DETAILED ACTION
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-30 are pending.
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.
Claims 1-12, 15-22, and 30 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by BEHZADI et al. (US 20200205709 A1, 2020-07-02) (hereinafter “BEHZADI”).
Regarding claim 1, BEHZADI teaches a computer-implemented method, comprising: identifying, by a computing system (e.g., [0058]), a value of a heart rate variability metric that indicates a variation in a heart waveform of a patient (e.g., [0011]-[0016]; [0036]-[0037]); identifying, by the computing system, a value of a brain activity metric that indicates a type of electrical activity represented by a brain waveform of the patient (e.g., [0014]-[0015]); providing, by the computing system, values for a collection of metrics to a computational model, the values for the collection of metrics including the value for the heart rate variability metric and the value for the brain activity metric (e.g., [0016], [0031]); and receiving, by the computing system from the computational model as a result of having provided the values for the collection of metrics to the computational model, an indication of mental state of the patient (e.g., [0022]-[0025]) (as recited in claim 1); wherein the computational model comprises a machine learning model that has been trained (e.g., [0036], [0065], [0092]-[0107]) (as recited in claim 2); comprising: training, by the computing system, the machine learning model by providing training data that includes, for each respective patient of multiple patients: (i) a respective value of the heart rate variability metric for the respective patient; (ii) a respective value of the brain activity metric for the respective patient; and (iii) a respective indication of mental state for the respective patient (e.g., [0036], [0065], [0092]-[0107]) (as recited in claim 3); wherein: the heart waveform comprises a waveform from an electrocardiogram of the patient; and the brain waveform comprises a waveform from an electroencephalogram of the patient (e.g., [0086], [0142]) (as recited in claim 4); comprising: determining, by the computing system, a value for a time-domain heart rate variability metric that indicates variance among lengths of heart beat intervals over a period of time in the heart waveform of the patient, wherein the value for the heart rate variability metric comprises the value for the time- domain heart rate variability metric (e.g., [0011]-[0016]; [0036]-[0037]) (as recited in claim 5); wherein the period of time is a combination of all instances of REM sleep stage during a sleep session (e.g., [0015]) (as recited in claim 6); comprising: determining, by the computing system, a value for a frequency-domain heart rate variability metric that indicates a categorization of frequencies within the heart waveform of the patient over a period of time into a collection of different heart beat frequency ranges, wherein the value for the heart rate variability metric comprises the value for the frequency-domain heart rate variability metric (e.g., [0036]-[0037) (as recited in claim 7); wherein the period of time is a particular sleep stage of the patient, such that the frequency-domain heart rate variability metric does not indicate categorization of frequencies within a sleep stage other than the particular sleep stage (e.g., [0036]-[0037]) (as recited in claim 8); wherein the particular sleep stage is a first N3 sleep stage or a first REM sleep stage of a sleep session (e.g., [0015], [0036]-[0037]) (as recited in claims 9, 15, and 20); wherein: the frequency-domain heart rate variability metric is determined by combining: (i) a first categorization of frequencies within the heart waveform of the patient over a first portion of the period of time, with a second categorization of frequencies within the heart waveform of the patient over a second portion of the period of time; and the first portion of the period of time and the second portion of the period of time are a same length of time (e.g., [0036]-[0037]) (as recited in claim 10); wherein the categorization of frequencies within the heart waveform of the patient over the period of time into the collection of different heart beat frequency ranges indicates intensities for each frequency range within the collection of different heart beat frequency ranges (e.g., [0036]-[0037], [0046], [0064]) (as recited in claim 11); determining, by the computing system, a value for a non-linear heart rate variability metric that indicates an amount of non-linear variability over a period of time in the heart waveform of the patient, wherein the value for the heart rate variability metric comprises the value for the non- linear heart rate variability metric (e.g., [0011]-[0016]; [0036]-[0037]) (as recited in claim 12); determining, by the computing system, a value for a frequency-domain heart rate variability metric that indicates a categorization of frequencies within the heart waveform of the patient over a second sleep stage that is different from the particular sleep stage, wherein the value for the heart rate variability metric comprises the value for the frequency-domain heart rate variability metric (e.g., [0036]-[0037], [0046], [0064]) (as recited in claim 16); wherein the frequency-domain heart rate variability metric does not indicate categorization of frequencies within a sleep stage other than the second sleep stage (e.g., [0036]-[0037], [0046], [0064]) (as recited in claim 17); wherein: the values for the collection of metrics provided to the computational model and on which the indication of the metal state of the patient is based includes a value for a sleep onset metric that indicates an amount of time before the patient experienced a particular sleep stage (e.g., [0036]-[0037], [0046], [0061]-[0062], [0133]-[0134]) (as recited in claim 18); determining, by the computing system based on analysis of the brain waveform of the patient:(i) a starting time within the brain waveform at which the patient began experiencing the particular sleep stage; and(ii) an ending time within the brain waveform at which the patient stopped experiencing the particular sleep stage (e.g., [0036]-[0037], [0046], [0061]-[0062], [0133]-[0134], [0144]-[0150], claim 5) (as recited in claim 19); wherein the amount of time before the patient experienced the particular sleep stage represents an amount of time between the patient being determined to have fallen asleep and the patient beginning to experience the particular sleep stage (e.g., [0074], [0174]) (as recited in claim 21); determining, by the computing system, a value for a heart rate frequency metric that indicates an intensity of a range of frequencies in the heart waveform of the patient over a period of time, wherein the values for the collection of metrics that are provided to the computational model and on which the indication of the metal state of the patient is based includes the value for the heart rate frequency metric (e.g., [0036]-[0037], [0046], [0064]) (as recited in claim 22); and, as discussed above, a computing system, comprising: one or more processors; and one or more computer-readable devices including instructions that, when executed by the one or more processors, cause the computing system to perform operations that include: the method of any one of claims 1-29.identifying, by the computing system, a value of a heart rate variability metric that indicates a variation in a heart waveform of a patient; identifying, by the computing system, a value of a brain activity metric that indicates a type of electrical activity represented by a brain waveform of the patient; providing, by the computing system, values for a collection of metrics to a computational model, the values for the collection of metrics including the value for the heart rate variability metric and the value for the brain activity metric; and receiving, by the computing system from the computational model as a result of having provided the values for the collection of metrics to the computational model, an indication of mental state of the patient as recited in claim 30).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over BEHZADI in view of BACH et al. (US 20220133194 A1, 2022-05-05) (hereinafter “BACH”).
