DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
The office action is in response to the claims filed on February 4, 2026 for the application filed July 29, 2025 which claims priority to a provisional application filed on January 31, 2020. Claims 1-2, 6-7, 13, 15, 21-22, 24, 26, 29, 32, 35, 38-39, 50, 52, 53, 57, 69-70, 72-74, 78-81 and 83-84 are currently pending and have been examined.
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 1-2, 6-7, 13, 15, 21-22, 24, 26, 29, 32, 35, 38-39, 50, 52, 53, 57, 69-70, 72-74, 78-81 and 83-84 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.
Eligibility Step 1:
Under step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, claims 1-2, 6-7, 13, 15, 21-22, 24, 26, 29, 32, 35, 38-39, 50, 52, 53, 57, 69-70, 72-74, 78-81 and 83-84 are directed towards a method (i.e. a process), which is a statutory category. Since the claims are directed toward statutory categories, it must be determined if the claims are directed towards a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea). In the instant application, the claims are directed towards an abstract idea.
Eligibility Step 2A, Prong One:
Under step 2A, prong one of the 2019 Revised Patent Subject Matter Eligibility Guidance, independent claims 1 and 78 are determined to be directed to an judicial exception because an abstract idea is recited in the claims which fall within the subject matter groupings of abstract ideas.
The abstract idea (identified in bold) recited in claim 1 is identified as:
A method of analyzing data related to use of a respiratory therapy system by a user during a sleep session, comprising:
receiving a first type of data related to the use of the respiratory therapy system by the user during the sleep session;
determining a first value of a first parameter related to the use of the respiratory therapy system by the user based at least in part on the first type of data;
identifying a desired second type of data, based at least in part on detecting, from the first type of data, an indicator of a medical condition and determining that the first type of data is insufficient to characterize the medical condition;
transmitting to the user a request for consent to receive the second type of data;
in response to receiving consent from the user, receiving the second type of data; and
determining, based at least in part on determining an inspiration/expiration ratio of the user during the sleep session based at least in part on the second type of data, (i) a second value of the first parameter, (ii) a value of a second parameter, or (iii) both (i) and (ii).
The identified limitations of “transmitting to the user a request for consent to receive the second type of data” and “in response to receiving consent from the user, receiving the second type of data” fall within the subject matter grouping of certain methods of organizing human activity and the sub grouping of commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations). Requesting consent from a user to receive data and receiving consent from the user is a commercial and legal interaction between two parties to enter into a legal obligation/agreement. For example, Regulation (EU) 2016/679 (General Data Protection Regulation) requires transmitting a request for consent to a user and receiving consent from a user in order to transmit and process data from a user device to a controller/processor (i.e. a company).
The identified limitations of “determining a first value…”, “identifying a desired second type of data based at least in part on detecting, from the first type of data, an indicator of a medical condition and determining that the first type of data is insufficient to characterize the medical condition” and " determining, based at least in part on the second type of data, (i) a second value of the first parameter, (ii) a value of a second parameter, or (iii) both (i) and (ii)” fall within the subject matter grouping of mental processes. The determining a first value, identifying a desired second type of data and determining a second value based on a determined inspiration/expiration ratio can be performed in the human mind using observations, evaluations, judgments and opinions. If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.
Accordingly, independent claim 1 recites an abstract idea under step 2A, prong one.
The abstract idea (identified in bold) recited in claim 78 is identified as:
A method of analyzing data related to use of a respiratory therapy system by a user during a current sleep session, comprising:
storing a plurality of historical values of a first parameter related to the user;
receiving a first type of data related to the user during the current sleep session;
determining a current value of the first parameter based at least in part on the first type of data;
comparing the current value of the first parameter and the plurality of historical values of the first parameter;
in response to the comparison between the current value of the first parameter and the plurality of historical values of the first parameter satisfying a threshold,
identifying a desired second type of data;
transmitting to the user a request for consent to receive the second type of data;
in response to receiving content from the user, receiving the second type of data; and
determining, based at least in part on determining an inspiration/expiration ratio of the user during the sleep session based at least in part on the second type of data, (i) a second value of the first parameter, (ii) a value of a second parameter, or (iii) both (i) and (ii).
The identified limitation of “transmitting to the user a request for consent to receive the second type of data” and “in response to receiving content from the user, receiving the second type of data” falls within the subject matter grouping of certain methods of organizing human activity related and the sub grouping of commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations). Requesting consent from a user to receive data and receiving consent from the user is a commercial and legal interaction between two parties to enter into a legal obligation/agreement. For example, Regulation (EU) 2016/679 (General Data Protection Regulation) requires transmitting a request for consent to a user and receiving consent from a user in order to transmit and process data from a user device to a controller/processor (i.e. a company).
The identified limitations of “determining a current value …”, “comparing the current value of the first parameter and the plurality of historical values of the first parameter” and "in response to the comparison between the current value of the first parameter and the plurality of historical values of the first parameter satisfying a threshold, identifying a desired second type of data” and “determining, based at least in part on determining an inspiration/expiration ratio of the user during the sleep session based at least in part on the second type of data, (i) a second value of the first parameter, (ii) a value of a second parameter, or (iii) both (i) and (ii)” fall within the subject matter grouping of mental processes. The determining of a current value, comparing the current value to historical values, determining if a threshold is satisfied, identifying a desired second type of data in response to the threshold being satisfied and determining a second value based on a determined inspiration/expiration ratio can be performed in the human mind using observations, evaluations, judgments and opinions. If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.
Accordingly, independent claim 78 recites an abstract idea under step 2A, prong one.
