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 .
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, 10, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Trivedi (US 2015/0258301) in view of Yang (US 2006/0183980).
Regarding claim 1, Trivedi teaches obtaining a first biological signal collected when a first sleep-aiding audio signal in a sleep- aiding audio library is played, wherein the first biological signal is a biological signal of a first user; and updating the sleep-aiding audio library based on a sleep state of the first user. (Paragraph [0020], “Sleep state data 130 may be determined based on sensor data received from one or more sensors coupled to smartphone 121, band 122, media device 125, or another wearable device or device” and “Various sensors may be used to capture various sensor data, including physiological data, activity or motion data, location data, environmental data, and the like. Physiological data may include, for example, heart rate, body temperature, bioimpedance, galvanic skin response (GSR), blood pressure, and the like”; Paragraph [0018], “Sleep state manager 110 may be configured to select a portion or piece of audio content 150 from a plurality of portions or pieces of audio content stored in an audio content library 140 as a function of sleep state data 130. Sleep state manager 110 may present white noise to help user 120 transition from sleep preparation to being asleep. If user 120 does not fall asleep within a time period (e.g., 30 minutes), for example, sleep state manager 110 may select audio content 150 to provide or state a recommendation to user 120. Sleep state manager 110 may select audio content 150 that helps user 120 transition between sleep states quickly. Sleep state manager 110 may select audio content 150 that helps user 120 transition between sleep states gradually, which may be more comfortable or desirable for user 120, for example, because he is not suddenly woken from deep sleep or REM sleep”). The audio is played while the user sleeps and so the biological signals collected while the user is asleep is collected when the first sleep-aiding audio signal is played. The examiner is interpreting the switching of audio content as updating the sleep-aiding audio library based on sleep state.
However, Trivedi does not teach determining a sleep quality of the first user based on the first biological signal and updating the sleep-aiding audio library based on the sleep quality of the first user.
Yang teaches determining a sleep quality of a first user based on the first biological signal and updating audio based on the sleep quality of the first user. (Claim 11. “the close-range monitoring system judges sleep quality through when the user goes to bed, time it takes the user to fall asleep, heartbeat, breathing pattern, muscular relaxation level, number of times the user gets up at night, and the duration of sleep. If sleep quality is poor, the system will suggest medication and behavior therapy. Then the system will continuously monitor, follow-up, and analyze if there is any improvement.” Claim 23. “As stated in claim 1, if sleep quality is poor, the close-range monitoring system can provide corrective suggestions; including sleep hygiene, relaxation therapy, stimulus control therapy, or other therapies. Through images, voice, words, 3-D projections, virtual reality software, feel, vibration, et cetera, it can give suggestions to the user. The system will continuously monitor and follow-up.”) The examiner is interpreting heartbeat, breathing pattern and muscular relaxation level of claim 11 to be the first biological signal and the voice, words, et cetera of claim 23 to be updating audio.
It would be prima facie obvious to one of ordinary skill in the art to modify the system in Trivedi to incorporate determining a sleep quality of as taught by Yang. One of ordinary skill in the art would be able to recognize users have differing lifestyles and living habits that change regularly and can cause many people to not eat, sleep, and function properly. Lack of sleep due to poor habits can lead to poor physical and mental health and reduction in quality of life. Therefore, it is important to measure sleep quality based on an individual’s lifestyle, emotional, and physical changes for a continuous long-term period. See Yang paragraphs [0004] – [0007].
It would also be prima facie obvious to one of ordinary skill in the art to modify the sleep-aiding audio library of Trivedi to incorporate modifying the sleep-aiding audio library/an audio based on sleep quality as taught by Yang to improve the quality of sleep of the user.
