Prong Two:
The claims, including dependent claims, are analyzed as a whole to determine whether additional limitations are recited such that the claims amount to significantly more than the abstract idea.
Regarding claim 1, the additional element of “a wearable ring device” amounts to insignificant extra solution activity.
Regarding claim 1, the additional element of “emitting light from a set of light emitting elements of the wearable ring device;” amounts to insignificant extra solution activity.
Regarding claim 1, the additional element of “measuring a first signal comprising a representation of one or more physiological phenomena and a noise component from the set of light emitting elements, wherein the noise component corresponds to a movement associated with the wearable ring device;” amounts to insignificant extra solution activity.
Regarding claim 7, the additional element of “one or more light emitting diodes (LEDs)”, amounts to insignificant extra solution activity.
Regarding claimed noised filtering for the wearable ring device disclosed in the applicant’s specification, the specification does not sufficiently describe a concrete technological improvement attributable to the claimed invention of noise filtering although the specification describes the wearable ring device as a physiological signal detection device, the applicant describes the wearable ring as a computing device as recited (Paragraph, 0023: “ Example wearable devices 104 may include wearable computing devices, such as a ring computing device (hereinafter “ring”) configured to be worn on a user’s 102 finger, a wrist computing device (e.g., a smart watch, fitness band, or bracelet) configured to be worn on a user’s 102 wrist, and/or a head mounted computing device (e.g., glasses/goggles). Wearable devices 104 may also include bands, straps (e.g., flexible or inflexible bands or straps), stick-on sensors, and the like, that may be positioned in other locations, such as bands around the head (e.g., a forehead headband), arm (e.g., a forearm band and/or bicep band), and/or leg (e.g., a thigh or calf band), behind the ear, under the armpit, and the like. Wearable devices 104 may also be attached to, or included in, articles of clothing”).
Insignificant extra solution activity does not integrate a judicial exception. Regarding claims 1-8, the judicial exception is not integrated into a practical application.
Step 2B:
The claims 1-8 do not include additional elements that are sufficient to amount significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements, including “wearable ring device,” “emitting” step, “measuring” step [claim 1], and “light emitting diodes (LED)” [claim 7] are recited at a high level of generality and are used for extra-solution activity, such as data gathering necessary to perform the abstract idea as well as part of generic computer equipment used to perform extra-solution data delivery computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)). Therefore, these elements do not provide an inventive concept sufficient to transform the claimed abstract idea into patent eligible subject matter.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 1-2, 6, and 8 are rejected under 35 U.S.C. 103 as being unpatentable over “Alghorani” (US 20220296169 A1) in view of “Jeon” (KR 20180077629 A).
Regarding claim 1,
Alghorani teaches a method for noise filtering for a wearable ring device (Abstract and Paragraphs 0026-0027 and 0059; Examiners Note: Alghorani discloses a wearable physiological sensor including a wristwatch. The wristwatch wearable sensor could be considered to fall within the scope of a wearable ring device because the claim does not further limit the ring device to a particular ring structure, and a wristwatch when worn is in a ring shape) comprising:
emitting light from a set of light emitting elements of the wearable ring device (Paragraph 0059: “FIG. 1 shows edge computing technology for remote patient monitoring, in which physiological signals (e.g., PPG, ECG, skin temperature) are collected through wearable biosensors (e.g., wristwatch, vital patch)”; Fig 5 illustrating waveform data from PPG sensors; Examiners Note: Alghorani discloses PPG sensing, which necessarily involved illumination of tissue with emitted light and detection of the transmitted light. The PPG sensor is reasonably considered as light emitting elements and light detection elements too.);
measuring a first signal comprising a representation of one or more physiological phenomena and a noise component (Paragraph 0049: “a wearable sensor configured for attaching to the monitored subject and configured to measure a biosignal, from which the physiological parameter is deducible, so as to form a measured signal including data representative of the physiological parameter and noise data”, and Paragraph 0089: “Wearable biosensors generate large amounts of patient data that contain motion artifacts and interference that can distort PPG-ECG signals and reduce the detection accuracy of physiological parameters during patient movement”),
wherein the noise component corresponds to a movement associated with the wearable ring device (Paragraph 0113: “In the illustrated arrangement, since the step of measuring the biosignal at 30 is performed using a wearable sensor such as 12, the noise data comprises noise associated with movement of the wearable sensor. Movement of the sensor primarily stems from movement of the subject to whom the sensor is generally fixedly attached and who is free to move around when wearing wearable sensors that are wirelessly communicated with the processing unit in the form of a portable computing device such as a smartphone. As such, preferably, the prescribed threshold for discarding frequency components is based on noise associated with movement of a wearable sensor”, and Paragraph 0114: “In the illustrated arrangement, the data collection method further includes measuring motion of the monitored subject to form motion data usable to remove the noise data from the measured biosignal, as indicated at 39. This is performed concurrently with measuring the biosignal”);