Eligibility Step 2A, Prong Two:
Under step 2A, prong two of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether the identified abstract ideas are integrated into a practical application. As detailed below, the additional elements, other than the abstract idea per se, when considered both individually and as an ordered combination, amount to no more than a recitation of: generally linking the abstract idea to a particular technological environment or field of use; insignificant extra-solution activity to the judicial exception; and/or adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract ide, which do not integrate the abstract idea into a practical application.
The additional elements recited in claim 1 are identified in italics as:
A method of analyzing data related to use of a respiratory therapy system by a user during a sleep session, comprising:
receiving a first type of data related to the use of the respiratory therapy system by the user during the sleep session;
determining a first value of a first parameter related to the use of the respiratory therapy system by the user based at least in part on the first type of data;
identifying a desired second type of data, based at least in part on detecting, from the first type of data, an indicator of a medical condition and determining that the first type of data is insufficient to characterize the medical condition;
transmitting to the user a request for consent to receive the second type of data;
in response to receiving consent from the user, receiving the second type of data; and
determining, based at least in part on determining an inspiration/expiration ratio of the user during the sleep session based at least in part on the second type of data, (i) a second value of the first parameter, (ii) a value of a second parameter, or (iii) both (i) and (ii).
The additional limitation of “receiving… data” is determined to be no more than insignificant extra-solution activity to the judicial exception under MPEP §2106.05(g). Receiving data is mere necessary data gathering which does not meaningfully limit the process of determining types of desired data to be received for which consent is requested or determining values.
Accordingly, claim 1 does not recite additional elements which integrate the abstract idea into a practical application.
The additional elements recited in claim 78 are identified in italics as:
A method of analyzing data related to use of a respiratory therapy system by a user during a current sleep session, comprising:
storing a plurality of historical values of a first parameter related to the user;
receiving a first type of data related to the user during the current sleep session;
determining a current value of the first parameter based at least in part on the first type of data;
comparing the current value of the first parameter and the plurality of historical values of the first parameter;
in response to the comparison between the current value of the first parameter and the plurality of historical values of the first parameter satisfying a threshold,
identifying a desired second type of data;
transmitting to the user a request for consent to receive the second type of data;
in response to receiving content from the user, receiving the second type of data; and
determining, based at least in part on determining an inspiration/expiration ratio of the user during the sleep session based at least in part on the second type of data, (i) a second value of the first parameter, (ii) a value of a second parameter, or (iii) both (i) and (ii).
The additional limitations of “storing.. values” and “receiving… data” are determined to be no more than insignificant extra-solution activity to the judicial exception under MPEP §2106.05(g). Storing and receiving values and data is mere necessary data gathering which do not meaningfully limit the process of determining types of desired data to be received for which consent is requested or determining values.
Accordingly, claim 78 does not recite additional elements which integrate the abstract idea into a practical application.
Eligibility Step 2B:
Under step 2B of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether provide an inventive concept by determining if the claims include additional elements or a combination of elements that are sufficient to amount to significantly more than the judicial exception. After evaluation, there is no indication that an additional element or combination of elements 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 limitations reciting storing and receiving data/values is determined to be no more than insignificant extra-solution activity to the judicial exception under MPEP §2106.05(g). Evidence that storing data and receiving data is well-understood, routine and conventional is provided by MPEP §2106.05(d), subsection II.
Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements amounts to an inventive concept.
Dependent Claims:
The dependent claims merely present additional abstract information in tandem with further details regarding the elements from the independent claims and are, therefore, directed to an abstract idea for similar reasons as given above. None of these limitations are deemed to integrate the claims into a practical application or to amount to significantly more than the abstract idea because, as detailed below.
Claim 2 recites limitations which are directed to the abstract idea of mental processes.
Claim 6 merely defines what the first data and first parameters are and is therefore encompassed by the abstract ideas of claim 1. Furthermore, limiting the types of data received to certain types of data is still mere data gathering which does not integrate the abstract idea into a practical application or amount to significantly more.
Claims 7 defines what the second type of data is, which is encompassed by the abstract ideas of claim 1. The transmitting step merely defines purpose of the consent which is encompassed by the abstract idea falling under the grouping of certain methods of organizing human activity. The determining step is determined to be directed to a mental process.
Claim 13 recites generating an alert to the user bases upon determined data which is a method of organizing human activity, such as managing the interactions between people (i.e. a user and a person/company generating the alert based on user data). Additionally, generating a alert based on data can be considered a mental process and providing the alert to a user can be considered the insignificantly extra-solution activity of data outputting.
Claim 15 merely defines the second values and is encompassed by the abstract idea of claim 1.
Claim 21, 22 and 24 further defines the information provided in the request for consent which is encompassed by the abstract ideas of claim 1.
Claim 26 recites the mental processes of determining if data satisfies a threshold and the human activity of transmitting a notification associated with actions for a user. Additionally, determining a notification to provide a user can be considered a mental process and transmitting the notification to a user can be considered the insignificantly extra-solution activity of data outputting.
Claim 29 merely defines what the second data includes and defines the information provided in the request consent which is encompassed by the abstract ideas of claim 1.
Claim 32 recites generating estimations which are directed to a mental process and recited transmitting a request for consent which is directed to certain methods of organizing human activity for similar reasons as detailed with respect to claim 1.
Claim 35 merely defines the second data and is encompassed by the abstract idea of claim 1.
Claim 38 merely defines the second data and second values and second and is encompassed by the abstract idea of claim 1.
Claim 39 merely defines the second data and second values and second and is encompassed by the abstract idea of claim 1.
Claim 50 defines the first type of data and is encompassed by the abstract idea of claim 1. The transmitting request for consent, receiving consent and transmitting a notification to a user recite certain methods of organizing human activity. The analyzing and estimating steps recite mental processes.
Claim 52 defines the first type of data and is encompassed by the abstract idea of claim 1. The transmitting request for consent and receiving consent recite certain methods of organizing human activity. The analyzing to detect step recite mental processes.
