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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 06/07/2024 and 08/08/2024 have been considered by the examiner.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Objections
Claims 1 and 20 are objected to because of the following informalities:
In claim 1, line 5, the claim reads “PPG data”. Before the use of acronyms, the word/phrase should be fully written followed by the acronym.
In claim 20, lines 6-7, the claim read “physiological detection signal quality evaluation method comprises:”. This should read “physiological detection signal quality evaluation method comprising:”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 8-9 and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 8, the claim recites “calculating an upper threshold U1 and a lower threshold U2 of the abnormal data” in line 5 and “determining that data that is in the energy ratio list and that is less than or equal to the lower threshold or greater than or equal to the upper threshold is the abnormal data” in lines 6-7. It is unclear how the thresholds can be calculated of the abnormal data if this step occurs before the abnormal data is determined. These limitations appear to be circular in nature. Clarification is requested.
For the purposes of examination, line 5 of the claim is interpreted as “calculating an upper threshold U1 and a lower threshold U2 of the energy ratio list”.
Regarding claim 9, the claim recites the terms a and b within a mathematical equation in lines 2-3. These terms are undefined and therefore the metes and bounds defined by the claims are unclear. Clarification is requested.
For the purposes of examination, the claim is interpreted as “wherein the upper threshold is U1 = a*Q3E - b*Q1E, and the lower threshold is U2 = a*Q3E + b*Q1E, wherein a and b are preset values.”
Regarding claim 20, the claim recites “enabled to perform the physiological detection signal quality evaluation method” in line 6. There is insufficient antecedent basis for this limitation.
For the purposes of examination, the claim is interpreted as “enabled to perform a physiological detection signal quality evaluation method”.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claim 1 follows.
Step 1
Regarding claim 1, the claim recites a series of steps or acts, including evaluating quality of a physiological detection signal collected by the detection device. Thus, the claim is directed to a process, which is one of the statutory categories of invention.
Step 2A, Prong One
The claim is then analyzed to determine whether it is directed to any judicial exception. The step of evaluating, based on the quality evaluation parameters, quality of a physiological detection signal collected by the detection device sets forth a judicial exception. This step describes a concept performed in the human mind (including an observation, evaluation, judgment, opinion). Thus, the claim is drawn to a Mental Process, which is an Abstract Idea.
Step 2A, Prong Two
Next, the claim as a whole is analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. The claim fails to recite an additional element or a combination of additional elements to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. Claim 1 recites evaluating quality of a physiological detection signal collected by the detection device, which is the judicial exception. The evaluating step does not provide an improvement to the technological field, the method does not effect a particular treatment or effect a particular change based on the evaluated quality, nor does the method use a particular machine to perform the Abstract Idea.
Step 2B
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception. Besides the Abstract Idea, the claim recites additional steps of obtaining a sample data set, selecting sample data of a scenario from the sample data set and dividing the sample data, calculating an energy ratio, and separately calculating quality evaluation parameters. Obtaining a sample data set of a device in different conditions and selecting sample data from a particular condition and calculating an energy ratio (i.e., signal-to-noise ratio) is well-understood, routine and conventional activity for those in the field of medical diagnostics. Further, the obtaining, selecting, calculating, and separately calculating steps are each recited at a high level of generality such that it amounts to insignificant presolution activity, e.g., mere data gathering step necessary to perform the Abstract Idea. When recited at this high level of generality, there is no meaningful limitation, such as a particular or unconventional step that distinguishes it from well-understood, routine, and conventional data gathering and comparing activity engaged in by medical professionals prior to Applicant's invention. Furthermore, it is well established that the mere physical or tangible nature of additional elements such as the obtaining step does not automatically confer eligibility on a claim directed to an abstract idea (see, e.g., Alice Corp. v. CLS Bank Int'l, 134 S.Ct. 2347, 2358-59 (2014)).
Consideration of the additional elements as a combination also adds no other meaningful limitations to the exception not already present when the elements are considered separately. Unlike the eligible claim in Diehr in which the elements limiting the exception are individually conventional, but taken together act in concert to improve a technical field, the claim here does not provide an improvement to the technical field. Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claim as a whole does not amount to significantly more than the exception itself. The claim is therefore drawn to non-statutory subject matter.
