DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
35 U.S.C 112(b)
The applicant’s amended response filed September 2, 2026 to the prior 112(b) rejection is persuasive, as it removes the grounds for rejection; thus, the 112(b) rejection is withdrawn.
35 U.S.C 103
Applicant’s remarks filed September 2, 2026, pages 8-14, have been considered in view of the amended claims.
Applicant argues that Molony's method 1300 determines only a single pairwise relationship rather than separate correlations corresponding to respective activity indicators. Applicant further contends that method 1400 prioritizes custom messages using parameter-pair weights rather than generating a visual presentation indicating the claimed plurality of correlations.
The Examiner respectfully disagrees. Under MPEP § 2111, the amended claims require multiple correlations corresponding to respective activity indicators, but do not require correlations for every available indicator or different measurement types. Molony identifies multiple activity measurements ([0087]), permits determining a plurality of parameters ([0211-0212]), and determines parameter relationships through statistical correlation ([0214-0217]; Fig. 13). A person of ordinary skill in the art would have had reason to apply Molony's existing correlation technique separately to selected activity measurements to identify their respective relationships with sleep scores, thereby providing more activity-specific behavioral feedback consistent with Molony's custom-message functionality ([0222], [0241]). This would predictably produce identifiable correlations using the disclosed technique.
Molony further generates visual indications of determined relationships ([0222]) and communicates them through a display device ([0227]). Presenting the resulting correlation indications together would enable users to distinguish activity-specific relationships and obtain more complete feedback using Molony's existing display functionality.
Applicant argues that Kayyali is limited to reactive, session-level PAP adjustments and does not determine the claimed plurality of individualized sleep/activity correlations or suggest using those correlations to determine respiratory-therapy.
The Examiner respectfully disagrees. Kayyali describes using multi-night patient data, calculated conditions, and trends to evaluate treatment effectiveness and determine PAP adjustments (Kayyali, Fig. 7 and accompanying description). Molony describes determining sleep/activity relationships through statistical correlation ([0217]) and using determined relationships to predict health outcomes and generate feedback ([0221]–[0222]). A person of ordinary skill in the art would have had reason to incorporate Molony's separately determined sleep/activity correlations into Kayyali's treatment-setting analysis to provide activity-specific information when evaluating sleep outcomes and determining therapy modifications, consistent with Kayyali's objective of using richer longitudinal data for titration. Because Kayyali already analyzes multiple patient measurements to determine therapy settings, incorporating Molony's calculated relationships as additional inputs would predictably extend that analysis with a reasonable expectation of successful implementation.
Applicant argues that the combination lacks a specific reason to modify Molony to determine separate activity-indicator correlations and use the resulting plurality for both visual presentation and respiratory-therapy modification.
The Examiner respectfully disagrees. Claim 1 requires the determined plurality of correlations to inform both operations. Molony permits determining a plurality of parameters and analyzing their relationships through correlation, paragraphs 0211-0212 and 0217. Applying that analysis separately to selected activity measurements would distinguish their respective associations with sleep outcomes. Presenting the resulting indications together would communicate those associations through Molony’s existing relationship-based feedback and display functions, paragraphs 0222, 0227, and 0241. The reason for these modifications is to provide activity-specific information supporting Molony’s recommendations for improving measured sleep and health parameters, paragraph 0224.
Kayyali supplies a further reason to use that information in therapy selection: its stated objective is titration using a rich data set over multiple nights, column 2, lines 38–47. Its processor relates patient signals to select treatment settings and evaluates calculated conditions and treatment-effectiveness trends, column 25, lines 4–25, and column 46, lines 41–65. A POSITA would therefore have incorporated Molony’s separately determined correlations into that assessment to identify how sleep outcomes vary with measured activities across days, providing longitudinal patient context when selecting the next therapy setting. The proposed implementation evaluates the plurality alongside respiratory measurements using Kayyali’s disclosed relationship algorithms, then generates the corresponding adjustment command. Those established analysis and control functions support a reasonable expectation of producing correlation-informed setting selection and device adjustment. This is a proposed integration of the teachings, rather than express disclosure of the complete arrangement. Accordingly, Applicant’s objection does not overcome this articulated rationale for claim 1’s disputed combination.
Applicant argues that claims 5–7, 18, and 19–20 remain patentable because their additional references do not cure the alleged deficiency in claim 1.
The Examiner respectfully disagrees regarding that inherited deficiency for the reasons explained above. Futch, Mohammed, and Wright need not supply the correlation and presentation modifications attributed to Molony in the articulated combination. Applicant identifies no separate deficiency concerning the additional limitations supplied by those references.
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.
Claim(s) 1-4, 8-15 and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over US-20230245780A1- Molony and further in view of Kayyali- US7942824.
