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 .
DETAILED ACTION
In the response filed on 09 October 2025, the following has occurred: no claims have been amended, canceled or amended.
Now claims 1-26 are pending.
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 1-26 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 and 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite methods and system for creating a personalized classifier for a subject: The limitations of:
Claim 17, which is representative of claims 1 and 15
obtain sleep data from biosignals received from the subject of a High-Accuracy Sleep Study (HASS) and sleep data from biosignals received from the subject in a Simplified Sleep Study (SSS), the data from the High-Accuracy Sleep Study (HASS) being obtained from the subject during a period of time that is simultaneous with the period during which the data from the Simplified Sleep Study (SSS) is obtained from the subject; develop a high-resolution HASS sleep profile from the sleep data of the High-Accuracy Sleep Study (HASS); create a personalized sleep classifier that outputs a SSS sleep profile of the subject based on the sleep data from the Simplified Sleep Study (SSS); calibrate the personalized sleep classifier such the SSS sleep profile output by the personalized sleep classifier based on the Simplified Sleep Study (SSS) of the subject approaches or aligns with the high-resolution HASS sleep profile based on the High-Accuracy Sleep Study (HASS) of the subject, wherein the biosignals received from the subject in the Simplified Sleep Study (SSS) are obtained by […] be[ing] self-applied by the subject of the Simplified Sleep Study (SSS) or applied without requiring professional assistance.
, as drafted, is a system which under its broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions) via the recitation of generic computer components. That is, by a human user interacting with a computer with a processing system (claims 1 and 15), one or more processors and one or more computer-readable storage devices (claim 17), the claimed invention amounts to managing personal behavior or interaction between people, the Examiner notes as stated in 2106.04(a)(2), “certain activity between a person and a computer… may fall within the “certain methods of organizing human activity” grouping”. For example, via human user interaction with a computer with a processing system (claims 1 and 15), one or more processors and one or more computer-readable storage devices (claim 17), the claim encompasses collection of data about a subject(s) from two studies, organizing the data collected data into a profile and a classifier and using the profile to tune the classifier for a particular subject(s). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a computer with a processing system (claims 1 and 15), one or more processors and one or more computer-readable storage devices (claim 17), which implements the abstract idea. The one or more processors and one or more computer-readable storage devices are recited at a high-level of generality (i.e., a general-purpose computers/ computer components implementing generic computer functions; see Applicant’s Specification Figure 7, paragraph [0085]) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these 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.
The claim recites the additional elements of “one or more biosensors that are configured to be self-applied by the subject of the Simplified Sleep Study (SSS) or applied without requiring professional assistance” to implement the abstract idea. The “one or more biosensors that are configured to be self-applied by the subject of the Simplified Sleep Study (SSS) or applied without requiring professional assistance” steps are recited at a high-level of generality (i.e., using a generic off-the electrodes to collect an electrical signal) and amounts to generally linking the abstract idea to a particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a computer with a processing system (claims 1 and 15), one or more processors and one or more computer-readable storage devices (claim 17), are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these 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.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “one or more biosensors that are configured to be self-applied by the subject of the Simplified Sleep Study (SSS) or applied without requiring professional assistance” were considered generally linking the abstract idea to particular technological environment. The “one or more biosensors that are configured to be self-applied by the subject of the Simplified Sleep Study (SSS) or applied without requiring professional assistance” steps have been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Westbrook (2010/0240982): see below but at least paragraph [0030]); Levendowski (20180333558): paragraph [0149]; Burton (20040193068): paragraph [0162]; self-application of sensors are well-understood, routine and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 2-14, 16 and 18-26 are similarly rejected because either further define the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible.
Claims 2 further describes simultaneous collection of data, however does not recite any additional elements and therefore cannot provide a practical application and/or significantly more.
Claim 3 recites the additional element of “retrieving the sleep data… from a memory storage”, however these steps are recited at a high-level of generality (i.e., as a general means of receiving/transmitting data) and amounts to the mere transmission and/or receipt of data, which is a form of extra-solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “retrieving the sleep data… from a memory storage” was considered extra-solution activity. This has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in MPEP 2106.05(d)(II)(i) “Receiving or transmitting data over a network” is well-understood, routine, and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 4-5, 13 and 21-23 further describe the type of signals obtained, however does not recite any additional elements and therefore cannot provide a practical application and/or significantly more.
Claim 6 describes determination of variance in previous recordings, however does not recite any additional elements and therefore cannot provide a practical application and/or significantly more.
Claims 7 and 8 further describe the type of sleep study performed, however does not recite any additional elements and therefore cannot provide a practical application and/or significantly more.
Claim 9 recites the additional element of “training or retraining at least a part of the personalized sleep classifier”, however these steps are recited at a high-level of generality. (i.e., training an off the shelf machine learning model) and amounts to generally linking the abstract idea to particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements were considered to be generally linking the abstract idea to particular technological environment. This has been re-evaluated under the "significantly more" analysis and determined to amount to be well- understood, routine, and conventional elements/functions. As described in 20200146620: paragraph [0095]; 20180049678: paragraphs [0156]-[0157]; training a classifier is well-understood routine and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 10-12, 16 and 20 further describe determination of sleep stages, diagnosing a subject and determination of efficacy of a treatment, however do not recite any additional elements and therefore cannot provide a practical application and/or significantly more.
Claims 14, 18 and 23-26 describes the hardware components and sensors that were already considered above and incorporated herein.
Claim 19 recites the additional element of a receiver, however this is recited at a high-level of generality (i.e., a general-purpose computers/ computer components implementing generic computer functions; see Applicant’s Specification Figure 7, paragraph [0085]) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these 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.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a receiver, are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components. 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.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-5, 7-12, 14, 17-20 and 23-26 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 2020/0146620 (hereafter “Christopherson”; already of record in the IDS) in view of U.S. Patent Pub. No. 2010/0240982 (hereafter “Westbrook”).
