DETAILED CORRESPONDENCE
This is a non-final office action on merits in response to the arguments and/or amendments filed on 05/04/2026 and the request for continued examination filed on 05/04/2026.
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 .
Status of claims
Amendments to claims 1, 5-10, 12, 14, 18-20 are acknowledged and have been carefully considered. Claims 1-20 are pending and considered below.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/04/2026 has been entered.
Subject Matter Free of Art
Claims 1-20 include subject matter that is free of prior art. The cited prior art of record fails to expressly teach or suggest, either alone or in combination, the features found within independent claims 1, 14, and 20.
For claims 1, 14, and 20, the cited prior art of record fails to expressly teach or suggest, either alone or in combination, determining, for each of a plurality of insomnia-related signals detected while a user is asleep, a set of insomnia-indicating signals by comparing each insomnia-related signal to a corresponding user-specific threshold determined while the user is asleep; discarding the insomnia-related signals that are not part of the insomnia-indicating signals while retaining the insomnia-indicating signals for each sleep session; and, at the end of a plurality of sleep sessions over a plurality of nights, determining, using a trained machine-learning model and based on the retained sets of insomnia-indicating signals, and without using the discarded insomnia-related signals, an insomnia condition of the user.
The closest prior art of record includes 1) Leon et al. (JP Publication 2023544515 A), referred to hereinafter as Leon; 2) Nofzinger (International Publication No. WO 2017201088 A1), referred to hereinafter as Nofzinger; 3) Irish et al. (Irish et al. The role of sleep hygiene in promoting public health: A review of empirical evidence. 2014. Sleep Medicine Reviews. 22. 23-36 (Year: 2014)), referred to hereinafter as Irish; and 4) Shariff et al. (U.S. Patent Publication 2016/0302671 A1), referred to hereinafter as Shariff.
Leon teaches monitoring a user during sleep using wearable sensors and physiological data, including movement, respiratory rate, heart rate, heart rate variability, temperature, and other physiological parameters. Leon further teaches determining a baseline score from physiological data when the user is relaxed, including when the user is falling asleep; evaluating physiological parameters relative to criteria associated with the user; learning levels from the user over multiple sleep sessions; and using previously recorded physiological and sleep data in connection with machine learning algorithms. However, Leon fails to teach or suggest determining, for each insomnia-related signal, a corresponding user-specific threshold determined while the user is asleep, selectively discarding signals that do not satisfy the corresponding thresholds while retaining the insomnia-indicating signals for each sleep session, and subsequently determining an insomnia condition using a trained machine learning model based on the retained sets of insomnia-indicating signals over the plurality of nights without using the discarded signals.
Nofzinger teaches monitoring sleep and physiological parameters over multiple nights using sensors, including EEG, heart rate variability, skin temperature, and motion sensors, and teaches determining sleep stages and physiological changes occurring during sleep. Nofzinger further teaches storing and reviewing physiological and sleep parameters over multiple nights and filtering certain sensor data, such as eliminating artifacts from EEG data before subsequent analysis. However, Nofzinger fails to teach or suggest establishing a corresponding user specific threshold determined while the user is asleep for each insomnia-related signal, identifying insomnia-indicating signals based on comparison to such thresholds, selectively discarding the signals that fail the corresponding thresholds, or determining an insomnia condition with a trained machine-learning model using the remaining threshold selected signals without using the discarded signals.
Irish teaches sleep practices and discusses relationships between behavioral and environmental factors and sleep, including exercise, light exposure, and other behaviors that affect sleep quality. Irish further discusses the relationship between exercise duration, frequency, timing, and sleep outcomes. However, Irish fails to teach or suggest a wearable multisensor system that establishes corresponding user specific thresholds while the user is asleep, compares detected insomnia-related physiological signals to those thresholds, selectively retains and discards signals based on the comparisons, or provides the retained insomnia-indicating signals from a plurality of sleep sessions to a trained machine-learning model to determine an insomnia condition.
Shariff teaches collecting physiological data using a wearable device, establishing individualized baseline values from historical physiological data associated with a user, comparing physiological data to corresponding baseline values and thresholds, and identifying physiological data that varies from the baseline by more than a threshold amount as atypical. Shariff further teaches selectively providing threshold exceeding physiological data to a probabilistic classification model and, in some embodiments, not analyzing physiological data with the classification model when the data varies less than the threshold amount from the baseline. Shariff also teaches machine learning probabilistic classification models for determining a health state. However, Shariff fails to teach or suggest determining an insomnia condition and the claimed combination of each corresponding threshold being a user-specific threshold determined while the user is asleep, insomnia-related signals are evaluated against those sleep-specific user thresholds during a plurality of sleep sessions, non-insomnia-indicating signals that are discarded while insomnia-indicating signals are retained for the sleep sessions, and a trained machine learning model to determine an insomnia condition at the end of the plurality of nights based on the retained sets of insomnia-indicating signals and without using the discarded signals.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Under step 1, the analysis is based on MPEP 2106.03, and claims 1-13 are drawn to a method, claims 14-19 are drawn to wearable device, and claim 20 is drawn to one or more non-transitory computer readable storage media. Thus, each claim, on its face, is directed to one of the statutory categories (i.e., useful process, machine, manufacture, or composition of matter) of 35 U.S.C. 101.
