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 .
Response to Arguments
With regards to the double patenting rejection based on U.S. Patent No. 12268530; and provisional double patenting rejections based on copending Applications Nos. 17/733864, 18/145645, 18/584745, 18/455048, 18/455514 and 18/612879 the double patenting rejection and provisional rejections are withdrawn based on the amendments.
Applicants’ arguments filed 6/2/26 have been fully considered but they are not persuasive.
Applicants first argue with regards to the 101 rejections: (1) arguing the claims do not recite a judicial exception, (2) that if they do include a judicial exception then the claims integrate it into a practical application, and (3) that if they do include a judicial exception they improve the technology.
With regards to (1) applicants argue that the “acquiring . . .”, “identifying . . .”, “classifying . . .” and “causing” steps do not recite a judicial exception and the steps are not mental processes. Examiner disagrees, the claims do not recite a judicial exception. The “acquiring …” is an additional element and not part of the abstract idea but it is extrasolution activity, as presolution data gathering. The acquiring is how PPG gathers data, light is transmitted to the body and the response is received (see for example, Heneghan [0134], [0144] or Allen in the pertinent art section in the conclusion). This data gathering is presolution activity, the gathered data is then used in the abstract idea. The two steps, “identifying . . .” and “classifying . . .”, represent the mental process abstract idea. A person can mentally or with pen and paper perform the “identifying . . .” relevant features (based on prior data, or by extracting features from the current data) and classifying based on the features a particular sleep stage, based on their knowledge in view of gathered data. The “causing . . .” step is an additional element and not part of the abstract idea but it is extrasolution activity, as postsolution activity of outputting the result. For the above reasons these arguments are not persuasive.
With regards to (2) Applicants argue the claims recite additional elements which integrate the claims into a practical application by improving the technology. Examiner disagrees. Applicants point to elements which are part of the judicial exception/abstract idea and not the additional elements. Applicants are arguing that the abstract idea is improved, more specifically that the model is more accurate. However, the additional elements are the data gathering using PPG and outputting the result of the abstract idea, which do not integrate the judicial exception into a practical application. The improvement Applicants are arguing is not in the additional elements, Applicants are not arguing for example an “improved” ppg sensor which is more sensitive or less noisy etc. but arguing that the claimed model is more accurate. MPEP 2106.05(a): "However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology."
Applicants mention the Cardionet case (Examiner notes the others were not found eligible) arguing the claims the analogous to those. The facts and claims of the current case are different than those of the Cardionet case and Cardionet is non-analogous because of the differences. Applicants are not claiming something akin to the adaptive real time analysis and their arguments are not directed to the additional elements.
Applicants next argue the current claims are analogous to Ex Parte Desjardins, Examiner disagrees. The facts and claims of the current case are different than those of the Cardionet case and Cardionet is non-analogous because of the differences. The claims in Desjardins had specific elements which provided the path to eligibility the claims in the current application do not provide elements similar to those in Desjardins. Desjardins was eligible because “In particular, the Appellant identifies certain limitations of independent claim 1 and asserts that "the claimed subject matter provides technical improvements over conventional systems by addressing challenges in continual learning and model efficiency by reducing storage requirements and preserving task performance across sequential training," citing paragraph 21 of the Specification for support. Id. at 7-9; see also id. at 8 ("This training strategy allows the model to preserve performance on earlier tasks even as it learns new ones, directly addressing the technical problem of 'catastrophic forgetting' in continual learning systems.").”; phrased differently holding the first model constant saved memory which was a technical improvement. The current application is reciting using machine learning models and that the currently claimed one is more accurate, which is not the same as what occurred in Desjardins. For the above reasons Applicants arguments are not persuasive.
With regards to (3) similar to the discussion above Applicants are arguing it is an “inventive concept” because it is significantly more as an improvement to the technology under step 2b. Again, this is not persuasive. Applicants point to elements which are part of the judicial exception/abstract idea and not the additional elements. Applicants are arguing that the abstract idea is improved, more specifically that the model is more accurate. However, the additional elements are the data gathering using PPG and outputting the result of the abstract idea. The improvement Applicants are arguing is not in the additional elements, Applicants are not arguing for example an “improved” ppg sensor which is more sensitive or less noisy etc. but arguing that the claimed model is more accurate. Per MPEP 2106.05(I) “An inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself." Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016).”. For the above reasons this argument is not persuasive. Further for the above reasons none of the 101 arguments are found persuasive.
Applicant’s arguments, see pg. 15, filed 6/2/26, with respect to the rejection(s) of claim(s) 1 and 17 (and by extension the dependent claims) under 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Heneghan in view of Kinnunen in further view of Guazzi.
Applicants’ 103 argument can be summarized, that Heneghan does not disclose the circadian rhythm feature being identified. Examiner agrees that while Heneghan discloses identifying a plurality features it may not explicitly disclose the circadian rhythm feature. Examiner notes Applicants discussion of Heneghan is based off of [0005] and the Heneghan references teaching is not limited to that portion.