Regarding claim 10 and 19, Trivedi teaches an apparatus for updating sleep-aiding audio signals, comprising at least one processor and a memory, wherein the at least one processor is coupled to the memory, and is configured to read and execute instructions in the memory, to cause the apparatus to perform operations and a non-transitory computer readable medium, storing computer program code, and when the computer program code is run on a computer, the computer is enabled to perform operations. (paragraph [0039], Fig 10, “ Computing platform 1010 includes a bus 1001 or other communication mechanism for communicating information, which interconnects subsystems and devices, such as processor 1019, system memory 1020 (e.g., RAM, etc.), storage device 1018 (e.g., ROM, etc.), a communications module 1017 (e.g., an Ethernet or wireless controller, a Bluetooth controller, etc.) to facilitate communications via a port on communication link 1023 to communicate, for example, with a computing device, including mobile computing and/or communication devices with processors” and paragraph [0040], “instructions may be embedded in software or firmware. The term ‘computer readable medium’ refers to any tangible medium that participates in providing instructions to processor 1019 for execution”). Trivedi further teach obtaining a first biological signal collected when a first sleep-aiding audio signal in a sleep- aiding audio library is played, wherein the first biological signal is a biological signal of a first user; determining a sleep quality of the first user based on the first biological signal; and updating the sleep-aiding audio library based on a sleep quality of the first user (see rejection of claim 1).
Claims 2, 3, 5, 6, 11, 12, 14, 15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Trivedi in view of Yang further in view of Garcia Molina (2019/0083028).
Regarding claims 2, 11, and 20 Trivedi teaches determining at least one sleep stage of a plurality of sleep stages based on the first biological signal. (paragraph [0020], “Sleep state data 130 may be determined based on sensor data received from one or more sensors coupled to smartphone 121, band 122, media device 125, or another wearable device or device” and “Various sensors may be used to capture various sensor data, including physiological data, activity or motion data, location data, environmental data, and the like. Physiological data may include, for example, heart rate, body temperature, bioimpedance, galvanic skin response (GSR), blood pressure, and the like” and “Sensor data may be processed to determine a sleep state of user”; paragraph [0032], “Sleep states may be sleep preparation 401, sleeping or being asleep 402, light sleep 403, deep sleep 404, wakefulness 405, and the like. Sleep state transitions or continuations may be sleep onset 421, sleep continuity 422, transitioning between light sleep and deep sleep 423, waking up 424, and sleep continuity 425.”; Claim 21, “The method of claim 3, wherein the one or more sub-sleep states comprise light sleep, deep sleep, and REM sleep.”)
However, Trivedi does not teach determining, based on the at least one sleep stage, a sleep quality corresponding to the at least one sleep stage.
Garcia Molina teaches determining, based on the at least one sleep stage, a sleep quality corresponding to the at least one sleep stage. (Paragraph [0033]. Sleep score assessment system 120 is configured to receive the user data associated with a sleep session and determine one or more sleep metrics associated with the first sleep session. For example, sleep architecture, sleep continuity, a sleep onset, and/or a sleep wakeup time are all exemplary types of metrics capable of being determined for a particular sleep session. The sleep architecture, for instance, includes duration of each sleep stage (e.g., N3, N2, N1, REM), a latency of each sleep stage, and/or a survival curve of each sleep stage. Paragraph [0088]. The sleep session score threshold value is a parameter than indicates a quality of a sleep session score value. For example, if a sleep session score value exceeds the sleep session score threshold value, then that particular sleep session may be classified as being “good” sleep, whereas sleep session score values less than the sleep session score threshold value may be classified as being “poor” sleep. The particular value attributed to the threshold is capable of being set by user 170 and/or sleep score assessment system 120, and alternatively, the value is capable of being determined based on a history of sleep sessions score values of a user. For example, an average sleep session score value for a particular amount of time (e.g., one week, one month, etc.) may be used as the sleep session score threshold value, however persons of ordinary skill in the art will recognize that this is merely exemplary.”) The examiner is interpreting the sleep session score value as an indicator of sleep quality which depends on various sleep metrics such as the duration of a sleep stage.
It would prima facie obvious to one of ordinary skill in the art to modify the teachings of Trivedi and Yang to incorporate the teachings of Garcia Molina of determining sleep quality based on sleep stage for the purpose of a more robust determination of sleep quality based on more user sleep data.