Regarding claims 13-14, BEHZADI teaches a computer-implemented method, except comprising: determining, by the computing system, a value for a brain state metric that indicates an amount of the electrical activity represented by the brain waveform of the patient that falls into a particular frequency band from among a collection of multiple different frequency bands, wherein the value for the brain activity metric comprises the value for the brain state metric.
BACH teaches analysis of brain states based on decomposing physiological data into frequency-banded components (e.g., [0019]-[0022], [0325], [0515], [0541]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of BACH with the invention taught by BEHZADI such that the invention further comprises determining, by the computing system, a value for a brain state metric that indicates an amount of the electrical activity represented by the brain waveform of the patient that falls into a particular frequency band from among a collection of multiple different frequency bands, wherein the value for the brain activity metric comprises the value for the brain state metric (as recited in claim 13); wherein the brain state metric indicates the amount of electrical activity that falls into the particular frequency band within a particular sleep stage of the patient, such that the brain state metric does not indicate electrical activity that falls within a sleep stage other than the particular sleep stage (as recited in claim 14) in order to improve the accuracy of invention.
Claims 27-28 are rejected under 35 U.S.C. 103 as being unpatentable over BEHZADI in view of WU et al. (US 20160081575 A1, 2022-05-05) (hereinafter “WU”).
Regarding claims 27-28, BEHZADI teaches a computer-implemented method, except compriosing: identifying, by the computing system, physiological data recorded from the patient while the patient was in a transitional period between sleep stages; and determining, by the computing system, a value for a transition-specific metric that indicates a value for another type of metric during the transitional period between sleep stages, and wherein the values for the collection of metrics that are provided to the computational model and on which the indication of the metal state of the patient is based include the value for the transition-specific metric.
WU teaches fully characterizing sleep structure using various metrics and models based on time-domain and spectral analysis of EEG and ECG data. See, e.g., [0076], [0117], [0147].
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of WU with the invention taught by BEHZADI such that the invention further comprises identifying, by the computing system, physiological data recorded from the patient while the patient was in a transitional period between sleep stages; and determining, by the computing system, a value for a transition-specific metric that indicates a value for another type of metric during the transitional period between sleep stages, and wherein the values for the collection of metrics that are provided to the computational model and on which the indication of the metal state of the patient is based include the value for the transition-specific metric (as recited in claim 27); identifying, by the computing system, physiological data recorded from the patient during a period of time that preceded the patient falling asleep; and determining, by the computing system, a value for a pre-sleep activity metric that indicates a value for another type of metric during the period of time that preceded the patient falling asleep, and wherein the values for the collection of metrics that are provided to the computational model and on which the indication of the metal state of the patient is based include the value for the pre-sleep activity metric (as recited in claim 28) ) in order to improve the accuracy of invention.
Allowable Subject Matter
Claim 23-26 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The prior art of record does not teach or suggest the claimed invention of the computer-implemented method of claim 1 comprising: determining, by the computing system, a value for an intra-stage diversity metric that indicates a ratio between: (i) an intensity of brain activity or heart activity within a first frequency band during an instance of a particular sleep stage from the brain waveform or the heart waveform of the patient; and (ii) an intensity of brain activity or heart activity within a second frequency band during the instance of the particular sleep stage from the brain waveform or the heart waveform of the patient, wherein the value for the brain state metric or the heart rate variability metric comprises the intra-stage diversity metric (as recited in claim 23); determining, by the computing system, a value for an inter-stage diversity metric that indicates a ratio between:(i) an intensity of brain activity or heart activity within a particular frequency band during a first instance of a particular sleep stage from the brain waveform or the heart waveform of the patient; and(ii) an intensity of brain activity or heart activity within the particular frequency band during a second instance of the particular sleep stage from the brain waveform or the heart waveform of the patient, wherein the value for the brain state metric or the heart rate metric comprises the value for the inter-stage diversity metric (as recited in claim 24); determining, by the computing system, a value for a brainwave diversity metric that indicates a difference between:(i) an intensity of brain activity recorded by a first electrode on a first side of a head of the patient; and(ii) an intensity of brain activity recorded by a second electrode on a second side of the head of the patient opposite the first side of the head of the patient, wherein the value for the brain state metric comprises the value for the brainwave diversity metric (as recited in claim 25); determining, by the computing system, a value for an inter-stage coupling metric that indicates a coupling between: (i) a ratio between a first metric and a second metric during a first sleep stage; and (ii) a ratio between the first metric and the second metric during a second sleep stage, wherein the values for the collection of metrics that are provided to the computational model and on which the indication of the metal state of the patient is based include the value for the inter-stage metric coupling metric (as recited in claim 26).
For these reasons the claims are believed to be allowable over the art of record.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SCOTT T LUAN whose telephone number is (571)270-1860. The examiner can normally be reached on 9am-5pm, M-F (generally).
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Gary Jackson, can be reached on 571-272-4697. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Scott Luan
/SCOTT LUAN/Primary Examiner, Art Unit 3792
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