Examiner notes that Applicants statement “Heneghan, however, does not disclose or suggest extracting a circadian feature representative of a circadian rhythm of a user from physiological data (e.g., the motion and/or the cardiopulmonary pulse-related data) covering the first time window.” This argument implies that Applicants extract a circadian feature from the physiological data, which is not the BRI of what is claimed. Additionally, based on Applicants specification, the circadian feature is determined by a model but the “circadian rhythm adjustment model” doesn’t seem to receive as input the physiological features. It is seemingly trained with the data from the other sensors, or adjusted/individualized based on them. See for example, [0149] “circadian features that are sensor-independent (ACC+T+HRV+C models)” with the “(ACC+T+HRV+C models)” being the features input into the sleep stage model (note the “C models” which is the “circadian rhythm adjustment model”). Examiner also notes that what the “circadian features” is not seemingly explicitly recited; the circadian features are described in terms of being the outputs of the models (see previous sentence) and generally can be used to “weight” the other features (see [0129] for example).
Applicants finally mention the additional references recited beyond Heneghan, but do not provide any specific arguments directed toward them beyond a general statement they don’t teach the elements. Examiner disagrees, see the rejection below.
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 1-4, 6-20 are rejected under 101 see the analysis below.
Step 1
The invention claimed in claims 1-20 are directed to statutory subject matter as the claims recite an apparatus or process.
Step 2A, Prong 1
Regarding Claims 1 and 17, the recited steps of “identifying” and “classifying” into sleep stages based on the physiological data are directed to a mental process of performing concepts in the human mind (including by a human using the aid of pen and paper). For example, this limitation simply amounts to the mental process of a clinician reviewing data (such as physiological data) and performing a mental analysis of reviewing the information to derive/identify features and use their knowledge to classify the user it into different stages based on the features. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong 2
Regarding Claims 1 and 17, the judicial exception is not integrated into a practical application. The claims include the additional elements of “acquiring” data (sub steps of “sampling” and “determining”), “inputting” and causing a display to output the result. The steps of “acquiring” data (sub steps of “sampling” and “determining”) and “inputting” the data into the computing system amounts to insignificant, extra-solution activity in that it is data gathering. The step of “causing . . .” the display to output the result is extra solution as merely outputting a result. The processor (i.e., “processor”, “computer processor”, “cloud-computing device”, “mobile device”, “user device”, see Applicants [0147] using the Pg Pub for paragraph numbers) in computing steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of determining outputs from inputs) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B
Regarding Claims 1 and 17, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As with step 2A, Prong 2 above, the additional elements of “acquiring” data (sub steps of “sampling” and “determining”), “inputting” and causing a display to output the result. The steps of “acquiring” data (sub steps of “sampling” and “determining”) and “inputting” the data into the computing system amounts to insignificant, extra-solution activity in that it is data gathering. The step of “causing . . .” the display to output the result is extra solution as merely outputting a result. The processor (i.e., “processor”, “computer processor”, “cloud-computing device”, “mobile device”, “user device”, see Applicants [0147] using the Pg Pub for paragraph numbers) in computing steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of determining outputs from inputs) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Additionally, per the Berkheimer requirement, wearable sensor, user device display, processing and data storage: (1) Heneghan see citations below; (2) Kinnunen see citations below; (3) Haakma see citations below including abstract, [0037]-[0038], [0049]-[0050], Fig. 7; (4) US 20150190086– see [0021]-[0024], Figs. 2-4. Furthermore, more specifically the wearable sensor be a ring: (1) Kinnunen see Fig. 2-3 and citations below; (2) US 20220031233 to Han et al. see [0032], [0072], [0079]-[0080], [0088], Figs. 1-3B, 5, 11-13; (3) US 20210169345 see [0013], [0026]-[0027], [0064], Figs. 1, 5 . As such the elements are shown to be WRC.
The claim limitations when viewed individually and in combination therefore do not amount to significantly more than the abstract idea itself. The claims are therefore ineligible.
Claims 2-4, 6-16, 18-20 only further define the data gathering or displaying of the result (insignificant, extra-solution activity) or further define elements of the inputting/classifying (i.e., only further define the mental process). Therefore, the claims do not include any additional elements that show integration into a practical application and do not include any additional elements that amount to significantly more than the abstract idea. The claims are ineligible.
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.
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.
Claim(s) 1-6, 8-10, 13-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20180064388 to Heneghan et al. (hereinafter Heneghan – Cited in IDS dated 1/6/26) in view of US 20180042540 to Kinnunen et al. (hereinafter Kinnunen) in further view of US 20200397366 to Guazzi et al. (hereinafter Guazzi).