Claim 53 merely defines the first type of data and second type of data and is encompassed by the abstract idea of claim 1. The transmitting a request for consent to activate a sensor recites an abstract idea falling subject matter grouping of certain methods of organizing human activity for similar reasons as detailed in claim 1.
Claim 57 merely defines the second type of data and is encompassed by the abstract idea of claim 1.
Claim 69 recites analyzing data associated with users which recites a mental processes. Transmitting requests to each user for consent and receiving consent recites a certain method of organizing human activity for similar reasons as detailed in claim 1. The transmission of requests for consent according to a respective order recites both the mental process of determining the order and the certain method of organizing human activity of a commercial/legal interactions according to specific rules instructions. Receiving data from two or more of the users is the insignificant extra-solution activity of mere data gathering which is well-understood, routine and conventional. Analyzing the data received to determine an optimal order recites a mental process.
Claim 70 merely defines what the optimal order results in and therefore the analysis for determining the optimal order, which is also directed to a mental process.
Claim 72 recites analyzing data to determine an optimal time of day for transmitting requests for consent which recites a mental process.
Claim 73 recites sorting users into populations and determining the optimal order for each population which also recites a mental process.
Claim 74 recites determining a manner in which consent was received to determine the optimal order which also recites a mental process.
Claim 79 and 80 further defines the information provided in the request for consent which is encompassed by the abstract ideas of claim 78.
Claim 81 recites determining a statistical parameter based on historical values which is a mental process. Furthermore, determining averages, medians, running averages and running medians are specific mathematical calculations such that the claim is also directed to mathematical concepts.
Claim 83 recites comparing current values to historical values or a statistical parameter which recites a mental process.
Claim 84 merely defines the comparison and therefore also recites a mental process.
Therefore, whether taken individually or as an ordered combination, 1-2, 6-7, 13, 15, 21-22, 24, 26, 29, 32, 35, 38-39, 50, 52, 53, 57, 69-70, 72-74, 78-81 and 83-84 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1-2, 6-7, 13, 15, 21-22, 24, 26, 29, 38-39, 50, 53 and 57 are rejected under 35 U.S.C. 103 as being unpatentable over Heneghan et al. (U.S. Pub. No. 2016/0270718) in view of Regulation (EU) 2016/679 and Bates (U.S. Pub. No. 2017/0323071).
Regarding claim 1, Heneghan discloses a method of analyzing data related to use of a respiratory therapy system by a user during a sleep session (Abstract, The system (100) may include one or more data sources, such as a non-obtrusive sleep sensor, configured to generate objective sleep measures of the user. The system may also include a fatigue monitoring module, which may be configured to generate an assessment, such as in one or more processors, of the fatigue state of the user based on the data from the one or more data sources. Paragraph [0055], the sleep sensor may be a sensor integrated with a respiratory pressure therapy device from which the user may receive or is receiving CPAP therapy.), comprising:
receiving a first type of data related to the use of the respiratory therapy system by the user during the sleep session (Paragraph [0072], the fatigue monitoring module 110 can be implemented “in the cloud”, i.e. at a remote server connected to the various data sources (data 115 to 170) over a network. Paragraph [0073], In various forms of the present technology, some subset of the data sources (data 115 to 170) are used by the fatigue monitoring module 110 to produce the estimated or predicted fatigue state 180.);
determining a first value of a first parameter related to the use of the respiratory therapy system by the user based at least in part on the first type of data (Paragraph [0073], In various forms of the present technology, some subset of the data sources (data 115 to 170) are used by the fatigue monitoring module 110 to produce the estimated or predicted fatigue state 180. Paragraph [0163], The fatigue assessment 180 could be the fatigue index value f. Paragraph [0141], a number of input parameters greater than or less than three are linearly combined by the linear classifier 4000 to generate the fatigue index f. Also see paragraph [0085], Objective sleep measures 120 extracted from the sleep sensor signal(s).);
identifying a desired second type of data (Paragraph [0144], establish the “best” set of input parameters and corresponding coefficients from a training set. Also see paragraph [0114], The fatigue monitoring and management system 100 can gain more insight if it is supplied with work pattern information 135 and paragraph [0048], Snoring measures obtained from audio processing may be combined with sleep sensor data processing and other measures of sleep-disordered breathing to improve accuracy.),
Paragraph [0073], In various forms of the present technology, some subset of the data sources (data 115 to 170) are used by the fatigue monitoring module 110 to produce the estimated or predicted fatigue state 180.); and
determining, based at least in part on determining an inspiration/expiration ratio of the user adding the sleep session based at least in part on the second type of data, (i) a second value of the first parameter, (ii) a value of a second parameter, or (iii) both (i) and (ii) (Paragraph [0073], In various forms of the present technology, some subset of the data sources (data 115 to 170) are used by the fatigue monitoring module 110 to produce the estimated or predicted fatigue state 180. Paragraph [0163], The fatigue assessment 180 could be the fatigue index value f. Paragraph [0141], a number of input parameters greater than or less than three are linearly combined by the linear classifier 4000 to generate the fatigue index f. Paragraph [0093], a major contributor to fatigue is the presence of sleep-disordered breathing (SDB). Paragraph [0095], Additionally, snoring is an SDB measure 125 that may be quantified. Paragraph [0104], A further advantage of the simultaneous acquisition of snoring audio signals and vibrations from a movement sensor, is that since snoring is much more common during inspiration, the snoring may be used to decide whether a movement signal represents inspiration or expiration. A further clue can be obtained by evaluating the inspiration to expiration ratio (which is typically 1:2 in normal subjects).).