processing the first signal to filter one or more characteristics including the noise component from the first signal based at least in part on a filtering procedure (Paragraph 0021, “converting the signal to a vector having a plurality of different frequency components each with a corresponding magnitude coefficient”, Paragraph 0022: “discarding from the vector select ones of the frequency components with coefficients below a prescribed threshold to form a reduced vector;”, Paragraph 0023: “communicating the reduced vector to a computing device for processing to deduce the physiological parameter”, Paragraph 0024: “This arrangement accounts for noise inadvertently captured during measurement of the biosignal and provides reduced computational burden for the computing device by removing components from the measured signal which are immaterial to the physiological parameter, such that the computing device receives a smaller amount of transmitted data”, and Paragraph 0029: “Preferably, the method further includes measuring motion of the monitored subject to form motion data usable to remove the noise data from the measured biosignal”),
wherein the filtering procedure comprises decomposing the first signal into a plurality of sets of coefficients, and wherein each set of coefficients is associated with a frequency (Paragraph 0021: “converting the signal to a vector having a plurality of different frequency components each with a corresponding magnitude coefficient”, Paragraph 0037: “typically, the noisy signal is in the form of a vector having a plurality of different frequency components each with a corresponding magnitude coefficient”, and Paragraph 0072: “the source biosignals are collected by wearable biosensors (e.g., wristwatch, vital patch) and compressed by a digital CS model [64] to discard the small frequency coefficients of the source biosignals vector s(t)=[s.sub.1(t), . . . , s.sub.N(t)] due to motion artifacts being measured by a motion sensor (accelerometer), i.e., many frequency coefficients are set to zero after adding a quantization step to the inverse discrete cosine transform vector Ψ=[Ψ.sub.1, . . . , Ψ.sub.N] (where Ψ.sub.1∈custom-character.sup.n×n is a unitary matrix that can discard the small coefficients of s.sub.i) to produce a sparse vector, x(t)=Ψs(t), where we can design the deep neural network to have fewer layers and thus the exploding gradient problem is fixed”); and
calculating a second signal based at least in part on filtering the one or more characteristics including the noise component from the first signal (Paragraph 0049: “a wearable sensor configured for attaching to the monitored subject and configured to measure a biosignal, from which the physiological parameter is deducible, so as to form a measured signal including data representative of the physiological parameter and noise data”, Paragraph 0050: “wherein the wearable sensor comprises a non-transitory memory and a processor configured to execute instructions stored on the non-transitory memory to substantially remove, from the measured signal, the noise data so as to form a cleaned signal”, Paragraph0051: “the portable computing device comprises a non-transitory memory and a processor configured to execute instructions stored on the non-transitory memory of the portable computing device to determine the physiological parameter from the transmitted signal”, and Paragraph 0057: “Preferably, the wearable sensor comprises a plurality of wearable sensors each measuring a different biosignal of the monitored subject from which a common physiological parameter is deducible”).
Alghorani further discloses that the filtering procedure comprises decomposing the first signal into a plurality of sets of coefficients, and wherein each set of coefficients is associated with a frequency (see above) but does not explicitly teach that the frequency comprises a frequency range.
Jeon teaches a method for denoising photoplethysmography signal (“Method for denoising photoplethysmography signal”) wherein the frequency comprises a frequency range (Paragraph 0066: “A discrete wavelet transform step (208) for signal processing of the real part includes decomposing (236, 238, 240) a low frequency wavelet coefficient and removing (242) noise from the high frequency wavelet coefficient based on a threshold, and a discrete wavelet transform step (210) for signal processing of the imaginary part includes decomposing (244, 246, 248) the low frequency wavelet coefficient and removing (250) noise from the high frequency wavelet coefficient based on the threshold”, and Paragraph 0067: “In the reconstructing of the ppg signal 250, the ppg signal from which noise is removed is reconstructed and output based on the decomposed low frequency wavelet coefficients and high frequency coefficients from which noise is removed, which are output through the discrete wavelet transform for signal processing of the real part, and the decomposed low frequency wavelet coefficients and high frequency coefficients from which noise is removed, which are output through the discrete wavelet transform for signal processing of the imaginary part”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Alghorani’s wearable sensor with Jeon because incorporating the wavelet coefficient decomposition and noise removal into the wearable ring device would have effectively removed noise from the PPG signals, thereby obtaining a clean PPG waveform (Jeon, Paragraph 0006, 0067).