Regarding (Previously Presented) claim 1, Christopherson teaches a computer-implemented method for creating a personalized sleep classifier for a subject (Christopherson: Figure 3, paragraph [0104], “a method 390 of correlating sleep study parameters with physiologic parameters”, paragraph [0040], “Components and methods of the present disclosure,
including but not limited to memory module 111, may be implemented in hardware via a microprocessor… software on one or more computer-readable media”), the method comprising:
--obtaining, by a processing system of a computing system having one or more processors, sleep data from biosignals received from the subject in a High-Accuracy Sleep Study (HASS) (Christopherson: Figure 4E, paragraphs [0052]-[0054], “controller 110 of IPG 100 comprises one or more processing units and associated memories 111 configured to generate control signals directing the operation of IPG 100, including the operation of at least sensing module 102,”, paragraph [0104], “A second set of sleep study parameters is tracked, independent of the IPG system, via the polysomnography study (at 395)”. Also see, paragraph [0040]) and
--sleep data from biosignals received from the subject in a Simplified Sleep Study (SSS) (Christopherson: Figure 4E, paragraph [0104], “At 394, a first set of physiologic parameters is tracked, during the polysomnography study, via the IPG system in its monitoring mode”),
--the data from the High-Accuracy Sleep Study (HASS) being obtained from the subject during a period of time that is simultaneous with the period during which the data from the Simplified Sleep Study (SSS) is obtained from the subject (Christopherson: Figure 4E, paragraph [0104], “at 392 the method 390 includes operating, substantially simultaneously with a polysomnography (PSG) study, an implantable pulse generator (IPG) system in a monitoring mode”, paragraph [0155], “sensing module 102 of IPG 100 (FIG. 2) is active during the sleep study so that parameters sensed via IPG 100 are correlated with, and calibrated, relative to known sleep study parameters that are indicative of various apnea-related physiologic events, patterns, and behaviors”);
--developing, by the processing system, a high-resolution HASS sleep profile from the sleep data of the High-Accuracy Sleep Study (HASS) (Christopherson: Figure 4E, paragraph [0104], “At 396, method 390 includes identifying sleep disordered breathing patterns (e.g. apneas and/or hypopneas) from the tracked second set of sleep study parameters and identifying which of these parameters are indicative of sleep disordered breathing for a particular patient”. Also see, paragraphs [0062], [0105]. The Examiner notes determining patterns for a particular patient reads on a profile under the broadest reasonable interpretation);
--creating, by the processing system, a personalized sleep classifier that outputs a SSS sleep profile of the subject based on the sleep data from the Simplified Sleep Study (SSS) (Christopherson: Figure 4E, paragraphs [0018]-[0019], “detection of an apnea via a bio-impedance signal… detection of an apnea via an respiratory pressure signal”, paragraph [0051], “the detection monitor 180 observes, via sensing module 102, physiologic conditions of the patient to detect whether sleep disordered breathing is occurring”, paragraph [0071], “a probabilistic polling profile 336 (for sensing of potential apneas)”, paragraph [0095], “system 200 is trained by operating simultaneously with a sleep study… different sleep stages (among other patterns) are recognized and in which a patient's different physiologic parameters are tracked relative to those sleep stages.”, paragraph [0107]-[0109], “training IPG 100 via a sleep study, the internally measured blood oxygen saturation parameter 417 (via IPG 100 and system 200) will be calibrated relative to the externally measured blood oxygen saturation parameter 411 to account for such differences… provide sleep disordered breathing scoring (e.g., apnea scoring)… after initially calibrating the IPG 100 during a sleep study”. Also see, paragraph [0083]);
--calibrating, by the processing system, the personalized sleep classifier such that the SSS sleep profile output by the personalized sleep classifier based on the Simplified Sleep Study (SSS) of the subject approaches or aligns with the high-resolution HASS sleep profile based on the High-Accuracy Sleep Study (HASS) of the subject (Christopherson: Figure 4E, paragraphs [0104]-[0105], “By looking at profiles of sleep disordered breathing behavior, a correlation is made between the IPG parameters 418 and the PSG parameters 402 such that the IPG 100 is calibrated to sleep study data”, paragraphs [0106]-[0109], “after initially calibrating the IPG 100 during a sleep study, the IPG 100 is permitted to operate during a second sleep study so that the IPG 100 is able to self-learn in direct association with the data provided via the PSG… physiologic data sensed via the IPG 100 that is indicative of an sleep disordered breathing behavior is matched with indicative physiologic data/observations sensed via the PSG monitoring system that is indicative of sleep disordered breathing behavior”, paragraph [0115], “sensing parameters of the IPG 100 and system 200 are calibrated relative to conventional sensing parameters of a formal sleep study by operating the IPG 100 and system 200 in a sensing mode ( e.g. second state 204 in FIG. 4A) during the formal sleep study”, paragraph [0155], “via the sleep study the IPG 100 learns a patient's physiologic patterns surrounding an apnea event, which helps to establish a baseline”),
wherein the biosignals received from the subject in the Simplified Sleep Study (SSS) are obtained by one or more biosensors […] (Christopherson: paragraph [0030], “system 10 also comprises additional sensors to obtain further physiologic data associated with respiratory functions. For example, system 10 may include various sensors”, paragraph [0041], “the sensing module 102 of IPG 100 receives and tracks signals from various physiologic sensors in order to determine a respiratory state of a patient, such as whether or not the patient is asleep or awake, and other respiratory-associated indicators, etc.”).
The Examiner notes [...] has been interpreted to teach (underlined below for clarity):
[…] a high-resolution HASS sleep profile […];
In the event that Christopherson does not explicitly teach this feature, Westbrook teaches this feature at paragraphs [0096] and [0099]. The motivation to combine is Westbrook within Christopherson is the same as that noted below.
Christopherson may not explicitly teach (underlined below for clarity):
wherein the biosignals received from the subject in the Simplified Sleep Study (SSS) are obtained by one or more biosensors that are configured to be self-applied by the subject of the Simplified Sleep Study (SSS) or applied without requiring professional assistance by a certified or credentialed technologist.
Westbrook teaches wherein the biosignals received from the subject in the Simplified Sleep Study (SSS) are obtained by one or more biosensors that are configured to be self-applied by the subject of the Simplified Sleep Study (SSS) or applied without requiring professional assistance by a certified or credentialed technologist (Westbrook: paragraph [0012], “The apparatus can be integrated with or connected to a sensor strip and can also integrate with or be connected to a nasal mask to obtain the physiological signals for the user. The form factor of this apparatus is comfortable, easy to self-apply, and results in less data artifacts than conventional techniques for capturing physiological data for analyzing sleep quality. Neuro-cardio-respiratory signals are analyzed using means to extract more accurate definitions of the frequency and severity of sleep discontinuity, sleep disordered breathing and patterns of sleep architecture”, paragraph [0030], “Thus, embodiments use electrodes and sensors that can be self-applied with limited skin or scalp preparation, and which monitors signal quality during use and provides user feedback when signal quality problems are detected”).
One of ordinary skill in the art before the effective filing date would have found it obvious to include using self-applied sensors to generate sleep profiles as taught by Westbrook within the calibration of a sleep classifier as taught by Christopherson with the motivation of “easy to self-apply, and results in less data artifacts than conventional techniques for capturing physiological data” (Westbrook: Abstract).