Claim 1. Molony teaches, A method comprising:
generating, by a control system, a plurality of sleep scores for a user, wherein the plurality of sleep scores for the user are associated with a plurality of days, wherein the sleep scores are generated based on data from a respiratory therapy device;
Molony describes a "control system 110" with processors and memory ([0042], [0043]) that generates a "set of component scores" including a "sleep score" and a "total health score" (which incorporates the sleep score) for each "sleep session" or "given day" ([0004], [0125], [0170]). These scores are presented in a "calendar portion 510" showing scores for a "plurality of date indications" (e.g., May 23-29), thus associating them with a "plurality of days" ([0119], [0120]). The "sleep score" is explicitly stated to be associated with therapy from a "respiratory therapy system 120" and can be based on "medical device usage data" from such a system, including usage, interface, and event scores ([0176], [0182]).
receiving, by the control system from an external device, a plurality of activity indicators, wherein the plurality of activity indicators are associated with the plurality of days;
Molony's "control system 110" receives "physiological data" from sources including an "activity tracker 190" (a wearable external device) ([0045], [0161], [0041], [0087], [0088]). This data is used to determine an "activity measurement" which includes a plurality of indicators like "number of steps, distance traveled, heart rate, calories burned" ([0087]). The system calculates a "total health score for given day" based in part on an "activity measurement for that day," and trends for activity measurements are shown over multiple days in a calendar view, thus associating these indicators with a "plurality of days" ([0125], [0145]).
determining, by the control system based on the plurality of sleep scores and the plurality of activity indicators, a plurality of correlations between the plurality of sleep scores and respective ones of the plurality of activity indicators, wherein each Molony, par. 0042: “control system 110 … analyze data … can be used to carry out one or more steps of any of the methods”; par. 0208: “the first parameter is a sleep score”; par. 0214: “the determined first parameter can have a plurality of data points, where each data point corresponds to one day in the time period”; par. 0087: “activity measurement can include … a number of steps, a distance traveled … duration … type … intensity … or any combination thereof”; par. 0211: “the determined second parameter is … an activity measurement”; par. 0217: “determining a relationship between the determined first parameter … and the determined second parameter … based on a statistical analysis, such as a correlation … or a regression analysis.”; par. 0213 … first portion of the physiological data for determining the first parameter (step 1302) is generated by a first one of the one or more sensors 130 (FIG. 1 ), while the second portion of the physiological data for determining the second parameter (step 1303)… the first portion of the physiological data can be generated by the pressure sensor 132 and/or the flow rate sensor 134, which are physically coupled to or integrated in the respiratory therapy system 1…; par. 0212 … one or both of step 1302 and step 1303 include determining a plurality of parameters … ; … multiple pairs of the plurality of parameters… par. 0240; … are associated with a custom message that can be generated using the method 1300… par. 0241; …the determined trend indication associated with each of the plurality of parameters of the one pair of the plurality of parameters… a sleep score and sleepiness score pair, a sleep score and blood pressure measurement pair, a sleep score and activity level pair.. par. 0249; … is indicative of a positive valence between the parameters in the pair…. Par. 0250; …. In some implementations, the custom message is only associated with the one of the first day, second day, one of the plurality of parameters…; … activity measurement can include, for example, a number of steps, a distance traveled … par. 0087; see also figure 8A-8B and 13 )
Molony describes a control system determining sleep scores and activity-related measurements, including steps and duration of physical activity, over multiple days (paragraphs [0087], [0208], [0211], [0214]–[0215]). Molony further describes determining multiple parameters and statistically correlating first and second parameters (paragraphs [0212], [0217]).
generating, by the control system based on the plurality of correlations between the plurality of sleep scores and the respective ones of the par. 0087: “activity measurement can include … a number of steps, a distance traveled … duration … type … intensity … or any combination thereof”; par. 0211: “the determined second parameter is … an activity measurement”; par. 0214: “the determined first parameter can have a plurality of data points”; par. 0217: “determining a relationship between the determined first parameter … and the determined second parameter … such as a correlation”; par. 0222: “the control system 110 generates the custom message based at least in part on … the determined relationship” and the message can include “a fifth indication associated with the determined relationship” using “alphanumeric text, image(s), video(s), graphic(s), symbol(s), color(s), or any combination thereof”; par. 0227: “control system 110 can cause the generated custom message to [be] communicated to the user via the display device 172 … using one or more visual indications.” multiple pairs of the plurality of parameters (par. 0241))
Molony describes determining correlations between sleep scores and activity measurements (pars. 0208, 0211-0212, 0217), generating visual indications of determined relationships (pars. 0222, 0227), and associating multiple parameter pairs with custom messages generated using method 1300 (pars. 0240–0241). These passages support the claimed correlation-based visual-presentation functionality
causing presentation, by the control system via a display device of a user device, the visual presentation.
