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 listing of references in the specification is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered.
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 2, 7, and 17 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.
In re claim 2, the limitation "the values" in line 2 has insufficient antecedent basis for this limitation in the claim.
In re claim 17, see above (In re claim 2).
In re claim 7, the limitation "the group" in line 2 has insufficient antecedent basis for this limitation in the claim.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-15 do not include additional elements that integrate the exception into a practical application of the exception or that are sufficient to amount to significantly more than the judicial exception for the reasons provided below which are in line with the 2014 Interim Guidance on Patent Subject Matter Eligibility (Federal Register, Vol. 79, No. 241, p. 74618, December 16, 2014), the July 2015 Update on Subject Matter Eligibility (Federal Register, Vol. 80, No. 146, p. 45429, July 30, 2015), the May 2016 Subject Matter Eligibility Update (Federal Register, Vol. 81, No. 88, p. 27381, May 6, 2016), and the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 4, p. 50, January 7, 2019).
Step 1: Claim 1 is drawn to a method.
Step 2A – Prong 1:
Claim 1 is drawn to an abstract idea, that under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components to collecting and processing data. In particular, claim 1 recites the following limitations:
a method of analyzing sleep, the method comprising
receiving, from a wearable device used by a patient, heart rate data from over a plurality of circadian cycles;
analyzing, with a computer system, heart rate data from multiple hours of wake time and sleep time in each of the plurality of circadian cycles and
creating a circadian model for the patient with a defined operation for applying sleep labels to new data from the wearable device;
receiving test data from the wearable device;
applying the circadian model to the test data to identify a sleep interval; and
assigning, using a classifier, sleep stages to epochs within the sleep interval.
In re claim 16, see above (In re claim 1). Substantially, the same reasoning applies.
These limitations of claim 1 are drawn to an abstract idea because they are processes that, under their broadest reasonable interpretation, are steps merely comprised of mental processes.
Step 2A – Prong Two:
Claim 14 recites the following emphasized (indicated in bold) additional elements that are beyond the judicial exception:
receiving, from a wearable device used by a patient, heart rate data from over a plurality of circadian cycles;
analyzing, with a computer system, heart rate data from multiple hours of wake time and sleep time in each of the plurality of circadian cycles and
creating a circadian model for the patient with a defined operation for applying sleep labels to new data from the wearable device;
receiving test data from the wearable device;
The additional elements do not integrate the exception into a practical application of the exception because the elements are directed to insignificant extra-solution activity. The wearable device amounts to no more than pre-solution activity of data gathering to receive the heart rate data and test data. The computer system is a computer that carries out abstract steps described in claim 1 (see 2106.05(g) and 2106.05(f)). Accordingly, each of the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limitations on practicing the abstract idea. Further, the judicial exception does not integrate the claim as a whole into a practical application because the claimed invention does not improve another technology or technical field. The alleged improvement made by the claimed invention as argued by the application above sets forth the improvement in a conclusory manner and the claim does not include the components or steps of the invention that the improvement described.
In re claim 16, see above (In re claim 1). Substantially, the same reasoning applies.
Step 2B:
Claim 14 does not recite additional elements that amount to significantly more than the judicial exception itself. Under 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity. The wearable device amounts to no more than pre-solution activity of data gathering to receive the heart rate data and test data. The computer system is a computer that carries out abstract steps described in claim 1. All uses of the recited abstract idea require the pre-solution data gathering.
In re claim 16, see above (In re claim 1). Substantially, the same reasoning applies.
Claims 15-18 and 17-20 recite the same abstract idea as their respective parent claims. Furthermore, these claims only contain recitations that further limit the abstract idea.
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, 6 - 7, 15 - 16, 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kinnunen et al (US 20220375591).
