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 Claims
Claims 1-11, 15-19, and 21-22 are rejected. Claims 12-14 and 20 are canceled.
Response to Arguments
Claim Rejections - 35 USC § 112
The previous 112(a) rejection of claims 1-12 and 14-20 has been withdrawn in view of the amendment.
Claim Rejections - 35 USC § 103
Applicant’s arguments with respect to claims 1-11, 15-19, and 21-22 have been considered but are moot because the new ground of rejection of claims 1 and 17 does not rely on the Raymann reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claims 1, 16-17, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Connor (US 20210379388 filed on 8/24/21) in view of Kahn (US 20160073951 filed on 11/23/15) and Almen (US 20050177051 filed on 2/25/05).
Regarding claims 1 and 17, Connor teaches a method and a system for managing sleep, comprising: a wearable device configured to be worn on a finger of a user and comprising one or more light-emitting components and one or more light-receiving components (¶720-a finger ring which functions as a wearable photoplethysmography (PPG) device with at least one light emitter, wherein changes in the amount (and/or spectrum) of light from the light emitter which is received by the light receiver caused by reflection of the light from (or transmission of the light through) the finger are analyzed in order to measure one or more biometric parameters); and one or more processors (¶286-processor) coupled with the wearable device, the user device, or both (¶337- a wearable device can further comprise a data processor; ¶343-a wearable device can be a finger ring), wherein the one or more processors are configured to: acquire, via the one or more light-emitting components and the one or more light-receiving components of the wearable device (¶720-a finger ring which functions as a wearable photoplethysmography (PPG) device with at least one light emitter, wherein changes in the amount (and/or spectrum) of light from the light emitter which is received by the light receiver caused by reflection of the light from (or transmission of the light through) the finger are analyzed in order to measure one or more biometric parameters), physiological data associated with the user, the physiological data comprising at least heart rate data associated with the user (¶718- wherein changes in the amount (and/or spectrum) of light from the light emitter which is received by the light receiver caused by reflection of the light from (or transmission of the light through) body tissue are analyzed in order to measure heart rate).
However, Connor does not teach a user device communicatively coupled with the wearable device; detect autonomously, via the one or more processors, that the user is napping based at least in part on detecting that the user is entering a sleep state of a set of sleep states during a first time interval within a day; and based at least in part on detecting that the user is napping and based at least in part on a timer lapsing, the response comprising a tactile vibration response, wherein a duration of the timer is based at least in part on the physiological data.
Kahn relates to motion sensing, and more particularly to monitoring a user's motions to improve rest (¶2). Kahn further teaches the invention using the following steps:
a user device communicatively coupled with the wearable device (¶23-the sleep system may be coupled to a mobile device, such as a smart phone); detect autonomously, via the one or more processors, that the user is napping based at least in part on detecting that the user is entering a sleep state of a set of sleep states during a first time interval within a day (¶31-timer system 250 may also use the current time of day to determine whether the sleep is a power nap; ¶43-the power nap allows the user to go through REM, and N1, but just before the user starts drifting into N2); and based at least in part on detecting that the user is napping and based at least in part on a timer lapsing (¶41-at the first transition to N2, the deeper sleep phase, the user is wakened. In one embodiment, this is based on the sleep timer 270; ¶43-the power nap allows the user to go through REM, and N1, but just before the user starts drifting into N2, the alarm is sounded, and the user is awakened; ¶40-the default power nap time is 26.5 minutes, which is the average optimal duration for adults. In one embodiment, the sleep timer 270 may be adjusted based on the historical sleep data collected for the particular user), the response comprising a tactile vibration response (¶45-when the sleep timer 270 determines that it is time for the user to wake, it sends a signal to alarm 280 . In one embodiment, the user may be wakened via an alarm, which may be…tactile; ¶85-tactile output device (e.g. vibrations, etc.)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Connor to include a user device communicatively coupled with the wearable device; detect autonomously, via the one or more processors, that the user is napping based at least in part on detecting that the user is entering a sleep state of a set of sleep states during a first time interval within a day; and based at least in part on detecting that the user is napping and based at least in part on a timer lapsing, the response comprising a tactile vibration response of Kahn in order for a user to take a power nap that is the right length to refresh, without taking too much time, and without making the user sluggish upon waking (Kahn, ¶15).