Molony states its "control system 110... can cause the generated custom message... to be communicated to the user via the display device 172" ([0227]). The "user device 170" includes this "display device 172" and can be a "smart phone" or "tablet" ([0084]). The "generated custom message" is the visual presentation indicating the correlations;
determining, by the control system automatically based on the plurality of correlations between the plurality of sleep scores and the respective ones of the plurality of activity indicators, a (Molony, par. 0207-0208, 0211, 0217, 0222, 0224, 0240-0249)
Molony describes a system that calculates correlations between sleep scores and other health indicators (such as blood pressure or activity measurements).
(Molony, par. 0044, “The medical information can include, for example, including indicative of one or more medical conditions associated with the user, medication usage by the user, or both”)
and causing, by the control system . (Molony, par. 0054 “The APAP system automatically varies the air pressure delivered to the user based on, for example, respiration data associated with the user.”)
35 U.S.C 103 Rationale:
Molony teaches determining correlations between sleep scores and activity indicators, as shown by Molony par. 0211, 0214, 0222, 0217.
However, Molony does not teach determining a modification for respiratory therapy on the respiratory therapy device for the user; generating … a command to adjust one or more parameters of the respiratory therapy; and causing … the respiratory therapy device to adjust the one or more parameters.
Kayyali teaches the missing machine-control part. In Kayyali, the treatment device can be adjusted automatically by a closed-loop control system using data or signals from the diagnostic device to operate the treatment system. Kayyali also describes calculating a command signal to titrate or adjust the PAP or CPAP device, and sending that command signal periodically to adjust the flow of pressurized gas. In simpler terms, Kayyali shows three things: deciding what therapy change is needed, generating a command for that change, and causing the PAP/CPAP device to make the adjustment. Refer to col. 3, ll. 40-56, col. 7, ll. 30-45, and col. 8, ll. 15-35
A POSITA would have combined Molony with Kayyali before the filing date to make Molony’s sleep-score/activity correlation not only shown to the user, but also used to adjust PAP/CPAP therapy.
Molony supplies the need and starting system: users may stop respiratory therapy when they do not see benefit, APAP already automatically changes delivered pressure, and the control system determines correlations involving sleep and activity measurements, Molony pars. 0003, 0054, 0211, 0217.
Kayyali supplies the missing control action: adjusting treatment gas flow or pressure using multi-night diagnostic data and closed-loop CPAP titration, Kayyali col. 2, ll. 27-67.
The modification would use Molony’s correlation as an input for selecting a therapy change, then use Kayyali’s command path to adjust PAP/CPAP pressure or flow, Kayyali col. 7, ll. 30-45; col. 8, ll. 20-35. This would predictably produce Molony’s correlation-based insight with Kayyali’s known automatic therapy adjustment.
Molony describes determining sleep scores and activity measurements, including step count and distance traveled ([0087], [0208], [0211-0212]). Method 1300 determining multiple parameters ([0211-0212]) and their relationships through statistical correlation ([0217]; Fig. 13).
Molony does not expressly separate correlations for respective activity measurements, applying its disclosed correlation analysis to selected measurements would identify their individual relationships with sleep scores, thereby providing more activity-specific information for Molony's behavioral feedback ([0222], [0241]). Each application would predictably produce a corresponding correlation using the existing technique. Molony further generates visual indications of determined relationships and displays them to the user ([0222], [0227]). A person of ordinary skill would have had reason to include the separately determined correlation indications together in a visual presentation to communicate and distinguish the activity-specific relationships, rather than limiting the output to one prioritized message. This would provide more complete actionable feedback using Molony's existing relationship-indication and display functions, with a reasonable expectation of success.
Note: Claim 21 is rejected with claim 1 for being very similar and in the view of the following different limitations in claim 21 rejected below:
determining, by the control system automatically based on the correlations between the plurality sleep scores and the plurality of activity indicators, a recommendation; (Molony, par. 0222 “The control system 110 generates the custom message based at least in part on... the determined relationship...”)
causing transmission, by the control system, of the recommendation; (Molony, par. 0228, “Step 1309 of the method 1300 includes causing the generated custom message to be communicated to the user”)
;
and
.
35 U.S.C 103 Rational:
Molony teaches receiving, in response to the causing the transmission of the recommendation, by stating “the recommendation can communicate recommended steps or actions for improving the determined first parameter” (para. [0224]) and “causing the generated custom message to be communicated to the user via the display device 172” (para. [0227]). However, Molony fails to disclose the specific bidirectional handshake of receiving... a command to adjust one or more parameters of the respiratory therapy that is generated specifically as a response to that transmission.