Regarding claim 2, Alghorani in view of Jeon teaches the method of claim 1 (See rejection of claim 1 above) wherein the filtering procedure comprises discrete wavelet transform procedure (Jeon, Paragraph 0006: “Disclosure technical problem an object to be achieved by the present invention is to provide a photoplethysmography signal denoising method that has a clear mathematical result and low computational complexity, can overcome shift sensitivity and frequency aliasing in discrete wavelet transform, and has excellent noise removal performance in ppg signals”, and Paragraph 0065: “step 202 includes performing a discrete wavelet transform (dwt) for signal processing of a real part on the signal output in the morphological filtering step 200 (208), performing a discrete wavelet transform (dwt) for signal processing of an imaginary part on the signal output in the morphological filtering step (210) and reconstructing a ppg signal based on a result of the discrete wavelet transform for signal processing of the real part and a result of the discrete wavelet transform for signal processing of the imaginary part (212)”).
Regarding claim 6, Alghorani in view of Jeon teaches the method of claim 1 (See rejection of claim 1 above).
Alghorani teaches wherein the second signal is associated with a clean signal (Paragraph 0049: “a wearable sensor configured for attaching to the monitored subject and configured to measure a biosignal, from which the physiological parameter is deducible, so as to form a measured signal including data representative of the physiological parameter and noise data”, Paragraph 0050: “wherein the wearable sensor comprises a non-transitory memory and a processor configured to execute instructions stored on the non-transitory memory to substantially remove, from the measured signal, the noise data so as to form a cleaned signal”, and Paragraph 0051: “a transmitted signal therefrom, wherein the portable computing device comprises a non-transitory memory and a processor configured to execute instructions stored on the non-transitory memory of the portable computing device to determine the physiological parameter from the transmitted signal”).
Regarding claim 8, Alghorani in view of Jeon teaches the method of claim 1 (See rejection of claim 1 above).
Alghorani teaches wherein the one or more physiological phenomena comprise blood oxygen levels, heart rate measurements, or both (Paragraph 0092: “Developing a low - complexity and cost sensor method that can provide continuous monitoring for the five physiological parameters ( e.g. , temperature , BP , RR , HR , SpO2 ) while walking or exercising”, Paragraph 0093: “Removing motion artifacts from PPG - ECG signals during patient movement and addressing the coexistence problem of WiFi , Bluetooth , and ZigBee technologies ( which results in RF interference and lower detection accuracy of the physiological parameters ) due to the increase in the number of IoT devices operating in the ISM band”, and Paragraph 0096: “Unlike competitors in the e - health market today who don't offer a continuous remote BP monitoring feature while the patient is in motion , our sensor system can monitor the five physiological parameters ( including BP ) simultaneously in real - time during patient movement”).
Claim 3 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Alghorani in view of Jeon, and further “Kwon” (US20180263566A1).
Regarding claim 3, Alghorani in view of Jeon teaches the method of claim 1 (See rejection of claim 1 above).
Alghorani teaches wherein processing the first signal to filter the one or more characteristics including the noise component further comprises (Paragraph 0021: “converting the signal to a vector having a plurality of different frequency components each with a corresponding magnitude coefficient”, Paragraph 0022: “discarding from the vector select ones of the frequency components with coefficients below a prescribed threshold to form a reduced vector”, Paragraph 0023: “communicating the reduced vector to a computing device for processing to deduce the physiological parameter”, Paragraph 0024: “This arrangement accounts for noise inadvertently captured during measurement of the biosignal and provides reduced computational burden for the computing device by removing components from the measured signal which are immaterial to the physiological parameter, such that the computing device receives a smaller amount of transmitted data.”, and Paragraph 0112: “In the illustrated arrangement, measuring the biosignal comprises measuring at least one of body temperature, heartbeat, and blood flow. When there are multiple sensors, such as those indicated at 12 through 14, each measures a different biosignal of the monitored subject from which the common physiological parameter is deducible. This may improve accuracy of the calculated or determined physiological parameter.’):
removing one or more sets of coefficients of the plurality of sets of coefficients, wherein the one or more sets of coefficients are associated with one or more respective frequency that fall outside of a threshold frequency (Paragraph 0022: “discarding from the vector select ones of the frequency components with coefficients below a prescribed threshold to form a reduced vector”, and Paragraph 0113: “since the step of measuring the biosignal at 30 is performed using a wearable sensor such as 12, the noise data comprises noise associated with movement of the wearable sensor. Movement of the sensor primarily stems from movement of the subject to whom the sensor is generally fixedly attached and who is free to move around when wearing wearable sensors that are wirelessly communicated with the processing unit in the form of a portable computing device such as a smartphone. As such, preferably, the prescribed threshold for discarding frequency components is based on noise associated with movement of a wearable sensor’).