Regarding (Original) claim 2, Christopherson and Westbrook teach the limitations of claim 1, and further teaches wherein obtaining the sleep data from the High-Accuracy Sleep Study (HASS) and the sleep data from the Simplified Sleep Study (SSS) includes both performing the High-Accuracy Sleep Study (HASS) on the subject and simultaneously performing the Simplified Sleep Study (SSS) on the subject (Christopherson: Figure 4E, paragraph [0104], “at 392 the method 390 includes operating, substantially simultaneously with a polysomnography (PSG) study, an implantable pulse generator (IPG) system in a monitoring mode”, paragraph [0155], “sensing module 102 of IPG 100 (FIG. 2) is active during the sleep study so that parameters sensed via IPG 100 are correlated with, and calibrated, relative to known sleep study parameters that are indicative of various apnea-related physiologic events, patterns, and behaviors”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 3, Christopherson and Westbrook teach the limitations of claim 1, and further teaches wherein obtaining the sleep data from the High-Accuracy Sleep Study (HASS) and the sleep data from the Simplified Sleep Study (SSS) includes retrieving the sleep data from the High-Accuracy Sleep Study (HASS) or the sleep data from the Simplified Sleep Study (SSS) as pre-recorded data from a memory storage (Christopherson: paragraph [0059]-[0064], “a history of the therapy is stored in records 228 (i.e. a portion of memory 111) of IPG 100… this information (e.g., AHI data) is used by the physician to further program the IPG to be more aggressive or less aggressive… the history communicated from the patient to the physician via records parameter 228 includes… records parameter 228 tracks a volume, frequency, and severity of sleep disordered breathing event”. The Examiner notes previously recorded sleep data of the IPG reads on pre-recorded sleep data from a SSS).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 4, Christopherson and Westbrook teach the limitations of claim 1, and further teaches wherein the biosignals received from the subject in the High-Accuracy Sleep Study (HASS) include Electroencephalography (EEG), Electrooculography (EOG), Electromyography (EMG), signals obtained from a nasal cannula, thoracic and/or abdomen, or pulse oximetry signals (Christopherson: paragraphs [0104]-[0107], “a polysomnography (PSG) study… one sleep study parameter includes an airflow measured at the mouth-nostril airflow pathway (410)… blood oxygen saturation parameter”. The Examiner notes a PSG study includes EEG, EOG, EMG and ECG studies1 and teaches what is required of the claim under the broadest reasonable interpretation).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 5, Christopherson and Westbrook teach the limitations of claim 1, and further teaches wherein the biosignals received from the subject in the Simplified Sleep Study (SSS) include one or more of a thoracic RIP signal, an abdomen RIP signal, a pulse signal, an activity signal, or an oximetry signal (Christopherson: paragraph [0111], “an array of IPG-related parameters 426, such as a heart rate, a blood oxygen saturation, a bio-impedance, and/or a respiratory pressure”, paragraph [0116], “a measurement of trans-thoracic impedance is used to track the relative amplitude of the respiratory waveform.”. Also see, paragraph [0029]).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 7, Christopherson and Westbrook teach the limitations of claim 1, and further teaches wherein the High-Accuracy Sleep Study (HASS) is a standard polysomnography (PSG) (Christopherson: paragraphs [0104]-[0107], “a polysomnography (PSG) study… one sleep study parameter includes an airflow measured at the mouth-nostril airflow pathway (410)… blood oxygen saturation parameter”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 8, Christopherson and Westbrook teach the limitations of claim 1, and further teaches wherein the High-Accuracy Sleep Study (HASS) is a Self-Applied Somnography (SAS) (Christopherson: paragraphs [0113]-[0114], “provide the physician with a pseudo sleep study performed in the home of the patient… calibrating an IPG system via an in-home pseudo sleep study via sensing physiologic parameters, as surrogates for conventional sleep study parameters”; Westbrook: paragraph [0012], “The apparatus can be integrated with or connected to a sensor strip and can also integrate with or be connected to a nasal mask to obtain the physiological signals for the user. The form factor of this apparatus is comfortable, easy to self-apply, and results in less data artifacts than conventional techniques for capturing physiological data for analyzing sleep quality. Neuro-cardio-respiratory signals are analyzed using means to extract more accurate definitions of the frequency and severity of sleep discontinuity, sleep disordered breathing and patterns of sleep architecture”, paragraph [0030], “Thus, embodiments use electrodes and sensors that can be self-applied with limited skin or scalp preparation, and which monitors signal quality during use and provides user feedback when signal quality problems are detected”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 9, Christopherson and Westbrook teach the limitations of claim 1, and further teaches wherein calibrating the personalized sleep classifier include one or more of statisitical scaling, Platt scaling, or isotonic regression or scaling of input data to the personlized classifier; statisitical scaling, Platt scaling, or isotonic regression or scaling of an output of the personlized sleep classifier; training or retraining at least a part of the personalized sleep classifier; normalization of input data to the personlized classifier; using known personal information of the subject; increasing the classifier parameter training dataset (Christopherson: paragraph [0095], “system 200 is trained by operating simultaneously with a sleep study (e.g., polysomnography) in which different sleep stages (among other patterns) are recognized and in which a patient's different physiologic parameters are tracked relative to those sleep stages. By correlating these sleep study parameters with sensed physiologic parameters of the IPG 100 (FIG. 3A), the IPG 100 becomes calibrated for a particular patient to recognize sleep stages and other sleep patterns which are useful in some embodiments for applying and/or suspending therapy via the IPG 100”, paragraph [0107]-[0110], “While there are some differences between these two respective parameters 417 and 411, by training IPG 100 via a sleep study, the internally measured blood oxygen saturation parameter 417 (via IPG 100 and system 200) will be calibrated relative to the externally measured blood oxygen saturation parameter 411 to account for such differences… FIG. 4F further illustrates such additional parameters which are correlated with the PSG-related primary parameters and the IPG-related primary parameters… Other parameters, such as patient demographic factors 424 are tracked as well, such as age, sex, smoker, weight, neck, hypertension, etc”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Previously Presented) claim 10, Christopherson and Westbrook teach a method for identifying sleep stages or sleep events of the subject, or providing a sleep profile for a subject (Christopherson: paragraph [0104], “a method 390 of correlating sleep study parameters with physiologic parameters”) comprising;
--creating a personalized sleep classifier for a subject according to claim l (Christopherson and Westbrook: see claim 1, above); and
--identifying, by the processing system, sleep stages or sleep events of the subject, or providing a sleep profile of the subject, based on further sleep data from the subject from a further Simplified Sleep Study (SSS) using the personalized sleep classifier (Christopherson: paragraph [0095], “one or more different stages of sleep may act as triggers to warrant suspending therapy to check whether or not any sleep disordered breathings behavior is occurring… different sleep stages (among other patterns) are recognized and in which a patient's different physiologic parameters are tracked relative to those sleep stages. By correlating these sleep study parameters with sensed physiologic parameters of the IPG 100 (FIG. 3A), the IPG 100 becomes calibrated for a particular patient to recognize sleep stages and other sleep patterns which are useful in some embodiments for applying and/or suspending therapy via the IPG 100”. Also see, paragraph [0110]),
--wherein the further sleep data is obtained from further Simplified Sleep Study (SSS) of a different period of time, either before or after, the sleep data from the biosignals received from the subject in the Simplified Sleep Study (SSS) were recorded or obtained (Christopherson: paragraph [0050], “evaluate the severity of sleep disordered breathing behavior both before and after the application”, paragraph [0064]-[0065], “a trend report within a night or for a period of multiple nights that enable detection of patterns or changes in the patient's health and/or enable evaluation of adjustments made to the therapy by the physician during the multiple night period… the IPG 100 and system 200 is operated in second state 204 for an extended period of time (or even all night) to provide the physician with an in-home pseudo sleep study”, paragraph [0109]-[0112], “after initially calibrating the IPG 100 during a sleep study, the IPG 100 is permitted to operate during a second sleep study so that the IPG 100 is able to self-learn in direct association with the data provided via the PSG… after a number of IPG-based systems 456 (e.g., IPG 1, IPG 2, etc.) are calibrated relative to sleep study parameters 454 (e.g. PSG 1, PSG 2, etc.) for a number of patients 452 (e.g. Joe, Fred, etc), embodiments of the present disclosure can use a correlation of the sleep study parameters relative to the IPG parameters to develop a predictive index 460”. The Examiner notes after calibration the device is used as a SSS).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Previously Presented) claim 11, Christopherson and Westbrook teach a method for diagnosing a sleep disorder of a subject (Christopherson: paragraph [0104], “a method 390 of correlating sleep study parameters with physiologic parameters”) comprising:
--creating a personalized sleep classifier for a subject according to claim l (Christopherson and Westbrook: see claim 1, above); and
--diagnosing, by the processing system, the sleep disorder by identifying sleep stages or sleep events of the subject, or providing a sleep profile of the subject, based on further sleep data from the subject from a further Simplified Sleep Study (SSS) using the personalized sleep classifier (Christopherson: paragraph [0051], “The apnea function 184 detects sleep disordered breathing, such as obstructive sleep apneas, hypopneas, and/or central sleep apneas, relative to the baseline breathing patterns of the patient. The hyperventilation function 186 is configured to assist identifying a sleep disordered breathing behavior based on parameters associated with a hyperventilation period following the sleep disordered breathing behavior.”, paragraph [0095], “one or more different stages of sleep may act as triggers to warrant suspending therapy to check whether or not any sleep disordered breathings behavior is occurring… different sleep stages (among other patterns) are recognized and in which a patient's different physiologic parameters are tracked relative to those sleep stages. By correlating these sleep study parameters with sensed physiologic parameters of the IPG 100 (FIG. 3A), the IPG 100 becomes calibrated for a particular patient to recognize sleep stages and other sleep patterns which are useful in some embodiments for applying and/or suspending therapy via the IPG 100”, paragraph [0140], “counting the number of apneas/hypopneas within a given time period, the method also looks at the intensity and/or duration of one or more apnea events to determine whether the measured patient behavior is above or below a setpoint”. Also see, paragraph [0110]),
--wherein the further sleep data is obtained from further Simplified Sleep Study (SSS) of a different period of time, either before or after, the sleep data from the biosignals received from the subject in the Simplified Sleep Study (SSS) were recorded or obtained (Christopherson: paragraph [0050], “evaluate the severity of sleep disordered breathing behavior both before and after the application”, paragraph [0064]-[0065], “a trend report within a night or for a period of multiple nights that enable detection of patterns or changes in the patient's health and/or enable evaluation of adjustments made to the therapy by the physician during the multiple night period… the IPG 100 and system 200 is operated in second state 204 for an extended period of time (or even all night) to provide the physician with an in-home pseudo sleep study”, paragraph [0109]-[0112], “after initially calibrating the IPG 100 during a sleep study, the IPG 100 is permitted to operate during a second sleep study so that the IPG 100 is able to self-learn in direct association with the data provided via the PSG… after a number of IPG-based systems 456 (e.g., IPG 1, IPG 2, etc.) are calibrated relative to sleep study parameters 454 (e.g. PSG 1, PSG 2, etc.) for a number of patients 452 (e.g. Joe, Fred, etc), embodiments of the present disclosure can use a correlation of the sleep study parameters relative to the IPG parameters to develop a predictive index 460”. The Examiner notes after calibration the device is used as a SSS).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Previously Presented) claim 12, Christopherson teaches a method for determining an efficacy of a treatment of a subject (Christopherson: paragraph [0052], “efficacy of therapy”, paragraph [0104], “a method 390 of correlating sleep study parameters with physiologic parameters”) comprising:
--creating a personalized sleep classifier for a subject according to claim l (Christopherson and Westbrook: see claim 1, above); and
--determining, by the processing system, the efficacy of a sleep treatment by identifying sleep stages or sleep events of the subject, or providing a sleep profile of the subject, based on further sleep data from the subject from a further Simplified Sleep Study (SSS) using the personalized sleep classifier (Christopherson: paragraph [0048], “efficacy is measured according to the number of apnea/hypopnea events and/or an apnea severity score (e.g. severity score parameter 759 in FIG. 9) that also incorporates a duration or an intensity (e.g., a decrease in blood oxygen) of each apnea/hypopnea event”, paragraph [0095], “one or more different stages of sleep may act as triggers to warrant suspending therapy to check whether or not any sleep disordered breathings behavior is occurring… different sleep stages (among other patterns) are recognized and in which a patient's different physiologic parameters are tracked relative to those sleep stages. By correlating these sleep study parameters with sensed physiologic parameters of the IPG 100 (FIG. 3A), the IPG 100 becomes calibrated for a particular patient to recognize sleep stages and other sleep patterns which are useful in some embodiments for applying and/or suspending therapy via the IPG 100”. Also see, paragraph [0110]),
--wherein the further sleep data is obtained from further Simplified Sleep Study (SSS) of a different period of time, either before or after, the sleep data from the biosignals received from the subject in the Simplified Sleep Study (SSS) were recorded or obtained (Christopherson: paragraph [0050], “evaluate the severity of sleep disordered breathing behavior both before and after the application”, paragraph [0064]-[0065], “a trend report within a night or for a period of multiple nights that enable detection of patterns or changes in the patient's health and/or enable evaluation of adjustments made to the therapy by the physician during the multiple night period… the IPG 100 and system 200 is operated in second state 204 for an extended period of time (or even all night) to provide the physician with an in-home pseudo sleep study”, paragraph [0109]-[0112], “after initially calibrating the IPG 100 during a sleep study, the IPG 100 is permitted to operate during a second sleep study so that the IPG 100 is able to self-learn in direct association with the data provided via the PSG… after a number of IPG-based systems 456 (e.g., IPG 1, IPG 2, etc.) are calibrated relative to sleep study parameters 454 (e.g. PSG 1, PSG 2, etc.) for a number of patients 452 (e.g. Joe, Fred, etc), embodiments of the present disclosure can use a correlation of the sleep study parameters relative to the IPG parameters to develop a predictive index 460”. The Examiner notes after calibration the device is used as a SSS).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 14, Christopherson and Westbrook teaches a hardware storage device having stored thereon computer executable instructions which, when executed by one or more processors of a computer system (Christopherson: paragraph [0040], “memory module 111, may be implemented in hardware via a microprocessor, programmable logic, or state machine, in firmware, or in software within a given device.”),
--configure the computer system to perform the method according to claim 1 (Christopherson and Westbrook: see claim 1, above).
REGARDING CLAIM(S) 17
Claim(s) 17 is/are analogous to Claim(s) 1, thus Claim(s) 17 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 1.
Regarding (Original) claim 18, Christopherson and Westbrook teaches the limitations of claim 17, and further teaches a storage device that stores the data from the High-Accuracy Sleep Study (HASS) and the data from the Simplified Sleep Study (SSS) (Christopherson: Figures 1-3, paragraph [0054], “mass storage device, or some other persistent storage, as represented by a memory 111 associated with controller 110”).
The motivation to combine is the same as in claim 17, incorporated herein.
Regarding (Original) claim 19, Christopherson and Westbrook teaches the limitations of claim 17, and further teaches a receiver configured to receive as input the data from the High-Accuracy Sleep Study (HASS) and the data from the Simplified Sleep Study (SSS) (Christopherson: Figures 1-3, paragraph [0058], “the communication module 112 of the IPG 100 is configured to facilitate wireless communication to and from the IPG 100”).
The motivation to combine is the same as in claim 17, incorporated herein.
Regarding (Previously Presented) claim 20, Christopherson and Westbrook teaches the limitations of claim 17, and further teaches identify sleep stages of the subject based on further sleep data from subsequent biosignals received from the subject in a subsequent Simplified Sleep Study (SSS) using the personalized sleep classifier (Christopherson: paragraph [0095], “one or more different stages of sleep may act as triggers to warrant suspending therapy to check whether or not any sleep disordered breathings behavior is occurring… different sleep stages (among other patterns) are recognized and in which a patient's different physiologic parameters are tracked relative to those sleep stages. By correlating these sleep study parameters with sensed physiologic parameters of the IPG 100 (FIG. 3A), the IPG 100 becomes calibrated for a particular patient to recognize sleep stages and other sleep patterns which are useful in some embodiments for applying and/or suspending therapy via the IPG 100”. Also see, paragraph [0110]).
The motivation to combine is the same as in claim 17, incorporated herein.