Heneghan does not appear to explicitly disclose but Regulation (EU) 2016/679 teaches that it was old and well known in the art of processing of personal data at the time of the filing to transmit to the user a request for consent to receive the second type of data; and in response to receiving consent from the user, receiving the second type of data (I. 119/38, article 9, Paragraph 1, Processing of data concerning health shall be prohibited. Paragraph 2. Paragraph 1 shall not apply if one of the following applies: (a) the data subject has given explicit consent to the processing of those personal data for one or more specified purposes. Also see I. 119/6, (32), Consent should be given by a clear affirmative act establishing a freely given, specific, informed and unambiguous indication of the data subject's agreement to the processing of personal data relating to him or her, such as by a written statement, including by electronic means, or an oral statement.).
Therefore, it would have been obvious to one of ordinary skill in the art of processing of personal data at the time of the filing to modify the method of Heneghan to include transmitting to the user a request for consent to receive the second type of data; and in response to receiving consent from the user, receiving the second type of data, as taught by Regulation (EU) 2016/679, in order to comply with Regulation (EU) 2016/679.
Heneghan further discloses detecting, from the first type of data, an indicator of a medical condition (Paragraph [0093], In addition to poor sleep hygiene (such as self-imposed sleep restrictions), a major contributor to fatigue is the presence of sleep-disordered breathing (SDB). Paragraph [0095], snoring is an SDB measure 125 that may be quantified.), but does not appear to explicitly disclose that the desired second type of data is identified based at least in part on detecting, from the first type of data, an indicator of a medical condition and determining that the first type of data is insufficient to characterize the medical condition.
Bates teaches that it was old and well known in the art of remote medical diagnostics at the time of the filing to identify a desired type of data to be requested from a patient based at least in part on detecting, from a first type of data, an indicator of a medical condition and determining that the first type of data is insufficient to characterize the medical condition (Bates, paragraph [0066], At step 308, based on, for example, system-patient interaction, symptom keyword trustability, symptom relationship weight factors, and symptom association scores, medical instrument measurements, medical instrument measurement accuracy scores, and other patient data, diagnosis probabilities are assigned to the potential illnesses. Paragraph [0067], At step 310, based on the diagnoses probabilities, additional patient input, e.g., answers to follow-up medical questions and/or medical instrument measurements are requested. Paragraph [0069], At step 313, it is determined whether the diagnosis probabilities for a group of illnesses having the highest diagnosis probabilities (i.e., top illnesses) meet a threshold. Otherwise, if the threshold is not met at step 313, then process 300 may return to step 310 to request additional patient responses and/or medical instrument measurement data.) to ensure reliable remote patient diagnosis so that physicians can allocate patient time more efficiently and, in some instances, allow individuals to manage their own health, thereby, reducing health care costs (Bates, Paragraph [0006]).
Therefore, it would have been obvious to one of ordinary skill in the art of remote medical diagnostics at the time of the filing to modify the method of Heneghan such that the desired second type of data is identified based at least in part on detecting, from the first type of data, an indicator of a medical condition and determining that the first type of data is insufficient to characterize the medical condition, as taught by Bates, in order to ensure reliable remote patient diagnosis so that physicians can allocate patient time more efficiently and, in some instances, allow individuals to manage their own health, thereby, reducing health care costs
Regarding claim 2, Heneghan further discloses wherein the desired second type of data is further based at least in part on the determined first value of the first parameter, the first type of data, an accuracy of the determined first value of the first parameter, or any combination thereof (Paragraph [0048], Snoring measures obtained from audio processing may be combined with sleep sensor data processing and other measures of sleep-disordered breathing to improve accuracy. Also see Bates, paragraph [0069], At step 313, it is determined whether the diagnosis probabilities for a group of illnesses having the highest diagnosis probabilities (i.e., top illnesses) meet a threshold). Otherwise, if the threshold is not met at step 313, then process 300 may return to step 310 to request additional patient responses and/or medical instrument measurement data.).
Regarding claim 6, Heneghan further discloses wherein the first type of data is physiological data associated with the user during the sleep session, and wherein the first parameter is a sleep-related parameter for the user during the sleep session (Paragraph [0085], Objective sleep measures 120 extracted from the sleep sensor signal(s) may include sleep statistics such as the duration of sleep, quality of sleep (amount of actual sleep during the expected sleep period), and the number of interruptions of sleep. The time-to-bed as well as wake-up time may also be extracted from the sleep sensor signal(s). REM sleep periods may also be extracted, along with deep sleep (slow wave sleep) periods. As mentioned above, REM sleep duration indicates cognitive recharge, while deep sleep duration indicates physical recharge. The relative ratios of deep sleep, light sleep, and REM sleep duration may also be extracted. Another sleep statistic is sleep inertia, representing the expected tiredness level immediately following waking up, particularly in the case where a user wakes directly from deep sleep. Wake after sleep onset (WASO) provides an aggregated estimate of fragmented sleep, which may be used in conjunction with the number of interruptions detected. Time to sleep (sleep latency) and sleep stage upon waking may also be extracted.).