In re claim 1, Kinnunen discloses a method of analyzing sleep (abstract), the method comprising
receiving [0015, 0069], from a wearable device (Fig. 2: 104) used by a patient (Fig. 1: “User”), heart rate data [0015, 0069] from over a plurality of circadian cycles [0138, 0139];
analyzing, with a computer system (Fig. 7: 705; [0155]: 705 may include a processor, [0025]), heart rate data [0069] from multiple hours of wake time and sleep time in each of the plurality of circadian cycles [0138, 0139] and
creating a circadian model (Fig. 7: 730, 735; [0132]) for the patient [0133] with a defined operation for applying sleep labels to new data ([0159, 0167]: whatever process labels the data for the machine learning, which is inherent, corresponds to the recited “defined operation”) from the wearable device [0159];
receiving test data from the wearable device [0204];
applying the circadian model to the test data to identify a sleep interval [0167, 0204]; and
assigning, using a classifier (Fig. 7: portions of 730, 735), sleep stages to epochs within the sleep interval [0104, 0116, 0204].
In re claim 16, see above (In re claim 1). Kinnunen further discloses:
a processor ([0229], Fig. 7: 705) coupled to memory containing instructions operable to cause the system to … [0207].
In re claim 5, Kinnunen discloses wherein the computer system builds the circadian model from a period of at least 2 consecutive circadian cycles [0138, 0139, 0142].
In re claim 6, Kinnunen discloses wherein the computer system is operable to use the circadian model and the classifier to detect episodes of daytime sleep by the patient ([0139], the circadian model is adjusted to account for day-time sleep and is input into the classifier [0159]).
In re claim 20, see above (In re claim 6).
In re claim 7, Kinnunen discloses wherein
the classifier is provided by a machine learning system that assigns the sleep stage to the epochs [0159], and
wherein each sleep stage is selected from the group consisting of wake, REM sleep, and non-REM sleep [0169].
In re claim 8, Kinnunen discloses the method of claim 7, wherein the machine learning system
includes at least one neural network [0111] that captures time dependencies [145, 146] and
has been trained on labeled training data from multiple subjects [128, 132].
In re claim 15, Kinnunen discloses
the classifier is provided by a machine learning system (abstract) and
the assigning step includes:
processing heart rate and accelerometer data from the wearable device into a feature per epoch that includes a measure of heart rate variability [0101, 0108] and activity [0098];
providing the features into the machine learning system to classify the epochs into sleep stages ([0113]: part of “physiological data”); and
updating a medical record for the patient with the sleep stages [0079].
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 2 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Kinnunen et al (US 20220375591) in view of Gowda et al (WO 2019036661).
In re claim 2, Kinnunen discloses wherein the circadian model weighs the probability metrics based on the time duration from the multiple hours of wake time and sleep time in the plurality of circadian cycles [0154, 0167].
Kinnunen lacks:
wherein the circadian model includes a threshold heart rate value, set at a fixed quantile of a cumulative distribution function of the values in the heart rate data from the multiple hours of wake time and sleep time in the plurality of circadian cycles.
Gowda discloses a system for monitoring sleep in a patient wherein heart rate data in the form of a quantile of the heart rate distribution or as a threshold over the interval is used to determine a change in the breathing disruption (sleep state) of the user [0057, 0058].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the method taught by Kinnunen by using a threshold heart rate value as taught by Gowda because this would allow the model to use normal baseline from which to compare the new data, making it easier to determine sleep labels.
In re claim 17, see above (In re claim 2).
Claims 3 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kinnunen et al (US 20220375591) in view of Gowda et al (WO 2019036661) in view of Karnik (US 20190082968).
In re claim 3, it is apparent that Kinnunen collects and needs wake data [0138, 0139, 0142]. However, Kinnunen lacks:
wherein the computer system sends the patient a notification when the heart rate data for the plurality of circadian cycles does or does not include enough hours of wake time to create the circadian model.
Karnik discloses a method of health monitoring wherein a prompt (i.e. notification) is sent to the user when there has not been enough data collected [0142].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the proposed system by sending the patient a notification to confirm if the heart rate data includes enough hours of wake time to create the circadian model so that the patient is aware that they need to wear the wearable device longer in order to have the circadian model effective in sleep analysis.
In re claim 18, see above (In re claim 3).
Claim 4 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kinnunen et al (US 20220375591) in view of Coleman et al (US 20190113973).
In re claim 4, Kinnunen discloses
wherein the computer system has built the circadian model using data for a plurality of circadian cycles for the patient [0133],
wherein each circadian cycle includes data from a wearable device for at least 12 hours of a 24-hour period [0014].