Regarding (Previously Presented) claim 23, Christopherson and Westbrook teach the limitations of claim 1, and further teaches wherein the biosignals received from the subject in the Simplified Sleep Study (SSS) include an activity-graph signal (Christopherson: Figures 4-7, paragraph [0042], “sensing module 102 comprises a body parameter 130, which includes at least one of a position-sensing component 132 or a motion-sensing component 134… the motion-sensing component 134 tracks sensing of "seismic" activity (via an accelerometer or a piezoelectric transducer) that is indicative of walking, body motion, talking, etc… tracks sensing of a body position or posture via an accelerometer or other transducer”, paragraphs [0068]-[0071], “a brief occasion of activity and/or cyclic periods of activity may be indicative of sleep disordered breathing behavior… sensing is performed according to a dynamic schedule based on the amount of body activity measured at a particular sensing time… a magnitude of a time interval between consecutive samples 332 (y-axis) is mapped relative to an amount of sensed body activity 334 (x-axis)”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Previously Presented) claim 24, Christopherson and Westbrook teach the limitations of claim 1, and further teach wherein the biosignals received from the subject in the Simplified Sleep Study (SSS) are obtained by one or more biosensors that are configured to be self-applied by the subject of the Simplified Sleep Study (SSS) (Westbrook: paragraph [0012], “The apparatus can be integrated with or connected to a sensor strip and can also integrate with or be connected to a nasal mask to obtain the physiological signals for the user. The form factor of this apparatus is comfortable, easy to self-apply, and results in less data artifacts than conventional techniques for capturing physiological data for analyzing sleep quality. Neuro-cardio-respiratory signals are analyzed using means to extract more accurate definitions of the frequency and severity of sleep discontinuity, sleep disordered breathing and patterns of sleep architecture”, paragraph [0030], “Thus, embodiments use electrodes and sensors that can be self-applied with limited skin or scalp preparation, and which monitors signal quality during use and provides user feedback when signal quality problems are detected”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Previously Presented) claim 25, Christopherson and Westbrook teach the limitations of claim 1, and further teach wherein the biosignals received from the subject in the Simplified Sleep Study (SSS) are obtained by one or more biosensors that are configured to be applied without requiring professional assistance by a certified or credentialed technologist (Westbrook: paragraph [0012], “The apparatus can be integrated with or connected to a sensor strip and can also integrate with or be connected to a nasal mask to obtain the physiological signals for the user. The form factor of this apparatus is comfortable, easy to self-apply, and results in less data artifacts than conventional techniques for capturing physiological data for analyzing sleep quality. Neuro-cardio-respiratory signals are analyzed using means to extract more accurate definitions of the frequency and severity of sleep discontinuity, sleep disordered breathing and patterns of sleep architecture”, paragraph [0030], “Thus, embodiments use electrodes and sensors that can be self-applied with limited skin or scalp preparation, and which monitors signal quality during use and provides user feedback when signal quality problems are detected”. The Examiner notes self-applied reads on what is required under the broadest reasonable interpretation).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Previously Presented) claim 26, Christopherson and Westbrook teach the limitations of claim 1, and further teach further comprising the one or more biosensors that are configured to be self-applied by the subject of the Simplified Sleep Study (SSS) (Westbrook: paragraph [0012], “The apparatus can be integrated with or connected to a sensor strip and can also integrate with or be connected to a nasal mask to obtain the physiological signals for the user. The form factor of this apparatus is comfortable, easy to self-apply, and results in less data artifacts than conventional techniques for capturing physiological data for analyzing sleep quality. Neuro-cardio-respiratory signals are analyzed using means to extract more accurate definitions of the frequency and severity of sleep discontinuity, sleep disordered breathing and patterns of sleep architecture”, paragraph [0030], “Thus, embodiments use electrodes and sensors that can be self-applied with limited skin or scalp preparation, and which monitors signal quality during use and provides user feedback when signal quality problems are detected”).
The motivation to combine is the same as in claim 1, incorporated herein.
Claim(s) 6 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 2020/0146620 (hereafter “Christopherson”; already of record in the IDS) and U.S. Patent Pub. No. 2010/0240982 (hereafter “Westbrook”) as applied to claim 1 above, and further in view of “The Dreem Headband compared to polysomnography for electroencephalographic signal acquisition and sleep staging” (hereafter “Arnal”; already of record in the IDS).
Regarding (Original) claim 6, Christopherson and Westbrook teaches the limitations of claim 1, and further teaches further comprising […] running the personalized sleep classifier on previous nights of sleep recorded in the Simplified Sleep Study (SSS) […] (Christopherson: paragraph [0105], “a comparison of some sleep study parameters 402 relative to some IPG therapy parameters 408… the IPG therapy parameters 408 are calibrated relative to one or more related sleep study parameters 402… By looking at profiles of sleep disordered breathing behavior, a correlation is made between the IPG parameters 418 and the PSG parameters 402 such that the IPG 100 is calibrated to sleep study data”).
Christopherson and Westbrook may not explicitly teach (underlined below for clarity):
--determining an accuracy of the personalized sleep classifier by running the personalized sleep classifier on previous nights of sleep recorded in the Simplified Sleep Study (SSS) and determining the variance of the SSS sleep profile and the HASS sleep profile for the same previous nights of sleep.
Arnal teaches determining an accuracy of the personalized sleep classifier by running the personalized sleep classifier on previous nights of sleep recorded in the Simplified Sleep Study (SSS) and determining the variance of the SSS sleep profile and the HASS sleep profile for the same previous nights of sleep (Arnal: page 1, “monitoring brain activity during sleep… assess the signal acquisition and the performance of the automatic sleep staging algorithms of a reduced-montage dry-electroencephalographic (EEG) device (Dreem headband, DH) compared to the gold-standard polysomnography (PSG)… assessed (1) similarity of measured EEG brain waves between the DH and the PSG; (2) the heart rate, breathing frequency, and respiration rate variability (RRV) agreement between the DH and the PSG; and (3) the performance of the DH's automatic sleep staging according to American Academy of Sleep Medicine guidelines versus PSG sleep experts manual scoring… The mean percentage error between the EEG signals acquired by the DH and those from the PSG for the monitoring of… frequencies during sleep… The mean absolute error for heart rate… Automatic sleep staging reached an overall accuracy”. Also see, pages 4-6, The Examiner notes accuracy and variance are determined and teaches what is required of the claim under the broadest reasonable interpretation).
One of ordinary skill in the art before the effective filing date would have found it obvious to include determination of accuracy and variance as taught by Arnal with the comparison for calibration as taught by Christopherson and Westbrook with the motivation of “[…reducing…] risk of cardiovascular and neurodegenerative diseases and psychiatric disorders (Arnal: page 2).
Claim(s) 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 2020/0146620 (hereafter “Christopherson”; already of record in the IDS), in view of U.S. Patent Pub. No. 2010/0240982 (hereafter “Westbrook”), in further view of “The Dreem Headband compared to polysomnography for electroencephalographic signal acquisition and sleep staging” (hereafter “Arnal”; already of record in the IDS).