Step 2A Prong One
Claim 1 recites the limitations of, for each of a plurality of sleep sessions over a plurality of nights determining, for each of the plurality of insomnia-related signals, a set of insomnia- indicating signals by comparing each insomnia-related signal in that plurality of insomnia- related signals to a corresponding threshold, each corresponding threshold comprising a user-specific threshold determined while the user is asleep; and discarding the plurality of insomnia-related signals that are not part of the insomnia- indicating signals while retaining the set of insomnia-indicating signals for that sleep session; at the end of the plurality of sleep sessions, determining based on the determined sets of insomnia-indicating signals at the end of the plurality of nights, and without using the discarded plurality of insomnia-related signals, an insomnia condition of the user. These limitations recite evaluations and judgments that, under their broadest reasonable interpretation, encompass concepts that can be practically performed in the mind or by using a pen and paper. For example, a person can compare observed insomnia-related information to predetermine user specific thresholds, identify information satisfying the thresholds as insomnia-indicating information, disregard information that does not satisfy the thresholds while retaining information that does, and evaluate the retained insomnia-indicating information over multiple sleep sessions to determine whether the information indicates an insomnia condition. Thus, the recited limitations encompass observations, evaluations, and judgements performed using information and therefore fall within the mental process grouping of abstract ideas. The recitation that the determination of the insomnia conditions is performed “by a trained machine-learning model” does not alter the nature of the evaluation recited by the claim and instead specifies a technological implementation for performing the recited determination. Thus, the claim recites a mental process which is an abstract idea.
Independent claims 14 and 20 recite identical or nearly identical steps with respect to claim 1 (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and these claims are therefore determined to recite an abstract idea under the same analysis.
Under Step 2A Prong Two
The claimed limitations, as per claim 1, include:
for each of a plurality of sleep sessions over a plurality of nights:
detecting, by a wearable device worn by a user, that the user is asleep;
detecting, by each of a plurality of sensors of the wearable device worn by the user, a corresponding plurality of insomnia-related signals while the user is asleep;
determining, for each of the plurality of insomnia-related signals, a set of insomnia- indicating signals by comparing each insomnia-related signal in that plurality of insomnia- related signals to a corresponding threshold, each corresponding threshold comprising a user-specific threshold determined while the user is asleep; and
discarding the plurality of insomnia-related signals that are not part of the insomnia- indicating signals while retaining the set of insomnia-indicating signals for that sleep session; and
at the end of the plurality of sleep sessions, determining, by a trained machine-learning model and based on the determined sets of insomnia-indicating signals at the end of the plurality of nights, and without using the discarded plurality of insomnia-related signals, an insomnia condition of the user.
Examiner Note: underlined elements indicate additional elements of the claimed invention identified as performing the steps of the claimed invention.
The judicial exception expressed in claim 1 is not integrated into a practical application. The claim recites the additional elements of a trained machine learning model that determines, based on the determined sets of insomnia-indicating signals and without using the discarded plurality of insomnia-related signals, an insomnia condition of the user. However, the recitation of the trained machine learning model uses a computer tool to perform the recited abstract evaluation and does not impose a meaningful limit on the judicial exception. Specifically, the claim does not recite a specific machine learning algorithm, model configuration, or other technological implementation that improves the functioning of the machine learning model or computer itself. Instead, the trained machine learning model is invoked at a high level of generality to perform the determination of an insomnia condition based on the selected insomnia-indicating information. Therefore, the additional element applies the recited mental process using a computer tool and amounts to instructions to implement the abstract idea using a computer, which is insufficient to integrate the judicial exception into a practical application (see MPEP § 2106.05(f)).
The judicial exception expressed in claim 1 is not integrated into a practical application. The claim recites the additional elements of detecting, by a wearable device worn by a user, that the user is asleep; and detecting, by each of a plurality of sensors of the wearable device worn by the user, a corresponding plurality of insomnia-related signals while the user is asleep. These limitations are recited at a high level of generality (i.e., a general means of collecting sleep information used as input for the subsequent evaluation), and amount to merely data gathering. Specifically, detecting that the user is asleep and detecting the plurality of insomnia-related signals merely obtain the information that is subsequently evaluated by comparing the insomnia-related signals to corresponding thresholds to identify insomnia-indicating signals and ultimately determine an insomnia condition. This data-gathering activity is considered an insignificant extra-solution activity to the judicial exception. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application (see MPEP § 2106.05(g)).
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.
Under step 2B
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A, the claim as a whole merely describes how to generally “apply” the concept of evaluating insomnia-related information by comparing the information to corresponding thresholds, identifying insomnia-related information, and determining an insomnia condition based on the identified information in a computer environment. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea.