Regarding claim 7, Heneghan as modified by Regulation (EU) 2016/679 further discloses wherein the second type of data is personal data associated with the user (Data sources 115, 118, 120, 125, 130, 145, 135 and 155 are all construed as personal data.), and wherein the method further comprises:
transmitting to the user a request for consent to analyze the personal data to sort the user into one or more populations of users (Regulation (EU) 2016/679, I. 119/38, article 9, Paragraph 1, Processing of data concerning health shall be prohibited. Paragraph 2. Paragraph 1 shall not apply if one of the following applies: (a) the data subject has given explicit consent to the processing of those personal data for one or more specified purposes. Also see I. 119/6, (32), Consent should be given by a clear affirmative act establishing a freely given, specific, informed and unambiguous indication of the data subject's agreement to the processing of personal data relating to him or her, such as by a written statement, including by electronic means, or an oral statement.); and
determining a modified value of the first parameter based at least in part on the one or more populations of users into which the user is sorted, the modified value of the first parameter being more accurate than the first value of the first parameter (Paragraph [0203], The user's fatigue level is compared to population data 150, providing such parameters as the user's “real sleep age”. Researchers in the field of sleep medicine have drawn up a profile of the likely distribution of sleep stages as a function of age. FIG. 5 contains a chart 5000 (from Shambroom and Fabregas) representing an example distribution of sleep stages as a function of age. By comparing a user's actual distribution of sleep stages against the population distribution, a sleep age can be determined for the user. Paragraph [0051], the fatigue monitoring module may be further configured to generate the assessment of the fatigue state of the user based on a population database comprising data from the one or more data sources from multiple users of the system. Paragraph [0048], the fatigue monitoring module may be further configured to generate the assessment of the fatigue state of the user based on a population database comprising data from the one or more data sources from multiple users of the system.).
Regarding claim 13, Heneghan further discloses generating, based at least in part on (i) the value of first parameter, (ii) the one or more populations of users into which the user is sorted, or (iii) both (i) and (ii), an alert related to the user (Paragraph [0169], The user information module 185 generates and provides a report for the user containing the fatigue state assessment and, optionally, the sleep statistics forming part of the objective sleep measures. Paragraph [0173], The fatigue assessment 180 can be used to make recommendations to the user. As an example, consider a user that has had a poor night's sleep, and that objective sleep measures 120 are collected via a non-obtrusive sleep sensor as mentioned above. During the day, fatigue-related data is captured from the user, such as objective fatigue measurements 130, environmental data 160, physical activity data 115, location data, and diet data. This data is analysed by the fatigue monitoring module 110 to generate an assessment 180 of fatigue state. The user information module 185 estimates and recommends an “ideal time to sleep” for that day based on this assessment. The user will thus know in advance what this time is and can have the option to obey or ignore the recommendation. This adherence or compliance data itself becomes an input for further analysis. Alerts may be given in advance of the recommended ideal sleep time (i.e., a ‘time to sleep’ reminder, e.g. delivered via the user's smartphone). Over time, the fatigue monitoring and management system develops into an individual personalised “ideal sleep time” recommender. Also see paragraph [0072].).
Regarding claim 15, Heneghan further discloses wherein the second value of the first parameter is more accurate than the first value of the first parameter, and wherein the second value is based at least in part on (i) the first type of data, (ii) the first value of the first parameter, or (iii) both (i) and (ii) (Paragraph [0048], Snoring measures obtained from audio processing may be combined with sleep sensor data processing and other measures of sleep-disordered breathing to improve accuracy. Also see paragraph [0114].).
Regarding claim 21, Heneghan does not appear to explicitly disclose, but Regulation (EU) 2016/679 teaches that it was old and well known in the art of processing of personal data at the time of the filing to include transmitting to the user an explanation of a potential use for the second type of data (I. 119/38, article 9, Paragraph 1, Processing of data concerning health shall be prohibited. Paragraph 2. Paragraph 1 shall not apply if one of the following applies: (a) the data subject has given explicit consent to the processing of those personal data for one or more specified purposes. Also see I. 119/6, (32), Consent should be given by a clear affirmative act establishing a freely given, specific, informed and unambiguous indication of the data subject's agreement to the processing of personal data relating to him or her, such as by a written statement, including by electronic means, or an oral statement.).
Therefore, it would have been obvious to one of ordinary skill in the art of processing of personal data at the time of the filing to modify the method of Heneghan to include transmitting to the user an explanation of a potential use for the second type of data, as taught by Regulation (EU) 2016/679, in order to comply with Regulation (EU) 2016/679.
Regarding claim 22, Heneghan as modified by Regulation (EU) 2016/679 does not appear to explicitly disclose wherein the explanation of the potential use for the second type of data includes an indication that the second type of data enables the second value of the first parameter to be determined with a larger confidence interval than the first value of the first parameter.
However, claim 22 is determined to be nonfunctional descriptive material without patentable weight as this limitation is directed to conveying a message or meaning to a human reader.
Regarding claim 24, Heneghan further discloses wherein the explanation of the potential use for the second type of data includes an indication of a correlation between the first value of first parameter and a potential medical condition (Determined to be nonfunctional descriptive material without patentable weight as this limitation is directed to conveying a message or meaning to a human reader.), and wherein the method further comprises estimating, based at least in part on the second type of data, a percentage likelihood that the user has the medical condition (Paragraph [0163], the fatigue monitoring module 110 may map the computed fatigue index value f using one or more thresholds to one of a set of fatigue states. Such fatigue states may represent a more easily interpreted fatigue assessment 180. In one implementation of such a mapping, the set of possible fatigue states and corresponding thresholds is: “All OK”—whereby no worrisome fatigue state is detected (f<0.5). “At risk”—whereby the user has an elevated risk of fatigue (0.5<f<0.8). In this state, an indication of how this was derived, and the severity of same, is provided. For example, a user may be exhibiting the early stages of sleep deficit, and can correct via behavioral change before a state of chronic fatigue is experienced. “Acute fatigue”—whereby a relatively short-term (in the order of 1-2 day) sleep restriction or very poor quality sleep is flagging likely acute fatigue (0.8<f<1). Based on work pattern information 135 or other data, this may be deemed to be a high-risk state (e.g., user is to perform a safety-critical function, or a user-reported planned long drive behind the wheel of a car etc.). This may initially manifest as physical fatigue, followed by increased mental fatigue (with associated cognitive impairment). “Chronic fatigue” (0.5<f<0.8 for several days) can be as a result of longer term sleep restriction or general poor sleep hygiene (including poor diet, and/or a mix of caffeinated/energy drinks to stay awake, followed by alcohol and sleeping tablets to sleep)/insomnia. An underlying SDB condition can also be a root cause.).).