Kinnunen lacks:
wherein the computer system has stored therein a circadian model for each of a plurality of patients,
wherein the computer system has built each circadian model using data for a plurality of circadian cycles for each patient.
Coleman discloses a method for determining the mental state of the user wherein a prediction model is developed and stored in a computer system for each patient [0058, 0150].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the method of sleep stage detection taught by Kinnunen by developing and storing the circadian model in a computer system as taught by Coleman as this would optimize computing resources and the aggregation of information across a number of users (Coleman [0058]).
In re claim 19, see above (In re claim 4).
Claims 9 - 12 are rejected under 35 U.S.C. 103 as being unpatentable over Kinnunen et al (US 20220375591) in view of Garcia Molina et al (US 20190192069).
In re claim 9, Kinnunen lacks wherein the neural network that captures time dependencies includes one or more of
a recurrent neural network and
a long short-term memory (LSTM) neural network.
Garcia Molina discloses a method to identify sleep stages that utilizes a recurrent neural network [0043] and a long-short term memory neural network [0043] to identify sleep stages.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the method taught by Kinnunen by using a recurrent neural network or long short-term memory (LSTM) neural network to determine the sleep stage of the patient as they can model high-level abstractions in data, and the LSTM would further benefit the classifier as the strong memory can use the historical data to refine prediction accuracy (Garcia Molina [0043]),.
In re claim 10, Kinnunen discloses
creating a record of sleep stages for the patient ([0079, 0157]: the results are displayed and stored in a database), and
providing access to the record to a registered user of the computer system (inherent: it is well known that computer systems have registered users).
Kinnunen lacks:
providing access to the record to a clinician who is a registered user of the computer system.
Garcia Molina discloses a method to identify sleep stages wherein the data and results provided to the patient and caregiver (e.g. clinician) from the system through computing devices [0060].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the method taught by Kinnunen by providing record access to the clinician via the computer system as taught by Garcia Molinia as the clinician would be able to use the record to inform their treatment of the patient and to update their medical record of the patient. The computing device would easily allow the clinician to access the record immediately after the data is collected, eliminating any potential scheduling delays, and allow the clinician to conduct their own analysis (Garcia Molina [0060]). Accordingly, such a modification would yield “providing access to the record to a clinician who is a registered user of the computer system”.
In re claim 11, Kinnunen wherein the record of sleep stages is displayed to the patient via an app ([0079]; Fig. 4).
In re claim 12, Kinnunen discloses, further comprising appending the record of sleep stages to an electronic medical record [0079] for the patient.
Claims 13 - 14 are rejected under 35 U.S.C. 103 as being unpatentable over Kinnunen et al (US 20220375591) in view of Moturu et al (US 20170189641).
In re claim 13, Kinnunen discloses wherein the computer system is operable to receive data transfers (Fig. 1: arrows directing flow of data to 110)
from different first (104-a) and second (104-b) wearable devices used by respective first (102-a) and second patients (102-b).
Kinnunen lacks wherein the computer system is operable to receive data transfers
wherein the data transfers from the respective devices have different formats or different content.
Moturu discloses a method for characterizing sleep wherein data can be gathered from a variety of mobile devices in which the data have different formats [0052].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the method taught by Kinnunen by allowing the computer system to accept data in different formats as taught by Moturu because it would make the method applicable to a wider range of users who use different types of devices.
In re claim 14, Kinnunen discloses wherein the computer system
performs the analyzing [0028] and assigning steps for each patient [0113].
Kinnunen lacks wherein the computer system
standardizes the data transfers.
Moturu discloses a method for characterizing sleep wherein data is standardized [0052].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the method taught by Kinnunen by standardizing the data as taught by Moturu as it would allow for the data to be analyzed and assigned in the same manner across different types of devices that may have different formats or different content.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HALLE M WELCH whose telephone number is (571)272-0168. The examiner can normally be reached Mon-Fri, 7:30 am to 5:00 pm..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David E Hamaoui can be reached at (571) 270-5625. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HALLE MARGARET WELCH/Examiner, Art Unit 3796
/DAVID HAMAOUI/SPE, Art Unit 3796