DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to 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.
Regarding Claim 1, the claim recites an apparatus, which is one of the statutory categories of invention (Step 1).
The claim is then analyzed to determine whether it is directed to any judicial exception (Step 2A, Prong One). The following limitations set forth a judicial exception:
determine a frequency spectrum corresponding to time-domain data, wherein the time-domain data is based on a receive signal
determine a breathing rate frequency based on a maximum amplitude of the frequency spectrum
determine a plurality of section-frequency-spectra, wherein each of the plurality of section-frequency-spectra correspond to a respective section of the time-domain data based on the breathing rate frequency
determine an average of the section-frequency-spectra to obtain an averaged-frequency-spectrum
subtract the averaged-frequency-spectrum from the frequency spectrum to obtain a difference spectrum
estimate a heart rate of a target based on a maximum amplitude of the difference spectrum
These limitations describe a mathematical calculation and/or a mental process as the skilled artisan is capable of performing the recited limitations and making a mental assessment thereafter. Examiner also notes that nothing from the claims suggest that the limitations cannot be practically performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations and/or mental process steps in real time. Examiner also notes that nothing from the claims suggests an undue level of complexity that the mathematical calculations and/or the mental process steps cannot be practically performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations and/or mental process steps.
For example:
The plain meaning of the limitation “determine a frequency spectrum corresponding to time-domain data, wherein the time-domain data is based on a receive signal” includes mathematical calculations that can be performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations steps in real time.
The plain meaning of the limitation “determine a breathing rate frequency based on a maximum amplitude of the frequency spectrum” includes mathematical calculations that can be performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations steps in real time.
The plain meaning of the limitation “determine a plurality of section-frequency-spectra, wherein each of the plurality of section-frequency-spectra correspond to a respective section of the time-domain data based on the breathing rate frequency” includes mathematical calculations that can be performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations steps in real time.
The plain meaning of the limitation “determine an average of the section-frequency-spectra to obtain an averaged-frequency-spectrum” includes mathematical calculations that can be performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations steps in real time.
The plain meaning of the limitation “subtract the averaged-frequency-spectrum from the frequency spectrum to obtain a difference spectrum” includes mathematical calculations that can be performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations steps in real time.
The plain meaning of the limitation “estimate a heart rate of a target based on a maximum amplitude of the difference spectrum” includes mental processes that can be performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations and/or mental process steps in real time.
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, integrates the identified judicial exception into a practical application (Step 2A, Prong Two).
The following limitations amount to a recitation of the words "apply it" (or an equivalent) and/or nothing more than mere instructions to implement the abstract idea on a generic computer. See MPEP 2106.05(f).
a processor
a memory coupled to the processor with instructions stored thereon, wherein the instructions, when executed by the processor, enable the apparatus to...
Therefore, these additional limitations do not integrate the judicial exception into a practical application.
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, amounts to significantly more than the identified judicial exception (Step 2B):
The following limitations do not amount to significantly more than the abstract idea for substantially similar reasons applied in Step 2A, Prong Two.
a processor
a memory coupled to the processor with instructions stored thereon, wherein the instructions, when executed by the processor, enable the apparatus to...
The following limitations is/are considered to be well-understood, routine, and conventional (WURC).
The processor is considered to be well-understood, routine, and conventional based on statement from the applicant's specification filed 07/08/2024 ([0035]; [0125]).
The memory is considered to be well-understood, routine, and conventional based on statement from the applicant's specification filed 07/08/2024 ([0035]).
Independent Claim 14 is also not patent eligible for substantially similar reasons as it recites the same abstract idea(s) and additional element(s) as Claim 1 but as a process-type claim.
Independent Claim 20 is also not patent eligible for substantially similar reasons as it recites the same abstract idea(s) and additional element(s) as Claim 1 but as an additional apparatus-type claim.
Dependent Claims 2-12 and 15-18 also fail to add subject matter qualifying as significantly more to the abstract independent claims as they merely further limit the abstract idea.