While the combination of Connor and Kahn teaches a sleep timer designed to sound an alarm to wake the user after the optimal length power nap (Kahn, ¶39), the combination does not explicitly teach wherein a duration of the timer is based at least in part on the physiological data.
Almen relates generally to monitoring heart rate variability using a wrist worn monitor (¶2). Almen further teaches the invention using the following step:
wherein a duration of the timer is based at least in part on the physiological data (¶17-capable of monitoring HRV data to assist the user in a timed rest period or nap; ¶45-the heart rate variability data obtained through the invention is used to determine when the user has achieved sleep or a beneficial level of rest. When the heart rate itself is lowered to a target resting heart rate level, the device starts a timed alarm to wake the user; ¶95-the monitor then monitors and records the heart rate and associated variability 214 until…the waking prompt timer expires 216 which activates the waking prompt 218 and the heart rate monitoring is ended).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Connor to include wherein a duration of the timer is based at least in part on the physiological data of Almen in order to monitoring the stages of sleep by changes in the heart rate variability (Almen, ¶8).
Regarding claim 16, the combination of Connor, Kahn, and Almen teaches the method of claim 1, wherein the wearable device comprises a wearable ring device (Connor, ¶720-a finger ring which functions as a wearable photoplethysmography (PPG) device).
Regarding claim 22, the combination of Connor, Kahn, and Almen teaches the method of claim 1, wherein the sleep state is a light sleep state (Kahn, ¶36-the alarm 280 wakes the user up at the optimal time. In one embodiment, that optimal time is when they are transitioning from deep sleep to light sleep; ¶44-N1), the method further comprising: detecting, via the one or more processors, that the user is in the light sleep state when the timer lapses (Kahn, ¶44-the system times the waking to be at the N1), wherein outputting the response is based at least in part on the user being in the light sleep state when the timer lapses (Kahn, ¶36-the alarm 280 wakes the user up at the optimal time. In one embodiment, that optimal time is when they are transitioning from deep sleep to light sleep; ¶43-the power nap allows the user to go through REM, and N1, but just before the user starts drifting into N2, the alarm is sounded, and the user is awakened; ¶44-the system times the waking to be at the N1; ¶46- the sleep statistics 255 identifies the outer limit of N1 on 7 days. The system then averages them and calculates a new power nap length that is optimal for the user).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Connor to include wherein the sleep state is a light sleep state, the method further comprising: detecting, via the one or more processors, that the user is in the light sleep state when the timer lapses, wherein outputting the response is based at least in part on the user being in the light sleep state when the timer lapses of Kahn in order for a user to take a power nap that is the right length to refresh, without taking too much time, and without making the user sluggish upon waking (Kahn, ¶15).
Claims 2, 9-11, 15, 18-19, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Connor in view of Kahn and Almen as applied to claims 1, 16-17, and 22 above, and further in view of Raymann (US 20170094046 filed on 9/30/15).
Regarding claim 2, the combination of Connor, Kahn, and Almen teaches the method of claim 1. However, the combination of Connor, Kahn, and Almen does not teach detecting that the user is within a threshold from transitioning into a deep sleep state, wherein outputting the response is further based at least in part on detecting that the user is within the threshold from transitioning into the deep sleep state.
Raymann teaches detecting that the user is within a threshold from transitioning into a deep sleep state (Raymann, ¶64-when the user falls into a deep sleep, the user's heartrate and breathing will be reduced even more than initial sleep), wherein outputting the response is further based at least in part on detecting that the user is within the threshold from transitioning into the deep sleep state (Raymann, ¶64-when sleep logic 102 determines that the user's heart rate and/or breathing rate is near or below the deep sleep threshold rate, sleep logic 102 can notify sleep application 146 and sleep application 146 can sound the alarm to wake the user).
