DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention, under the 35 U.S.C. 101 analysis, recites elements directed to a judicial exception without significantly more. The claims 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 device that includes the following limitations.
a receiver circuit configured to receive cardiac information and respiratory information of the patient;
a storage device configured to store a trained hybrid sleep detection and classification model comprising:
a plurality of trained machine-learning (ML) models each trained to map cardiac and respiratory data into one of multiple distinct model-specific awake or sleep classes;
a trained regression model trained to map the model-specific awake or sleep classes produced by the plurality of ML models to a composite awake or sleep classification;
a controller circuit, comprising a sleep detector configured to apply the received cardiac and respiratory information to the trained hybrid sleep detection and classification model to determine a corresponding composite awake or sleep classification for the patient;
wherein the controller is configured to provide the determined composite awake or sleep classification to a user or a process executable by the medical-device system
Step 2A – Prong 1: Claim 1 recites the above bolded limitations that under its broadest reasonable interpretation, covers mental processes - concepts performed in the human mind (including an observation, evaluation, judgment, opinion) or performed with pen and paper. Receiving data to map into different classes and taking the mapped data to produce a composite classification can be performed in the human mind or with pen and paper.
Step 2A – Prong 2: Claim 1 recites the above underlined limitations that are beyond the judicial exception but do not integrate the exception into a practical application of the exception because they are elements directed to insignificant extra-solution activity or are recited at a high level of generality to perform the abstract idea (see MPEP 2106.04(d) and 2106.05(f)). . The receiver circuit, the storage device and the controller circuit are recited at a high level of generality. The ML and regression models and the providing the determined composite awake or sleep classification to a user or a process executable by the medical-device system does not integrate the exception into a practical application of the exception because it does not amount to more than generally linking the use of the exception to a particular technological environment or field of use. See MPEP 2106.05(h).
Step 2B: Claim 1 does not recite additional elements that amount to significantly more than the judicial exception itself. The receiver circuit, the storage device and the controller circuit are well-understood, routine, and conventional elements as evidenced by and not limited to Abolghasemiam et al. (US 2024/0156389) disclosing, “processing unit may receive a number of biomedical signals from a subject, including electrocardiogram (ECG)…and may send them for further processing to a conventional processor.” ([0047]). The ML and regression models and the providing the determined composite awake or sleep classification to a user or a process executable by the medical-device system is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016). The emphasized elements do not amount to significantly more than the judicial exception because these limitations are simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)).
In view of the above, the additional elements individually do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)).
Claims 2-12 depend on and recite the same judicial exception as claim 1. The limitations in the dependent claims only further limit the mental process or recite additional insignificant extra-solution activity.
Claim 13 is directed to a method reciting substantially the same limitations as claim 1.
Steps 2A - Prong 1-2, 2B: the judicial exceptions and additional elements identified for claim 1 correspond to the judicial exceptions and additional elements recited in claim 13 and are similarly not eligible under 35 U.S.C. 101.
Claims 14-20 depend on and recite the same judicial exception as claim 1. The limitations in the dependent claims only further limit the mental process or recite additional insignificant extra-solution activity.
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.
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.
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, 5-8, 10, 12-13, 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Heneghan et al. (US 2019/0021607) in view of de Zambotti et al. (US 2023/0329631).
Regarding claims 1 and 13, Heneghan et al. discloses a method and a medical-device system (fig. 1) for monitoring and staging sleep in a patient, the system comprising: a receiver circuit ([0019]) configured to receive cardiac information and respiratory information of the patient ([0018]); a storage device (“memory” claim 17) configured to store a trained hybrid sleep detection and classification model comprising: a plurality of trained machine-learning (ML) models (“the three streams of data are segmented into time epochs…. The classification from epochs can be further combined with classification from other epochs” [0044]; “classifier model uses this statistical fact to provide a classification output for each epoch” [0080]; since there are a plurality of epochs, there are a plurality of models) each trained to map cardiac and respiratory data into one of multiple distinct model-specific awake or sleep classes (“WAKE, NON-REM SLEEP, REM SLEEP” [0080]); a controller circuit (“processor” [0087]) comprising a sleep detector configured to apply the received cardiac and respiratory information to the trained hybrid sleep detection and classification model to determine a corresponding composite awake or sleep classification for the patient (“These epoch classifications can then be combined over an entire night's recording to provide a so-called hypnogram. An important parameter that can be derived from the hypnogram is the sleep efficiency, which is the percentage of time asleep as a fraction of the total time in bed” [0080]); wherein the controller is configured to provide the determined composite awake or sleep classification to a user or a process executable by the medical-device system ([0081-0083]).