Dependent Claims 2-13 and 19 also fail to add subject qualifying as significantly more to the abstract independent claims as they recite limitations that do not integrate the claims into a practical application for substantially similar reasons as set forth above. The examiner notes that for Claim 13, the radar system is considered to be well-understood, routine, and conventional based on statement from the applicant's specification filed 07/08/2024 ([0029]).
Dependent Claims 2-13 and 19 also fail to add subject matter integrating the judicial exception or qualifying as significantly more to the abstract independent claims as they do not recite significantly more than the identified abstract idea for substantially similar reasons as set forth above. The examiner notes that for Claim 13, the radar system is considered to be well-understood, routine, and conventional based on statement from the applicant's specification filed 07/08/2024 ([0029]).
Therefore, Claims 1-20 are not patent eligible under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 7-8, and 13-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang et al (US 20230081472 A1, hereinafter Wang).
Regarding Claim 1, Wang discloses an apparatus (See Elements 940 and 1040, Figs. 9-10), comprising:
a processor (See Elements 902 and 1002, Figs. 9-10); and
a memory (See Elements 904 and 1004, Figs. 9-10) coupled to the processor with instructions stored thereon, wherein the instructions, when executed by the processor, enable the apparatus to (“The memory 904, which can include both read-only memory (ROM) and random access memory (RAM), can provide instructions and data to the processor 902”, [0311]; “the processor 1002, the memory 1004, the transceiver 1010 and the power module 1008 work similarly to the processor 902, the memory 904, the transceiver 910 and the power module 908 in the Bot 900”, [0320]; also see all of [0199]):
determine a frequency spectrum corresponding to time-domain data (See Figs. 6A-6B), wherein the time-domain data is based on a receive signal (Step 1104, Fig. 11),
determine a breathing rate frequency based on a maximum amplitude of the frequency spectrum (Step 1110, Fig. 11; See Fig. 20B, “respiration component” graph—the breathing rate frequency is estimated as the locally maximum amplitude(s) of the graph),
determine a plurality of section-frequency-spectra (“A beamforming may be performed on the channel information to get the Channel Impulse Response (CIR) at different range-azimuth bins”, [0389]), wherein each of the plurality of section-frequency-spectra correspond to a respective section of the time-domain data based on the breathing rate frequency (“obtaining a time series of channel information (TSCI) of the wireless channel based on the second wireless signal, wherein each CI comprises at least one of: a channel state information (CSI), channel impulse response (CIR), channel frequency response (CFR)”, [0048]; “The CI may be associated with a decomposition of the signal”, [0172]),
determine an average of the section-frequency-spectra to obtain an averaged-frequency-spectrum (“the motion trend is further estimated by smoothing spline...”, [0383]),
subtract the averaged-frequency-spectrum from the frequency spectrum to obtain a difference spectrum (“the motion trend is further estimated by smoothing spline and then eliminated”, [0389]), and
estimate a heart rate of a target based on a maximum amplitude of the difference spectrum (“The first sub-figure in FIG. 20A shows the phase measurement after motion cancellation”, [0414]; Step 1110, Fig. 11; See Fig. 20B, “heartbeat component” graph—the heartbeat is estimated as the locally maximum amplitude(s) of the graph after motion cancellation).