Algohorani teaches one or more respective frequency that falls outside of a threshold frequency (see above) but does not explicitly teach that the frequency comprises a frequency range.
Kwon teaches one or more respective frequency ranges that fall outside of a threshold frequency (Paragraph 0033: “Whenever the wavelet transform is performed, the frequency band is divided into two bands and a low frequency band component is A[n] and a high frequency band component is D[n]”, Paragraph 0048: “Whenever one wavelet transform is performed, the signal is divided into an approximation A[n] part and a detail D[n] part. A[n] represents a low frequency band and D[n] represents a high frequency band. In this case, the wavelet transform is repeated so that the frequency band of A[n] is 0 to 4 Hz”, and Paragraph 0063: “FIG. 8 is a graph illustrating a motion artifact detecting result according to the present invention. The PPG signal which generates the motion artifact is identified from three parts in which the PPI is short in FIG. 8A. After setting the reference signal to determine the motion artifact and setting the base function, the wavelet transform is performed to represent an A[n] period having a frequency range of 0 to 4 Hz and a D[n] period having a frequency range of 32 to 64 Hz. A period when the power of each period of the D[n] period is 1.5 times or higher than the power of the corresponding period of the reference signal is determined as a period where the motion artifact is generated”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Alghorani with Kwon because separating the PPG signal into frequency bands using wavelet transform would have enabled noise in frequency bands outside the desired PPG frequency band to be removed (Kwon, paragraphs 0033,0048, and 0063).
Regarding claim 5, Alghorani in view of Jeon and Kwon teaches the method of claim 3 (See rejections of claims 1 and 3 above).
Alghorani does not teach wherein the threshold frequency range is predetermined or dynamic.
Kwon teaches wherein the threshold frequency range is predetermined or dynamic (Paragraph 0033: “Whenever the wavelet transform is performed, the frequency band is divided into two bands and a low frequency band component is A[n] and a high frequency band component is D[n]”, Paragraph 0048: “Whenever one wavelet transform is performed, the signal is divided into an approximation A[n] part and a detail D[n] part. A[n] represents a low frequency band and D[n] represents a high frequency band. In this case, the wavelet transform is repeated so that the frequency band of A[n] is 0 to 4 Hz”, and Paragraph 0063: “FIG. 8 is a graph illustrating a motion artifact detecting result according to the present invention. The PPG signal which generates the motion artifact is identified from three parts in which the PPI is short in FIG. 8A. After setting the reference signal to determine the motion artifact and setting the base function, the wavelet transform is performed to represent an A[n] period having a frequency range of 0 to 4 Hz and a D[n] period having a frequency range of 32 to 64 Hz. A period when the power of each period of the D[n] period is 1.5 times or higher than the power of the corresponding period of the reference signal is determined as a period where the motion artifact is generated”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Alghorani with Kwon because using a predetermined threshold frequency range would have enabled noise outside the desired PPG frequency range to be identified and removed (Kwon, Paragraph 0033,0048, and 0063).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Alghorani in view of Jeon, Kwon, and further in view of “Rao” ("On the use of Wavelet Transform based Adaptive Filtering for de-noising of Pulse Oximeter signals", IEEE, International Instrumentation and Measurement Technology Conference (I2MTC), Glasgow, United Kingdom, 2021, pp. 1-4).
Regarding claim 4, Alghorani in view of Jeon, and further Kwon teaches the method of claim 3 (See rejection of claim 3 above).
Alghorani does not teach further comprising: calculating a noise reference based at least in part on the one or more sets of coefficients, wherein filtering the one or more characteristics including the noise component is based at least in part on the noise reference.