Heneghan et al. discloses determining a corresponding composite awake or sleep classification for the patient (“hypnogram” [0080]) but does not expressly disclose a trained regression model trained to map the model-specific awake or sleep classes produced by the plurality of ML models to a composite awake or sleep classification. de Zambotti et al. teaches a similar a medical-device system (fig. 1) for monitoring and staging sleep in a patient, the system comprising: a receiver circuit configured to receive cardiac information and respiratory information of the patient ([0007]); a storage device 104 configured to store a trained hybrid sleep detection and classification model comprising: a plurality of trained machine-learning (ML) models 790 each trained to map information into one of multiple distinct model-specific classes ([0138]). More importantly, de Zambotti et al. teaches it is known in the art to provide a trained regression model 792 trained to map the model-specific classes produced by the plurality of ML models to a composite awake or sleep classification (“The predictive data model 792 gives the probability of the transition to the sleep state occurring at a date and time based on the observed and/or collected data” [0147]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heneghan et al. to include a trained regression model trained to map the model-specific awake or sleep classes produced by the plurality of ML models to a composite awake or sleep classification as taught by de Zambotti et al. in order to provide a predictive data model to determine awake or sleep classification that is known in the art to be simple and capable of being trained on-the-fly as new data is available, the incremental learning techniques used to train the model on-the-fly ([0119]).
Regarding claims 5 and 17, Heneghan et al. does not expressly disclose wherein the plurality of trained ML models include binary classification models each trained to map the cardiac and respiratory data into one of two model-specific awake or sleep classes. de Zambotti et al. teaches inputs to a binary classification decision module 443 output into one of two model-specific outcomes ([0116]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heneghan et al. to apply a binary classification decision module as the plurality of trained ML models, each trained to map the cardiac and respiratory data into one of two model-specific awake or sleep classes as taught by de Zambotti et al. in order to indicate an occurrence of an event or otherwise ([0109]).
Regarding claims 6 and 18, Heneghan et al. does not expressly disclose the plurality of trained ML models include one or more trained decision tree models, one or more trained random forest models, or one or more neural network or deep neural network models. de Zambotti et al. teaches it is known in the art for the plurality of trained ML models to include one or more trained decision tree models, one or more trained random forest models, or one or more neural network or deep neural network models ([0112], [0119], [0142], [0145], [0147]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heneghan et al. to use one or more neural network or deep neural network models as taught by de Zambotti et al. in order to provide the capability of processing larger amounts of available data ([0112]), as using neural or deep neural networks is known in the art, the results of such a modification being reasonably predictable.
Regarding claims 7 and 19, Heneghan et al. discloses the model-specific awake or sleep classes include one or more of an awake state or a sleep state ([0080]).
Regarding claims 8 and 19, Heneghan et al. discloses wherein the model-specific awake or sleep classes include one or more sleep phases or stages selected from the group consisting of: a rapid eye movement (REM) phase of sleep; a non-REM phase of sleep; an N1 stage of non-REM sleep; an N2 stage of non-REM sleep; a combined N1-N2 stage of non-REM sleep; and an N3 stage of non-REM sleep ([0080]).
Regarding claim 10, Heneghan et al. in view of de Zambotti et al. disclose the trained regression model is a logistic regression model ([0147]).
Regarding claims 12 and 20, Heneghan et al. discloses an apnea detector circuit configured to detect sleep apnea during a time when the composite awake or sleep classification satisfies a specific sleep phase or stage requirement ([0045]; [0069-0074]).
Claim(s) 2, 9, 11, 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Heneghan et al. (US 2019/0021607) in view of de Zambotti et al. (US 2023/0329631) and further in view of Zhang et al. (US 2024/0257168).
Regarding claims 2 and 14, Heneghan et al. does not expressly disclose apply the received cardiac respiratory information to each of the plurality of trained ML models to determine a model-specific awake or sleep classification and a confidence score associated with the determined model-specific awake or sleep classification; and apply the confidence scores associated with the determined awake or sleep classes to the trained regression model to determine the corresponding composite awake or sleep classification for the patient. Zhang et al. teaches generating confidence scores from models of data ([0041]) and using the confidence score associated with the model to apply to a combinatorial modeling of the data ([0047-0048]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heneghan et al. to generate confidence scores for the determined model-specific awake or sleep classifications, and to apply the confidence scores associated with the determined awake or sleep classes to the trained regression model to determine the corresponding composite awake or sleep classification for the patient, as taught by Zhang et al. in order to provide indications of accuracy of the model-specific awake or sleep classifications, that can be weighted in confidence when used for the composite awake or sleep classification.