Regarding Claim 2, Wang discloses the apparatus of claim 1, wherein the instructions, when executed by the processor, further enable the apparatus to:
apply a high-pass filter to the time-domain data to provide high-pass filtered time-domain data (“An operation, pre-processing, processing and/or postprocessing may be applied to data (e.g. TSCI, autocorrelation, features of TSCI). An operation may be preprocessing, processing and/or postprocessing. The preprocessing, processing and/or postprocessing may be an operation. An operation may comprise... highpass filtering”, [0203]; under broadest reasonable interpretation of applying an operation to data, this high-pass filtering could be performed on time-domain data by routine optimization of the teachings of Wang);
calculate an average amplitude of the high-pass filtered time-domain data to obtain a noise-level average (“An operation, pre-processing, processing and/or postprocessing may be applied to data (e.g. TSCI, autocorrelation, features of TSCI). An operation may be preprocessing, processing and/or postprocessing. The preprocessing, processing and/or postprocessing may be an operation. An operation may comprise... weighted averaging, arithmetic mean, geometric mean, harmonic mean, averaging over selected frequency”, [0203]; under broadest reasonable interpretation of applying an operation to data, this average amplitude could be calculated for the previously high-pass filtered data by routine optimization of the teachings of Wang); and
determine the frequency spectrum (See Figs. 6A-6B; “The characteristics and/or STI (e.g. motion information) may comprise... frequency spectrum”, [0181]) in response to the noise-level average being under a pre-specified noise-level threshold (“The function (e.g. function of operands) may comprise... thresholding”, [0204]; under broadest reasonable interpretation of applying an operation to data, this thresholding could be performed on the previously high-pass filtered and averaged data of Wang by routine optimization).
Regarding Claim 3, Wang discloses the apparatus of claim 1, wherein the instructions, when executed by the processor, further enable the apparatus to:
calculate a mean value of amplitude values of the time-domain data (“An operation, pre-processing, processing and/or postprocessing may be applied to data (e.g. TSCI, autocorrelation, features of TSCI). An operation may be preprocessing, processing and/or postprocessing. The preprocessing, processing and/or postprocessing may be an operation. An operation may comprise... weighted averaging, arithmetic mean, geometric mean, harmonic mean, averaging over selected frequency”, [0203]; under broadest reasonable interpretation of applying an operation to data, the mean value could be calculated from time-domain data amplitudes by routine optimization of the teachings of Wang);
subtract the mean value from the amplitude values of the time-domain data to generate mean-removed time-domain data (“At step s6b1: process the raw signal by removing/suppressing influence of the dominant (larger magnitude) periodic signal (e.g. filter the raw signal, or estimate the dominant periodic signal and subtract it from the raw signal), wherein the dominant periodic signal may be estimated by an operation on the raw signal (e.g. smoothing, low pass filtering, spline interpolation, B-spline, cubic spline interpolation, polynomial fitting, polynomial fitting with order adaptively selected based on the distance/tap, etc.”, [0342]); and
determine the frequency spectrum for the mean-removed time-domain data (“The characteristics and/or STI (e.g. motion information) may comprise... frequency spectrum”, [0181]).
Regarding Claim 4, Wang discloses the apparatus of claim 1, wherein the instructions, when executed by the processor, further enable the apparatus to:
perform one or more range Fourier Transformations of the time-domain data (“An operation, pre-processing, processing and/or postprocessing may be applied to data (e.g. TSCI, autocorrelation, features of TSCI). An operation may be preprocessing, processing and/or postprocessing. The preprocessing, processing and/or postprocessing may be an operation. An operation may comprise... Fourier transform (FT)”, [0203]; under broadest interpretation of applying an operation to data, this Fourier Transform applied to the time domain data specifically could be disclosed by routine optimization of the teachings of Wang) to obtain one or more range buffers, each of the one or more range buffers being divided into a plurality of range bins (“FIG. 12 illustrates exemplary vital signals in different range-azimuth bins”, [0066]);
for each of the one or more range buffers, identify a range bin (“FIG. 12 illustrates exemplary vital signals in different range-azimuth bins”, [0066]) associated with a maximum amplitude to determine a range-time dataset (“At least one characteristics (e.g. characteristic value, or characteristic point) of a function (e.g. auto-correlation function, auto-covariance function, cross-correlation function, cross-covariance function, power spectral density, time function, frequency domain function, frequency transform) may be determined (e.g. by an object tracking server, the processor, the Type 1 heterogeneous device, the Type 2 heterogeneous device, and/or another device). The at least one characteristics of the function may include...a maximum”, [0213]; under broadest interpretation of determining a characteristic of a function, the maximum of a function, e.g. a maximum amplitude, could be applied to a range-azimuth bin specifically by routine optimization of the teachings of Wang); and
determine the frequency spectrum for the range-time dataset corresponding to the identified range bin (See Figs. 6A-6B; “The characteristics and/or STI (e.g. motion information) may comprise... frequency spectrum”, [0181]).