Regarding Claim 1, an interpretation of Heneghan discloses a method for automatically detecting sleep stages, comprising:
acquiring, via a wearable device throughout a time interval, physiological data from a user ([0125]-[0126] including “the sleep monitoring platform 200 comprises a wearable device 202, a secondary device 204, a network 206, and a backend system 208.”, [0127] including “a wearable device configured to be attached to the wearer's body, such as a wrist-worn band, watch, finger clip, ear-clip, chest-strap, ankle strap, or any other suitable device.”, [0131], [0134] including “collect pulse-related data and motion data during a sleep session for use in the techniques and systems discussed herein. . . . to obtain the motion data from the one or more motion sensors 410, obtain the pulse-related data from the one or more optical sensors 412, and to then transmit such data to other devices in the sleep monitoring platform.”, Figs. 2-5 see also [0005], [0116], [0129]-[0130], [0132]-[0133], [0135], [0230]; recites gathering physiological data during a sleep session on a wearable and transmit data to/receive the data at external devices), wherein the wearable device comprises one or more photoplethysmogram (PPG) sensors arranged on an inner surface of the wearable device ([0134] including “when such an optical sensor is placed adjacent to a person's skin, light from the light source(s) may diffuse into the person's flesh and then be emitted back out of the person's flesh such that the photodetector(s) can detect it.”, [0144] including “A typical photoplethysmographic (PPG) signal”, [0139], [0149] see also [0230]), and wherein acquiring the physiological data comprises ([0134] including “when such an optical sensor is placed adjacent to a person's skin, light from the light source(s) may diffuse into the person's flesh and then be emitted back out of the person's flesh such that the photodetector(s) can detect it.”, [0144] including “A typical photoplethysmographic (PPG) signal”, [0139], [0149] see also [0230]):
sampling a PPG signal generated by at least one optical receiver of the one or more PPG sensors, the PPG signal indicative of an amount of light received by the at least one optical receiver, and the light transmitted from at least one optical transmitter of the one or more PPG sensors ([0134] including “when such an optical sensor is placed adjacent to a person's skin, light from the light source(s) may diffuse into the person's flesh and then be emitted back out of the person's flesh such that the photodetector(s) can detect it.”, [0139], [0144] including “A typical photoplethysmographic (PPG) signal”, [0149], Figs. 2 and 4 see also [0055], [0218], [0230]); and
determining a pulse waveform of the user based on the PPG signal, wherein the physiological data is based at least in part on the pulse waveform ([0134] including “when such an optical sensor is placed adjacent to a person's skin, light from the light source(s) may diffuse into the person's flesh and then be emitted back out of the person's flesh such that the photodetector(s) can detect it.”, [0139] including “The pulse-related data may take the form of a waveform of detected light that varies in response to the blood volume pulsating within the skin of a wearer of the wearable device”, [0144] including “A typical photoplethysmographic (PPG) signal”, [0149], Fig. 6 see also [0230]);
inputting the physiological data into a machine learning classifier ([0118] including “various data-driven features for a given time interval of a sleep session may be derived from optical heart rate sensor data and/or accelerometer sensor data that is obtained in association with that time interval. These features may then be provided to a classifier that classifies that time interval into one of several classes or stages of sleep based on the values of one or more of the data-driven features.”, [0155]-[0156] see also [0230]);
identifying, using the machine learning classifier, a plurality of features associated with the physiological data ([0019], [0120]-[0121] including “Various tools for training, validating, and testing classifiers of various types may be found, for example, in the documentation for scikit-learn, which is a module developed for the Python programming language (http://scikit-learn.sourceforge.net).”, [0140], [0159], [0178] see also [0142]-[0156] discusses various features and the input of the features into a classifier, [0230]);
classifying the physiological data, using the machine learning classifier, into at least one sleep stage of a plurality of sleep stages for at least a portion of the time interval ([0116] including “a sleep session for a person's sleep may be divided into a number of intervals, which are often referred to as “epochs,” and each such interval may be evaluated to determine what sleep state or stage the person was in during that interval. For example, the American Academy of Sleep Medicine Guidelines define four classes of sleep: W, N1-N2, N3, and random eye movement (REM), which may be viewed as corresponding to Wake (or Awake), Light Sleep (which may include both N1 and N2), Deep Sleep, and REM Sleep, respectively. Other approaches may include fewer or greater numbers of sleep stages.”, [0118] including ”These features may then be provided to a classifier that classifies that time interval into one of several classes or stages of sleep based on the values of one or more of the data-driven features.”, [0155]-[0156] see also [0230]; data is input into a ML classifier which determines the sleep state/stage), wherein classifying the physiological data is based at least in part on the plurality of features ([0019], [0140], [0155]-[0156], [0160]-[0161], [0178] see also [0120]-[0121], [0142]-[0154] discusses various features, [0159], [0230]); and
causing a graphical user interface of a user device ([0122], [0132], [0180], [0215], Figs. 13, 15, 22, 24-25 see also [0181]-[0183], [0230]) to display an indication of the at least one sleep stage of the plurality of sleep stages based at least in part on classifying the physiological data ([0122], [0180]-[0181] including “Once a person's sleep session has been characterized using the sleep monitoring platform and its classifier, the resulting data set for the sleep session may be broken down into various formats or analyzed and the presented to the person”, [0182]-[0183] including “A more detailed presentation of the sleep data may be provided by way of a hypnogram 1326, which is a type of timeline in which time intervals during the timeline corresponding with particular sleep stages”, Figs. 13-14 see also [0230]).
