Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/22/2026 has been entered.
Status of Claims
This action is in response to applicant arguments filled on 04/22/2026 for application 18/342208.
Claims 1, 17, and 20 have been amended.
Claims 3, 16, and 19 have been canceled.
Claims 1-2, 4-15, 17-18, and 20 are currently pending and have been examined.
Detailed Action
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 4-15, 17-18, and 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Shouldice (US 2023/0173221 A1).
In claim 1, a computer-implemented method comprising:
Shouldice teaches:
collecting, by an application executing on a device associated with a user, and in communication with a network access point that manages a local-area network serving a plurality of client devices associated with the user (para. 11 wherein “the one or more control parameters of the respiratory therapy system, the one or more control parameters of the one or more devices in the environment of the user”. Para. 71 teaches “The respiratory therapy system can process data from one or more (such as single sensor, multi sensor, or multi modal processing) local or remote sensors in order to determine the user parameters and/or environmental parameters for determining a current sleep stage and/or promoting a sleep stage”), data related to sleep activity of the user and data, received via the network access point, characterizing operation of one or more of the plurality of client devise on the local-area network (Para. 44 recites “the control of one or more devices in an environment of a user, or a combination thereof to promote a desired sleep stage of the user”. Para. 220 wherein collected patient data to generate sleep reports is taught);
analyzing, by the application executing an artificial intelligence (AI) model, the collected data including the data related to sleep activity and the data characterizing operation of the one or more of the plurality of client devices (Para. 240-243 wherein data from client devices is analyzed during a sleep session. 263-265 wherein AI is used to analyze data in the environment of the user);
generating, by the application, based on the AI model-based analysis, a measure of the user’s sleep; (Para. 113 and 266);
further analyzing, by the application a correlation between the measure of the user’s sleep and measures of user sleep generated for other users associated with the demographic (Para. 77 wherein “The system can consider the average rate (or overall rate graph) for other purposes such as comparing to themselves over time, or indeed to someone in a similar demographic”. See also Para. 126); and
communicating, over the local-area network via the network access point, the correlation to the user via a digital display presented via a display element of a user device (Para. 212); and
causing, over the local-area network and via the network access point, a modification to a digital environment of the user based on the communicated correlation, the modification comprising the network access point toggling and reconfiguring an operating mode of at least one of the plurality of client devices on the local-area network, thereby altering how at least one of the plurality of client devices operate for the user (Para. 11 wherein “Aspects of the embodiment include the step of tracking an outcome of the adjusting of the one or more control parameters to validate an efficacy of the one or more models. Aspects of the embodiment include the step of updating the one or more models based on the outcome of the adjusting of the one or more control parameters to improve the one or more models with respect to optimizing the sleep of the user”… “A sound level could include an alarm, such as a “smart” alarm that is sleep stage- and/or sleep state-based, whereby the optimization is such as to predict a sleep stage during an alarm window and optionally making adjustments such that the user wakes with a reduced sleep inertia; for example, if a user if predicted to be in deep/SWS sleep during the anticipated alarm time, the actual alarm time may be adjusted within a window (e.g., such as a 15 or 30 min flexible alarm period) such that the user is woken from N1, N2, or REM (or if they are already awake, as a reminder to get up). The system could also act to nudge them from deep or REM to N2 prior to activating the alarm (particularly if a flexible alarm period is not desired”. Para. 209 teaches “ the same weighting as described above for control parameters can also be applied to environmental parameters, which can correspond to control parameters of one or more devices in the environment that can change the environmental parameters”).
As per claim 2, Shouldice teaches the method of claim 1, wherein the demographic comprises at least one of: an age; a gender; a weight; a BMI score; an occupation; a level of education; a family status; an ethnicity; a location; or a culture (Para. 73).
As per claim 4, Shouldice teaches the method of claim 1, wherein the measure of the user’s sleep and the measures of user sleep generated for the other users comprise a measure of at least one of sleep latency, total sleep duration, deep sleep duration, light sleep duration, sleep restlessness, or nighttime awakenings (Para. 242).
As per claim 5, Shouldice teaches the method of claim 1, further comprising:
identifying a subset of the users associated with the demographic whose measure of user sleep falls below a threshold value (para. 173, 176, 219, 281 wherein users can be compared to determine if thresholds of sleep data is met);
receiving, as an output from a trained model, a feature associated with the users within the subset of users whose measure of user sleep falls below the threshold value; and
communicating, over the network, the feature to the user via the digital display as part of a suggestion for improving the measure of the user’s sleep (Para. 11 wherein “ Aspects of the embodiment include the step of updating the one or more models based on the outcome of the adjusting of the one or more control parameters to improve the one or more models with respect to optimizing the sleep of the user. Aspects of the embodiment include the step of monitoring the one or more user parameters, the respiratory therapy system, the environment of the user, or a combination thereof to determine whether one or more events occur that satisfy a sleep disturbance threshold”).