Regarding Claim 7, Wang discloses the apparatus of claim 1, wherein the instructions, when executed by the processor, further enable the apparatus to:
determine the frequency spectrum for the time-domain data (See Figs. 6A-6B); and
subsequently apply a finite-impulse-response filter to amplitude values of the frequency spectrum (“An operation, pre-processing, processing and/or postprocessing may be applied to data... An operation may comprise...finite impulse response (FIR) filtering”, [0203]; under broadest interpretation of applying an operation to data, this FIR filtering to the time domain data specifically could be performed by routine optimization of the teachings of Wang).
Regarding Claim 8, modified Wang discloses the apparatus of claim 1, wherein the instructions, when executed by the processor, enable the apparatus to determine the breathing rate frequency by:
detecting a first frequency corresponding to a maximum amplitude value of the frequency spectrum (Step 1110, Fig. 11; See Fig. 20B, “respiration component” graph—the breathing rate frequency is estimated as the locally maximum amplitude(s) of the graph); and
detecting a second frequency corresponding to a local maximum of amplitude values in the frequency spectrum within a first pre-specified frequency range from twice the first frequency (Step 1110, Fig. 11; See Fig. 20B, “respiration component” graph—the tallest peak in the graph is in a frequency range at least twice of another locally maximum frequency).
Regarding Claim 13, Wang discloses a radar system (See Fig. 2 and 14; “In some embodiments, the disclosed system is built upon an FMCW radar”, [0391]), comprising:
the apparatus according to claim 1 (See Elements 940 and 1040, Figs. 9-10); and
a radar sensor configured to generate the receive signal (“a transceiver 1010 comprising a transmitter 1012 and a receiver 1014”, [0319]).
Regarding Claim 14, Wang discloses a method (“Methods, apparatus and systems for wireless vital sign monitoring are described”, Abstract), comprising:
determining a frequency spectrum corresponding to time-domain data (See Figs. 6A-6B), wherein the time-domain data is based on a receive signal (Step 1104, Fig. 11);
determining a breathing rate frequency based on a maximum amplitude of the frequency spectrum (Step 1110, Fig. 11; See Fig. 20B, “respiration component” graph—the breathing rate frequency is estimated as the locally maximum amplitude(s) of the graph);
determining a plurality of section-frequency-spectra (“A beamforming may be performed on the channel information to get the Channel Impulse Response (CIR) at different range-azimuth bins”, [0389]), wherein each of the plurality of section-frequency-spectra correspond to a respective section of the time-domain data based on the breathing rate frequency (“obtaining a time series of channel information (TSCI) of the wireless channel based on the second wireless signal, wherein each CI comprises at least one of: a channel state information (CSI), channel impulse response (CIR), channel frequency response (CFR)”, [0048]; “The CI may be associated with a decomposition of the signal”, [0172]);
determining an average of the section-frequency-spectra to obtain an averaged-frequency-spectrum (“the motion trend is further estimated by smoothing spline...”, [0383]);
subtracting the averaged-frequency-spectrum from the frequency spectrum to obtain a difference spectrum (“the motion trend is further estimated by smoothing spline and then eliminated”, [0389]); and
estimating a heart rate of a target based on a maximum amplitude of the difference spectrum (“The first sub-figure in FIG. 20A shows the phase measurement after motion cancellation”, [0414]; Step 1110, Fig. 11; See Fig. 20B, “heartbeat component” graph—the heartbeat is estimated as the locally maximum amplitude(s) of the graph after motion cancellation).