Raymann generally relates to human sleep detection (¶1).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Connor to include detecting that the user is within a threshold from transitioning into a deep sleep state, wherein outputting the response is further based at least in part on detecting that the user is within the threshold from transitioning into the deep sleep state of Raymann in order to realize the benefit of sleep without the grogginess that is experienced when a user is awakened from a deep sleep (Raymann, ¶64).
Regarding claim 9, the combination of Connor, Kahn, and Almen teaches the method of claim 1. However, the combination of Connor, Kahn, and Almen does not teach enabling the timer based at least in part on a condition; and disabling the timer based at least in part on the timer lapsing, wherein outputting the response is based at least in part on disabling the timer.
Raymann teaches enabling the timer based at least in part on a condition (Raymann, ¶61-the user can select a timed nap function by manipulating graphical element 402. For example, the timed nap function can wake the user with an alarm after a specified period of time); and disabling the timer based at least in part on the timer lapsing (Raymann, ¶61-when the timer runs down to zero, the napping function can wake the user with an (e.g., audible) alarm), wherein outputting the response is based at least in part on disabling the timer (Raymann, ¶61-a nap timer that will sound an alarm after the specified amount of time has elapsed).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Connor to include enabling the timer based at least in part on a condition; and disabling the timer based at least in part on the timer lapsing, wherein outputting the response is based at least in part on disabling the timer of Raymann in order to realize the benefit of sleep without the grogginess that is experienced when a user is awakened from a deep sleep (Raymann, ¶64).
Regarding clam 10, the combination of Connor, Kahn, Almen, and Raymann teaches the method of claim 9, further comprising: determining an activity the user is engaged in and a time associated with the activity the user is engaged in based at least in part on sensor data from the wearable device (Raymann, ¶19-sleep logic 102 can interact with various sensors of computing device 100 to detect sleep signals (e.g., user activities, biometric data, etc.) indicating when the user intends to sleep, when the user actually falls asleep, and when the user wakes up; ¶64-sleep logic 102 can determine that the user has fallen asleep when the heart rate and/or breathing rate fall below a sleep start threshold rate for heart rate or breathing rate; ¶35-sleep logic 120 can monitor the environment of computing device 102 to determine the user's activities all the time or on regular intervals (e.g., every 2 minutes, every 5 minutes, etc.) throughout the day to detect and identify the user's activities), the condition comprising the determined activity the user is engaged in (Raymann, ¶61-the napping function can begin the nap timer), wherein enabling the timer is based at least in part on the determined activity the user is engaged in and the time associated with the activity the user is engaged in (Raymann, ¶61-the user can select a timed nap function by manipulating graphical element 402. For example, the timed nap function can wake the user with an alarm after a specified period of time).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Connor to include determining an activity the user is engaged in and a time associated with the activity the user is engaged in based at least in part on sensor data from the wearable device, the condition comprising the determined activity the user is engaged in, wherein enabling the timer is based at least in part on the determined activity the user is engaged in and the time associated with the activity the user is engaged in of Raymann in order to realize the benefit of sleep without the grogginess that is experienced when a user is awakened from a deep sleep (Raymann, ¶64).
Regarding claim 11, the combination of Connor, Kahn, and Almen teaches the method of claim 1, wherein outputting the response comprises: outputting both of the tactile vibration response and an audio response (Raymann, ¶58-the alarm can be presented as a sound, a vibration, and/or a graphical notification presented by computing device 100).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Connor to include wherein outputting the response comprises: outputting both of the tactile vibration response and an audio response of Raymann in order to realize the benefit of sleep without the grogginess that is experienced when a user is awakened from a deep sleep (Raymann, ¶64).