The same rationale applies to claim 20.
Further regarding claim 20, the device recited in the claim is a generic device comprising generic computer components configured to perform the abstract idea. The recited memory and processor are generic computer components configured to perform the Abstract Idea. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application.
The dependent claims also fail to add something more to the abstract independent claims as they generally recite method steps pertaining to data gathering and the judicial exception. The evaluating step recited in the independent claim maintain a high level of generality even when considered in combination with the dependent claims.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claims 1-2, and 18-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US Patent Publication 2020/0337574 by Foroozan et al. – cited by Applicant, hereinafter “Foroozan”.
Regarding claim 1, Foroozan teaches a physiological detection signal quality evaluation method, wherein the method comprises: obtaining a sample data set collected by a detection device (optical sensor 104) in different scenarios ([0057]; FIG. 8 illustrates that for a single subject and during different activities of sitting, walking, and running, SQM is evaluated. [0006]; The determination of SQM is based on the PPG data.); selecting sample data of a scenario from the sample data set, and dividing the sample data into physiological data ([0037]; the physiological data can be considered the AC component of the PPG signal and is synchronized with the heartbeat) and PPG data (the PPG signal); calculating an energy ratio of the physiological data based on the physiological data and the PPG data ([0051; 0058]; Relative power was used as the clarity metric (SQM). This is defined as the ratio of the power of AC PPG signal after bandpass filtering (0.5-5 Hz) to the power of the raw AC PPG signal.); separately calculating quality evaluation parameters of sample data of a plurality of classified scenarios based on energy ratios of the physiological data corresponding to different scenarios ([0057]; Fig. 8 displays the PPG signal with signal quality ratings which are based signal quality levels (See Fig. 6A), which are based on the SQMs, during different activities of sitting, walking, and running); and evaluating, based on the quality evaluation parameters, quality of a physiological detection signal collected by the detection device ([0057]; The quality of Excellent, Acceptable, and Weak are evaluated based on the signal quality levels).
Regarding claim 2, Foroozan teaches the physiological detection signal quality evaluation method according to claim 1, wherein the obtaining a sample data set collected by a detection device in different scenarios comprises: setting a plurality of preset scenarios based on a label (Labels are seen in Fig. 8, indicating the beginning and end of activities and high motion labels. These labels correspond to the activities of sitting walking, and running); connecting the detection device to a data collection end (The optical sensor is connected to the processor, which is considered a data connection end.); collecting, by the detection device, data based on a single preset scenario to generate a single piece of sample data (During any of the scenarios, the detection device collects PPG data); and outputting a multi-scenario sample data set based on sample data of the plurality of preset scenarios (Fig. 8 comprises a multi-scenario sample data set).
Regarding claim 18, Foroozan teaches the physiological detection signal quality evaluation method according to claim 1, wherein the method further comprises: filtering out low-frequency and/or high-frequency noise in the PPG data in the sample data ([0045]; the pre-processing module 504 is responsible for filtering out low (below 0.5 Hz) and high (above 5Hz) frequencies).
Regarding claim 19, Foroozan teaches the physiological detection signal quality evaluation method according to claim 18, wherein the filtering out low-frequency and/or high-frequency noise in the PPG data in the sample data comprises: setting an upper threshold and a lower threshold of a band-pass frequency by using an f-order band-pass filter ([0045]; the upper threshold is set at 5Hz and the lower threshold is set at 0.5Hz. While Foroozan does not specify the order of the filter, a simple filter can be considered to be a first order filter, which reads on the limitation of an f-order filter); and inputting the PPG data into the band-pass filter, and filtering out the low-frequency and/or high-frequency noise in the PPG data based on the upper threshold and the lower threshold by using the band-pass filter ([0045]; The filter is employed to “remove high frequency components of the PPG signal as well as low frequency noise due”).