Regarding Claim 15, Wang discloses the method of claim 14, further comprising:
applying a high-pass filter to the time-domain data to provide high-pass filtered time-domain data (“An operation, pre-processing, processing and/or postprocessing may be applied to data (e.g. TSCI, autocorrelation, features of TSCI). An operation may be preprocessing, processing and/or postprocessing. The preprocessing, processing and/or postprocessing may be an operation. An operation may comprise... highpass filtering”, [0203]; under broadest reasonable interpretation of applying an operation to data, this high-pass filtering could be performed on time-domain data by routine optimization of the teachings of Wang);
calculating an average amplitude of the high-pass filtered time-domain data to obtain a noise-level average (“An operation, pre-processing, processing and/or postprocessing may be applied to data (e.g. TSCI, autocorrelation, features of TSCI). An operation may be preprocessing, processing and/or postprocessing. The preprocessing, processing and/or postprocessing may be an operation. An operation may comprise... weighted averaging, arithmetic mean, geometric mean, harmonic mean, averaging over selected frequency”, [0203]; under broadest reasonable interpretation of applying an operation to data, this average amplitude could be calculated for the previously high-pass filtered data by routine optimization of the teachings of Wang); and
determining the frequency spectrum (“The characteristics and/or STI (e.g. motion information) may comprise... frequency spectrum”, [0181]) in response to the noise-level average being under a pre-specified noise-level threshold (“The function (e.g. function of operands) may comprise... thresholding”, [0204]; under broadest reasonable interpretation of applying an operation to data, this thresholding could be performed on the previously high-pass filtered and averaged data of Wang by routine optimization).
Regarding Claim 16, Wang discloses the method of claim 14, further comprising:
calculating a mean value of amplitude values of the time-domain data (“An operation, pre-processing, processing and/or postprocessing may be applied to data (e.g. TSCI, autocorrelation, features of TSCI). An operation may be preprocessing, processing and/or postprocessing. The preprocessing, processing and/or postprocessing may be an operation. An operation may comprise... weighted averaging, arithmetic mean, geometric mean, harmonic mean, averaging over selected frequency”, [0203]; under broadest reasonable interpretation of applying an operation to data, the mean value could be calculated from time-domain data amplitudes by routine optimization of the teachings of Wang);
subtracting the mean value from the amplitude values of the time-domain data to generate mean-removed time-domain data (“At step s6b1: process the raw signal by removing/suppressing influence of the dominant (larger magnitude) periodic signal (e.g. filter the raw signal, or estimate the dominant periodic signal and subtract it from the raw signal), wherein the dominant periodic signal may be estimated by an operation on the raw signal (e.g. smoothing, low pass filtering, spline interpolation, B-spline, cubic spline interpolation, polynomial fitting, polynomial fitting with order adaptively selected based on the distance/tap, etc.”, [0342]); and
determining the frequency spectrum for the mean-removed time-domain data (See Figs. 6A-6B; “The characteristics and/or STI (e.g. motion information) may comprise... frequency spectrum”, [0181]).
Regarding Claim 17, Wang discloses the method of claim 14, further comprising:
performing one or more range Fourier Transformations of the time-domain data (“An operation, pre-processing, processing and/or postprocessing may be applied to data (e.g. TSCI, autocorrelation, features of TSCI). An operation may be preprocessing, processing and/or postprocessing. The preprocessing, processing and/or postprocessing may be an operation. An operation may comprise... Fourier transform (FT)”, [0203]; under broadest interpretation of applying an operation to data, this Fourier Transform applied to the time domain data specifically could be disclosed by routine optimization of the teachings of Wang) to obtain one or more range buffers, each of the one or more range buffers being divided into a plurality of range bins (“FIG. 12 illustrates exemplary vital signals in different range-azimuth bins”, [0066]);
for each of the one or more range buffers, identifying a range bin (“FIG. 12 illustrates exemplary vital signals in different range-azimuth bins”, [0066]) associated with a maximum amplitude to determine a range-time dataset (“At least one characteristics (e.g. characteristic value, or characteristic point) of a function (e.g. auto-correlation function, auto-covariance function, cross-correlation function, cross-covariance function, power spectral density, time function, frequency domain function, frequency transform) may be determined (e.g. by an object tracking server, the processor, the Type 1 heterogeneous device, the Type 2 heterogeneous device, and/or another device). The at least one characteristics of the function may include...a maximum”, [0213]; under broadest interpretation of determining a characteristic of a function, the maximum of a function, e.g. a maximum amplitude, could be applied to a range-azimuth bin specifically by routine optimization of the teachings of Wang); and
determining the frequency spectrum for the range-time dataset corresponding to the identified range bin (See Figs. 6A-6B; “The characteristics and/or STI (e.g. motion information) may comprise... frequency spectrum”, [0181]).