Claim 1 also recites the additional elements of detecting, by a wearable device worn by a user, that the user is asleep, and detecting, by each of a plurality of sensors of the wearable device, a corresponding plurality of insomnia-related signals while the user is asleep. These additional elements merely gather the sleep-related information that is subsequently used in performing the abstract evaluation. This data-gathering activity constitutes insignificant extra-solution activity and does not provide an inventive concept sufficient to transform the claimed abstract idea into patent eligible subject matter. As noted in Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016), the collection of information used as input for subsequent analysis does not provide an inventive concept, as merely selecting and gathering information for use in an abstract analysis constitutes insignificant extra solution activity. Accordingly, these additional elements, whether considered individually or in combination, do not amount to significantly more than the judicial exception.
Claims 2-10, 12, 15-17, and 19 recite no further additional elements, and only further narrow the abstract idea. The previously identified additional elements, individually and as a combination, do not integrate the narrowed abstract idea into a practical application for reasons similar to those explained above, and do not amount to significantly more than the narrowed abstract idea for reasons similar to those explained above.
Claims 11, 13, and 18 recite the additional elements of receiving, from the user, a description of the user's food intake, alcohol intake, or caffeine intake before the user's bedtime (claim 11), by the trained machine-learning model (claim 11), based on a signal obtained by an accelerometer of the wearable device (claim 13), by an EEG signal from a head-worn device of the user (claim 13), and the one or more processors of the wearable device (claim 18). However, these additional element amount to implementing an abstract idea on a generic computing device or mere data gathering (i.e., an insignificant extra-solution activity). As such, these additional elements, when considered individually or in combination with the previously identified additional elements, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Thus, as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible.
Therefore, the claims here fail to contain any additional element(s) or combination of additional elements that can be considered as significantly more and the claims are rejected under 35 U.S.C. 101 for lacking eligible subject matter.
Claim Rejections - 35 USC § 103
In view of Applicant’s amendments and arguments, the rejection of claims 1-20 under 35 U.S.C. 103 is withdrawn.
Response to Arguments
Applicant’s arguments and amendments, see Remarks/Amendments submitted on 05/04/2026 with respect to the rejection of the claims have been carefully considered and is addressed below.
Claim Rejections - 35 USC § 101
Examiner respectfully disagrees with Applicant’s argument that the claimed discarding and retaining of signals integrates the judicial exception into a practical application by reducing computational resources and improving insomnia detection. Although the specification states that discarding data that does not satisfy a threshold and providing only insomnia-indicating signals to the machine learning model may save computational resources, the claims do not recite a particular improvement to the operation of the computer or machine learning model itself. Instead, claim 1 recites comparing collected insomnia-related information to corresponding thresholds, retaining information that satisfies the thresholds while discarding information that does not, and using the retained information to make the abstract determination of an insomnia condition. The trained machine learning model is recited at a high level of generality as the tool that performs the ultimate determination, without reciting a particular machine learning algorithm or training technique. Therefore, the additional element applies the recited mental process using a computer tool and amounts to instructions to implement the abstract idea using a computer, which is insufficient to integrate the judicial exception into a practical application
Examiner also respectfully disagrees that the wearable device and plurality of sensors integrate the judicial exception into a practical application because the Specification describes advantages over clinical sleep studies and subjective self diagnosis. The claims recite the wearable device and sensors as acquiring the sleep-related information which the subsequent abstract evaluation operates. Specifically, the wearable device detects that the user is asleep, and the plurality of sensors detects the corresponding plurality of insomnia-related signals that are subsequently compared to thresholds and evaluated to determine an insomnia condition. Thus, these limitations constitute data-gathering activity in which the information used in the recited analysis. As noted in Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016), collecting information for subsequent analysis does not itself provide the inventive concept or transform the abstract analysis into patent-eligible subject matter.
Lastly, the Examiner respectfully disagrees that the wearable device and sensors necessarily integrate the exception into a practical application because the determination could not occur without first acquiring insomnia-related signals. The fact that data gathering is necessary to perform a subsequent analysis does not establish that the data gathering activity is integral to a practical application of the judicial exception. The claimed sensors acquire insomnia-related information and subsequently the information is compared to corresponding thresholds, selected or discarded based on the comparison, and the retained information is evaluated to determine an insomnia condition. Accordingly, the sensors provide the inputs required for the subsequent abstract evaluation, and considered individually and as an ordered combination, the additional elements do not impose a meaningful limit on the recited mental process or integrate the judicial exception into a practical application. Accordingly, Applicant’s arguments do not overcome the rejection under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In view of Applicant’s amendments and arguments, the rejection of claims 1-20 under 35 U.S.C. 103 is withdrawn.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
Shouldice et al. (U.S. Patent Publication 2020/0297955) teaches systems and methods for managing a user’s chronic disease that include a physiological monitor carried by the user to sense physiological parameters and a management device that can analyze physiological and/or environmental parameters to detect a trigger pattern indicative of an event and to generate treatment instructions.
Eleftheriou et al. (U.S. Patent Publication 2023/0301586 A1) teaches the method involves deriving insomnia profiles from biosignal timeseries collected by a wearable device across two time periods, selecting a treatment pathway based on the first profile, and evaluating the treatment’s effectiveness by comparing the first and second profiles.
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/K.R.L./Examiner, Art Unit 3685
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