An interpretation Heneghan may not explicitly disclose wherein the wearable sensor device is a ring.
However, in the same field of endeavor (medical diagnostic systems), Kinnunen teaches wherein the wearable sensor device is a ring ([0067]-[0068], Figs. 1-2 see also [0027], [0073], [0085] including “the hypnogram is determined based on the measured motion data, photoplethysmogram waveform dynamics and heart rate dynamics using a classification algorithm, a decision tree, a neural network or equivalent.”).
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the wearable device recited by Heneghan to more specifically be a ring as recited by Kinnunen because measurement on the finger is optimal for gathering HR/HRV optically and the ring wearable is lighter weight than other wearable sensor devices ([0173]).
An interpretation Heneghan may not explicitly disclose wherein the plurality of features comprise one or more circadian features representative of a circadian rhythm of the user.
However, in the same field of endeavor (medical diagnostic systems), Guazzi teaches wherein the plurality of features comprise one or more circadian features representative of a circadian rhythm of the user ([0003]-[0004], [0011], [0028]-[0029] including “circadian rhythm model”; circadian rhythm model outputs “feature”).
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the sleep staging analysis of Heneghan in view of Kinnunen to include circadian rhythm model element as disclosed by Guazzi because it is combining prior art elements (the analysis for sleep staging as recited by Heneghan to include the additional feature determination of the circadian rhythm model as recited by Guazzi) according to known methods to yield predictable results of having sleep stages which is based on the physiological data as well as the circadian rhythm.
Regarding Claim 2, an interpretation of Heneghan further discloses wherein classifying the physiological data ([0116], [0118], [0155]-[0156], Figs. 5, 10, 13-14 see also [0230]) comprises:
classifying the physiological data collected throughout the time interval into a plurality of sleep intervals within the time interval ([0116] including “a sleep session for a person's sleep may be divided into a number of intervals, which are often referred to as “epochs,” and each such interval may be evaluated to determine what sleep state or stage the person was in during that interval.”, [0118] including “various data-driven features for a given time interval of a sleep session may be derived from optical heart rate sensor data and/or accelerometer sensor data that is obtained in association with that time interval. These features may then be provided to a classifier that classifies that time interval into one of several classes or stages of sleep based on the values of one or more of the data-driven features.”, [0155]-[0156], [0175], Figs. 10, 12-14, 25-26 see also [0119], [0178], [0230]; Reference discloses the sleep session is made up of a series of time intervals and classifying a sleep stage (aka state etc.) each time interval); and
classifying each sleep interval of the plurality of sleep intervals into at least one of an awake sleep stage, a light sleep stage, a rapid eye movement sleep stage, or a deep sleep stage ([0116] including “For example, the American Academy of Sleep Medicine Guidelines define four classes of sleep: W, N1-N2, N3, and random eye movement (REM), which may be viewed as corresponding to Wake (or Awake), Light Sleep (which may include both N1 and N2), Deep Sleep, and REM Sleep, respectively. Other approaches may include fewer or greater numbers of sleep stages.”, [0118], [0155]-[0156], Figs. 10, 13-14 see also [0178], [0230]).
Regarding Claim 3, an interpretation of Heneghan further discloses causing the graphical user interface of the user device to display one or more sleep intervals of the plurality of sleep intervals ([0122], [0180]-[0181] including “Once a person's sleep session has been characterized using the sleep monitoring platform and its classifier, the resulting data set for the sleep session may be broken down into various formats or analyzed and the presented to the person”, [0182]-[0183] including “A more detailed presentation of the sleep data may be provided by way of a hypnogram 1326, which is a type of timeline in which time intervals during the timeline corresponding with particular sleep stages”, [0215], Figs. 13-14, 25-26 see also [0230]); and
causing the graphical user interface of the user device to display for each sleep interval of the one or more sleep intervals, a sleep stage corresponding to the respective sleep interval ([0122], [0180]-[0181] including “Once a person's sleep session has been characterized using the sleep monitoring platform and its classifier, the resulting data set for the sleep session may be broken down into various formats or analyzed and the presented to the person”, [0182]-[0183] including “A more detailed presentation of the sleep data may be provided by way of a hypnogram 1326, which is a type of timeline in which time intervals during the timeline corresponding with particular sleep stages”, Figs. 13-14, 24-26 see also [0230])
Regarding Claim 4, an interpretation of Heneghan further discloses performing one or more normalization procedures on the physiological data, resulting in normalized physiological data ([0082] including “may be normalized before those one or more pulse data features are extracted”, [0140], [0160]-[0161] see also [0230]), wherein inputting the physiological data into the machine learning classifier comprises inputting the normalized physiological data into the machine learning classifier ([0082], [0140], [0160]-[0161] see also [0230]; extracted features may be normalized and the normalized features input into the classifier).
Regarding Claim 6, an interpretation of Heneghan discloses wherein the plurality of features comprise a pattern between two or more parameters of the physiological data (0081]-[0082], [0160] see also [0219]-[0220]), an average data value of the physiological data ([0081]-[0082] including “a mean heart rate. . .”, [0140], [0160]), and/or a comparison of a data value of the physiological data to a baseline data value for the user (0081]-[0082], [0160] see also [0219]-[0220]), or any combination thereof.