Regarding Claim 19, Wang discloses a non-transitory computer readable medium with instructions stored thereon (“The memory may be volatile, non-volatile, random access memory (RAM), Read Only Memory (ROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), hard disk, flash memory, CD-ROM, DVD-ROM, magnetic storage, optical storage, organic storage, storage system, storage network, network storage, cloud storage, edge storage, local storage, external storage, internal storage, or other form of non-transitory storage medium known in the art”, [0199]), wherein the instructions, when executed by a processor (“he processor may comprise general purpose processor, special purpose processor, microprocessor, microcontroller, embedded processor, digital signal processor, central processing unit (CPU), graphical processing unit (GPU), multi-processor, multi-core processor, and/or processor with graphics capability, and/or a combination”, [0199]), enable the processor to perform the method according to claim 14 (“The set of instructions (machine executable code) corresponding to the method steps may be embodied directly in hardware, in software, in firmware, or in combinations thereof”, [0199]).
Regarding Claim 20, Wang discloses an apparatus (See Elements 940 and 1040, Figs. 9-10) comprising a processor (See Elements 902 and 1002, Figs. 9-10) configured to (See all of [0199]):
determine a frequency spectrum corresponding to time-domain data (See Figs. 6A-6B), wherein the time-domain data is based on a receive signal (Step 1104, Fig. 11);
determine a breathing rate frequency based on a maximum amplitude of the frequency spectrum (Step 1110, Fig. 11; See Fig. 20B, “respiration component” graph—the breathing rate frequency is estimated as the locally maximum amplitude(s) of the graph);
determine a plurality of section-frequency-spectra (“A beamforming may be performed on the channel information to get the Channel Impulse Response (CIR) at different range-azimuth bins”, [0389]), wherein each of the plurality of section-frequency-spectra correspond to a respective section of the time-domain data based on the breathing rate frequency (“obtaining a time series of channel information (TSCI) of the wireless channel based on the second wireless signal, wherein each CI comprises at least one of: a channel state information (CSI), channel impulse response (CIR), channel frequency response (CFR)”, [0048]; “The CI may be associated with a decomposition of the signal”, [0172]);
determine an average of the section-frequency-spectra to obtain an averaged-frequency-spectrum (“the motion trend is further estimated by smoothing spline...”, [0383]);
subtract the averaged-frequency-spectrum from the frequency spectrum to obtain a difference spectrum (“the motion trend is further estimated by smoothing spline and then eliminated”, [0389]); and
estimate a heart rate of a target based on a maximum amplitude of the difference spectrum (“The first sub-figure in FIG. 20A shows the phase measurement after motion cancellation”, [0414]; Step 1110, Fig. 11; See Fig. 20B, “heartbeat component” graph—the heartbeat is estimated as the locally maximum amplitude(s) of the graph after motion cancellation).
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.
Claims 5 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Lin et al (US 20180263502 A1, hereinafter Lin).
Regarding Claim 5, Wang discloses the apparatus of claim 4. Wang discloses the claimed invention except for expressly disclosing wherein the instructions, when executed by the processor, further enable the apparatus to:
check whether a peak-to-peak value for the range-time dataset is within pre-specified limits based on an average of a plurality of previous peak-to-peak values in the range-time dataset; and
determine the frequency spectrum for the range-time dataset in response to the peak-to-peak value being within its pre-specified limits.