Kayyali teaches the receiving... a command... in response to the... transmission, describing a system where a “remote monitoring station” receives a “processed signal” (the recommendation/report) and sends back a response: “a remote monitoring station comprising a transceiver for receiving the processed signal from the data acquisition system and transmitting a command signal based at least in part on the transmitted processed signal to the CPAP device” (col. 5, ll. 45–67). Kayyali further specifies the device receives a command signal via an “electrical connection” (col. 6, ll. 40-50, col. 8, ll. 1–35) to perform the adjustment (col. 6, ll. 1–5). In simple terms, this means that instead of just showing a message on a screen for a person to read, the system sends data out and then waits to receive back a specific instruction to change its own settings. This describes exactly what is missing in Molony, which only sends a one-way notification to a user but lacks the two-way "handshake" needed for the machine to receive a return command to fix the treatment.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of Molony with Kayyali because both references are directed to the shared field of respiratory therapy and address the common problem of providing clinical oversight for machine setting changes (Molony, para. [0003]; Kayyali, col. 2, ll. 55–65). Molony identifies that health correlations result in recommendations for “improving the determined first parameter” (Molony, para. [0224]). Kayyali teaches that setting modifications are most safely and accurately performed when a system “remotely evaluat[es] and approv[es] the set of final values” before the device is programmed (Kayyali, col. 8, ll. 40–60). A PHOSITA would have been motivated to integrate Kayyali’s bidirectional “handshake” protocol into Molony’s recommendation engine to ensure that machine adjustments are verified by an external monitor (such as a clinician) before the command is received and executed by the therapy device.
The combination makes the full limitation obvious because it establishes a closed-loop interactive feedback mechanism. A PHOSITA would integrate the receiving... in response... a command into the control system of Molony—utilizing Molony’s recommendation output as the trigger—to achieve the benefit of safe, verified therapy titration. As Kayyali teaches, this ensures that the device “is titrated or adjusted based on at least in part the retransmitted or transmitted signal and at least in part on the user input” or remote command (Kayyali, col. 6, ll. 12–21).
A PHOSITA would have had a reasonable expectation of success because the technical implementation of bidirectional command-response protocols is routine in networked medical systems. Molony establishes that the “control system 110” (para. [0042]) is capable of transmitting data over networks, and Kayyali describes the specific hardware, such as an “electrical connection for receiving the... transmitted signal” (Kayyali, col. 6, ll. 1–15), needed to facilitate the response. The integration requires only standard digital "handshake" logic to wait for and receive a return command after a recommendation is issued.
Molony teaches a respiratory therapy system where the "APAP system automatically varies the air pressure delivered to the user based on... respiration data" (para. [0054]) and the control system causes a "custom message to be communicated to the user" (para. [0227]). However, Molony fails to disclose causing, by the control system via the command, the respiratory therapy device to adjust the one or more parameters of the respiratory therapy system.
Kayyali teaches causing... the respiratory therapy device to adjust the one or more parameters, specifically describing a system that is "periodically delivering a command signal from the remote monitoring station to the PAP or CPAP device to adjust the flow of pressurized gas delivered by the PAP or CPAP device to the subject" (col. 8, ll. 19–35). Kayyali further specifies that the system is responsible for "programming the treatment device to deliver the set of final treatment values" (col. 25, ll. 50–55).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of Molony with Kayyali because both references are directed to respiratory therapy systems aimed at optimizing patient treatment (Molony, para. [0003]; Kayyali, col. 2, ll. 45–67). Molony identifies the need for "dynamic adjustments" based on health correlations to improve therapy (Molony, Abstract). Kayyali teaches that issuing a direct command to the machine provides "more accurate control" and ensures symptoms are treated "as they occur" (Kayyali, col. 4, ll. 15–25; col. 21, ll. 55–60). A PHOSITA would have been motivated to utilize Kayyali's command-delivery mechanism to execute the adjustments identified by Molony, transforming a passive notification system into an active, self-correcting device.
The combination makes the full limitation obvious because it provides the execution step for the control system's analysis. A PHOSITA would integrate the causing... via the command... to adjust into Molony's architecture to achieve the benefit of immediate, automated parameter updates. As Kayyali teaches, "the treatment device can be adjusted by either a closed loop control system (automatically)... to actuate a physical... treatment system" (Kayyali, col. 3, ll. 34–56), allowing the control system to exert direct control over the therapy parameters.
A PHOSITA would have had a reasonable expectation of success because the technical integration is routine. Molony discloses a "control system 110" and "respiratory therapy device 122" (para. [0042], [0046]), and Kayyali confirms such devices are "capable of receiving the... transmitted signal and delivering a flow of pressurized gas" (Kayyali, col. 6, ll. 65–67). Using standard digital commands to modify machine variables is a straightforward implementation of known interfaces.
Claim 2. Molony in view of Kayyali teaches, The method of claim 1, wherein the external device is one or more of the user device, a smartphone, a smartwatch, a fitness tracker, a step counter, a blood pressure monitor, a heartrate monitor, and a sleep tracker. (Molony, paras. [0041], [0084], [0087], [0088], [0086])
Molony's system 100 explicitly includes a "user device 170" (which can be a "smart phone" or "smart watch"), an "activity tracker 190" (which can be a "smartwatch" or "wristband" and is used to determine metrics like "number of steps" and "heart rate", thus functioning as a fitness tracker, step counter, and heartrate monitor), and a "blood pressure device 180" (a blood pressure monitor). These devices are external to the respiratory therapy system and provide data to the control system, fully meeting the specified list of external devices.