Regarding claim 20, Foroozan teaches an electronic device (Fig. 1; system 100) wherein the electronic device comprises a memory (Fig. 1; memory 115A) and a processor (Fig. 1; processor 110); the memory is configured to store program instructions; and the processor is configured to read and execute the program instructions stored in the memory, and when the program instructions are executed by the processor, the electronic device is enabled to perform the physiological detection signal quality evaluation method ([0031], “the processor can execute special instructions (stored on non-transitory computer readable-medium) for carrying out various methods of power reduction as described herein”) comprises: obtaining a sample data set collected by a detection device in different scenarios (See the rejection of claim 1); selecting sample data of a scenario from the sample data set, and dividing the sample data into physiological data and PPG data (See the rejection of claim 1); calculating an energy ratio of the physiological data based on the physiological data and the PPG data (See the rejection of claim 1); separately calculating quality evaluation parameters of sample data of a plurality of classified scenarios based on energy ratios of the physiological data corresponding to different scenarios (See the rejection of claim 1); and evaluating, based on the quality evaluation parameters, quality of a physiological detection signal collected by the detection device (See the rejection of claim 1).
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.
Claims 3 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Foroozan, as applied to claim 1, in view of US Patent Publication 2017/0249445 by Devries et al., hereinafter “Devries”.
Regarding claim 3, Foroozan teaches the physiological detection signal quality evaluation method according to claim 1, but does not teach wherein the calculating an energy ratio of the physiological data based on the physiological data and the PPG data comprises: separately calculating, based on the physiological data and the PPG data, a plurality of energy ratios of frequencies corresponding to a plurality of pieces of physiological data; and calculating an average value of the plurality of energy ratios of the frequencies corresponding to the plurality of pieces of physiological data, and using the average value as the energy ratio of the physiological data.
Devries teaches a method of calculating an energy ratio (signal-to-noise ratio (SNR)), wherein the ratio is calculated on a per-beat basis (Each beat comprises one of a plurality of pieces of physiological data) for all of the valid beats in a given window. The energy ratios from each beat is then averaged over all of the beats to obtain an average energy ratio ([0209]). By using the average of each beat, only the valid heartbeats are able to be selected from the heartbeats, which would result in a more accurate energy ratio.
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the method of Foroozan such that the calculating an energy ratio of the physiological data based on the physiological data and the PPG data includes: separately calculating, based on the physiological data and the PPG data, a plurality of energy ratios of frequencies corresponding to a plurality of pieces of physiological data; and calculating an average value of the plurality of energy ratios of the frequencies corresponding to the plurality of pieces of physiological data, and using the average value as the energy ratio of the physiological data, in order to have a more accurate energy ratio as taught by Devries ([0209]).
Regarding claim 17, Foroozan teaches the physiological detection signal quality evaluation method according to claim 1, but does not teach wherein the method further comprises: performing upsampling processing on the PPG data in the sample data.
Devries teaches up-sampling and interpolation of the pulse profile data (i.e., PPG signal). Up-sampling the data can be performed to improve the time resolution of the processing steps to follow ([0063]).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the method of Foroozan to include performing upsampling processing on the PPG data in the sample data in order to improve the time resolution of the processing steps to follow, as taught by Devries ([0063]).
Claims 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Foroozan in view of Devries as applied to claim 3, in view of US Patent Publication 2020/0237317 by Newberry et al., hereinafter “Newberry”.
Regarding claim 4, Foroozan in view of Devries teaches the physiological detection signal quality evaluation method according to claim 3, but does not teach wherein the separately calculating, based on the physiological data and the PPG data, a plurality of energy ratios of frequencies corresponding to a plurality of pieces of physiological data comprises: performing short-time Fourier transform on the PPG data to obtain power spectral density data of the PPG data, wherein a window type of the short-time Fourier transform is a Hamming window; determining time points corresponding to center locations of a plurality of Hamming windows, and obtaining physiological data corresponding to each time point; converting the physiological data into a frequency range; calculating, based on the power spectral density data, an energy ratio corresponding to the frequency range; and outputting an energy ratio list of the plurality of pieces of physiological data in the sample data based on energy ratios corresponding to a plurality of frequency ranges.