Rao teaches further comprising: calculating a noise reference based at least in part on the one or more sets of coefficients (Page 1, right column, Section Ⅱ, paragraph 3, lines 19-21: “an efficient adaptive filtering method for MA reduction, which uses a wavelet reconstructed secondary MA noise as reference signal, is presented”, Fig. 1 and Page 2, right column, Section Ⅱ. C.: “C. Proposed Wavelet transform based Adaptive Filter (WTB-AF).
The processing steps of proposed Wavelet transform based adaptive filter (WTB-AF) method are given below.
(i) Recorded MA corrupted PPG signals will be decomposed into corresponding approximate (Aj) and detail coefficients (Dj) using wavelet decomposition procedure
(ii) The approximate and detail coefficients will be modified according to the following procedure based on the kurtosis of Aj and Dj If kurtosis of Aj and Dj values are;
(a) Very high indicates, the random components corresponding to out-of-band MA noise and coefficients Aj and Dj remains unaltered.
(b) Moderate values indicating that it corresponds to combination of signal and in-band MA noise. Then thresholding is applied to concerned Aj and Dj to get MA noise.
(c) Low values indicating that it correspnds to only signal components. Then corresponding Aj and Dj are forced to zero.
(iii) The previous step will provide a way for the in-band and out-of –band MA noise, then wavelet reconstruction will be applied on modified Aj and Dj components”, Page 3, left column, Section Ⅲ, lines 7-12: “As part of the proposed method, a secondary noise representing MA is generated by wavelet decomposing the MA currupted PPG signal. The wavelet recostructed MA noise equivalent signal is shown in Fig. 3(b). This wavelet reconstructed signal will be used a reference noise signal for adaptive filter operation”, and Abstract: “The current work is focused on an efficient adaptive filtering method for MA reduction, which uses a wavelet reconstructed secondary MA noise as reference signal. It eliminates the use of”, and Page 1, Introduction, right column, paragraph 3, lines 19-21: “In this paper, an efficient adaptive filtering method for MA reduction, which uses a wavelet reconstructed secondary MA noise as reference signal, is presented”),
wherein filtering the one or more characteristics including the noise component is based at least in part on the noise reference (Page 1, Introduction, right column, paragraph 3, lines 23-26: “This method while reducing the MA, restored the PPG morphology and respiratory components facilitating accurate estimation SpO2 , heart rate (HR), and respiratory rate (RR), and ” The scenarios wherein the noise and signal overlap, then the adaptive filter will provide a best solution for noise separation”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Alghorani’s wearable sensor in view of Jeon and Kwon with Rao because using a wavelet-reconstructed secondary motion artifact (MA) noise as a reference signal for adaptive filtering would have provided noise separation when the noise and signal overlap (Rao, Page 2, right column, Section Ⅱ, paragraph 5, lines 36-38).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Alghorani in view of Jeon as applied in claim 1, and further in view of “Heikkinen” (WO2023064410A1).
Regarding claim 7, Alghorani in view of Jeon teaches the method of claim 1 (See rejection of claim 1 above).
Alghorani does not explicitly teach wherein the set of light emitting elements comprises one or more light emitting diodes (LEDs).
Heikkinen teaches wherein the set of light emitting elements comprises one or more light emitting diodes (LEDs) (Paragraph 0031: “the rings 104 (e.g., wearable devices 104) of the system 100 may be configured to collect physiological data from the respective users 102 based on arterial blood flow within the user’s finger. In particular, a ring 104 may utilize one or more LEDs (e.g., red LEDs, green LEDs) which emit light on the palmside of a user’s finger to collect physiological data based on arterial blood flow within the user’s finger. In some implementations, the ring 104 may acquire the physiological data using a combination of both green and red LEDs. The physiological data may include any physiological data known in the art including, but not limited to, temperature data, accelerometer data (e.g., movement/motion data), heart rate data, HRV data, blood oxygen level data, or any combination thereof”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Alghorani with Heikkinen because using the LEDs would have enabled the LEDs to emit light on the palm side of a user’s finger to collect physiological data based on arterial blood flow within the user’s finger (Heikkinen, Paragraph 0031).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MORGAN S SHIM whose telephone number is (571)272-9032. The examiner can normally be reached Mon-Fri 7:30AM-4:30PM.
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/MORGAN SANGJO SHIM/Examiner, Art Unit 3791
/PATRICK FERNANDES/Primary Examiner, Art Unit 3791
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