However, Lin, which is in the same field of invention (See Abstract), teaches wherein the instructions, when executed by the processor, further enable the apparatus to (See [0095]):
check whether a peak-to-peak value for the dataset is within pre-specified limits based on an average of a plurality of previous peak-to-peak values in the dataset (“If the quality of extracted heartbeat pulses is not good enough for accurate heart rate estimation (e.g., the variance of peak-to-peak intervals in the averaged waveform x[n] is above a preset threshold)”, [0083]; the inverse of this statement is the peak-to-peak intervals are considered good enough when below the preset threshold); and
perform the diagnostic method in response to the peak-to-peak value being within its pre-specified limits (“the algorithm can redo the depression of respiration signal, band pass filtering, and Tompkins's method for heartbeat pulses extraction described above with higher order polynomial curves to improve the signal quality”, [0083]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Lin into the apparatus of Wang such that the apparatus is enabled to: check whether a peak-to-peak value for the range-time dataset is within pre-specified limits based on an average of a plurality of previous peak-to-peak values in the range-time dataset; and determine the frequency spectrum for the range-time dataset in response to the peak-to-peak value being within its pre-specified limits, to maintain diagnostic accuracy and filter out faulty signals.
Regarding Claim 18, Wang discloses the method of claim 17. Wang discloses the claimed invention except for expressly disclosing the method further comprising:
checking whether a peak-to-peak value for the range-time dataset is within pre-specified limits based on an average of a plurality of previous peak-to-peak values in the range-time dataset; and
determining the frequency spectrum for the range-time dataset in response to the peak-to-peak value being within its pre-specified limits.
However, Lin, which is in the same field of invention (See Abstract), teaches checking whether a peak-to-peak value for the dataset is within pre-specified limits based on an average of a plurality of previous peak-to-peak values in the dataset (“If the quality of extracted heartbeat pulses is not good enough for accurate heart rate estimation (e.g., the variance of peak-to-peak intervals in the averaged waveform x[n] is above a preset threshold)”, [0083]; the inverse of this statement is the peak-to-peak intervals are considered good enough when below the preset threshold); and
performing the diagnostic method in response to the peak-to-peak value being within its pre-specified limits (“the algorithm can redo the depression of respiration signal, band pass filtering, and Tompkins's method for heartbeat pulses extraction described above with higher order polynomial curves to improve the signal quality”, [0083]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Lin into the method of Wang to recite checking whether a peak-to-peak value for the range-time dataset is within pre-specified limits based on an average of a plurality of previous peak-to-peak values in the range-time dataset; and determining the frequency spectrum for the range-time dataset in response to the peak-to-peak value being within its pre-specified limits, to maintain diagnostic accuracy and filter out faulty signals.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Dosenbach et al (US 20210398678 A1, hereinafter Dosenbach).
Regarding Claim 6, Wang discloses the apparatus of claim 4. Wang discloses the claimed invention except for expressly disclosing wherein the instructions, when executed by the processor, further enable the apparatus to:
determine a plurality of derivative values corresponding to a rate of change of a range at a respective time of the range-time dataset;
determine a variance of the plurality of derivative values;
check that each of the derivative values is within a threshold determined by the variance; and
determine the frequency spectrum for the range-time dataset in response to each derivative value being within the threshold.
However, Dosenbach, which is also directed towards signal processing of biologically-transduced signals (See Abstract), teaches
determine a plurality of derivative values corresponding to a rate of change of a range at a respective time of the range-time dataset (“Frame censoring (scrubbing) was computed on the basis of both frame-wise displacement (FD) and variance of derivatives (DVARS) measures with thresholds set individually for each participant”, [0089]);
determine a variance of the plurality of derivative values (“Frame censoring (scrubbing) was computed on the basis of both frame-wise displacement (FD) and variance of derivatives (DVARS) measures with thresholds set individually for each participant”, [0089]);
check that each of the derivative values is within a threshold determined by the variance (“Frame censoring (scrubbing) was computed on the basis of both frame-wise displacement (FD) and variance of derivatives (DVARS) measures with thresholds set individually for each participant”, [0089]); and
determine the frequency spectrum for the range-time dataset in response to each derivative value being within the threshold (“The data then were temporally bandpass filtered prior to nuisance regression, retaining frequencies between 0.005 Hz and 0.1 Hz”, [0089]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Dosenbach into the apparatus of Wang such that the apparatus is further enabled to: determine a plurality of derivative values corresponding to a rate of change of a range at a respective time of the range-time dataset; determine a variance of the plurality of derivative values; check that each of the derivative values is within a threshold determined by the variance; and determine the frequency spectrum for the range-time dataset in response to each derivative value being within the threshold, because this is a denoising process (See [0089] of Dosenbach).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Xue et al (CN 113688985 A, hereinafter Xue; an attached machine translation was relied upon for this rejection).