Regarding claims 3 and 12, Trivedi teaches wherein the sleep-aiding audio library comprises sleep- aiding audio signals corresponding to the plurality of sleep stages (Fig. 4, paragraph [0032], “Portions of audio content 421-425 may be selected as a function of sleep states 401-405. Portions of audio content 421-425 may also be selected as a function of sleep state transitions or continuations 421-425. In some examples, based on a sleep state being sleep preparation 401, audio content 451 may be selected and presented to facilitate sleep onset 421. In some examples, a sleep state may be sleeping 402. To maintain sleep continuity 422, audio content 452 may be selected. In some examples, an interference may be detected during sleeping 402, and audio content 452 may be selected to maintain sleep continuity 422. In some examples, data representing light sleep 403 or deep sleep 404 may be received, and audio content 453 may be selected to transition between them. In some examples, another audio content (not shown) may be selected to maintain continuity of light sleep 403 or deep sleep 404. A user may transition between light sleep 403 and deep sleep 404 multiple times while in the sleeping state 402. In some examples, a sleep state of wakefulness 405 may be detected, and audio content 455 may be selected to maintain sleep continuity 425. In some examples, a sleep state of sleeping 402 may be detected, and audio content 454 may be selected to facilitate waking up 424. In some examples, sleeping 402 may be detected after audio content 454 is presented, and another audio content (not shown) may be selected and presented.”) The examiner is interpreting the selecting and presenting of the audio content as the updating of the sleep-aiding audio signal. Trivedi, Yang, and Garcia Molina disclose as discussed for claims 2, 11 and 20 above, updating the sleep-aiding audio library based on the sleep quality of the first user where the sleep quality of the user is based on the sleep stage so in combination the three references teach updating a sleep-aiding audio signal corresponding to the at least one sleep stage in the sleep-aiding audio library (audio signal added/updated based on sleep quality which is based on sleep stage).
Regarding claim 5 and 14, Trivedi in view of Garcia Molina teaches determining, based on a duration of the at least one sleep stage, the sleep quality corresponding to the at least one sleep stage (Garcia Molina teaches determining, based on the at least one sleep stage, a sleep quality corresponding to the at least one sleep stage. (Paragraph [0033]. Sleep score assessment system 120 is configured to receive the user data associated with a sleep session and determine one or more sleep metrics associated with the first sleep session. For example, sleep architecture, sleep continuity, a sleep onset, and/or a sleep wakeup time are all exemplary types of metrics capable of being determined for a particular sleep session. The sleep architecture, for instance, includes duration of each sleep stage (e.g., N3, N2, N1, REM), a latency of each sleep stage, and/or a survival curve of each sleep stage, Paragraph [0088]. The sleep session score threshold value is a parameter than indicates a quality of a sleep session score value. For example, if a sleep session score value exceeds the sleep session score threshold value, then that particular sleep session may be classified as being “good” sleep, whereas sleep session score values less than the sleep session score threshold value may be classified as being “poor” sleep. The particular value attributed to the threshold is capable of being set by user 170 and/or sleep score assessment system 120, and alternatively, the value is capable of being determined based on a history of sleep sessions score values of a user. For example, an average sleep session score value for a particular amount of time (e.g., one week, one month, etc.) may be used as the sleep session score threshold value, however persons of ordinary skill in the art will recognize that this is merely exemplary.”) The examiner is interpreting the sleep session score value as an indicator of sleep quality which depends on various sleep metrics such as the duration of sleep stage.
However, Trivedi and Garcia Molina fail to teach determining, based on a reference value corresponding to the at least one sleep stage, the sleep quality corresponding to the at least one sleep stage.
Garcia Molina teaches determining, based on a reference value corresponding to the at least one sleep stage, the sleep quality corresponding to the at least one sleep stage (paragraph [0036], “For example, reference database 150 may store sleep information associated with an age of user 170, a gender of user 170, a chronotype of user 170, and the like. In a non-limiting embodiment, reference sleep information associated with user 170 is stored by reference database 150”. Paragraph [0088], “For instance, sleep session threshold values are capable of being stored by user history database 140 and/or reference database 150. The sleep session score threshold value is a parameter than indicates a quality of a sleep session score value….For example, if a sleep session score value exceeds the sleep session score threshold value, then that particular sleep session may be classified as being “good” sleep, whereas sleep session score values less than the sleep session score threshold value may be classified as being “poor” sleep.”)