Regarding claims 9 and 15, Heneghan et al. does not expressly disclose the trained regression model is trained to determine respective weights of confidence scores associated with determined awake or sleep classes, and to determine the composite awake or sleep classification using a weighted combination of the confidence scores each weighted by the respective weights. Zhang et al. teaches generating confidence scores from models of data ([0041]) and using the confidence score associated with the model to apply to a combinatorial modeling of the data ([0047-0048]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heneghan et al. to generate confidence scores for the determined model-specific awake or sleep classifications, and to apply the confidence scores associated with the determined awake or sleep classes to the trained regression model to determine the corresponding composite awake or sleep classification for the patient, as taught by Zhang et al. in order to provide indications of accuracy of the model-specific awake or sleep classifications, that can be weighted in confidence when used for the composite awake or sleep classification.
Regarding claim 11, Heneghan et al. does not expressly disclose a training module configured to generate the trained hybrid sleep detection and classification model, including: to generate the plurality of trained ML models using a first training dataset comprising input cardiac and respiratory data collected from a group of patients during known awake or sleep states or sleep stages; and to generate the trained regression model using a second trained dataset comprising awake or sleep classifications and confidence scores associated with the awake or sleep classifications and the known awake or sleep states or sleep stages. Zhang et al. teaches training a classifier model using training datasets (“seed segment”) collected from specific attribute groups, since training a model when treating attributes equally may fail to dynamically discover segment key attributes ([0032]). Zhang et al. further teaches utilizing confidence scores associated with classifications to indicate probability of entity classification into a respective candidate segment of a set of candidate segments ([0041]) and using the confidence score associated with the model to apply to a combinatorial modeling of the data ([0047-0048]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heneghan et al. to generate the plurality of trained ML models using seed segment data from specific attribute groups such as known awake or sleep states or sleep stages and to generate the trained regression model using a second training dataset comprising awake or sleep classification and confidence scores associated with the awake or sleep classifications and the known awake or sleep states or sleep stages as taught by Zhang et al. in order to increase the likelihood of discovering segment key attributes ([0032]) and to provide indications of accuracy of the model-specific awake or sleep classifications, that can be weighted in confidence.
Claim(s) 3-4 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Heneghan et al. (US 2019/0021607) in view of de Zambotti et al. (US 2023/0329631) and further in view of Fonseca et al. (US 10,363,388).
Regarding claim 3, Heneghan et al. does not expressly disclose the receiver circuit is electrically coupled to a cardiac sensor configured to sense the cardiac information of the patient including a surface or subcutaneous electrocardiogram or heart sound information and instead teaches a ballistocardiogram ([0043]). Fonseca et al. teaches a ballistocardiographic sensors are equivalent in the art to surface cardiac electrodes (“(in-ear) electrodes” col. 5, lines 10-25). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heneghan et al. to use a surface or subcutaneous electrocardiogram sensor as taught by Fonseca et al. as they are known equivalents in the art of cardiac signal detection, the results of such a modification being reasonably predictable.
Regarding claim 4, Heneghan et al. does not expressly disclose wherein the receiver circuit is electrically coupled to a respiratory sensor configured to sense the respiratory information of the patient including a respiratory rate or a tidal volume. Fonseca et al. teaches sensed respiratory information of a patient can include respiratory rate (col. 5, lines 26-32). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heneghan et al. and use respiratory rate as respiratory information of the patient as taught by Fonseca et al. as it is a known respiratory attribute for detecting sleep, the results of such a modification being reasonably predictable.
Regarding claim 16, Heneghan et al. does not expressly disclose the received cardiac information includes a surface or subcutaneous electrocardiogram or heart sound information and instead teaches a ballistocardiogram ([0043]). Fonseca et al. teaches a ballistocardiographic sensors are equivalent in the art to surface cardiac electrodes (“(in-ear) electrodes” col. 5, lines 10-25). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heneghan et al. to use a surface or subcutaneous electrocardiogram sensor as taught by Fonseca et al. as they are known equivalents in the art of cardiac signal detection, the results of such a modification being reasonably predictable. Heneghan et al. also does not expressly disclose the received respiratory information includes a respiratory rate or a tidal volume. Fonseca et al. teaches sensed respiratory information of a patient can include respiratory rate (col. 5, lines 26-32). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heneghan et al. and use respiratory rate as respiratory information of the patient as taught by Fonseca et al. as it is a known respiratory attribute for detecting sleep, the results of such a modification being reasonably predictable.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERICA S LEE whose telephone number is (571)270-1480. The examiner can normally be reached M-F 8-7pm, flex.
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/ERICA S LEE/Primary Examiner, Art Unit 3796
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