As per claim 6, Shouldice teaches the method of claim 5, wherein communicating the feature to the user comprises communicating the feature in response to at least one of:
determining that the measure of the user’s sleep falls below the threshold value (Para. 11); and
determining that the user is associated with the feature (Para. 11).
As per claim 7, Shouldice teaches the method of claim 5, further comprising:
for each particular user within the subset of users whose measure of user sleep falls below the threshold value, identifying one or more devices within a location associated the particular user (Para. 11 and 71);
collecting data from the identified devices (Para. 11 and 71); and
inputting the data collected from the identified devices to the trained model, wherein the feature is generated by the trained model as the output based on the inputted data (Para. 11 and 71).
As per claim 8, Shouldice teaches the method of claim 5, wherein the feature comprises an environmental feature (Para. 76).
As per claim 9, Shouldice teaches the method of claim 8, wherein the environmental feature comprises at least one of: a geographic area associated with the user; an altitude of a geographic area associated with the user; a measure of pollution at a geographic area associated with the user; and a population size of a geographic area associated with the user (Para. 227).
As per claim 10, Shouldice teaches the method of claim 5, wherein the feature comprises a pattern of activity (Para. 241).
As per claim 11, Shouldice teaches the method of claim 10, wherein the pattern of activity comprises at least one of: a pattern of screen usage; and a pattern of movement (Para. 241).
As per claim 12, Shouldice teaches the method of claim 10, wherein the pattern of activity is determined for each particular user within the subset in response to:
identifying a set of devices associated with a location of the particular user (Para. 251);
collecting and analyzing data from each of the set of devices (Para. 251); and
in response to analyzing the data from each of the set of devices, determining the pattern of activity for the particular user (Para. 251).
As per claim 13, Shouldice teaches the method of claim 12, wherein the set of devices corresponds to devices comprising sensor capabilities for tracking movements and information about the particular user at the location (Para. 251).
As per claim 14, Shouldice teaches the method of claim 5, wherein the feature comprises a biometric feature (Para. 72).
As per claim 15, Shouldice teaches the method of claim 14, wherein the biometric feature comprises at least one of: a weight of the user; a blood oxygen level of the user; a temperature of the user; a heartrate of the user; or a blood pressure of the user (Para. 145).
Claims 17-18 and 20 recite substantially similar limitations as seen above and hence are rejected for similar rationale as noted above.
Response to Augments
Applicant arguments/amendments over come the 101 rejection.
The Applicant argues the art rejection. The Applicant states that Shouldice does not teach a network access point that manages a local-area network serving a plurality of client devices associated with the user. The Applicant further argues that Shouldice does not disclose dual input analysis. The reference does not teach as an input to the AI model, data characterizing the operation of client devices on a local area network that are sperate from the sleep tracking sensor and that are reachable via a network access point. In addition, the Applicant argues that Shouldice does not teach a reconfiguration of a sperate client device on a local area network, and it is not performed by a network access point.
The Examiner respectfully disagrees. Shouldice in paragraph 71 teaches “The respiratory therapy system can process data from one or more (such as single sensor, multi sensor, or multi modal processing) local or remote sensors in order to determine the user parameters and/or environmental parameters for determining a current sleep stage and/or promoting a sleep stage”; i.e. a plurality of sensors or client devices can be monitored and analyzed. Paragraph’s 240-243 teach “The data generated by one or more of the sensors 830 can be one or more user parameters and/or one or more environmental parameters, or processed to determine one or more user parameters and/or one or more environmental parameters, and used by the control system 810 to promote a sleep stage.” i.e. environmental data and/or user data from client devices are analyzed during a sleep session. Paragraph 209 teaches “ the same weighting as described above for control parameters can also be applied to environmental parameters, which can correspond to control parameters of one or more devices in the environment that can change the environmental parameters” i.e. reconfiguring of a client device is taught.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAROUN P KANAAN whose telephone number is (571)270-1497. The examiner can normally be reached Monday-Friday 8:00-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached at (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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MAROUN P. KANAAN
Primary Examiner
Art Unit 3687
/MAROUN P KANAAN/Primary Examiner, Art Unit 3687
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