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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/16/25 has been entered.
Status of Claims
Claims 1-12 and 14-20 are rejected. Claim 13 is canceled.
Response to Arguments
Claim Rejections - 35 USC § 112
Applicant's arguments filed 12/16/25 have been fully considered but they are not persuasive.
Applicant asserts that there is support in the specification for “a first sleep state” (¶36; ¶38) and “a second sleep state” (¶38; ¶95). However, the specification additionally discloses that “different sleep stages, including an awake sleep stage, a REM sleep stage, a light sleep stage (NREM), and a deep sleep stage (NREM)” (¶30). The specification does not disclose “a first sleep state” and “a second sleep state,” and the claim language is broader than a light sleep state and a deep sleep being the first and second sleep states respectively, because there are other sleep states indicated.
Claim Rejections - 35 USC § 103
Applicant’s arguments with respect to claims 1-20 have been considered but are moot in view of
the new ground of rejection. Newly applied reference Raymann teaches the amended limitations of “detect autonomously, via one or more processors coupled with the wearable device or with a user device, that the user is napping based at least in part on detecting that the user is entering a first sleep state of a set of sleep states during a first time interval within a day” (¶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; ¶77-a time when computing device 100 first detected the sleep signals at step 808. Computing device 100 can determine that the user is asleep when computing device 100 detects the sleep signals for a period of time (e.g., 10 minutes)); and “a second sleep state” (¶64-the user can select a power nap function that wakes the user with an alarm when sleep logic 102 determines that the user is entering a deep sleep).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-12 and 14-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 17, and 20 recite “a first sleep state” and claims 1-3, 5, 8, 12, 14-15, and 17-20 recite “a second sleep state.” The specification discloses:
“ensure that the user 102-a (User 1) remains in a light sleep state and does not transition to a deep sleep state” (¶38); and
“classify periods of time into different sleep stages, including an awake sleep stage, a REM sleep stage, a light sleep stage (NREM), and a deep sleep stage (NREM)” (¶30).
However, there is insufficient written description under 35 U.S.C. 112(a), due to only disclosing some of the many species that encompass the broad or large genus of any or all possible sleep stages. MPEP 2163(II)(A)(3)(a)(ii) states:
The written description requirement for a claimed genus may be satisfied through sufficient description of a representative number of species by actual reduction to practice (see i)(A) above), reduction to drawings (see i)(B) above), or by disclosure of relevant, identifying characteristics, i.e., structure or other physical and/or chemical properties, by functional characteristics coupled with a known or disclosed correlation between function and structure, or by a combination of such identifying characteristics, sufficient to show the inventor was in possession of the claimed genus (see i)(C) above). See Eli Lilly, 119 F.3d at 1568, 43 USPQ2d at 1406. See Juno Therapeutics, Inc. v. Kite Pharma, Inc., 10 F.4th 1330, 1337, 2021 USPQ2d 893 (Fed. Cir. 2021) ( "[T]he written description must lead a person of ordinary skill in the art to understand that the inventor possessed the entire scope of the claimed invention. Ariad, 598 F.3d at 1353–54 ('[T]he purpose of the written description requirement is to ensure that the scope of the right to exclude, as set forth in the claims, does not overreach the scope of the inventor's contribution to the field of art as described in the patent specification.' (internal quotation marks omitted).").”
While ¶30 of the specification discloses that there are different sleep stages, including an awake sleep stage, a REM sleep stage, a light sleep stage (NREM), and a deep sleep stage (NREM), this differs from the broad claim language as Applicant indicates that “a first sleep state” corresponds to light sleep and “a second sleep state” corresponds to deep sleep (Remarks, filed 12/16/25, page 9). The claims recites a broad genus that does not encompass any or all possible sleep stages.
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 1-2, 9-12, 14-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Connor (US 20210379388 filed on 8/24/21) in view of Raymann (US 20170094046 filed on 9/30/15).
Regarding claims 1, 17, and 20, Connor teaches a method, an apparatus, and a non-transitory computer-readable medium for managing sleep, comprising: a processor (¶286-processor); memory coupled with the processor (¶286-memory); and instructions stored in the memory and executable by the processor (¶286) to cause the apparatus to: acquire, via one or more light-emitting components and one or more light-receiving components of a wearable device configured to be worn on a finger of a user, physiological data associated with the user (¶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), 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 to detect autonomously, via one or more processors coupled with the wearable device or with a user device, that the user is napping based at least in part on detecting that the user is entering a first sleep state of a set of sleep states during a first time interval within a day; detect, via the one or more processors coupled with the wearable device or with the user device, whether the user is beginning to transition into a second sleep state of the set of sleep states based at least in part on the acquired physiological data; and output a response based at least in part on the at least heart rate data before the user transitions into the second sleep state or based at least in part on a timer lapsing, the response comprising a tactile vibration response, wherein the outputting is in accordance with whether the user is scheduled to transition into the second sleep state.