Newberry teaches that the frequency spectrum of a PPG signal can be determined over a time period defined by using an FFT algorithm over a Hamming window. Newberry also teaches that the pulse rate (i.e., physiological data) can be obtained corresponding to that window as the highest power in the estimated frequency spectrum ([0170]).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the method taught by Foroozan in view of Devries such that the separately calculating, based on the physiological data and the PPG data, a plurality of energy ratios of frequencies corresponding to a plurality of pieces of physiological data comprises: performing short-time Fourier transform on the PPG data to obtain power spectral density data of the PPG data, wherein a window type of the short-time Fourier transform is a Hamming window; determining time points corresponding to center locations of a plurality of Hamming windows, and obtaining physiological data corresponding to each time point; converting the physiological data into a frequency range; calculating, based on the power spectral density data, an energy ratio corresponding to the frequency range; and outputting an energy ratio list of the plurality of pieces of physiological data in the sample data based on energy ratios corresponding to a plurality of frequency ranges, as taught by Newberry ([0170]).
It is noted calculating the energy ratios as taught by Foroozan in view of Devries includes calculating a plurality of energy ratios by obtaining the power over windows of periods of time. Newberry teaches a method of determining and obtaining power over a short period of time (by applying the Hamming window) and using a Fast Fourier Transform (i.e., short-time). It is further noted that the center location of the power spectrum is where the peak power is present, therefore obtaining the peak power in the combination of Foroozan, Devries, and Newberry is analogous to determining time points corresponding to center locations of a plurality of Hamming windows. This modification of Foroozan in view of Devries with the details of Newberry would merely comprise combining prior art elements according to known methods to yield predictable results. See MPEP 2143.I.A.
Regarding claim 5, the combination of Foroozan, Devries, and Newberry teach the physiological detection signal quality evaluation method according to claim 4, wherein the calculating, based on the power spectral density data, an energy ratio corresponding to the frequency range comprises: dividing a sum of energy values corresponding to a plurality of frequency values in the frequency range by a sum of energy values corresponding to all frequency values to obtain the energy ratio corresponding to the frequency range (Foroozan, [0056]; the relative power is defined as the ratio of the power of the filtered AC PPG signal (i.e., corresponding to a plurality of frequency values) to the power of the raw AC PPG signal (i.e., corresponding to all frequency values).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Foroozan in view of Devries in view of Newberry, as applied to claim 4, in view of US Patent Publication 2011/0257552 by Banet et al., hereinafter “Banet”.
The combination of Foroozan, Devries, and Newberry teach the physiological detection signal quality evaluation method according to claim 4, but does not teach wherein the separately calculating, based on the physiological data and the PPG data, a plurality of energy ratios of frequencies corresponding to a plurality of pieces of physiological data further comprises: performing interpolation processing on the power spectral density data in a time-domain zero-filling or frequency-domain padding manner.
Banet teaches a method of using time-domain samples to perform a Fourier Transform. Banet teaches that the number of samples in the time-domain has a significant influence on the resolution of the power spectrum. Adding a constant value such as zero into the time-domain signal, the number of samples is increased and the resolution of the power spectrum is also increased ([0138]).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the method taught by the combination of Foroozan, Devries, and Newberry such that the separately calculating, based on the physiological data and the PPG data, a plurality of energy ratios of frequencies corresponding to a plurality of pieces of physiological data further comprises: performing interpolation processing on the power spectral density data in a time-domain zero-filling or frequency-domain padding manner, in order to increase the resolution of the power spectrum, as taught by Banet ([0138]).
Claims 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Foroozan in view of Devries in view of Newberry, as applied to claim 4, in view of US Patent Publication 2018/0149946 by Sarnow et al., hereinafter “Sarnow”.