Claim 3. Molony in view of Kayyali teaches, The method of claim 1, wherein the activity indicators include one or more of step count, heartrate, calories burned, calories consumed, sleep quality, sleep duration, workouts, active minutes, stand hours, respiration rate, walking distance, and blood oxygen levels.
Molony's "activity measurement" obtained from the "activity tracker 190" explicitly includes "a number of steps", "heart rate", "a number of calories burned", "time spent standing", "respiration rate", "distance traveled" (which is walking distance), and "blood oxygen saturation" ([0087]).
Claim 4. Molony in view of Kayyali teaches, The method of claim 1, wherein the control system determines the correlations between the sleep scores and the plurality of activity indicators based on a machine learning algorithm.
Molony teaches that the control system determines operative relationships (correlations) between sleep scores and activity indicators based on a machine learning algorithm when it describes using a machine learning algorithm to adjust weighting values for component scores (which include sleep and activity scores) to achieve a desired health goal. (Molony, paras. [0170], [0202]). This process of the ML algorithm deriving how to treat or combine these scores based on learned patterns to achieve an objective constitutes "determining correlations... based on a machine learning algorithm" under BRI.
Claim 8. Molony in view of Kayyali teaches, The method of claim 1, further comprising:
identifying, by the control system, a trend in the plurality of sleep scores;
Molony describes displaying a "sleep score trend 830" which is "used to indicate a trend" ([Molony, Paragraph 0145]) and determining a "trend indication" from data points of the sleep score trend ([Molony, Paragraph 0148]). Molony's method 1300 further includes "determining a first trend associated with the determined first parameter," where that parameter can be a sleep score ([Molony, Paragraphs 0214, 0208]).
determining, by the control system, one or more of the activity indicators that correlate with the trend in the plurality of sleep scores;
Molony's method includes "determining a relationship" (which can be a "correlation" per [Molony, Paragraph 0207]) between a "first parameter" (e.g., sleep score, as per [Molony, Paragraph 0208]) and a "second parameter" (e.g., an activity indicator such as an "activity score" or an activity-influenced "blood pressure measurement", as per [Molony, Paragraphs 0170, 0211]). This determination is made "based at least in part on the determined first trend" (the sleep score trend) [Molony, Paragraph 0207].
generating, by the control system based on the one or more activity indicators that correlate with the trend in the plurality of sleep scores, a recommendation;
Molony's control system "generates a custom message" which "includes a recommendation" ([Molony, Paragraphs 0222, 0224]). This generation is explicitly stated to be "based at least in part on... the determined second parameter [which can be an activity indicator, see [Molony, Paragraph 0211]], the determined first trend [e.g., sleep trend, see [Molony, Paragraph 0214]] ... and the determined relationship (step 1306) [the correlation between the sleep score and activity indicator, considering the sleep trend, see [Molony, Paragraph 0207]] ..." ([Molony, Paragraph 0222]). Therefore, Molony teaches that the recommendation is generated based on the activity indicators that have been determined to correlate (via the "determined relationship") with the identified sleep score trend.
and causing presentation, by the control system via the display device of the user device, of the recommendation.
Molony's method includes "causing the generated custom message (step 1308) to be communicated to the user... via the display device 172" ([Molony, Paragraph 0227]). Since the custom message generated in step 1308 includes the "recommendation" (as per [Molony, Paragraph 0224]), this constitutes causing presentation of the recommendation.
Claim 9. Molony in view of Kayyali teaches, The method of claim 8, wherein the recommendation is associated with one or more of the activity indicators.
Molony's system generates a "recommendation" that "can communicate recommended steps or actions for improving... the determined second parameter (step 1303)" ([Molony, Paragraph 0224]). The "second parameter" is disclosed to be potentially "an activity measurement" ([Molony, Paragraph 0211]), which itself is based on activity indicators like "a count of steps" ([Molony, Paragraph 0181]). Thus, a recommendation for improving an "activity measurement" is directly associated with activity indicators.
Claim 10 Molony in view of Kayyali teaches, The method of claim 9, wherein the recommendation is one or more of to increase an activity associated with one or more of the activity indicators and decrease an activity associated with one or more of the activity indicators.
Molony's system identifies an "activity component" as a "component to be improved" if a user "regularly exhibits low activity" ([Molony, Paragraph 0199]). A recommendation to improve low activity would inherently be a recommendation to increase that activity. The system generates "custom message[s] to aid in encouraging a behavioral response" ([Molony, Paragraph 0222]) through "recommended steps or actions for improving" parameters ([Molony, Paragraph 0224]), which would logically include increasing beneficial activities or decreasing those found to be detrimental in the context of the user's overall health goals and data correlations.