Regarding claim 15, the combination of Connor and Raymann teaches the method of claim 1, further comprising: determining whether the user is allowed to transition into a deep sleep state based at least in part on a sleep cycle parameter defined within an application associated with the user device and the wearable device, or that the transition into the deep sleep state is within a threshold time to a bedtime of the user, or both (Raymann, ¶64-when the user falls into a deep sleep, the user's heartrate and breathing will be reduced even more than initial sleep. Sleep logic 102 can determine that the user has fallen asleep when the heart rate and/or breathing rate fall below a deep sleep threshold rate for heart rate or breathing rate that is lower than the sleep start threshold rate), wherein outputting the response is further based at least in part on the sleep cycle parameter or the transition into the deep sleep state being within the threshold time to the bedtime of the user, or both (Raymann, ¶64-when sleep logic 102 determines that the user's heart rate and/or breathing rate is near or below the deep sleep threshold rate, sleep logic 102 can notify sleep application 146 and sleep application 146 can sound the alarm to wake the user).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Connor to include determining whether the user is allowed to transition into the deep sleep state based at least in part on a sleep cycle parameter defined within an application associated with the user device and the wearable device, or that the transition into the deep sleep state is within a threshold time to a bedtime of the user, or both, wherein outputting the response is further based at least in part on the sleep cycle parameter or the transition into the deep sleep state being within the threshold time to the bedtime of the user, or both of Raymann in order to realize the benefit of sleep without the grogginess that is experienced when a user is awakened from a deep sleep (Raymann, ¶64).
Regarding claim 18, the combination of Connor, Kahn, and Almen teaches the system of claim 17. However, the combination of Connor, Kahn, and Almen does not teach wherein the one or more processors are further configured to: enable the timer based at least in part on a condition; and disable the timer based at least in part on the timer lapsing, wherein the response being output is based at least in part on disabling the timer.
Raymann teaches wherein the one or more processors are further configured to: enable the timer based at least in part on a condition (Raymann, ¶61-the user can select a timed nap function by manipulating graphical element 402. For example, the timed nap function can wake the user with an alarm after a specified period of time); and disable the timer based at least in part on the timer lapsing (Raymann, ¶61-when the timer runs down to zero, the napping function can wake the user with an (e.g., audible) alarm)), wherein the response being output is based at least in part on disabling the timer (Raymann, ¶61-a nap timer that will sound an alarm after the specified amount of time has elapsed).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Connor to include wherein the one or more processors are further configured to: enable the timer based at least in part on a condition; and disable the timer based at least in part on the timer lapsing, wherein the response being output is based at least in part on disabling the timer of Raymann in order to realize the benefit of sleep without the grogginess that is experienced when a user is awakened from a deep sleep (Raymann, ¶64).
Regarding claim 19, the combination of Connor, Kahn, Almen, and Raymann teaches the system of claim 18, wherein the one or more processors are further configured to: determine an activity the user is engaged in and a time associated with the activity the user is engaged in based at least in part on sensor data from the wearable device (Raymann, ¶19-sleep logic 102 can interact with various sensors of computing device 100 to detect sleep signals (e.g., user activities, biometric data, etc.) indicating when the user intends to sleep, when the user actually falls asleep, and when the user wakes up; ¶64-sleep logic 102 can determine that the user has fallen asleep when the heart rate and/or breathing rate fall below a sleep start threshold rate for heart rate or breathing rate; ¶35-sleep logic 120 can monitor the environment of computing device 102 to determine the user's activities all the time or on regular intervals (e.g., every 2 minutes, every 5 minutes, etc.) throughout the day to detect and identify the user's activities), the condition comprising the determined activity the user is engaged in (Raymann, ¶61-the napping function can begin the nap timer), wherein the timer being enabled is based at least in part on the determined activity the user is engaged in and the time associated with the activity the user is engaged in (Raymann, ¶61-the user can select a timed nap function by manipulating graphical element 402. For example, the timed nap function can wake the user with an alarm after a specified period of time).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Connor to include wherein the one or more processors are further configured to: determine an activity the user is engaged in and a time associated with the activity the user is engaged in based at least in part on sensor data from the wearable device, the condition comprising the determined activity the user is engaged in, wherein the timer being enabled is based at least in part on the determined activity the user is engaged in and the time associated with the activity the user is engaged in of Raymann in order to realize the benefit of sleep without the grogginess that is experienced when a user is awakened from a deep sleep (Raymann, ¶64).