Regarding Claim 8, an interpretation of Heneghan further discloses identifying a bed time associated with the user, a wake time associated with the user, or both, based at least in part on classifying the physiological data ([0130], [0198], Figs. 20, 22, 24); and causing the graphical user interface of the user device to display the bed time, the wake time, or both ([0198], Figs. 20, 22, 24 see also [0180], [0230]).
Regarding Claim 9, an interpretation of Heneghan further discloses transmitting, via the user device, the physiological data to one or more servers for classification ([0125]-[0126] including “the sleep monitoring platform 200 comprises a wearable device 202, a secondary device 204, a network 206, and a backend system 208.”, [0129] including “the wearable device 204 may access the backend system 208 via the secondary device 204.”, [0130]-[0131], [0134] including “collect pulse-related data and motion data during a sleep session for use in the techniques and systems discussed herein. . . . to obtain the motion data from the one or more motion sensors 410, obtain the pulse-related data from the one or more optical sensors 412, and to then transmit such data to other devices in the sleep monitoring platform.”, [0164], Figs. 2-5 see also [0005], [0116], [0127], [0129]-[0130], [0132]-[0133], [0135], [0230]; recites gathering physiological data during a sleep session (i.e. the time interval) on a wearable and transmit data to/receive the data at external devices).
Regarding Claim 10, an interpretation of Heneghan further discloses generating, using the user device, value/parameter based at least in part on the physiological data ([0130], [0132], [0135], [0177], Figs. 2-3, 5 see also [0126], [0129], [0230]).
An interpretation of Heneghan may not explicitly disclose generate one or more scores associated with the user based at least in part on the physiological data, the one or more scores comprising a Sleep Score, a Readiness Score, or both.
However, in the same field of endeavor (medical diagnostic systems), Kinnunen teaches generate one or more scores associated with the user based at least in part on the physiological data, the one or more scores comprising a Sleep Score, a Readiness Score, or both ([0087], [0092]-[0093], [0117], [0120]-[0121], Figs. 9-12 see also [0027], [0122]-[0131], [0177]; Data may be transferred from wearable gathering data to external device analyzing data and presenting a sleep score).
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the analysis recited by Heneghan to include scores as recited by Kinnunen because the determination and presentation of the scores and related information provides the user with more information and suggestions to improve those scores and optimize their use of free time ([0003], [0095], [0172]).
Regarding Claim 11, an interpretation of Heneghan may not explicitly disclose inputting a circadian rhythm adjustment model into the machine learning classifier, wherein classifying the physiological data is based at least in part on the circadian rhythm adjustment model.
However, in the same field of endeavor (medical diagnostic systems), Guazzi teaches inputting a circadian rhythm adjustment model into the machine learning classifier, wherein classifying the physiological data is based at least in part on the circadian rhythm adjustment model ([0003]-[0004], [0011], [0028]-[0029]; sleep staging incorporating the circadian rhythm).
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the sleep staging analysis of Heneghan in view of Kinnunen to include circadian rhythm model element as disclosed by Guazzi because it is combining prior art elements (the analysis for sleep staging as recited by Heneghan to include the additional consideration of circadian rhythm as recited by Guazzi) according to known methods to yield predictable results of having sleep stages which is based on the physiological data as well as the circadian rhythm.
Regarding Claim 13, an interpretation of Heneghan further discloses causing the graphical user interface of the user device to display at least a subset of the physiological data ([0199], Figs. 20, 22, 24).
Regarding Claim 14, an interpretation of Heneghan further discloses wherein the physiological data comprises accelerometer data ([0131], [0134], [0136]-[0137], Figs. 2-4 see also [0230]), heart rate ([0131], [0134], [0137], Figs. 2-4 see also [0230]).
Regarding Claim 15, an interpretation of Heneghan may not explicitly disclose wherein the wearable device collects the physiological data from the user based on arterial blood flow within a finger of the user.
However, in the same field of endeavor (medical diagnostic systems), Kinnunen teaches wherein the wearable ring device collects the physiological data from the user based on arterial blood flow within a finger of the user ([0067], [0173], Fig. 2).
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the wearable device recited by Heneghan to more specifically be a ring as recited by Kinnunen because measurement on the finger is optimal for gathering HR/HRV optically and the ring wearable is lighter weight than other wearable sensor devices ([0173]).
Regarding Claim 16, an interpretation of Heneghan further discloses wherein the wearable device collects the physiological data from the user using one or more red light emitting diodes and one or more green light emitting diodes ([0134] including “For example, green light is particularly well-suited for measuring heart rate, whereas a combination of red light and infrared light is particularly well-suited for measuring blood oxygenation levels.”).
An interpretation Heneghan may not explicitly disclose wherein the wearable sensor device is a ring.