Regarding (Previously Presented) claim 15, Christopherson teaches a computer-implemented method for creating a personalized sleep classifier for one or more subjects of a […] subjects (Christopherson: Figure 3, paragraph [0104], “a method 390 of correlating sleep study parameters with physiologic parameters”, paragraph [0040], “Components and methods of the present disclosure, including but not limited to memory module 111, may be implemented in hardware via a microprocessor… software on one or more computer-readable media”), the method comprising:
--obtaining, by a processing system of a computing system having one or more processors, sleep data from biosignals received from the focused group of subjects in a High-Accuracy Sleep Study (HASS) (Christopherson: Figure 4E, paragraphs [0052]-[0054], “controller 110 of IPG 100 comprises one or more processing units and associated memories 111 configured to generate control signals directing the operation of IPG 100, including the operation of at least sensing module 102,”, paragraph [0104], “A second set of sleep study parameters is tracked, independent of the IPG system, via the polysomnography study (at 395)”. Also see, paragraph [0040]) and
--sleep data from biosignals received from the […] subjects in a Simplified Sleep Study (SSS) (Christopherson: Figure 4E, paragraph [0104], “At 394, a first set of physiologic parameters is tracked, during the polysomnography study, via the IPG system in its monitoring mode”),
--the data from the High-Accuracy Sleep Study (HASS) being obtained from the […] subjects during a period of time that is simultaneous with the period during which the data from the Simplified Sleep Study (SSS) is obtained from the […] subjects (Christopherson: Figure 4E, paragraph [0104], “at 392 the method 390 includes operating, substantially simultaneously with a polysomnography (PSG) study, an implantable pulse generator (IPG) system in a monitoring mode”, paragraph [0155], “sensing module 102 of IPG 100 (FIG. 2) is active during the sleep study so that parameters sensed via IPG 100 are correlated with, and calibrated, relative to known sleep study parameters that are indicative of various apnea-related physiologic events, patterns, and behaviors”);
--developing, by the processing system, a high-resolution HASS sleep profile from the sleep data of the High-Accuracy Sleep Study (HASS) (Christopherson: Figure 4E, paragraph [0104], “At 396, method 390 includes identifying sleep disordered breathing patterns (e.g. apneas and/or hypopneas) from the tracked second set of sleep study parameters and identifying which of these parameters are indicative of sleep disordered breathing for a particular patient”. Also see, paragraphs [0062], [0105]. The Examiner notes determining patterns for a particular patient reads on a profile under the broadest reasonable interpretation);
--creating, by the processing system, a personalized sleep classifier that outputs a SSS sleep profile of the […] subjects based on the sleep data from the Simplified Sleep Study (SSS) (Christopherson: Figure 4E, paragraphs [0018]-[0019], “detection of an apnea via a bio-impedance signal… detection of an apnea via an respiratory pressure signal”, paragraph [0051], “the detection monitor 180 observes, via sensing module 102, physiologic conditions of the patient to detect whether sleep disordered breathing is occurring”, paragraph [0071], “a probabilistic polling profile 336 (for sensing of potential apneas)”, paragraph [0095], “system 200 is trained by operating simultaneously with a sleep study… different sleep stages (among other patterns) are recognized and in which a patient's different physiologic parameters are tracked relative to those sleep stages.”, paragraph [0107]-[0109], “training IPG 100 via a sleep study, the internally measured blood oxygen saturation parameter 417 (via IPG 100 and system 200) will be calibrated relative to the externally measured blood oxygen saturation parameter 411 to account for such differences… provide sleep disordered breathing scoring (e.g., apnea scoring)… after initially calibrating the IPG 100 during a sleep study”. Also see, paragraph [0083]);
--calibrating, by the processing system, the personalized sleep classifier for one or more of the […] subjects such that the SSS sleep profile output by the personalized sleep classifier based on the Simplified Sleep Study (SSS) of the one or more of the […] subjects approaches or aligns with the high-resolution HASS sleep profile based on the High-Accuracy Sleep Study (HASS) one or more of the […] subjects, […] (Christopherson: Figure 4E, paragraphs [0104]-[0105], “By looking at profiles of sleep disordered breathing behavior, a correlation is made between the IPG parameters 418 and the PSG parameters 402 such that the IPG 100 is calibrated to sleep study data”, paragraphs [0106]-[0109], “after initially calibrating the IPG 100 during a sleep study, the IPG 100 is permitted to operate during a second sleep study so that the IPG 100 is able to self-learn in direct association with the data provided via the PSG… physiologic data sensed via the IPG 100 that is indicative of an sleep disordered breathing behavior is matched with indicative physiologic data/observations sensed via the PSG monitoring system that is indicative of sleep disordered breathing behavior”, paragraph [0115], “sensing parameters of the IPG 100 and system 200 are calibrated relative to conventional sensing parameters of a formal sleep study by operating the IPG 100 and system 200 in a sensing mode ( e.g. second state 204 in FIG. 4A) during the formal sleep study”, paragraph [0155], “via the sleep study the IPG 100 learns a patient's physiologic patterns surrounding an apnea event, which helps to establish a baseline”),
wherein the biosignals received from the subject in the Simplified Sleep Study (SSS) are obtained by one or more biosensors […] (Christopherson: paragraph [0030], “system 10 also comprises additional sensors to obtain further physiologic data associated with respiratory functions. For example, system 10 may include various sensors”, paragraph [0041], “the sensing module 102 of IPG 100 receives and tracks signals from various physiologic sensors in order to determine a respiratory state of a patient, such as whether or not the patient is asleep or awake, and other respiratory-associated indicators, etc.”).
Christopherson may not explicitly teach (underlined below for clarity):
wherein the biosignals received from the subject in the Simplified Sleep Study (SSS) are obtained by one or more biosensors that are configured to be self-applied by the subject of the Simplified Sleep Study (SSS) or applied without requiring professional assistance.
Westbrook teaches wherein the biosignals received from the subject in the Simplified Sleep Study (SSS) are obtained by one or more biosensors that are configured to be self-applied by the subject of the Simplified Sleep Study (SSS) or applied without requiring professional assistance (Westbrook: paragraph [0012], “The apparatus can be integrated with or connected to a sensor strip and can also integrate with or be connected to a nasal mask to obtain the physiological signals for the user. The form factor of this apparatus is comfortable, easy to self-apply, and results in less data artifacts than conventional techniques for capturing physiological data for analyzing sleep quality. Neuro-cardio-respiratory signals are analyzed using means to extract more accurate definitions of the frequency and severity of sleep discontinuity, sleep disordered breathing and patterns of sleep architecture”, paragraph [0030], “Thus, embodiments use electrodes and sensors that can be self-applied with limited skin or scalp preparation, and which monitors signal quality during use and provides user feedback when signal quality problems are detected”).
One of ordinary skill in the art before the effective filing date would have found it obvious to include using self-applied sensors to generate sleep profiles as taught by Westbrook within the calibration of a sleep classifier as taught by Christopherson with the motivation of “easy to self-apply, and results in less data artifacts than conventional techniques for capturing physiological data” (Westbrook: Abstract).
Christopherson and Westbrook may not explicitly teach (underlined below for clarity):
--a computer-implemented method for creating a personalized sleep classifier for one or more subjects of a focused group of subjects, the method comprising: obtaining, by a processing system of a computing system having one or more processors, sleep data from biosignals received from the focused group of subjects in a High-Accuracy Sleep Study (HASS) and sleep data from biosignals received from the focused group of subjects in a Simplified Sleep Study (SSS), the data from the High-Accuracy Sleep Study (HASS) being obtained from the focused group of subjects during a period of time that is simultaneous with the period during which the data from the Simplified Sleep Study (SSS) is obtained from the focused group of subjects; developing, by the processing system, a high-resolution HASS sleep profile from the sleep data of the High-Accuracy Sleep Study (HASS); creating, by the processing system, a personalized sleep classifier that outputs a SSS sleep profile of the focused group of subjects based on the sleep data from the Simplified Sleep Study (SSS); calibrating, by the processing system, the personalized sleep classifier for one or more of the focused group of subjects such that the SSS sleep profile output by the personalized sleep classifier based on the Simplified Sleep Study (SSS) of the one or more of the focused group of subjects approaches or aligns with the high-resolution HASS sleep profile based on the High-Accuracy Sleep Study (HASS) one or more of the focused group of subjects, wherein the focused group of subjects share one or more same characteristics, including age, sex, diagnosed clinical condition, weight, race/ethnicity, BMI, treatment with a same medication, or health condition.