Claim 11. Molony in view of Kayyali teaches, The method of claim 1, further comprising:
identifying, by the control system, a trend in the plurality of activity indicators;
Molony describes displaying an "activity level measurement trend 850" which is "used to indicate a trend" ([Molony, Paragraph 0145]) and determining a "trend indication" from data points of the activity level trend ([Molony, Paragraph 0148]). Additionally, Molony's method 1300 includes "determining a second trend associated with the determined second parameter," where that parameter can be an activity measurement ([Molony, Paragraphs 0215, 0211]).
determining, by the control system, one or more of the sleep scores that correlate with the trend in the plurality of activity indicators;
Molony's method includes "determining a relationship" (which can be a "correlation" per [Molony, Paragraph 0207]) between a "first parameter" (e.g., sleep score, as per [Molony, Paragraph 0208]) and a "second parameter" (e.g., an activity indicator, as per [Molony, Paragraph 0211]). This determination is made "based at least in part on... the determined second trend" (the activity indicator trend) [Molony, Paragraph 0207].
generating, by the control system based on the one or more sleep scores that correlate with the trend in the plurality of activity indicators, a recommendation;
Molony explicitly states that its control system "generates the custom message [which includes a recommendation per [Molony, Paragraph 0224]] based at least in part on... the determined first parameter [which can be a sleep score per [Molony, Paragraph 0208]] ... the determined second trend [which can be an activity trend per [Molony, Paragraph 0215]] ... and the determined relationship [correlation] (step 1306)" ([Molony, Paragraph 0222]). This establishes that the recommendation is based on sleep scores as they correlate with the activity trend.
and causing presentation, via the display device of the user device, of the recommendation.
Molony clearly states that the control system causes the "generated custom message (step 1308)" (which contains the "recommendation" per [Molony, Paragraph 0224]) "to be communicated to the user via the display device 172" of the user device ([Molony, Paragraph 0227]).
Claim 12. Molony in view of Kayyali teaches, The method of claim 11, wherein the recommendation is associated with one or more of the activity indicators.
Molony's system is designed to provide recommendations that can target various parameters for improvement, including an "activity measurement" (which comprises activity indicators) ([Molony, Paragraphs 0211, 0224]). The system generates custom messages with recommendations based on an analysis that includes the "determined second parameter" (e.g., activity indicator) and its trend ([Molony, Paragraph 0222]).
Claim 13. Molony in view of Kayyali teaches, The method of claim 12, wherein the recommendation is one or more of to increase an activity associated with one or more of the activity indicators and decrease and activity associated with one or more of the activity indicators.
Molony's system identifies an "activity component" as a "component to be improved" if a user "regularly exhibits low activity" ([Molony, Paragraph 0199]); a recommendation for such improvement logically involves advising an increase in activity. The broader aim to provide "recommended steps or actions for improving" parameters ([Molony, Paragraph 0224]) to "encourage a behavioral response" ([Molony, Paragraph 0222]) supports that such recommendations would be directional, i.e., to increase or decrease specific activities as needed for health improvement.
Claim 14. Molony in view of Kayyali teaches, The method of claim 1, further comprising:
determining, by the control system based on the correlations between the plurality of sleep scores and the plurality of activity indicators, a trend in the plurality of sleep scores;
Molony teaches determining a trend in sleep scores ([Molony, Paragraph 0145]), and also teaches determining correlations between sleep scores and activity indicators where such determination considers the sleep trend ([Molony, Paragraph 0207]). Furthermore, Molony generates custom messages based on a holistic understanding of parameters, their trends, and the relationships between them ([Molony, Paragraph 0222]). For the system to act upon or message about a sleep trend in a manner that also reflects its correlation with activity (e.g., "sleep score improved while blood pressure improved" [Molony, Paragraph 0223].
and determining, by the control system based on the trend in the plurality of sleep scores one or more of motivational content and congratulatory content to present to the user;
Molony's system "generates a custom message" that can include "positive... reinforcement" such as "'Well done, you're taking steps towards better health!'" (), which is motivational/congratulatory. This message generation is "based at least in part on... the determined first trend", which can be the sleep score trend. (Molony, paragraph 0222, 0224)
and causing presentation, by the control system via the display device of the user device, of one or more of the motivational contents and the congratulatory content.
Molony's method includes "causing the generated custom message (step 1308) to be communicated to the user... via the display device 172" (Molony paragraphs 0227-0228). Since the "custom message" generated in step 1308 is based on trends and can include motivational/congratulatory feedback ([Molony, Paragraphs 0222, 0224]), this constitutes causing presentation of such content.
Claim 15. Molony in view of Kayyali teaches, The method of claim 14, wherein one or more of the motivational contents and the congratulatory content includes one or more of articles, messages, awards, and media.