However, in the same field of endeavor (medical diagnostic systems), Kinnunen teaches wherein the wearable sensor device is a ring with light sources ([0067]-[0068], [0077]-[0078], Figs. 1-2 see also [0027]).
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the wearable device recited by Heneghan to more specifically be a ring as recited by Kinnunen because measurement on the finger is optimal for gathering HR/HRV optically and the ring wearable is lighter weight than other wearable sensor devices ([0173]).
Regarding Claim 17, an interpretation of Heneghan discloses an apparatus for automatically detecting sleep stages (abstract, [0125]-[0126], Figs. 2-5 see also [0127]-[0129], [0230]), comprising:
a processor ([0132]-[0133], figs. 2-3 see also [0135], [0177], [0230]);
memory coupled with the processor ([0132]-[0133], figs. 2-3 see also [0135], [0230]); and
instructions stored in the memory and executable by the processor ([0131]-[0133], figs. 2-3 see also [0135], [0177], [0230]) to cause the apparatus to:
acquire, via a wearable device throughout a time interval, physiological data from a user ([0125]-[0126] including “the sleep monitoring platform 200 comprises a wearable device 202, a secondary device 204, a network 206, and a backend system 208.”, [0127] including “a wearable device configured to be attached to the wearer's body, such as a wrist-worn band, watch, finger clip, ear-clip, chest-strap, ankle strap, or any other suitable device.”, [0131], [0134] including “collect pulse-related data and motion data during a sleep session for use in the techniques and systems discussed herein. . . . to obtain the motion data from the one or more motion sensors 410, obtain the pulse-related data from the one or more optical sensors 412, and to then transmit such data to other devices in the sleep monitoring platform.”, Figs. 2-5 see also [0005], [0116], [0129]-[0130], [0132]-[0133], [0135], [0230]; recites gathering physiological data during a sleep session (i.e. the time interval) on a wearable and transmit data to/receive the data at external devices), wherein the wearable device comprises one or more photoplethysmogram (PPG) sensors arranged on an inner surface of the wearable device ([0134] including “when such an optical sensor is placed adjacent to a person's skin, light from the light source(s) may diffuse into the person's flesh and then be emitted back out of the person's flesh such that the photodetector(s) can detect it.”, [0144] including “A typical photoplethysmographic (PPG) signal”, [0139], [0149] see also [0230]), and wherein the acquisition of the physiological data ([0134] including “when such an optical sensor is placed adjacent to a person's skin, light from the light source(s) may diffuse into the person's flesh and then be emitted back out of the person's flesh such that the photodetector(s) can detect it.”, [0144] including “A typical photoplethysmographic (PPG) signal”, [0139], [0149] see also [0230]) comprises:
sampling a PPG signal generated by at least one optical receiver of the one or more PPG sensors, the PPG signal indicative of an amount of light received by the at least one optical receiver, and the light transmitted from at least one optical transmitter of the one or more PPG sensors ([0134] including “when such an optical sensor is placed adjacent to a person's skin, light from the light source(s) may diffuse into the person's flesh and then be emitted back out of the person's flesh such that the photodetector(s) can detect it.”, [0139], [0144] including “A typical photoplethysmographic (PPG) signal”, [0149], Figs. 2 and 4 see also [0055], [0218], [0230]); and
determining a pulse waveform of the user based on the PPG signal, wherein the physiological data is based at least in part on the pulse waveform ([0134] including “when such an optical sensor is placed adjacent to a person's skin, light from the light source(s) may diffuse into the person's flesh and then be emitted back out of the person's flesh such that the photodetector(s) can detect it.”, [0139] including “The pulse-related data may take the form of a waveform of detected light that varies in response to the blood volume pulsating within the skin of a wearer of the wearable device”, [0144] including “A typical photoplethysmographic (PPG) signal”, [0149], Fig. 6 see also [0230]);
input the physiological data into a machine learning classifier ([0118] including “various data-driven features for a given time interval of a sleep session may be derived from optical heart rate sensor data and/or accelerometer sensor data that is obtained in association with that time interval. These features may then be provided to a classifier that classifies that time interval into one of several classes or stages of sleep based on the values of one or more of the data-driven features.”, [0155]-[0156] see also [0230]);
identify, using the machine learning classifier, a plurality of features associated with the physiological data ([0019], [0120]-[0121] including “Various tools for training, validating, and testing classifiers of various types may be found, for example, in the documentation for scikit-learn, which is a module developed for the Python programming language (http://scikit-learn.sourceforge.net).”, [0140], [0178] see also [0142]-[0156] discusses various features and the input of the features into a classifier, [0159], [0230]);
classify the physiological data, using the machine learning classifier, into at least one sleep stage of a plurality of sleep stages for at least a portion of the time interval ([0116] including “a sleep session for a person's sleep may be divided into a number of intervals, which are often referred to as “epochs,” and each such interval may be evaluated to determine what sleep state or stage the person was in during that interval. For example, the American Academy of Sleep Medicine Guidelines define four classes of sleep: W, N1-N2, N3, and random eye movement (REM), which may be viewed as corresponding to Wake (or Awake), Light Sleep (which may include both N1 and N2), Deep Sleep, and REM Sleep, respectively. Other approaches may include fewer or greater numbers of sleep stages.”, [0118] including ”These features may then be provided to a classifier that classifies that time interval into one of several classes or stages of sleep based on the values of one or more of the data-driven features.”, [0155]-[0156] see also [0230]; data is input into a ML classifier which determines the sleep state/stage), wherein classification of the physiological data is based at least in part on the plurality of features ([0019], [0140], [0155]-[0156], [0160]-[0161], [0178] see also [0120]-[0121], [0142]-[0154] discusses various features, [0159], [0230]); and
cause a graphical user interface of a user device ([0122], [0132], [0180], [0215], Figs. 13, 15, 22, 24-25 see also [0181]-[0183], [0230]) to display an indication of the at least one sleep stage of the plurality of sleep stages based at least in part on classifying the physiological data ([0122], [0180]-[0181] including “Once a person's sleep session has been characterized using the sleep monitoring platform and its classifier, the resulting data set for the sleep session may be broken down into various formats or analyzed and the presented to the person”, [0182]-[0183] including “A more detailed presentation of the sleep data may be provided by way of a hypnogram 1326, which is a type of timeline in which time intervals during the timeline corresponding with particular sleep stages”, Figs. 13-14 see also [0230]).