It would be prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Trivedi in view of Yang (sleep quality corresponding to at least one sleep stage) with the teachings of Garcia Molina (reference value according to the at least one sleep stage). One of ordinary skill in the art would be able to recognize that sleep quality is unique to an individual and a meaningful and quantifiable metric for an individual to understand that would encourage healthy sleep habits (see Garcia Molina, paragraph [0003]).
Regarding claim 6 and 15, Trivedi teaches obtaining feedback information of the first user for the first sleep-aiding audio signal. (paragraph [0031], “, a user may use user interface 324 to enter biographical information, such as age, sex, and the like. Biographical information may be used by sleep state manager 310 to select, tailor, or customize audio content.”)
However, Trivedi fails to teach updating, based on the feedback information, the reference value corresponding to the at least one sleep stage.
Garcia Molina teaches updating, based on the feedback information, the reference value corresponding to the at least one sleep stage. (Paragraph [0078]. “In one embodiment, feedback provided by user 170 is also capable of being used to adjust an amount and magnitude of the deductions applied, as well as adjust an amount and magnitude of the bonuses applied…..if sleep score assessment system 120 determines that user 170 is continually having sleep sessions scores that are less than a certain value, sleep score assessment system 120 may be prompted to ask one or more questions (e.g., via a graphical user interface displayed by a display screen), the answers of which may be used to adjust and/or calibrate the deduction parameters and/or the bonus parameters.” )
It would be prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use modify the feedback information of biological information as taught by Trivedi to use the feedback information to update reference values as taught by Garcia Molina in order to tailor data to a specific user.
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Trivedi, Yang, Garcia Molina, and further in view of Wetmore (US 2014/0057232).
Regarding claims 4 and 13, Trivedi, Yang, and Garcia Molina further teaches determining a target sleep-aiding audio signal based on the updated sleep-aiding audio signal corresponding to the at least one sleep stage (Trivedi teaches updating and playing the sleep signal based on sleep stage, the played signal being interpreted as the target sleep-aiding audio signal [0018]), but fails to teach wherein the target sleep-aiding audio signal is to be played for the first user when the first user is in the at least one sleep stage.
Wetmore teaches wherein the target sleep-aiding audio signal is to be played for the first user when the first user is in the at least one sleep stage. (paragraph [0316], “…stimuli are delivered only for a portion of the night based on likelihood of a particular target stage of sleep occurring relative to sleep onset. Beneficial embodiments incorporate historical data from a user to optimize the timing of stimulation (sensory, electrical, or other forms of neuromodulation) based on when subject tends to be in a beneficial stage of sleep for affecting brain activity.”) The examiner is interpreting “target sleep-aiding audio” in the claim as an audio signal associated with a desired stage of sleep. Therefore, the prior art is being interpreted as a sensory stimulus (audio signal) being provided to a user to reach a beneficial or particular (target) sleep state.
It would be prima facie obvious to one of ordinary skill in the art to modify the teachings of Trivedi, Yang, and Garcia Molina to provide sleep-aiding audio when the user is a particular sleep stage as taught by Wetmore as slow-wave sleep is known to be beneficial for various cognitive functions, as well as other aspects of health outside of the nervous system such as the adaptive immune system. (Wetmore paragraphs [0005] – [0016]) so one would want to try to maximize being in the slow-wave sleep stage.
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Trivedi in view of Yang, and Garcia Molina further in view of Chang (US 2015/0187199).
Regarding claims 7 and 16, Trivedi in view of Yang, and Garcia Molina teach wherein the updating of the sleep-aiding audio library based on the quality.