Molony explicitly teaches that its system generates "custom messages" [Molony, Paragraph 0222] which include "positive... reinforcement" such as "'Well done, you're taking steps towards better health!'" [Molony, Paragraph 0224]. These clearly fall under the category of "messages." Molony also describes that when a patient achieves a high score, the device may present a "celebration, for example a fireworks animation" [Molony, Paragraph 0173]; a "fireworks animation" is a form of "media" and can also be interpreted as a type of "award" in a gamified context, serving a congratulatory purpose. Additionally, the "educational information" included in custom messages [Molony, Paragraph 0224] can take the form of short textual "articles" or textual "media."
Claim 17. Molony in combination with Kayyali teaches, The method of claim 1, wherein the control system causes the respiratory therapy device to adjust one or more parameters of the respiratory therapy by:
transmitting, by the control system via a communications network, the command to the respiratory therapy device. (Kayyali, Col. 3, ll. 57-67, “wireless link or some combination thereof “, Col. 25, ll. 5-26 “calculate the next appropriate treatment setting… signal… to determine that the PAP pressure should be increased…”)
Kayyali read on, a processing assembly managing a medical ventilator by sending digital instructions across a wireless or wired data infrastructure to modify pressure or flow settings
Claim 22.
Molony in view of Kayyali teaches,
The method of claim 1, further comprising: identifying, by the control system, a trend in the plurality of sleep scores; (Molony pars. 0144-0146, 0211, 0214-0217)
Molony discloses a sleep score trend and an activity level measurement trend over a plurality of dates, determines a first trend from plural sleep-score data points using a fitted line/slope, determines a second trend for the activity parameter, and determines a relationship using a correlation between the first parameter and the second parameter
determining, by the control system, one or more of the activity indicators that correlate with the trend in the plurality of sleep scores; (Molony, par. 0087, 0148, 0217)
Molony’s activity measurements read on activity indicators, the displayed sleep-score and activity-measurement data are used to determine trends, and Molony expressly determines a relationship/correlation between the first trend and second trend; where the first parameter is the sleep score and the second parameter is the activity measurement, this reads on determining activity indicators that correlate with the trend in the plurality of sleep scores.
and adjusting, by the control system based on the correlations between the sleep scores and the plurality of activity indicators, weights of the data from the respiratory therapy device to generate the plurality of sleep scores. (Molony, par. 0182-0183, 0196, 0198)
Molony teaches using respiratory-therapy-system data to generate or modify the sleep score, and teaches adjusting weighting values based on trends or inferences; because Molony also determines sleep/activity correlations, those correlations are reasonably read as the identified inference used to adjust the weights.
Claim(s) 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over US-20230245780-A1- Molony, and further in view of Kayyali- US7942824 and US20170132395- Futch.
Claim 5. Molony in view of Kayyali teaches, The method of claim 1, further comprising:
and determining, by the control system based on
Molony teaches a method where a control system determines correlations between an individual user's sleep scores (derived from a respiratory therapy device) and their activity indicators (from an external device) over multiple days, and presents these individual correlations and does not describes aggregating these individually determined correlations (or the underlying data from which these correlations are derived) for a plurality of users, nor does it teach determining group correlations based on such an aggregation of multi-user data.
Futch teaches the analysis of aggregated data from a plurality of users to derive population-level insights and "best paths," which are group correlations. References paragraph 0030, 0076,0082 It would have been obvious to combine Molony's individual analysis with Futch's population-level analytics to enhance the system's capabilities by deriving group correlations, as both references pertain to digital health platforms leveraging user data for health improvement insights. A person of ordinary skill in the art, seeking to enhance the insights provided by Molony's individual-focused system, would have been motivated to incorporate Futch's population-level data aggregation and group correlation analysis techniques to “to provide unique recommendations to the user that steer the user onto the optimal path to reach their goal.” Par. 0076 Futch
Claim 6.Molony in combination with Kayyali and Futch teaches, The method of claim 5, further comprising:
generating, by the control system for the user based on the group correlations, insights for the user;
Futch describes its system analyzing "input data... together with data from other users on the platform" (which represents the basis for group correlations) to provide "unique recommendations to the user" [Futch, Paragraph 0076]. These "unique recommendations" derived from analyzing collective user data are "insights for the user based on the group correlations." Furthermore, Futch mentions that "artificial intelligence and personalized data analytics feedback provides the user with... insights that the system has gained from analyzing the data points of users" (plural) [Futch, Paragraph 0029]. Deriving "best paths for health improvement" from grouped user data and sharing these predictively also constitutes generating insights based on group correlations [Futch, Paragraph 0082].
and causing presentation, via the display device of the user device, the insights for the user.
Futch explicitly states that "recommendations are determined for the user... and are transmitted... to the user device and displayed on the user device" [Futch, Paragraph 0081]. These recommendations, which are based on analysis including data from other users (group correlations), are presented on the user's device via a "user application" and "user interface" [Futch, Paragraphs 0077, 0113].