Arnal teaches a computer-implemented method for creating a personalized sleep classifier for one or more subjects of a focused group of subjects, the method comprising: obtaining, by a processing system of a computing system having one or more processors, sleep data from biosignals received from the focused group of subjects in a High-Accuracy Sleep Study (HASS) and sleep data from biosignals received from the focused group of subjects in a Simplified Sleep Study (SSS), the data from the High-Accuracy Sleep Study (HASS) being obtained from the focused group of subjects during a period of time that is simultaneous with the period during which the data from the Simplified Sleep Study (SSS) is obtained from the focused group of subjects; developing, by the processing system, a high-resolution HASS sleep profile from the sleep data of the High-Accuracy Sleep Study (HASS); creating, by the processing system, a personalized sleep classifier that outputs a SSS sleep profile of the focused group of subjects based on the sleep data from the Simplified Sleep Study (SSS); calibrating, by the processing system, the personalized sleep classifier for one or more of the focused group of subjects such that the SSS sleep profile output by the personalized sleep classifier based on the Simplified Sleep Study (SSS) of the one or more of the focused group of subjects approaches or aligns with the high-resolution HASS sleep profile based on the High-Accuracy Sleep Study (HASS) one or more of the focused group of subjects, wherein the focused group of subjects share one or more same characteristics, including age, sex, diagnosed clinical condition, weight, race/ethnicity, BMI, treatment with a same medication, or health condition (Arnal: page 3, “A total of 31 volunteers were recruited without regard to gender or ethnicity from the local community by study advertisement flyers. Volunteers were eligible if they were between the ages of 18 and 65 years and capable of providing informed consent. Exclusion critetia included current pregnancy or nursing; severe cardiac, neurological, or psychiatric comorbidity in the last 12 months; morbid obesity (BMI >=40); or use of benzodiazepines, non-benzodiazepines {Z-drugs), or y-hydroxybutyrate on the day of the study.”. The Examiner notes inclusion and exclusion criteria for the group consists of characteristics of age and condition and teaches what is require of the claim under the broadest reasonable interpretation).
One of ordinary skill in the art before the effective filing date would have found it obvious to include using a group of subjects as taught by Arnal within the subject as taught by Christopherson and Westbrook with the motivation of “[…reducing…] risk of cardiovascular and neurodegenerative diseases and psychiatric disorders (Arnal: page 2).
Regarding (Previously Presented) claim 16, Christopherson, Westbrook and Arnal teach the limitations of claim 15, and further teaches a method for identifying sleep stages or sleep events of the subject, or providing a sleep profile for a subject (Christopherson: paragraph [0104], “a method 390 of correlating sleep study parameters with physiologic parameters”) comprising;
--creating, by the processing system, a personalized sleep classifier for a subject according to claim 15 (Christopherson, Westbrook and Arnal: see claim 15, above); and
--identifying, by the processing system, sleep stages or sleep events of the subject, or providing a sleep profile of the subject, based on further sleep data from the subject from a further Simplified Sleep Study (SSS) using the personalized sleep classifier(Christopherson: paragraph [0095], “one or more different stages of sleep may act as triggers to warrant suspending therapy to check whether or not any sleep disordered breathings behavior is occurring… different sleep stages (among other patterns) are recognized and in which a patient's different physiologic parameters are tracked relative to those sleep stages. By correlating these sleep study parameters with sensed physiologic parameters of the IPG 100 (FIG. 3A), the IPG 100 becomes calibrated for a particular patient to recognize sleep stages and other sleep patterns which are useful in some embodiments for applying and/or suspending therapy via the IPG 100”. Also see, paragraph [0110]),
--wherein the further sleep data is obtained from further Simplified Sleep Study (SSS) of a different period of time, either before or after, the sleep data from the biosignals received from the subject in the Simplified Sleep Study (SSS) were recorded or obtained (Christopherson: paragraph [0050], “evaluate the severity of sleep disordered breathing behavior both before and after the application”, paragraph [0064]-[0065], “a trend report within a night or for a period of multiple nights that enable detection of patterns or changes in the patient's health and/or enable evaluation of adjustments made to the therapy by the physician during the multiple night period… the IPG 100 and system 200 is operated in second state 204 for an extended period of time (or even all night) to provide the physician with an in-home pseudo sleep study”, paragraph [0109]-[0112], “after initially calibrating the IPG 100 during a sleep study, the IPG 100 is permitted to operate during a second sleep study so that the IPG 100 is able to self-learn in direct association with the data provided via the PSG… after a number of IPG-based systems 456 (e.g., IPG 1, IPG 2, etc.) are calibrated relative to sleep study parameters 454 (e.g. PSG 1, PSG 2, etc.) for a number of patients 452 (e.g. Joe, Fred, etc), embodiments of the present disclosure can use a correlation of the sleep study parameters relative to the IPG parameters to develop a predictive index 460”. The Examiner notes after calibration the device is used as a SSS).
The motivation to combine is the same as in claim 15, incorporated herein.
Claim(s) 13 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 2020/0146620 (hereafter “Christopherson”; already of record in the IDS) amd U.S. Patent Pub. No. 2010/0240982 (hereafter “Westbrook”) as applied to claim 1 above, and further in view of U.S. Patent Pub. No. 2018/0049678 (hereafter “Hoskuldsson”; already of record in the IDS).
Regarding (Previously Presented) claim 13, Christopherson and Westbrook teaches a method for identifying sleep stages of a subject (Christopherson: paragraph [0104], “a method 390 of correlating sleep study parameters with physiologic parameters”) comprising;
--creating a personalized sleep classifier for a subject according to claim l (Christopherson and Westbrook: see claim 1, above); and
--using, by the processing system, […] signals obtained in a subsequent Simplified Sleep Study (SSS) to estimate wake, […] sleep stages in the subject (Christopherson: paragraph [0095], “one or more different stages of sleep may act as triggers to warrant suspending therapy to check whether or not any sleep disordered breathings behavior is occurring… different sleep stages (among other patterns) are recognized and in which a patient's different physiologic parameters are tracked relative to those sleep stages. By correlating these sleep study parameters with sensed physiologic parameters of the IPG 100 (FIG. 3A), the IPG 100 becomes calibrated for a particular patient to recognize sleep stages and other sleep patterns which are useful in some embodiments for applying and/or suspending therapy via the IPG 100”. Also see, paragraph [0110]).
Christopherson and Westbrook may not explicitly teach (underlined below for clarity):
--using, by the processing system, chest and abdomen respiratory inductance plethysmography (RIP) signals obtained in a subsequent Simplified Sleep Study (SSS) to estimate wake, REM sleep and non-REM sleep stages in the subject.
Hoskludsson teaches using, by the processing system, chest and abdomen respiratory inductance plethysmography (RIP) signals obtained in a subsequent Simplified Sleep Study (SSS) to estimate wake, REM sleep and non-REM sleep stages in the subject (Hoskludsson: Figures 1-2, paragraph [0021], “Chest (Thorax) and Abdomen signals above are typical RIP signals”, paragraph [0110], “the thorax contribution, which is also of interest as the physiology suggests that the ratio of thorax contribution vs. abdomen contribution changes with the level of sleep, the respiratory muscular activity being different during REM sleep compared with the Nl , N2, and N3 sleep stages. Sudden changes in the contribution ratio are therefore strongly related with REM onset and offset”, claim 10, “wherein the thoracic effort signal (T) and abdomen effort signal (A) are obtained by a Respiratory Inductive Plethysmograph (RIP) system”. Also see, paragraph [0138]).
One of ordinary skill in the art before the effective filing date would have found it obvious to include using RIP signals to determine REM sleep as taught by Hoskludsson within the determination of sleep stages as taught by Christopherson and Westbrook with the motivation of “improve the respiratory effort measure.” (Hoskludsson: paragraph [0186]).
Regarding (Previously Presented) claim 21, Christopherson and Westbrook teaches the limitations of claim 1, but may not explicitly teach wherein the biosignals received from the subject in the Simplified Sleep Study (SSS) include a thoracic RIP signal and/or an abdomen RIP signal.