Regarding claim 26, Heneghan further discloses wherein the method further comprises, in response to the estimated percentage likelihood satisfying a threshold, transmitting a notification associated with the medical condition, a suggested treatment routine, a suggested appointment with a healthcare provider, or any combination thereof (Paragraph [0056], In some cases, the method may further include generating or making a recommendation to the user based on the fatigue state assessment, such as via an output device associated with a processor. In some cases, the recommendation may be an ideal time for the user to go to sleep. In some cases, the recommendation may be an optimal time for the user to wake up. In some cases, the user may be undergoing CPAP therapy, and the recommendation may be a recommendation to improve or change the CPAP therapy. Paragraph [0163], the fatigue monitoring module 110 may map the computed fatigue index value f using one or more thresholds to one of a set of fatigue states. Such fatigue states may represent a more easily interpreted fatigue assessment 180. In one implementation of such a mapping, the set of possible fatigue states and corresponding thresholds is: “All OK”—whereby no worrisome fatigue state is detected (f<0.5). “At risk”—whereby the user has an elevated risk of fatigue (0.5<f<0.8). In this state, an indication of how this was derived, and the severity of same, is provided. For example, a user may be exhibiting the early stages of sleep deficit, and can correct via behavioral change before a state of chronic fatigue is experienced. “Acute fatigue”—whereby a relatively short-term (in the order of 1-2 day) sleep restriction or very poor quality sleep is flagging likely acute fatigue (0.8<f<1). Based on work pattern information 135 or other data, this may be deemed to be a high-risk state (e.g., user is to perform a safety-critical function, or a user-reported planned long drive behind the wheel of a car etc.). This may initially manifest as physical fatigue, followed by increased mental fatigue (with associated cognitive impairment). “Chronic fatigue” (0.5<f<0.8 for several days) can be as a result of longer term sleep restriction or general poor sleep hygiene (including poor diet, and/or a mix of caffeinated/energy drinks to stay awake, followed by alcohol and sleeping tablets to sleep)/insomnia. An underlying SDB condition can also be a root cause.). Also see paragraph [0177].).
Regarding claim 29, Heneghan further discloses wherein the second type of data includes an electronic medical record of the user (Paragraph [0136], The fatigue monitoring and management system 100 may also gather information from the user on conditions/disease states that are related to fatigue, including anxiety, headache and nasal congestion, asthma, anemia (e.g., related to menstruation), depression, arthritis, diabetes, and sleep apnea.), and wherein the explanation of the potential use for the electronic medical record of the user includes an indication that the electronic medical record of the user enables identification of a desired additional parameter based on the first type of data to be identified (Determined to be nonfunctional descriptive material without patentable weight as this limitation is directed to conveying a message or meaning to a human reader.).
Regarding claim 38, Heneghan further discloses wherein the second type of data is indicative of one or more characteristics of the respiratory therapy system, and wherein the second value of the first parameter is based at least in part on the one or more characteristics of the respiratory therapy system (Paragraph [0084], In implementations in which a user is receiving CPAP therapy for obstructive sleep apnea from an RPT device via a patient interface such as a mask, the sleep sensors may be the sensors integrated with the RPT device or patient interface, such as pressure or flow rate sensors. Paragraph [0073], In various forms of the present technology, some subset of the data sources (data 115 to 170) are used by the fatigue monitoring module 110 to produce the estimated or predicted fatigue state 180.).
Regarding claim 39, Heneghan further discloses wherein the respiratory therapy system includes a respiratory therapy device, a conduit, and an interface, the user being connected to the respiratory therapy device via the conduit and the interface, and wherein one or more characteristics of the respiratory therapy system include (i) one or more characteristics of a conduit, (ii) one or more characteristics of the interface, or (iii) both (i) and (ii), and wherein the second value of the first parameter is more accurate than the first value of the first parameter (Paragraphs [0017]-[0019], Continuous Positive Airway Pressure (CPAP) therapy has been used to treat Obstructive Sleep Apnea (OSA). The hypothesis is that continuous positive airway pressure acts as a pneumatic splint and may prevent upper airway occlusion by pushing the soft palate and tongue forward and away from the posterior oropharyngeal wall. The application of a supply of air at positive pressure to the entrance of the airways of a patient is facilitated by the use of a patient interface, such as a nasal mask, a full-face mask, or nasal pillows. The air at positive pressure may be supplied to the airway of a patient by a respiratory pressure therapy (RPT) device such as a motor-driven blower. The outlet of the RPT device is connected via a flexible delivery conduit to a patient interface as described above. Paragraph [0084], In implementations in which a user is receiving CPAP therapy for obstructive sleep apnea from an RPT device via a patient interface such as a mask, the sleep sensors may be the sensors integrated with the RPT device or patient interface, such as pressure or flow rate sensors. Paragraph [0073], In various forms of the present technology, some subset of the data sources (data 115 to 170) are used by the fatigue monitoring module 110 to produce the estimated or predicted fatigue state 180. Paragraph [0144], Multiple linear regression is a supervised way to establish the “best” set of input parameters and corresponding coefficients from a training set. In a multiple linear regression approach, the fatigue monitoring and management system 100 learns the “best” set of input parameters and corresponding coefficients to estimate or predict the fatigue state of a person.).
