DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Election/Restrictions
Applicant’s election without traverse of claims 1-23 and 28 in the reply filed on 04/27/2026 is acknowledged.
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 3, 9, 13, 18, and 19 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 claims 3, 9, 18, and 19, the phrase "for example" renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Claim 13 recites the limitation “wherein the input cardiorespiratory signals are derived from video images of the test subject” and then further “optionally wherein the method further comprises deriving the input cardiorespiratory signals from the video images of the test subjects”. As the second limitation states that the derivation of input signals is “optional” but the first limitation stipulates that they are derived from the video image, it is unclear if the limitation is required. If the second limitation is instead referring to “subjects” as in a plural number of test subjects, the claim lacks antecedent basis.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 2-3, 10, 13-14, rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 2 recites the limitation “training the feature extractor using the reference cardiorespiratory signals.”, while claim 1 recites the limitation “using a feature extractor trained using reference cardiorespiratory signals”.
Claim 3 is rejected due to dependency.
Claim 10 recites the limitation “further comprising deriving the at least one cardiorespiratory feature of the test subject using the feature extractor” which fails to limit claim 1 which recites “the input data comprise: … and at least one cardiorespiratory feature of the test subject; the input data are derived from video images of the test subject”
Claim 13 recites the limitation “wherein the input cardiorespiratory signals are derived from video images of the test subject,” which fails to limit claim 1 which recites “the input data comprise: … and at least one cardiorespiratory feature of the test subject; the input data are derived from video images of the test subject”
Claim 14 recites the limitation “further comprising deriving the at least one measure of test subject movement from the video images of the test subject” which fails to limit claim 1 which recites “the input data comprise; at least one measure of test subject movement;… the input data are derived from video images of the test subject”
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-23 and 28 is/are rejected under 35 U.S.C. 101 because the claimed invention, considering all claim elements both individually and in combination as a whole, do not amount to significantly more than a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea).
Claim 1 is a claim to a process, machine, manufacture, or composition of matter and therefore meets one of the categorical limitations of 35 U.S.C. 101. However, claim 1 meets the first prong of the step 2A analysis because it is directed to a/an abstract idea, as evidenced by the claim language of “determining a sleep state of a test subject using input data from the test subject,”, “the input data are derived from video images of the test subject”, “the at least one cardiorespiratory feature of the test subject is derived using a feature extractor trained using reference cardiorespiratory signals for each of a plurality of reference subjects, the feature extractor comprising a neural network, and the reference cardiorespiratory signals derived from time-resolved measurements”. This claim language, under the broadest, reasonable interpretation, encompasses subject matter that may be performed by a human using mental steps or with pen and paper that can involve basic critical thinking, which are types of activities that have been found by the courts to represents abstract ideas (i.e., the mental comparison in Ambry Genetics, or the diagnosing an abnormal condition by performing clinical tests and thinking about the results in Grams). In this case, observing sensor data, extracting features from images, and determining a sleep state consist of steps that may be performed in the mind with the use of a pen and paper. The use of a neural-network does not qualify the claim as more than a mental process as a generic computer structure is not significantly more according to Alice v. CLS.
The claim language also meets prong 2 of the step 2A analysis because the above-recited claim language does not integrate the abstract idea into a practical application. The disclosed technologies do not improve a technical field (see MPEP 2106.05(a)), affect a particular treatment for a disease or medical condition (see MPEP 2106.04(d)(2)), effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.04(d)(2)), apply the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), or apply the judicial exception in some meaningful way beyond generally linking the use of the abstract idea to a particular technological environment (MPEP 2106.04(d)(2) and 2106.05(e)). As a result, step 2A is satisfied and the second step, step 2B, must be considered.