An interpretation Heneghan may not explicitly disclose wherein the wearable sensor device is a ring.
However, in the same field of endeavor (medical diagnostic systems), Kinnunen teaches wherein the wearable sensor device is a ring ([0067]-[0068], Figs. 1-2 see also [0027], [0073], [0085] including “the hypnogram is determined based on the measured motion data, photoplethysmogram waveform dynamics and heart rate dynamics using a classification algorithm, a decision tree, a neural network or equivalent.”).
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the wearable device recited by Heneghan to more specifically be a ring as recited by Kinnunen because measurement on the finger is optimal for gathering HR/HRV optically and the ring wearable is lighter weight than other wearable sensor devices ([0173]).
An interpretation Heneghan may not explicitly disclose wherein the plurality of features comprise one or more circadian features representative of a circadian rhythm of the user.
However, in the same field of endeavor (medical diagnostic systems), Guazzi teaches wherein the plurality of features comprise one or more circadian features representative of a circadian rhythm of the user ([0003]-[0004], [0011], [0028]-[0029] including “circadian rhythm model”; circadian rhythm model outputs “feature”).
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the sleep staging analysis of Heneghan in view of Kinnunen to include circadian rhythm model element as disclosed by Guazzi because it is combining prior art elements (the analysis for sleep staging as recited by Heneghan to include the additional feature determination of the circadian rhythm model as recited by Guazzi) according to known methods to yield predictable results of having sleep stages which is based on the physiological data as well as the circadian rhythm.
Regarding Claim 18, an interpretation of Heneghan further discloses: classify the physiological data collected throughout the time interval into a plurality of sleep intervals within the time interval ([0116] including “a sleep session for a person's sleep may be divided into a number of intervals, which are often referred to as “epochs,” and each such interval may be evaluated to determine what sleep state or stage the person was in during that interval.”, [0118] including “various data-driven features for a given time interval of a sleep session may be derived from optical heart rate sensor data and/or accelerometer sensor data that is obtained in association with that time interval. These features may then be provided to a classifier that classifies that time interval into one of several classes or stages of sleep based on the values of one or more of the data-driven features.”, [0155]-[0156], [0175], Figs. 10, 12-14, 25-26 see also [0119], [0178], [0230]; Reference discloses the sleep session is made up of a series of time intervals and classifying a sleep stage (aka state etc.) each time interval); and
classify each sleep interval of the plurality of sleep intervals into at least one of an awake sleep stage, a light sleep stage, a rapid eye movement sleep stage, or a deep sleep stage ([0116] including “For example, the American Academy of Sleep Medicine Guidelines define four classes of sleep: W, N1-N2, N3, and random eye movement (REM), which may be viewed as corresponding to Wake (or Awake), Light Sleep (which may include both N1 and N2), Deep Sleep, and REM Sleep, respectively. Other approaches may include fewer or greater numbers of sleep stages.”, [0118], [0155]-[0156], Figs. 10, 13-14 see also [0178], [0230]).