Regarding claim 21, the combination of Connor, Kahn, and Almen teaches the method of claim 1. However, the combination of Connor, Kahn, and Almen does not teach displaying, via the user device, an indication of the duration of the timer based at least in part on determining the duration of the timer; and receiving, via the user device, a user input confirming the duration of the timer, wherein outputting the response is based at least in part on reception of the user input confirming the duration of the timer.
Raymann teaches displaying, via the user device, an indication of the duration of the timer based at least in part on determining the duration of the timer (¶61-the user can enter an amount of time in graphical element 404 (e.g., a text box) to specify an amount of time to nap (e.g., nap window); Fig. 4-404 shows an indication of the duration of the timer); and receiving, via the user device, a user input confirming the duration of the timer (¶61- When the timed nap function is selected by the user, the user can enter an amount of time in graphical element 404 (e.g., a text box) to specify an amount of time to nap (e.g., nap window), the user can press and hold an area of the display of computing device 100 (as indicated by graphical element 408) to begin a nap timer that will sound an alarm after the specified amount of time has elapsed; Fig. 4), wherein outputting the response is based at least in part on reception of the user input confirming the duration of the timer (¶61-when the user initially applies pressure to the display, the napping function can begin the nap timer. When the timer runs down to zero, the napping function can wake the user with an (e.g., audible) alarm).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Connor to include displaying, via the user device, an indication of the duration of the timer based at least in part on determining the duration of the timer; and receiving, via the user device, a user input confirming the duration of the timer, wherein outputting the response is based at least in part on reception of the user input confirming the duration of the timer of Raymann in order for the user to select a type of nap (Raymann, ¶60).
Claims 3 is rejected under 35 U.S.C. 103 as being unpatentable over Connor in view of Kahn, Almen, and further in view of Raymann as applied to claim 2 above, and further in view of Capodilupo (US 20210177342 filed on 12/17/20), hereinafter referred to as Cap.
Regarding claim 3, the combination of Connor, Kahn, Almen, and Raymann teaches the method of claim 2. However, the combination of Connor, Kahn, Almen, and Raymann does not teach determining a sleep cycle associated with the user based at least in part on a learning model, wherein detecting whether the user is within the threshold from transitioning into the deep sleep state of the set of sleep states is based at least in part on determining the sleep cycle associated with the user.
Cap teaches determining a sleep cycle associated with the user based at least in part on a learning model (Cap, ¶43-according to the foregoing, sleep of a user may be monitored to detect various sleep states, transitions, and other sleep-related information. For example, the device may monitor/detect the duration of sleep states, the transitions between sleep states, the number of sleep cycles or particular states, the number of transitions, the number of waking events, the transitions to an awake state, and so forth; ¶112-employ machine learning based on individual user behavior to learn individualized characteristics of significant sleep and physiological cycle), wherein detecting whether the user is within the threshold from transitioning into the deep sleep state of the set of sleep states is based at least in part on determining the sleep cycle associated with the user (¶43-device may monitor/detect the duration of sleep states, the transitions between sleep states, the number of sleep cycles or particular states, the number of transitions, the number of waking events, the transitions to an awake state, and so forth; ¶109- if a sleep is received/detected but it is short (e.g., <2 hours) and only crossed the PCE range beginning, it may be considered to be a nap and not a significant sleep. This nuance may not be possible using a strict endpoint technique to predicted cycle ends; ¶40-stage 3 of non-REM sleep generally includes a state of deep sleep, where a person is not easily awakened. Stage 3 is often referred to as delta sleep, deep sleep, or slow wave sleep (i.e., from the high amplitude but small frequency brain waves typically found in this stage). Slow wave sleep is thought to be the most restful form of sleep, which relieves subjective feelings of sleepiness and restores the body).
Cap generally relates to physiological monitoring, and more specifically to management of cycles of physical activity monitored with a wearable device (¶2).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Connor to include determining a sleep cycle associated with the user based at least in part on a learning model, wherein detecting whether the user is within the threshold from transitioning into the deep sleep state of the set of sleep states is based at least in part on determining the sleep cycle associated with the user of Cap in order to employ machine learning based on individual user behavior to learn individualized characteristics of significant sleep and physiological cycles (Cap, ¶112).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Connor in view of Kahn, Almen, and Raymann, and further in view of Cap as applied to claim 3 above, and further in view of Baker (WO 2022006119 filed on 6/29/21).