Regarding claims 7-9, the combination of Foroozan, Devries, and Newberry teach the physiological detection signal quality evaluation method according to claim 4, but does not teach wherein the separately calculating, based on the physiological data and the PPG data, a plurality of energy ratios of frequencies corresponding to a plurality of pieces of physiological data further comprises: deleting abnormal data in the energy ratio list of the plurality of pieces of physiological data; wherein the deleting abnormal data in the energy ratio list of the plurality of pieces of physiological data comprises: calculating an upper quartile Q1E and a lower quartile Q3E in the energy ratio list; calculating an upper threshold U1 and a lower threshold U2 of the abnormal data; determining that data that is in the energy ratio list and that is less than or equal to the lower threshold or greater than or equal to the upper threshold is the abnormal data; and outputting an energy ratio list from which the abnormal data is deleted; or wherein the upper threshold is U1=a* Q3E−b* Q1E, and the lower threshold is U2=a* Q1E −b* Q3E.
Sarnow teaches a method of monitoring a biological signal and determining a physiological parameter based on the biological signal. Sarnow also teaches that an upper and lower fence value (i.e., threshold) for the data set can be determined by using the formulas Q3+1.5*IQR and Q1-1.5*IQR, respectively, and any values outside of the thresholds can be discarded as outliers (i.e., abnormal data) ([0151-0153]).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the method taught by the combination of Foroozan, Devries, and Newberry such that the separately calculating, based on the physiological data and the PPG data, a plurality of energy ratios of frequencies corresponding to a plurality of pieces of physiological data further comprises: deleting abnormal data in the energy ratio list of the plurality of pieces of physiological data; wherein the deleting abnormal data in the energy ratio list of the plurality of pieces of physiological data comprises: calculating an upper quartile Q1E and a lower quartile Q3E in the energy ratio list; calculating an upper threshold U1 and a lower threshold U2 of the abnormal data; determining that data that is in the energy ratio list and that is less than or equal to the lower threshold or greater than or equal to the upper threshold is the abnormal data; and outputting an energy ratio list from which the abnormal data is deleted; or wherein the upper threshold is U1=a* Q3E−b* Q1E, and the lower threshold is U2=a* Q1E −b* Q3E, as taught by Sarnow ([0151-0154]). Removing outliers provides a more accurate assessment of the biological parameter within the dataset. It is noted that the formula for defining the upper and lower thresholds as taught by Sarnow can be rewritten in the form of a* Q3E−b* Q1E and a* Q1E −b* Q3E, wherein a =2.5 and b=1.5.
Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Foroozan, as applied to claim 1, in view of US Patent Publication 2019/0192253 by Yang et al., hereinafter “Yang”.
Regarding claim 10, Foroozan teaches the physiological detection signal quality evaluation method according to claim 1, but does not teach wherein the quality evaluation parameters comprise a lower quartile, a median, and an upper quartile of physiological data energy ratios of a plurality of scenarios in the classified scenarios.
Yang teaches a method of analyzing the quality of an acquired signal by comparing energy ratios (i.e., SNR). The quality evaluation parameters of median, lower (i.e., first) quartile, and upper (i.e., third) quartile that are based on the SNR values were compared between conditions to determine differences in the quality of signals ([0098]).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the method of Foroozan such that the quality evaluation parameters comprise a lower quartile, a median, and an upper quartile of physiological data energy ratios of a plurality of scenarios in the classified scenarios, as taught by Yang. It is noted that the using the median, lower quartile, and upper quartile is a method of using descriptive statistics to compare a plurality of data points. As Fig. 6A of Foroozan displays a scatter of points, these points can be analyzed by using descriptive statistics. Therefore, the modification of Foroozan in view of Yang merely comprises the application of a known technique to a known method ready for improvement to yield predictable results. See MPEP 2143.I.D.