Regarding Claim 19, an interpretation of Heneghan further discloses cause the graphical user interface of the user device to display one or more sleep intervals of the plurality of sleep intervals ([0122], [0180]-[0181] including “Once a person's sleep session has been characterized using the sleep monitoring platform and its classifier, the resulting data set for the sleep session may be broken down into various formats or analyzed and the presented to the person”, [0182]-[0183] including “A more detailed presentation of the sleep data may be provided by way of a hypnogram 1326, which is a type of timeline in which time intervals during the timeline corresponding with particular sleep stages”, [0215], Figs. 13-14, 25-26 see also [0230]); and
cause the graphical user interface of the user device to display, for each sleep interval of the one or more sleep intervals, a sleep stage corresponding to the respective sleep interval ([0122], [0180]-[0181] including “Once a person's sleep session has been characterized using the sleep monitoring platform and its classifier, the resulting data set for the sleep session may be broken down into various formats or analyzed and the presented to the person”, [0182]-[0183] including “A more detailed presentation of the sleep data may be provided by way of a hypnogram 1326, which is a type of timeline in which time intervals during the timeline corresponding with particular sleep stages”, Figs. 13-14, 24-26 see also [0230])
Regarding Claim 20, an interpretation of Heneghan further discloses perform one or more normalization procedures on the physiological data, resulting in normalized physiological data ([0082] including “may be normalized before those one or more pulse data features are extracted”, [0140], [0160]-[0161] see also [0230]), wherein inputting of the physiological data into the machine learning classifier comprises inputting the normalized physiological data into the machine learning classifier ([0082], [0140], [0160]-[0161] see also [0230]; extracted features may be normalized and the normalized features input into the classifier).
Claim Rejections - 35 USC § 103
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Heneghan in view of Kinnunen in further view of Guazzi in further view of US 20180125418 to Haakma et al. (hereinafter Haakma).
Regarding Claim 7, an interpretation of Heneghan discloses causing the graphical user interface of the user device to display a maximum and minimum of the heart rate ([0199], Figs. 20, 22, 24).
An interpretation of Heneghan may not explicitly disclose the max and min parameters are features of the plurality of features.
However, in the same field of endeavor (medical diagnostic systems), Haakma teaches the max and min parameters are features of the plurality of features ([0037]-[0038] including “a feature refers to any parameter that can be obtained from evaluating a signal and thus is also termed as a signal feature. For instance, a signal feature can be a maximum or minimum value of the sensor signal during a specified time period”, [0050]).
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the features of sleep staging analysis of Heneghan in view of Kinnunen in further view of Guazzi to include max/min values of parameters as recited by Haakma because it is “Obvious to try” – choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success. Haakma recites that “signal feature” is known term in signal processing and recites a finite number of identified and predictable features generic signals to serve as features in in a ML model which have a reasonable expectation of success.
Claim Rejections - 35 USC § 103
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Heneghan in view of Kinnunen in further view of Guazzi in further view of US 20110230790 to Kozlov (hereinafter Kozlov).
Regarding Claim 12, an interpretation of Heneghan further discloses receiving additional physiological data associated with the user from the wearable device, the physiological data collected via the wearable device throughout a second time interval ([0125]-[0127], [0131], [0134], [0201], Figs. 2-5, 22 see also [0005], [0116], [0129]-[0130], [0132]-[0133], [0135], [0230]; recites gathering physiological data during a plurality of sleep sessions on a wearable and transmit data to/receive the data at external devices);
inputting the additional physiological data into the machine learning classifier ([0118], [0155]-[0156] see also [0230]);
classifying the additional physiological data, using the machine learning classifier, into at least one sleep stage of the plurality of sleep stages for at least a portion of the second time interval ([0116], [0118], [0155]-[0156] see also [0230]; data is input into a ML classifier which determines the sleep state/stage), wherein classifying the additional physiological data is based at least in part on inputting the additional physiological data ([0116], [0118], [0155]-[0156] see also [0230]; data is input into a ML classifier which determines the sleep state/stage); and
causing the graphical user interface of the user device ([0122], [0132], [0180], [0215], Figs. 13, 15, 22, 24-25 see also [0181]-[0183], [0230]) to display an indication of the at least one sleep stage of the plurality of sleep stages for at least a portion of the second time interval based at least in part on classifying the additional physiological data ([0122], [0180]-[0181], [0182]-[0183], Figs. 13-14 see also [0230]).
An interpretation Heneghan may not explicitly disclose wherein the wearable sensor device is a ring.
However, in the same field of endeavor (medical diagnostic systems), Kinnunen teaches wherein the wearable sensor device is a ring with light sources ([0067]-[0068], [0077]-[0078], Figs. 1-2 see also [0027]).
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the wearable device recited by Heneghan to more specifically be a ring as recited by Kinnunen because measurement on the finger is optimal for gathering HR/HRV optically and the ring wearable is lighter weight than other wearable sensor devices ([0173]).
An interpretation of Heneghan may not explicitly disclose the classification based on prior data (i.e. the physiological data) and current data (i.e. the additional physiological data).
However, in the same field of endeavor (medical diagnostic systems), Kozlov teaches refining a model based on including a first set of data which then increases the accuracy of the analysis for the additional second set of data (abstract, [0179] see also [0137]-[0138]).
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the sleep staging analysis of Heneghan in view of Kinnunen in further view of Guazzi to include the updating of the model using previous data as recited by Kozlov because it provides more accurate determinations ([0179]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
John Allen. Photoplethysmography and its application in clinical physiological measurement. Physiol Meas. 2007 Mar;28(3):R1-39. doi: 10.1088/0967-3334/28/3/R01. Epub 2007 Feb 20. PMID: 17322588. https://iopscience.iop.org/article/10.1088/0967-3334/28/3/R01. viewed on 9/23/26
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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