However, Trivedi in view of Yang fails to teach updating a sequence of the sleep-aiding audio signals in the sleep-aiding audio library based on the sleep quality of the first user; and/or deleting one or more sleep-aiding audio signals from the sleep-aiding audio library based on the sleep quality of the first user.
Chang teaches updating a sequence of the sleep-aiding audio signals in the sleep-aiding audio library based on the sleep quality of the first user; and/or deleting one or more sleep-aiding audio signals from the sleep-aiding audio library based on the sleep quality of the first user, and wherein the first sleep-aiding audio signal is a newly added sleep-aiding audio signal. (paragraph [0035], “When the user enters sleep, the server can generates corresponding feedback signal 116 which commands the electronic device 130 to stop playing the audio file or stream or enter into standby mode. In addition, when the received parameter signal 114 indicates the user's brainwave state is not in deep sleep, the server 120 can command the electronic device 130 to switch the currently played audio file or stream in order to aid the user's sleep quality”, paragraph [0050], “The associated information of all played audio files or streams are kept in record along with the concurrent brainwave state represented by the sleepless degree parameters, so when the sleep aid system determines which audio file or stream should be played, priorities of the audio files or streams that are less effective in aiding the user's sleep will be lowered in the determination process based on the history records. Hence any audio tile or stream with a lower priority will be prevented from being played firstly”, paragraph [0037], “For instance, the speaker device 150 can be utilized to play an audio file or stream, transmit data indicating the currently played audio file or stream to the server 120, receive the feedback signal 116 from the server 120, and switch the currently played audio file or stream to another audio file or stream according to the feedback signal 116”). The examiner is interpreting “stream with a lower priority” and “prevented from being played firstly” as deleting one or more sleep-aiding audio signals from the sleep-aiding audio library. The examiner is interpreting “switch the currently played audio file or stream to another according to the feedback signal” as “updating a sequence of the sleep-aiding audio signal in the sleep-aiding audio library” and as “the first sleep-aiding audio signal is a newly added sleep-aiding audio signal.
It would be prima facie obvious to someone of ordinary skill in the art to modify the sleep-aiding audio signals of Trivedi to incorporate the teachings of Chang of update the sequence and/or deleting an audio signal. One of ordinary skill in the art would have recognized that sleep is an individualized trait that would need to be programmed for each user based on both biological and sleep data. In general, it's easier for people to fall asleep in a comfortable and familiar environment. Some people may like to listen to calm music in bed in order to aid sleep. In other cases, playing music might be disturbing their sleep patterns (paragraphs [0005] – [0007] of Chang). Therefore, it would have been obvious to prioritize a sequence of sleep-aiding audio signals based on the sleep quality and sleep stages of the user.
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Trivedi, Yang, and further in view of Chang.
Regarding claim 8, Trivedi and Yang fail to disclose wherein the first sleep-aiding audio signal is a newly added sleep-aiding audio signal Chang teaches wherein the first sleep-aiding audio signal is a newly added sleep-aiding audio signal (Paragraph [0037]. “In another embodiment of the present invention as shown in FIG. 1D, the sleep aid system 100 can further include a speaker device 150. For instance, the speaker device 150 can be utilized to play an audio file or stream, transmit data indicating the currently played audio file or stream to the server 120, receive the feedback signal 116 from the server 120, and switch the currently played audio file or stream to another audio file or stream according to the feedback signal 116.”) The examiner is interpreting the switch from the currently played audio file to another audio file as the newly added sleep-aiding audio signal.
It would be prima facie obvious to one of ordinary skill in the art before the effective filing date of the application to modify the teachings in Trivedi and Yang with the teachings in Chang. One of ordinary skill in the art would have been able to recognize that having quality sleep is vital to one's health. It is generally easier for people to fail asleep in a comfortable and familiar environment. However, environmental factors may not always be in control of the user and people may lose the feeling of sleepiness easily which may cause sleep disorder issues. Accordingly, a system for establishing or improving a comfortable sleep environment is strongly needed, so as to help a person to smoothly enter into sleep (see paragraphs [0005] – [0007] of Chang) and a system can only improve the environment by being able to receive updated signals throughout the duration of a person’s sleep.