With regard to the second step, the claim does not appear to recite additional elements that amount to significantly more. The additional elements are “sensors” and “neural-network”. However, these elements are not “significantly more” because they are well-known, routine, and/or conventional as evidenced by para [0038]: “posture and a body movement amount, and physiological information such as brain waves, a heart rate, blood pressure, skin potential, muscle potential, stomach sound, perspiration, and temperature are measured by using a well-known sensor” of Kaida et al. (US 20200359958 A1). Regarding the “neural network”. a generic computer structure such as is not significantly more according to Alice v. CLS. Therefore, these elements do not add significantly more and thus the claim as a whole does not amount to significantly more than a judicial exception.
Additionally, the ordered combination of elements do not add anything significantly more to the claimed subject matter. Specifically, the ordered combination of elements do not have any function that is not already supplied by each element individually. That is, the whole is not greater than the sum of its parts.
In view of the above, independent claim 1 fails to recite patent-eligible subject matter under 35 U.S.C. 101. Dependent claim(s) 2-23 and 28 fail to cure the deficiencies of independent claim 1 by merely reciting additional abstract ideas, further limitations on abstract ideas already recited, and/or additional elements that are not significantly more. Thus, claim(s) 1-23 and 28 is/are rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-23, and 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chan et al. (US 20220386944 A1) in view of in view of Kordari et al. (WO 2023214957 A1)
Regarding claim 1, Chan discloses a method comprising determining a sleep state of a test subject using input data from the test subject (abstract), wherein: the input data comprise: at least one measure of test subject movement ([0012]: "a fraction of time labeled as movement by the activity detector"); and at least one cardiorespiratory feature of the test subject ([0013]: "generating, with a breath detector, one or more streams of breath cycle lengths and breath cycle amplitudes;", [0031]: "heart beats"); and the at least one cardiorespiratory feature of the test subject is derived using a feature extractor ([0027]: “Sensor signal(s) 202 from a sleep/wake tracking device (e.g., an in bed sensor) are input into feature extractor 201, which extracts multiple features, as described in reference to FIGS. 3-5 . Some example features include but are not limited to: respiratory rate variability (RRV), respiratory amplitude variability (RAV) and motion”)
Chan fails to disclose the input data are derived from video images of the test subject and a feature extractor trained using reference cardiorespiratory signals for each of a plurality of reference subjects, the feature extractor comprising a neural network, and the reference cardiorespiratory signals derived from time-resolved measurements from a plurality of sensors worn by the reference subjects.
Kordari discloses input data are derived from video images of the test subject ([0007]: “the device having one or more cameras thereon configured to capture an image of one or more users”) and a feature extractor trained using reference cardiorespiratory signals for each of a plurality of reference subjects, the feature extractor comprising a neural network ([0031]: “, the machine learning model may be configured as a deep learning network. The deep learning network may be a convolutional neural network.”), and the reference cardiorespiratory signals derived from time-resolved measurements from a plurality of sensors worn by the reference subjects ([0029]: “providing a time series of video frames collected from a region of a body of a subject that is associated with a blood volume change within a tissue of the body; providing a time series of R-R intervals, the R-R intervals provided as ground truth data; synchronizing the time series of the video frames and the time series of the R-R intervals to provide an input pair to provide a synchronized time series of video frames and time series of R-R intervals; and training a machine learning model to estimate R-R intervals from the synchronized time series of video frames and time series of R-R intervals using the time series of R-R intervals as the ground truth data.”).
It would have been obvious to a person of ordinary skill in the art prior to the effective filing date to modify the method of obtaining input data disclosed by Chan to the method of deriving input data from video images as disclosed by Kordari in order to allow for non-contact monitoring of a patient ([0295]). It would also have been obvious to a person ordinary skill in the art prior to the effective filing date to modify the feature extractor disclosed by Chan to the feature extractor comprising a neural network and the reference cardiorespiratory signals derived from time-resolved measurements from a plurality of sensors worn by the reference subjects as disclosed by Kordari in order to improve the quality of features derived (Kordari).
Regarding claim 2, Kordari discloses further comprising training the feature extractor using the reference cardiorespiratory signals ([0035]: providing a time series of R-R intervals as ground truth data; and training a machine learning model to determine a time domain biometric value from the time series of R-R intervals.”, [0163]).