Regarding Claim 12, Wang discloses the apparatus of claim 1. Wang discloses the claimed invention except for expressly disclosing wherein the instructions, when executed by the processor, further enable the apparatus to perform multiple heart rate estimations, wherein each estimation is applied to an iteration of a recursive model for obtaining a refined heart rate estimate. However, Xue, which also discloses heart rate estimations (See Abstract), teaches wherein the instructions, when executed by the processor, further enable the apparatus to perform multiple heart rate estimations (“judging whether the current iteration times reaches the iteration threshold value in the heart rate estimation model, wherein the iteration threshold value is set to be 50 to 500”, page 3), wherein each estimation is applied to an iteration of a recursive model for obtaining a refined heart rate estimate (“it can ensure the iteration times of the heart rate estimation model to reach the preset iteration threshold value, further ensuring the training effect of the model”, page 8).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang with Xue, for the advantage of a more accurate heart rate estimation.
Examiner’s Note
The Examiner notes that Claims 9-11 are not currently rejected under prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
See Wang et al (US 20200300972 A1).
See McMichael (US 20210030480 A1).
See Chian et al (US 20210121075 A1) ([0017]).
See Rahman et al (US 20210386318 A1) ([0045]).
Cordie et al (US 20230047069 A1) discloses processor instructions to: subtract the averaged-frequency-spectrum from the frequency spectrum to obtain a difference spectrum (“the range signals obtained from the received radar waves are converted from the time domain to the frequency domain in a step 24, for instance by applying a Fast Fourier Transform (FFT), followed by a step 26 of subtracting an average of a plurality of frequency domain range signals obtained from radar waves received in a plurality of previously executed iterations of the steps”, [0078]). However, Cordie does not teach this step being performed on a plurality of section-frequency-spectra, wherein each of the plurality of section-frequency-spectra correspond to a respective section of the time-domain data based on the breathing rate frequency. Rather, Cordie teaches a Fast Fourier transform to the whole signal ([0078]).
See Коваленко (RU 2004133270 A).
See Teresawa (JP 2009279143 A).
See Han et al (CN 107167802 A), which is the closest prior art to Claim 11.
See Shimada et al (JP 2019069101 A).
See Chiou et al (JP 2020099661 A) (““Determination of whether the maximum average difference is greater than a threshold value or determination of whether the sum of maximum average differences is greater than the threshold value”).
See Cui et al (CN 112462336 A) (“"searching the spectrum peak frequency (difference frequency) in the spectrum; calculating the sliding window length according to the sampling rate and the difference frequency; calculating the sliding average value on the window length of the filtered signal; obtaining the leakage signal”).
See Zhang (CN 112754431 A).
See Ji et al (CN 115299891 A) (“S1, pre-baseband the I/Q orthogonal signal, obtaining the heartbeat signal component enhanced phase differential signal; S2, selecting the phase difference signal enhanced by the heartbeat signal component in the specific frequency range, calculating the Gaussian weighted average value in the sliding window by using the bimodal Gaussian model to perform frequency spectrum fitting, fitting two central frequencies in each sliding window for fitting the main peak frequency of the two main harmonic waves”, Claim 3).
See Luo (CN 116087935 A).
See Song (KR 20230105761 A).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN EPHRAIM COOPER whose telephone number is (571)272-2860. The examiner can normally be reached Monday-Friday 7:30AM-5:30PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jacqueline Cheng can be reached at (571) 272-5596. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JONATHAN E. COOPER/Examiner, Art Unit 3791
/JACQUELINE CHENG/Supervisory Patent Examiner, Art Unit 3791
Prosecution Insights