Claim 7. Molony in combination with Kayyali and Futch teaches, The method of claim 6, wherein the insights for the user include descriptions of why one or more of the activity indicators are correlated with one or more of the plurality of sleep scores.
Futch describes providing users with "insight comments" [Futch, Paragraph 0023] and "educational tips from health experts and relevant expert articles, videos and blogs relating to their specific health improvement goals" [Futch, Paragraph 0033]. These insights and educational materials are informed by an "artificial intelligence and personalized data analytics" system that analyzes "data points of users " ([Futch, Paragraph 0029]) and data "from other users on the platform" ([Futch, Paragraph 0076]). Given that Futch addresses health pillars including "physical activity" and "sleep" ([Futch, Paragraph 0040, Table 1]).
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US-20230245780-A1- Molony, and further in view of Kayyali- US7942824 and US-20210050089-A1. MOHAMMED
Claim 18. Molony in view of Kayyali teaches, The method of claim 1, further comprising:
adjusting, by the control system based on the correlations between the sleep scores and the plurality of activity indicators, weights of the data from the respiratory therapy device to generate the plurality of sleep scores.
Molony does not explicitly teach adjusting, based on these specific correlations, the internal weights of the data from the respiratory therapy device that are used to generate the sleep score itself.
MOHAMMED teaches a health management platform utilizing machine-learned models, such as a "Sleep Twin Module 655" ([MOHAMMED, Paragraph 0107, Figure 6]), where model "Parameter values" that "describe the weight that is associated with at least one of the featured input values" ([MOHAMMED, Paragraph 0119]) are "continuously updated" through model training based on "input biosignals and metabolic state outcomes" ([MOHAMMED, Paragraph 0124]).
It would have been obvious to one of ordinary skill in the art to modify Molony's system, which determines relevant sleep/activity correlations, by incorporating MOHAMMED's advanced machine learning approach of continuously training and updating models. This would involve using the correlations found by Molony to dynamically adjust the weights of the input data from the respiratory therapy device when generating Molony's sleep scores, thereby making these scores more accurate and reflective of the user's holistic health state as understood through MOHAMMED's adaptive modeling techniques. The motivation would be to improve the precision and clinical relevance of the sleep scores by making their calculation adaptive to learned interrelations with other health factors like activity, a known goal in developing sophisticated health monitoring systems as shown by MOHAMMED's pursuit of "precision treatment" ([MOHAMMED, Paragraph 0006]).
Claim(s) 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US-20230245780-A1- Molony, and further in view of Kayyali- US7942824 and US-11134888-B2- Wright.
Claim 19. Molony in view of Kayyali teaches, The method claim 1, further comprising:
determining, by the control system based on the correlations between the sleep scores and the plurality of activity indicators, one or more modifications to automation policies; and
causing, by the control system, one or more automated devices to alter their operation based on the one or more modifications to automation policies.
However, Molony does not explicitly teach using these specific correlations to determine "modifications to automation polices" for general automated devices, nor causing such general automated devices to alter their operation based on these policies. Molony's system provides insights and recommendations to the user ([Molony, Paragraph 0224]).
Wright teaches a smart home control system that determines "an appliance control schedule" (an automation policy) for "one or more appliances in a user environment" (such as "lights or lighting systems, and heating, air conditioning and/or ventilation (HVAC) systems" [Wright, Paragraph 0012]) based on "data indicative of a sleep state of the user" and "environmental stimuli" to "reduce sleep disruption" ([Wright, Paragraphs 0010, 0017, 0046]), and then "controls the one or more appliances in dependence on the determined control actions" or schedule ([Wright, Paragraph 0010]), for example, "The Appliance Schedule is sent to the Connected Appliances, which enact the desired functions at the scheduled times" ([Wright, Paragraph 0035]).
It would have been obvious to one of ordinary skill in the art to modify Molony's system, which determines correlations between sleep scores and external activity indicators, to further incorporate Wright's teachings of using health-related data (sleep states) to determine and implement modifications to automation policies for general automated devices. One of ordinary skill would be motivated to enhance Molony's system by incorporating the environmental control capabilities taught by Wright. Wright discloses a system that actively "controls one or more appliances in a user environment" like "lights or lighting systems, and heating, air conditioning and/or ventilation (HVAC) systems" ([Wright, Paragraphs 0010, 0012]) based on "data indicative of a sleep state of the user" with the explicit goal to "reduce sleep disruption" and "improve sleep quality" ([Wright, Paragraphs 0010, 0046]).
20. Molony in combination with Wright teaches, The method of claim 19, wherein the automated devices include one or more of lights, alarm systems, audio systems, entertainment systems, and mobile devices.
Wright teaches that the automated devices controlled by its appliance control schedule include "lights or lighting systems" ([Wright, Paragraph 0012]), "Smart Speaker[s]" (audio systems) ([Wright, Figure 4]), and "TV" or "radio" (entertainment/audio systems) ([Wright, Paragraph 0052]).
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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/J.D.R/
Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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