Regarding claim 3, Chan further discloses wherein training the feature extractor comprises combining the feature extractor with a reference classifier to form a transfer network (Fig 2) and training the transfer network to determine a sleep state for each of the reference subjects ([0045]: “Classifier 500 can be trained using back propagation techniques”), optionally by training the transfer network to minimize a loss function, for example a cross-entropy loss
Regarding claim 4, Chan further discloses wherein the reference cardiorespiratory signals in respect of each reference subject are temporally divided into a plurality of epochs, each epoch being labelled with one of a set of sleep states comprising wake, rapid-eye movement (REM) sleep, light non-rapid eye movement (NREM) sleep, or deep NREM sleep, optionally wherein in the set of sleep states further comprises intermediate NREM sleep ([0027]: "These features are input into ML classifier 202 (e.g., as a feature vector), as described in reference to FIG. 5 . ML classifier 202 predicts either a “sleep” or “wake” state based on the input features. In another embodiment, it is also possible for the ML classifier 202 to predict specific sleep stages such as REM, NREM1, NREM2, and NREM3 based on the input features.", [0028]: "For example, if ML classifier 202 predicts an epoch of “sleep” with a 0.45 probability, that epoch would be classified as “wake” because its probability would be less than a specified threshold value (e.g., 0.55 in this example; sleep and wake probabilities add to one). Different probability thresholds can be used to tune the algorithm to more likely predict sleep or wake for any given epoch. ").
Regarding claim 5, Kordari further discloses wherein the reference cardiorespiratory signals comprise one or more of heart rate, respiratory rate, a pulse waveform, and a respiratory waveform ([0030]: “The time series of R-R intervals may be calculated from inter-beat intervals measured by an ECG”).
Regarding claim 6, Kordari discloses wherein the reference cardiorespiratory signals comprise a first plurality of reference cardiorespiratory signals and a second plurality of reference cardiorespiratory signals, wherein the first and second pluralities of reference cardiorespiratory signals differ in one or more signal characteristics ( [0195-0197]: “A windowed version of this algorithm enhances accuracy by cutting the target series into overlapping windows and calculating the median and normalization factor for each window separately… a pattern-based windowed impulse response (PWIR) algorithm was tested for which good performance on non-pathological R-R datasets was reported.”.), optionally wherein the signal characteristics comprise sampling rate and duration.
Regarding claim 7, Kordari discloses wherein the feature extractor extracts a first plurality of features from the first plurality of reference cardiorespiratory signals and a second plurality of features from the second plurality of reference cardiorespiratory signals ([0029]: “providing a time series of R-R intervals, the R-R intervals provided as ground truth data; synchronizing the time series of the video frames and the time series of the R-R intervals to provide an input pair to provide a synchronized time series of video frames and time series of R-R intervals”, wherein the pairs are different per interval).
Regarding claim 8, Kordari discloses wherein the feature extractor combines the first and second pluralities of features into a plurality of output features, optionally wherein the plurality of output features is smaller than a total number of features in the first and second pluralities of features ([0127]: “The “Stress Score” reading is an output that is a calculated value that is calculated using algorithms that receive trended HRV, HR, and respiration as inputs.”).
Regarding claim 9, Kordari discloses the method further comprises deriving the reference cardiorespiratory signals from the time-resolved measurements, for example by down sampling and/or filtering the time-resolved measurements ([0029]).
Regarding claim 10, Chan discloses deriving the at least one cardiorespiratory feature of the test subject using the feature extractor (Fig 2 element 201).
Regarding claim 11, Chan discloses wherein the at least one cardiorespiratory feature of the test subject is derived from input cardiorespiratory signals using the feature extractor, optionally wherein the input cardiorespiratory signals comprise one or more of heart rate, respiratory rate, a pulse waveform, and a respiratory waveform (Fig 2 element 201, [0027]: “respiratory rate variability (RRV), respiratory amplitude variability (RAV) and motion. Other example features include but are not limited to heart rate (HR) and HR variability (HRV)”).