Regarding claim 4, the combination of Connor, Kahn, Almen, Raymann, and Cap teaches the method of claim 3. However, the combination of Connor, Kahn, Almen, Raymann, and Cap does not teach wherein the learning model determines relationships between one or more of a respective heart rate data associated with the user, a respective sleep state of the set of sleep states associated with the user, a respective time for the user to transition into the respective sleep state of the set of sleep states associated with the user, or a combination thereof.
Baker teaches wherein the learning model determines relationships between one or more of a respective heart rate data associated with the user, a respective sleep state of the set of sleep states associated with the user, a respective time for the user to transition into the respective sleep state of the set of sleep states associated with the user, or a combination thereof (page 8, lines 7-8-the predictive data model can be associated with a plurality of different patterns; page 8, lines 12-15-the patterns can be indicative of different probabilities of the user transitioning to the sleep state at a particular date and time and/or different intervention actions which can improve the probability and/or improve the transition time).
Baker relates to managing sleep for a user (page 1, ¶1).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Connor to include wherein the learning model determines relationships between one or more of a respective heart rate data associated with the user, a respective sleep state of the set of sleep states associated with the user, a respective time for the user to transition into the respective sleep state of the set of sleep states associated with the user, or a combination thereof of Baker in order to provide a sleep intervention strategy to increase or improve sleep for the user (Baker, page 7, lines 27-29).
Claims 5-6 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Connor in view of Kahn and Almen as applied to claim 1 above, and further in view of Cap.
Regarding claim 5, the combination of Connor, Kahn, and Almen teaches the method of claim 1. However, the combination of Connor, Kahn, and Almen does not teach determining a sleep cycle associated with the user based at least in part on a sleep cycle parameter defined within an application associated with the user device and the wearable device, wherein detecting whether the user is beginning to transition into a deep sleep state of the set of sleep states is based at least in part on determining the sleep cycle associated with the user.
Cap teaches determining a sleep cycle associated with the user based at least in part on a sleep cycle parameter defined within an application associated with the user device and the wearable device (¶43-the device may monitor/detect…the number of sleep cycles or particular states; ¶122-the user interface 702 may be rendered on a smart phone, tablet, laptop, desktop, or any other suitable user device; Fig. 7), wherein detecting whether the user is beginning to transition into the a deep sleep state of the set of sleep states is based at least in part on determining the sleep cycle associated with the user (¶43-device may monitor/detect the duration of sleep states, the transitions between sleep states, the number of sleep cycles or particular states, the number of transitions, the number of waking events, the transitions to an awake state, and so forth; ¶109- if a sleep is received/detected but it is short (e.g., <2 hours) and only crossed the PCE range beginning, it may be considered to be a nap and not a significant sleep. This nuance may not be possible using a strict endpoint technique to predicted cycle ends; ¶40-stage 3 of non-REM sleep generally includes a state of deep sleep, where a person is not easily awakened. Stage 3 is often referred to as delta sleep, deep sleep, or slow wave sleep (i.e., from the high amplitude but small frequency brain waves typically found in this stage). Slow wave sleep is thought to be the most restful form of sleep, which relieves subjective feelings of sleepiness and restores the body).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Connor to include determining a sleep cycle associated with the user based at least in part on a sleep cycle parameter defined within an application associated with the user device and the wearable device, wherein detecting whether the user is beginning to transition into a deep sleep state of the set of sleep states is based at least in part on determining the sleep cycle associated with the user of Cap in order to employ machine learning based on individual user behavior to learn individualized characteristics of significant sleep and physiological cycles (Cap, ¶112).