Regarding claim 50, Heneghan further discloses wherein the first type of data is respiration data associated with the user of the respiratory therapy system by the user during the sleep session (Paragraph [0055], In some cases, the sleep sensor may be a sensor integrated with a respiratory pressure therapy device from which the user may receive or is receiving CPAP therapy.), and wherein the method further comprises:
transmitting, to the user, a request for consent to analyze the respiration data (See claim 1 above) to determine an inspiration/expiration ratio of the user during the sleep session (Paragraph [0104], A further advantage of the simultaneous acquisition of snoring audio signals and vibrations from a movement sensor, is that since snoring is much more common during inspiration, the snoring may be used to decide whether a movement signal represents inspiration or expiration. A further clue can be obtained by evaluating the inspiration to expiration ratio (which is typically 1:2 in normal subjects).);
in response to receiving consent from the user, analyzing the respiration data to determine the inspiration/expiration ratio of the user during the sleep session (Paragraph [0093], a major contributor to fatigue is the presence of sleep-disordered breathing (SDB). Paragraph [0104] evaluating the inspiration to expiration ratio.);
estimating a percentage likelihood that the user has a medical condition based at least in part on the determined inspiration/expiration ratio of the user (Paragraph [0163], the fatigue monitoring module 110 may map the computed fatigue index value f using one or more thresholds to one of a set of fatigue states. Such fatigue states may represent a more easily interpreted fatigue assessment 180. In one implementation of such a mapping, the set of possible fatigue states and corresponding thresholds is: “All OK”—whereby no worrisome fatigue state is detected (f<0.5). “At risk”—whereby the user has an elevated risk of fatigue (0.5<f<0.8). In this state, an indication of how this was derived, and the severity of same, is provided. For example, a user may be exhibiting the early stages of sleep deficit, and can correct via behavioral change before a state of chronic fatigue is experienced. “Acute fatigue”—whereby a relatively short-term (in the order of 1-2 day) sleep restriction or very poor quality sleep is flagging likely acute fatigue (0.8<f<1). Based on work pattern information 135 or other data, this may be deemed to be a high-risk state (e.g., user is to perform a safety-critical function, or a user-reported planned long drive behind the wheel of a car etc.). This may initially manifest as physical fatigue, followed by increased mental fatigue (with associated cognitive impairment). “Chronic fatigue” (0.5<f<0.8 for several days) can be as a result of longer term sleep restriction or general poor sleep hygiene (including poor diet, and/or a mix of caffeinated/energy drinks to stay awake, followed by alcohol and sleeping tablets to sleep)/insomnia. An underlying SDB condition can also be a root cause.).); and
transmitting a notification to (i) the user, (ii) a healthcare provider, or (iii) both (i) and (ii), the notification indicating the percentage likelihood that the user has the medical condition (Paragraph [0169], The user information module 185 generates and provides a report for the user containing the fatigue state assessment and, optionally, the sleep statistics forming part of the objective sleep measures.).
Regarding claim 57, Heneghan further discloses wherein the second type of data is related to (i) use of the respiratory therapy system by the user, (ii) activities of the user occurring outside of the sleep session, or (iii) both (i) and (ii) (Paragraph [0181], An increase in fatigue index in a CPAP patient can suggest less than optimal compliance with prescribed CPAP therapy. Also see paragraphs [0127].).
Claims 32 and 35 are rejected under 35 U.S.C. 103 as being unpatentable over Heneghan et al. (U.S. Pub. No. 2016/02707185) in view of Regulation (EU) 2016/679, Bates (U.S. Pub. No. 2017/0323071) and Colla et al. (U.S. Patent No. 6,091,973).
Regarding claim 32, Heneghan as modified by Regulation (EU) 2016/679 further disclose transmitting to the user a request for consent to analyze the second type of data to determine whether the user is asleep (See claim 1 above, consent to receive the second type of data includes the specified purpose and the specified purpose is intended use.) and determining when a user is asleep (See fig, 2 and paragraph [0082]).
Heneghan does not appear to explicitly disclose, but Colla teaches that it was old and well known in the art of sleep apnea at the time of the filing to include generating, based on the first type of data, a first estimation of a number of respiratory events per hour experienced by the user (Colla, column 1, lines 20-51, PSG involves the measurement of sleep and respiratory variables including EEG, EOG, chin EMG, ECG, respiratory activity, nasal airflow, chest and abdominal movements, abdominal effort and oxygen saturation. The data gathered leads to a calculation of the Respiratory Disturbance Index (RDI) which is the average number of arousals per hour due to respiratory disturbance. To detect an arousal, the EEG, once displayed, requires interpretation either visually by a skilled operator or automatically by a computer-based analysis system.);
and
generating, based on the first type of data and a determination of whether user is asleep, a second estimation of the number of respiratory events per hour experienced by the user, the second estimation being more accurate than the first estimation (Colla, column 7, lines 9-19, the correlation of skin conductance with one or more other physiological variables (whether associated with the sympathetic nervous system or not) or any two or more sympathetic physiological variables (for example blood pressure and heart rate), is an accurate determinant of the occurrence of an arousal associated with an A/H episode. Further, it is believed that the correlation of two sympathetic physiological variables, not necessarily including skin conductance, and for example heart rate and blood pressure, also is an accurate determinant.) to determine of an index of sleep quality (Colla, column 2, lines 3-6).
Therefore, it would have been obvious to one of ordinary skill in the art of sleep apnea at the time of the filing modify the method of Heneghan to include the limitations above, as taught by Colla, in order to determine of an index of sleep quality.
Regarding claim 35, Heneghan further discloses wherein the second type of data includes (i) movement data indicative of movement of the user during the sleep session, (ii) movement data indicative of movement of a component of the respiratory therapy system during the sleep session, (iii) audio data indicative of noise generated by the user during the sleep session, (iv) audio data indicative of noise generated by the respiratory therapy system during the sleep session, or (v) any combination of (i)-(iv) (Paragraphs [0083]-[0106]).