Hoskludsson teaches wherein the biosignals received from the subject in the Simplified Sleep Study (SSS) include a thoracic RIP signal and/or an abdomen RIP signal (Hoskludsson: Figures 1-2, paragraph [0021], “Chest (Thorax) and Abdomen signals above are typical RIP signals”, paragraph [0110], “the thorax contribution, which is also of interest as the physiology suggests that the ratio of thorax contribution vs. abdomen contribution changes with the level of sleep, the respiratory muscular activity being different during REM sleep compared with the Nl , N2, and N3 sleep stages. Sudden changes in the contribution ratio are therefore strongly related with REM onset and offset”, claim 10, “wherein the thoracic effort signal (T) and abdomen effort signal (A) are obtained by a Respiratory Inductive Plethysmograph (RIP) system”. Also see, paragraph [0138]).
The motivation to combine is the same as in claim 13, incorporated herein.
Claim(s) 22 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 2020/0146620 (hereafter “Christopherson”; already of record in the IDS) in view of.
are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 2020/0146620 (hereafter “Christopherson”; already of record in the IDS), in view of U.S. Patent Pub. No. 2010/0240982 (hereafter “Westbrook”), in further view of U.S. Patent No. 8,365,730 (hereafter “Baker”).
Regarding (Previously Presented) claim 22, Christopherson and Westbrook teaches the limitations of claim 1, but may not explicitly teach wherein the biosignals received from the subject in the Simplified Sleep Study (SSS) include a photo-plethysmography (PPG) signal.
Baker teaches wherein the biosignals received from the subject in the Simplified Sleep Study (SSS) include a photo-plethysmography (PPG) signal (Baker: Figures 1-3, Column 1, lines, 45-55, “Pulse oximeters typically utilize a non-invasive sensor that 45 transmits light through a patient's tissue and that photoelectrically detects the absorption and/or scattering of the transmitted light in such tissue. A photo-plethysmographic waveform, which corresponds to the cyclic attenuation of optical energy through the patient's tissue, may be generated from the detected light”, Column 6, lines 25-45, “microprocessor 48 may then determine the patient's physiological parameters, such as SpO2, pulse rate, respiratory effort, and so forth, based on the values of the received signals corresponding to the light received by the detector 18 (e.g., the photo-plethysmographic waveform) and the information about the sensor 12”).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the invention was made to combine the noted features of Baker within teaching of Christopherson and Westbrook since the combination of the two references is merely simple substitution of one known element for another producing a predictable result (KSR rationale B). Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself—that is, in the substitution of the use of photo-plethysmography (PPG) signals as taught by Baker for the health parameters as taught by Christopherson and Westbrook. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Response to Arguments
Applicant's arguments filed on 09 October 2025 have been fully considered but they are not persuasive. Applicant's arguments will be addressed below in the order in which they appear in the response filed on 09 October 2025.
Rejection under 35 U.S.C. § 101
Regarding the rejection of claim 1-26, the Examiner has considered the Applicant's arguments but does not find them persuasive. The Examiner has attempted to address all of the arguments presented by the Applicant; however, any arguments inadvertently not addressed are not persuasive for at least the following reasons:
Applicant argues:
Applicant thus respectfully reiterates that claim 1 cannot be considered to cover human activity. Rather claim 1 expressly requires that these steps are implemented by a computer. The "broadest reasonable inteipretation" cannot be applied by setting aside the terms and features of the claims themselves… Applicant respectfully submits that the features of claims 1, 15, and 17 cannot simply be set aside in an effort to give a "broadest reasonable inteipretation" that the claims are actually directed to organizing human behavior… As further described in paragraphs [52] and [54]-[57] of Applicant's originally-filed specification, the classifier allows to achieve the advantages such as accuracy of HASS with a simpler, self-appliable system of a SSS. The classifier provides sufficient measurements to determine the sleep stages without requiring the many sensors and data processing of a HASS, such as a PSG… Thus, for reasons described below, Applicant's claimed invention provides an inventive concept that transforms any judicial exception into patent eligible subject matter under Step 2B of the Alice/Mayo two-part test.
The Examiner respectfully disagrees.
It is respectfully submitted, the claims under the broadest reasonable interpretation are directed toward organizing how human users interact with various generic off-the-shelf computer components to collect data about a human user, organizing the collected data, and providing to the human user a result of the organized data, which as stated in 2106.04(a)(2), “certain activity between a person and a computer… may fall within the “certain methods of organizing human activity” grouping”. The claims are directed toward organizing data between a human user and various generic computer components and is directed toward the certain method of organizing human activity grouping of abstract ideas.
The claims do not recite any additional elements which provide either a technical solution to a technical problem recited in Applicant’s specification, and/or an improvement in the functionality of the computer. First, looking to the argued paragraphs, the paragraphs do not recite any technical problems rooted in computer hardware technology, classification of sleep is not a technical problem rooted in computer technology, at best the paragraphs address human activity problems of “require[ing] professional assistance to accurately setup and perform”, the claims may provide human activity solutions to human activity problems to provide an improved abstract idea, nevertheless an improved abstract idea is still an abstract idea. Therefore, as the claims do not recite any additional elements that improve the performance of the computer or provide a technical solution to a technical problem recited in the specification the argument is unpersuasive.
Rejection under 35 U.S.C. § 103
Regarding the rejection of claim 1-26, the Examiner has considered the Applicant's arguments but does not find them persuasive. The Examiner has attempted to address all of the arguments presented by the Applicant; however, any arguments inadvertently not addressed are not persuasive for at least the following reasons:
Applicant argues:
In the Office Action, Christopherson and Westbrook are relied on in the obviousness based rejection of claim 1, with Christopherson being relied on as the primary reference in the rejection… The Office Action concedes that Christopherson does not teach that the biosignals are obtained by one or more biosensors that are configured to be self-applied by the subject of the SSS or applied without requiring professional assistance by a certified or credentialed technologist… According to the Office Action, it would be obvious for the skilled person to exchange the implantable pulse generator (IPG) of Christopherson with the nasal mask of Westbrook to arrive at the subject matter of claim 1… Therefore, the nasal mask of Westbrook is not configured to perform the task described in Christopherson, like stimulating nerves or measuring respiratory pressure. Thus, Applicant submit that one of ordinary skill in the art would have no motivation to exchange the sensors and nerve simulators of Christopherson with the self-applied nasal mask of Westbrook. How would the self-applied nasal mask of Westbrook serve to stimulate the hypoglossal nerve as intended with the surgically implanted device of Christopherson? … An exchange of the respective sensors would only make sense when the skilled person would have had knowledge about the present invention that teaches to exchange PSG sensors of a High-Accuracy Sleep Study (HASS) with the self applied sensors of a Simplified Sleep Study (SSS). Without this knowledge or motivation, skilled person would not be able to arrive at the claimed subject matter.
The Examiner respectfully disagrees.
It is respectfully submitted, the combination of Westbrook within Christopherson expressly teaches receiving biosignals from a sensor that is self-applied, and provides a motivation that one of ordinary skill in the art would find prima facie obvious to combine with the motivation of “easy to self-apply, and results in less data artifacts than conventional techniques for capturing physiological data” (Westbrook: Abstract). The collection of data from another sensor would not render the system of Christopherson inoperability, the MPEP states at 2143(B), “An assessment of whether a combination would render the device inoperable must not "ignore the modifications that one skilled in the art would make”, the Examiner notes both Westbrook and Christopherson are directed toward collection of data to assess/classify sleep, one of ordinary skill in the art before the effective filing date would find prima facie obvious to combine the references with the motivation above.
Conclusion
THIS ACTION IS MADE FINAL. 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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/A.E.L./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
1 See Polysomnography-Wikipedia 2019 (https://web.archive.org/web/20190130205159/https://en.wikipedia.org/wiki/Polysomnography)