Regarding claim 11, Foroozan in view of Yang teaches the physiological detection signal quality evaluation method according to claim 10, wherein the separately calculating quality evaluation parameters of sample data of a plurality of classified scenarios based on energy ratios of the physiological data corresponding to different scenarios comprises: classifying the different scenarios to determine the plurality of classified scenarios (Foroozan, Fig. 8 teaches different energy ratios for a plurality of classified scenarios (sitting, walking, running)); obtaining, based on an analysis object (Foroozan, the analysis object is considered to be the activity performed), a plurality of physiological data energy ratios of classified scenarios separately corresponding to at least two evaluation dimensions (Foroozan, the different types of activities are considered evaluation dimensions (i.e., sitting, walking, and running)); and calculating a lower quartile, a median, and an upper quartile of the physiological data energy ratios of classified scenarios separately corresponding to the at least two evaluation dimensions (In the combination of Foroozan and Yang, the quality evaluation parameters comprise a lower quartile, a median, and an upper quartile. Therefore, they are calculated for each scenario).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Foroozan in view of Yang, as applied to claim 11, in view of US Patent Publication 2008/0108908 by Maddess et al., hereinafter “Maddess”.
Foroozan in view of Yang teaches the physiological detection signal quality evaluation method according to claim 11, but does not teach wherein the evaluating, based on the quality evaluation parameters, quality of a physiological detection signal collected by the detection device comprises: determining, based on an increase or decrease ratio of a quality evaluation parameter of a classified scenario corresponding to a first evaluation dimension to a quality evaluation parameter of a classified scenario corresponding to a second evaluation dimension, whether the quality of the physiological detection signal collected by the detection device is improved or degraded.
Maddess teaches a method comparing results (i.e. increases or decreases) from different scenarios by comparing the median of the signal to noise ratios ([0069]). The comparison of these rates can be used to determine improvements in the signal to noise ratio among conditions ([0069]).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date to have modified the method as taught by Foroozan in view of Yang such that wherein the evaluating, based on the quality evaluation parameters, quality of a physiological detection signal collected by the detection device comprises: determining, based on an increase or decrease ratio of a quality evaluation parameter of a classified scenario corresponding to a first evaluation dimension to a quality evaluation parameter of a classified scenario corresponding to a second evaluation dimension, whether the quality of the physiological detection signal collected by the detection device is improved or degraded, in order to determine improvements in the signal to noise ratio among conditions, as taught by Maddess ([0069]).
Examiner’s Note
The following is a statement of reasons for the lack of prior art rejections for claims 13-16:
Claim 13 recites: wherein the determining, based on an increase or decrease ratio of a quality evaluation parameter of a classified scenario corresponding to a first evaluation dimension to a quality evaluation parameter of a classified scenario corresponding to a second evaluation dimension, whether the quality of the physiological detection signal collected by the detection device is improved or degraded comprises: calculating a lower quartile increase ratio, a median increase ratio, and an upper quartile increase ratio of a plurality of physiological data energy ratios of the classified scenario corresponding to the first evaluation dimension to a plurality of physiological data energy ratios of the classified scenario corresponding to the second evaluation dimension; and determining, based on the lower quartile increase ratio, the median increase ratio, and the upper quartile increase ratio, whether the quality of the physiological detection signal collected by the detection device is improved or degraded.
The closest prior art is identified as the combination of Foroozan, Yang, and Maddess as applied to claim 12 above. The combination teaches calculating a median increase ratio of a plurality of physiological data energy ratios of the classified scenario corresponding to the first evaluation dimension to a plurality of physiological data energy ratios of the classified scenario corresponding to the second evaluation dimension; and determining, based on the median increase ratio whether the quality of the physiological detection signal collected by the detection device is improved or degraded (see the rejection of claim 12). The combination fails to teach the calculation of a lower quartile increase ratio or an upper quartile increase ratio, or using these ratios to determine whether the quality of the physiological detection signal collected by the detection device is improved or degraded.
The limitations of this claim and the dependent claims are patentably distinct over the prior art cited in this Office action and any other prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Patent Publication 2016/0235312 by Jeanne et al. teaches a method of binning energy ratios of PPG signals and comparing them statistically for different conditions.
US Patent Publication 2017/0181691 by Olivier et al. teaches a system that calculates a quality metric for a signal that is calculated by taking the Fourier transform of the signal and obtaining a ratio of powers.
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/NELSON ALEXANDER GLOVER/Examiner, Art Unit 3791
/ADAM J EISEMAN/Primary Examiner, Art Unit 3791