Claim 52 is rejected under 35 U.S.C. 103 as being unpatentable over Heneghan et al. (U.S. Pub. No. 2016/02707185) in view of Regulation (EU) 2016/679, Bates (U.S. Pub. No. 2017/0323071) and McMahon (U.S. Pub No. 2018/0256069).
Regarding claim 52, Heneghan does not appear to explicitly disclose wherein the respiratory therapy system includes a microphone (Paragraph [0052], Optionally, the sleep sensor data may be combined with audio data from an audio sensor to obtain the snoring measure.), and wherein the method further comprises:
transmitting, to the user, a request for consent to receive audio data from the microphone (see claim 1 above);
in response to receiving consent from the user (see claim 1 above), receiving the audio data from the microphone (Paragraph [0052], Optionally, the sleep sensor data may be combined with audio data from an audio sensor to obtain the snoring measure.); and
analyzing the audio data to detect Paragraph [0052], Optionally, the sleep sensor data may be combined with audio data from an audio sensor to obtain the snoring measure. Some subset of the data sources (data 115 to 170) are used by the fatigue monitoring module 110 to produce the estimated or predicted fatigue state 180.).
Heneghan does not appear to explicitly disclose, but McMahon teach that it was old and well known in the art of detection, diagnosis and prevention of respiratory-related conditions at the time of the filing to analyze audio data to detect coughing, (McMahon, paragraph [0118], signal type process 140 may be applied to characterize portions of input signal 12 that correspond with one or more exemplary signal types for further processing. For example, process 140 may characterize frames of the input signal 12 based upon their detected characteristics. Paragraph [0119], As illustrated in FIGS. 6 A-D portions of signal 12 associated with each of quiet breathing (FIG. 6H), snoring (FIG. 6A, 6C), apnea snoring (FIG. 6B), and coughing (FIG. 6D) may be distinguishable from each other in terms of frequency, time and amplitude.) to assist in raising awareness of SDB conditions (McMahon, paragraph [0019]).
Therefore, it would have been obvious to one of ordinary skill in the art of detection, diagnosis and prevention of respiratory-related conditions at the time of the filing to modify the method of Heneghan to include analyzing the audio data to detect coughing or wheezing from the user, the detected coughing or wheezing aiding in estimating the percentage likelihood that the user has the medical condition, as taught by McMahon, in order to assist in raising awareness of SDB conditions
Claim 53 is rejected under 35 U.S.C. 103 as being unpatentable over Heneghan et al. (U.S. Pub. No. 2016/02707185) in view of Regulation (EU) 2016/679, Bates (U.S. Pub. No. 2017/0323071) and Gray (Always On: Privacy Implications of Microphone-Enabled Devices).
Regarding claim 53, Heneghan further discloses wherein the respiratory therapy system includes a first sensor configured to generate the first type of data, and a second sensor configured to generate the second type of data,
Heneghan does not appear to explicitly disclose wherein the method further comprises transmitting to the user a request for consent to activate the second sensor and to generate the second type of data.
Gray teach that it was old and well known in the art of microphone enabled devices at the time of the filing to transmitting to the user a request for consent to activate the second sensor and to generate the second type of data (Gray, page 9, (4) The core principles of trust and informed consent dictate that users should understand when a device is on and recording. Page 8, V. , manufacturers should emphasize user awareness, consent-based features, and control over the device.) to alleviate privacy concerns and build consumer trust in the ways that data is collected, stored, and analyzed (Gray, page 3).
Therefore, it would have been obvious to one of ordinary skill in the art of microphone enabled devices at the time of the filing to modify the method of Heneghan to include transmitting to the user a request for consent to activate the second sensor and to generate the second type of data
Claims 69 and 78 and claims depending therefrom are determined to not be disclosed, taught or suggested by the prior art in the context of respiratory therapy systems for acquiring user consent to receive data related to sleep sessions.
Response to Arguments
Applicant's arguments filed February 4, 2026 regarding claims 1-2, 6-7, 13, 15, 21-22, 24, 26, 29, 32, 35, 38-39, 50, 52, 53, 57, 69-70, 72-74, 78-81 and 83-84 being rejected under 35 U.S.C. §101 have been fully considered but they are not persuasive.
Applicant argues that the claim as amended recite implementation specific signal/physiology-processing operations tied to a respiratory therapy system in session which meaningfully limits the claims to a practical application under step 2A, prong two.
In response, step 2A, prong two considers if an additional element (or combination of elements) integrates the exception into a practical application by applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. In the instant claims, the argued elements are all determined to be directed to the judicial exception and are not additional elements and therefore cannot integrated the exception into a practical application.
Applicant argues under step 2B that the order combination supplies an inventive concept beyond well-understood, routine and conventional extra-solution activity.
In response, the argued elements are all determined to be directed to an abstract idea and are not additional elements. The inventive concept cannot be furnished by the abstract idea itself.
Applicant's arguments filed February 4, 2026 regarding claims 1-2, 6-7, 13, 15, 21-22, 24, 26, 29, 32, 35, 38-39, 50, 52, 53 and 57 being rejected under 35 U.S.C. §103 have been fully considered but they are not persuasive or moot in view of the new grounds of rejection.
Arguments pertaining to the medical condition-triggered, insufficiency-driven selection of additional data in the context of an in-session respiratory therapy workflow are moot in view of the new grounds of rejection.
Applicant argues that Heneghan does not teach or suggest determining the inspiration/expiration ratio during a sleep session based on the second type of data and basing the parameter determinations on the computed ration.
In response, this is disclosed by Heneghan in paragraph [0104] which uses movement data to evaluate the inspiration to expiration ratio of a user during sleep in order to quantify snoring used to determine a fatigue state of a user. When combined with EU regulations, the movement data would be consented data.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/DEVIN C HEIN/Examiner, Art Unit 3686
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