Raymann generally relates to human sleep detection (¶1). Raymann further teaches the invention using the following steps:
detect autonomously, via one or more processors coupled with the wearable device or with a user device, that the user is napping based at least in part on detecting that the user is entering a first sleep state of a set of sleep states during a first time interval within a day (¶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; ¶77-a time when computing device 100 first detected the sleep signals at step 808. Computing device 100 can determine that the user is asleep when computing device 100 detects the sleep signals for a period of time (e.g., 10 minutes)); detect, via the one or more processors coupled with the wearable device or with the user device, whether the user is beginning to transition into a second sleep state of the set of sleep states based at least in part on the acquired physiological data (¶64-the user can select a power nap function that wakes the user with an alarm when sleep logic 102 determines that the user is entering a deep sleep, computing device 100 (e.g., a wearable device such as a watch) can detect when the user falls into (or is about to fall into) a deep sleep state. For example, sensors built into computing device 100 can detect the rate and variability of heartbeats and breathing); and output a response based at least in part on the at least heart rate data before the user transitions into the second sleep state or based at least in part on a timer lapsing (¶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; ¶61-a nap timer that will sound an alarm after the specified amount of time has elapsed), the response comprising a tactile vibration response (¶58-the alarm can be presented as a sound, a vibration, and/or a graphical notification presented by computing device 100), wherein the outputting is in accordance with whether the user is scheduled to transition into the second sleep state (¶64-the user can select a power nap function that wakes the user with an alarm when sleep logic 102 determines that the user is entering a deep sleep; ¶60-GUI 400 can present graphical element 402. 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; ¶61).
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 autonomously, via one or more processors coupled with the wearable device or with a user device, that the user is napping based at least in part on detecting that the user is entering a first sleep state of a set of sleep states during a first time interval within a day; detecting, via the one or more processors coupled with the wearable device or with the user device, whether the user is beginning to transition into a second sleep state of the set of sleep states based at least in part on the acquired physiological data; and outputting a response based at least in part on the at least heart rate data before the user transitions into the second sleep state or based at least in part on a timer lapsing, the response comprising a tactile vibration response, wherein the outputting is in accordance with whether the user is scheduled to transition into the second 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 claims 2 and 18, the combination of Connor and Raymann teaches the method and the apparatus of claims 1 and 17, further comprising: detecting that the user is within a threshold from transitioning into the second sleep state based at least in part on at least the heart rate data associated with the user (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 based at least in part on detecting that the user is within the threshold from transitioning into the second 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).
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 the second sleep state based at least in part on at least the heart rate data associated with the user, wherein outputting the response is based at least in part on detecting that the user is within the threshold from transitioning into the second 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 and Raymann teaches the method of claim 1, further comprising: 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 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 and Raymann 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 12, the combination of Connor and Raymann teaches the method of claim 1, wherein the second sleep state comprises a non-rapid eye movement (NREM) sleep state (Raymann, ¶64- entering a deep sleep).
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 second sleep state comprises a non-rapid eye movement (NREM) 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 14, the combination of Connor and Raymann teaches the method of claim 1, wherein the sleep state comprises a deep sleep state (Raymann, ¶64- entering a deep sleep).
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 comprises a 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 15, the combination of Connor and Raymann teaches the method of claim 1, further comprising: determining whether the user is allowed to transition into the second 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 second 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 based at least in part on the sleep cycle parameter or the transition into the second 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 second 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 second sleep state is within a threshold time to a bedtime of the user, or both, wherein outputting the response is based at least in part on the sleep cycle parameter or the transition into the second 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 16, the combination of Connor and Raymann 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).
Claims 3, 5, 7-8, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Connor in view of Raymann as applied to claims 1-2, 9-12, 14-18, and 20 above, and further in view of Capodilupo (US 20210177342 filed on 12/17/20), hereinafter referred to as Cap.
Regarding claims 3 and 19, the combination of Connor and Raymann teaches the method and the apparatus of claims 1 and 17. However, the combination of Connor 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 beginning to transition into the second 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 beginning to transition into the second 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 beginning to transition into the second 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 5, the combination of Connor and Raymann teaches the method of claim 1. However, the combination of Connor and Raymann 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 the second 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 second 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 the second 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 7, the combination of Connor, Raymann, and Cap teaches the method of claim 5, 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 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 8, the combination of Connor, Raymann, 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 second 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 second 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 Raymann and further in view of Cap as applied to claims 1 and 3 above, and further in view of Baker (WO 2022006119 filed on 6/29/21).
Regarding claim 4, the combination of Connor, Raymann, and Cap teaches the method of claim 3. However, the combination of Connor, 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).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Connor in view of Raymann and Cap as applied to claim 5 above, and further in view of Kahn (US 20160073951 filed on 11/23/15).
Regarding claim 6, the combination of Connor, Raymann, and Cap teaches the method of claim 5. However, the combination of Connor, Raymann, and Cap does not teach 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 teaches 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).
Kahn relates to motion sensing, and more particularly to monitoring a user's motions to improve rest (¶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, Raymann, and Cap 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).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAURA HODGE whose telephone number is (571) 272-7101. The examiner can normally be reached M-F: 8:00 am-5:00 pm.
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/L.N.H./Examiner, Art Unit 3792
/UNSU JUNG/Supervisory Patent Examiner, Art Unit 3792