Regarding claim 6, the combination of Connor, Kahn, Almen, and Cap teaches the method of claim 5, comprising: receiving an input from the user defining the sleep cycle parameter via a graphical user interface and within the application associated with the user device and the wearable device, wherein determining the sleep cycle associated with the user is based at least in part on the received input from the user defining the sleep cycle parameter (Kahn, ¶37-the user may define a number of sleep cycles he or she would like to sleep for, through the user interface 275).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Connor to include receiving an input from the user defining the sleep cycle parameter via a graphical user interface and within the application associated with the user device and the wearable device, wherein determining the sleep cycle associated with the user is based at least in part on the received input from the user defining the sleep cycle parameter of Kahn in order to optimize the length of a power nap for each individual based on analysis of previously obtained sleep and nap patterns and additional user information or wake up the subject before he or she crosses into non-REM sleep that would cause sluggishness (Kahn, ¶15).
Regarding claim 8, the combination of Connor, Kahn, Almen, and Cap teaches the method of claim 5, further comprising: determining the sleep cycle associated with the user based at least in part on a profile of the user (Cap, ¶40-sleep cycles), the profile of the user comprising an age of the user, an average sleep cycle associated with the user, a Readiness Score associated with the user, a Sleep Score associated with the user, or a combination thereof (Cap, ¶54-calculating a sleep score and communicating this score to a user; ¶112-individual user behavior), wherein detecting whether the user is beginning to transition into the deep sleep state of the set of sleep states is based at least in part on determining the sleep cycle associated with the user (Cap, ¶43-device may monitor/detect the duration of sleep states, the transitions between sleep states, the number of sleep cycles or particular states, the number of transitions, the number of waking events, the transitions to an awake state, and so forth; ¶109- if a sleep is received/detected but it is short (e.g., <2 hours) and only crossed the PCE range beginning, it may be considered to be a nap and not a significant sleep. This nuance may not be possible using a strict endpoint technique to predicted cycle ends; ¶40-stage 3 of non-REM sleep generally includes a state of deep sleep, where a person is not easily awakened. Stage 3 is often referred to as delta sleep, deep sleep, or slow wave sleep (i.e., from the high amplitude but small frequency brain waves typically found in this stage). Slow wave sleep is thought to be the most restful form of sleep, which relieves subjective feelings of sleepiness and restores the body).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Connor to include determining the sleep cycle associated with the user based at least in part on a profile of the user, the profile of the user comprising an age of the user, an average sleep cycle associated with the user, a Readiness Score associated with the user, a Sleep Score associated with the user, or a combination thereof, wherein detecting whether the user is beginning to transition into the deep sleep state of the set of sleep states is based at least in part on determining the sleep cycle associated with the user of Cap in order to employ machine learning based on individual user behavior to learn individualized characteristics of significant sleep and physiological cycles (Cap, ¶112).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Connor in view of Kahn and Almen, and further in view of Cap as applied to claim 5 above, and further in view of Raymann.
Regarding claim 7, the combination of Connor, Kahn, Almen, and Cap teaches the method of claim 5. However, the combination of Connor, Kahn, Almen, and Cap does not teach wherein the sleep cycle parameter is predefined.
Raymann teaches wherein the sleep cycle parameter is predefined (Raymann, ¶47-sleep logic 102 can use pressure sensor 114 to determine when the user intends to sleep and the start of sleep. Sleep logic 102 (or sleep application 146 ) can present a graphical user interface on a touch sensitive display of computing device 100; ¶60-graphical element 402 (e.g., a nap type selector) can be manipulated (e.g., selected, slid, etc.) by the user to select a type of nap).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Connor to include wherein the sleep cycle parameter is predefined of Raymann in order to adjust the alarm clock settings based on a user's sleep goal (Raymann, ¶56).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20210150873: the user can tell the device “I am taking a nap.” The interactive audio device can then audibly help the user fall asleep and then, by monitoring sleep and/or time, guide a person to an appropriate duration of nap based on the expected available time, an estimate of current sleep deficit, time of day, and user request. For example, it may optimize to target durations such as a 20 min, 30 min, 60 min or 90 min (full sleep cycle) (¶256).
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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/L.N.H./Examiner, Art Unit 3792
/AMANDA L STEINBERG/Examiner, Art Unit 3792
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