Regarding claim 12, Chan discloses wherein the input cardiorespiratory signals comprise a first plurality of input cardiorespiratory signals and a second plurality of input cardiorespiratory signals, wherein the first and second pluralities of input cardiorespiratory signals differ in one or more signal characteristics, optionally wherein the signal characteristics comprise sampling rate and duration ([0040]: “the time-domain movement detection path includes an activity detection module 402 that outputs a stream of movement states (“moving” or “not-moving”) and associated movement amplitudes whenever movement occurred. Window function 403 accumulates all movement periods and amplitudes within a window of N seconds (e.g., N=60). This window is shifted across the processing duration (e.g., whole night). Time-domain movement feature extractor 404 computes, from the window of movement periods and amplitudes, a mean movement amplitude, a maximum movement amplitude and a fraction of time the movement periods were labeled as movement by the classifier in the activity detection module 402.”)
Regarding claim 13, Kordari discloses wherein the input cardiorespiratory signals are derived from video images of the test subject ([0029]: “video frames collected from a region of a body of a subject that is associated with a blood volume change within a tissue of the body;”), optionally wherein the method further comprises deriving the input cardiorespiratory signals from the video images of the test subjects ([0029]: “blood volume change”).
Regarding claim 14, Kordari discloses deriving the at least one measure of test subject movement from the video images of the test subject ([0161]: “eye movement/blinking rate”).
Regarding claim 15, Kordari discloses wherein the at least one measure of test subject movement comprises one or more of a measure of subject movement in a head region of the subject, a measure of movement in a torso region of the subject, and a measure of movement in an outer region of the video image ([0161]: “eye movement/blinking rate”).
Regarding claim 16, Chan further discloses wherein determining the sleep state of the test subject comprises applying a machine-learning algorithm to the input data, wherein the machine-learning algorithm outputs a determination of the sleep state of the test subject on the basis of training data in respect of a plurality of training subjects (Fig 2 ML classifier and sleep/wake, [0045]: “Classifier 500 can be trained using back propagation techniques and annotated training data sets comprising data collected from sleep study participants.”, wherein the learning model is trained using back propagation and thus would do a forward pass using the same input extraction method); the training data are derived from video images of the training subjects (as modified by Dcruz above in claim 1); the training data comprise for each of the training subjects: at least one measure of subject movement (Fig 2 motion); and at least one cardiorespiratory feature of the subject (Fig 2 RAV and RRV); and the at least one cardiorespiratory feature for each of the training subjects is derived using the feature extractor (Fig 2 element 201).
Regarding claim 17, Chan discloses wherein the machine learning algorithm is a regression algorithm ([0045]: “logistic regression, support vector machines (SVM), Extra Trees, Random Forests, Gradient Boosted Trees, Extreme Learning Machine (ELM), Perceptron and multi-layered convolutional neural networks.”), and the machine-learning algorithm determines the sleep state of the test subject using a continuous measure of sleep depth ([0027]: “to predict specific sleep stages such as REM, NREM1, NREM2, and NREM3 based on the input features”), optionally wherein the regression algorithm is a neural network ([0045]).
Regarding claim 18, Chan discloses wherein: the training data in respect of each training subject are temporally divided into a plurality of epochs, each epoch being labelled with one of a set of sleep states, for example wherein the set of sleep states comprises wake, rapid-eye movement (REM) sleep, light non-rapid eye movement (NREM) sleep, or deep NREM sleep, optionally wherein in the set of sleep states further comprises intermediate NREM sleep ([0028]: “For example, if ML classifier 202 predicts an epoch of “sleep” with a 0.45 probability, that epoch would be classified as “wake” because its probability would be less than a specified threshold value (e.g., 0.55 in this example; sleep and wake probabilities add to one). Different probability thresholds can be used to tune the algorithm to more likely predict sleep or wake for any given epoch. From a full night of sleep/wake predictions, other derived metrics can be computed. For example, the Total Sleep Time metric can be computed by summing the sleep times between Sleep Onset and Sleep Offset. In another embodiment, ML classifier 202 generates a probability for each sleep stage (Wake, REM, NREM1, NREM2, NREM3), and the stage with the highest probability is chosen as the predicted stage.”); and the machine learning algorithm is a classification algorithm that classifies the sleep state of the test subject as one of the set of sleep states ([0027]), optionally wherein the classification algorithm is one of a logistic regression, k-nearest neighbours, a random forest classifier, and a neural network ([0045])
Regarding claim 19, Chan further discloses the training data in respect of each training subject are temporally divided into a plurality of epochs, each epoch being labelled with one of a set of sleep states, for example wherein the set of sleep states comprises wake, rapid-eye movement (REM) sleep, light non-rapid eye movement (NREM) sleep, or deep NREM sleep, optionally wherein in the set of sleep states further comprises intermediate NREM sleep ([0028]); the machine learning algorithm is a classification algorithm, optionally one of a logistic regression, k-nearest neighbours, a random forest classifier, and a neural network ([0045]); the classification algorithm generates scores for each sleep state of the set of sleep states, the scores representing a confidence of the sleep state of the test subject being that sleep state of the set of sleep states ([0028]: “a probability of sleep or wake as a measure of confidence in the prediction (hereinafter, also referred to as “confidence score”)”); and the machine-learning algorithm determines the sleep state of the test subject using a continuous measure of sleep depth, the continuous measure being determined from a weighted combination of the scores for each sleep state of the set of sleep states ([0028]: “Different probability thresholds can be used to tune the algorithm to more likely predict sleep or wake for any given epoch. From a full night of sleep/wake predictions, other derived metrics can be computed. For example, the Total Sleep Time metric can be computed by summing the sleep times between Sleep Onset and Sleep Offset. In another embodiment, ML classifier 202 generates a probability for each sleep stage (Wake, REM, NREM1, NREM2, NREM3), and the stage with the highest probability is chosen as the predicted stage.”, wherein the probability is the weight).
Regarding claim 20, Chan discloses wherein the at least one cardiorespiratory feature for each of the training subjects is derived from training cardiorespiratory signals using the feature extractor (Fig 2), optionally wherein the training cardiorespiratory signals comprise one or more of heart rate, respiratory rate, a pulse waveform, and a respiratory waveform (Fig 2).
Regarding claim 21, Chan discloses wherein the training cardiorespiratory signals comprise a first plurality of training cardiorespiratory signals and a second plurality of training cardiorespiratory signals, wherein the first and second pluralities of training cardiorespiratory signals differ in one or more signal characteristics (([0040]: “the time-domain movement detection path includes an activity detection module 402 that outputs a stream of movement states (“moving” or “not-moving”) and associated movement amplitudes whenever movement occurred. Window function 403 accumulates all movement periods and amplitudes within a window of N seconds (e.g., N=60). This window is shifted across the processing duration (e.g., whole night). Time-domain movement feature extractor 404 computes, from the window of movement periods and amplitudes, a mean movement amplitude, a maximum movement amplitude and a fraction of time the movement periods were labeled as movement by the classifier in the activity detection module 402.”) optionally wherein the signal characteristics comprise sampling rate and duration.
Regarding claim 22, Kordari discloses wherein the training cardiorespiratory signals are derived from video images of the training subjects ([0029]).
Regarding claim 23, Chan further comprising training the machine-learning algorithm using the training data.
Regarding claim 28, Chan further discloses A computer program comprising, or a non-transitory computer- readable storage medium having stored thereon, instructions which, when carried out by a computer, cause the computer to carry out a method according to claim 1 ([0015]: “an apparatus, computing device and non-transitory, computer-readable storage medium.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Fonseca et al. (US 20170360308 A1) – discloses cardiorespiratory sleep stage classification
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/KAVYA SHOBANA BALAJI/Examiner, Art Unit 3791
/DEVIN B HENSON/Primary Examiner, Art Unit 3791
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