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Trivedi, Yang further in view of McLaughlin (US 2018/0193589).
Regarding claims 9 and 18, Trivedi teaches determining a sleep state of a second user, wherein the sleep state of the second user is determined based on a second biological signal, the second biological signal is a biological signal of the second user, and the second biological signal is collected when a second sleep-aiding audio signal in the sleep-aiding audio library is played. (Fig 6, paragraph [0034] FIG. 6 illustrates a network of devices of a plurality of users, the devices to be used with sleep state managers, according to some examples. As shown, FIG. 6 includes server or node 680, audio content library 640, and users 621-623. Each user 621-623 may use one or more devices having a sleep state manager. The devices of users 621-623 may communicate with each other over a network, and may be in direct data communication with each other, or be in data communication with server 680. Server 680 may include audio content library 640. Audio content library 640 may store one or more portions of audio content. Users 621-623 may upload, share, or store audio content on audio content library 640, and may retrieve or download audio content from audio content library 640”).
However, Trivedi does not teach determining a sleep quality of the second user based on the second biological signal and updating the sleep-aiding audio library based on the sleep quality of the second user.
Yang teaches determining a sleep quality of a user based on the first biological signal and updating audio based on the sleep quality of the user. (Claim 11. “the close-range monitoring system judges sleep quality through when the user goes to bed, time it takes the user to fall asleep, heartbeat, breathing pattern, muscular relaxation level, number of times the user gets up at night, and the duration of sleep. If sleep quality is poor, the system will suggest medication and behavior therapy. Then the system will continuously monitor, follow-up, and analyze if there is any improvement.” Claim 23. “As stated in claim 1, if sleep quality is poor, the close-range monitoring system can provide corrective suggestions; including sleep hygiene, relaxation therapy, stimulus control therapy, or other therapies. Through images, voice, words, 3-D projections, virtual reality software, feel, vibration, et cetera, it can give suggestions to the user. The system will continuously monitor and follow-up.”) The examiner is interpreting heartbeat, breathing pattern and muscular relaxation level of claim 11 to be the first biological signal and the voice, words, et cetera of claim 23 to be updating audio. The examiner is modifying the sleep-aiding audio library of Trivedi to incorporate modifying the sleep-aiding audio library/an audio based on sleep quality as taught by Yang to improve the quality of sleep of the both a first and second user.
It would be prima facie obvious to one of ordinary skill in the art to modify the system in Trivedi to incorporate determining a sleep quality of as taught by Yang. One of ordinary skill in the art would be able to recognize users have differing lifestyles and living habits that change regularly and can cause many people to not eat, sleep, and function properly. Lack of sleep due to poor habits can lead to poor physical and mental health and reduction in quality of life. Therefore, it is important to measure sleep quality based on an individual’s lifestyle, emotional, and physical changes for a continuous long-term period. See Yang paragraphs [0004] – [0007].
However, Trivedi also fails to teach updating the sleep-aiding audio library based on the sleep quality of the first user and the sleep quality of the second user.
Mclaughlin teaches updating the sleep-aiding audio library based on the sleep quality of the first user and the sleep quality of the second user. (paragraph [0035], “the properties of the individual's surroundings can include signals from other individuals or animals that are close to the individual (e.g., a person's bedtime partner, a pet dog, etc.)…. measure a noise (e.g., snoring) coming from a person's bedtime partner. The immersive environment system would dynamically create or adjust the individual's immersive audio environment based on the other person's snoring to modify, decrease, cancel, drown out, or filter the offending snoring noise in order to improve sleep quality”)
It would be prima facie obvious to one of ordinary skill in the art to modify the system in Trivedi and Yang to incorporate the teachings of McLaughlin. One of ordinary skill in the art would be able to recognize that users of sleep system do not always sleep in isolation and are prone to environmental disturbances. Therefore, it would benefit the users to have a sleep system that can adjust to the disturbances to allow for quality sleep. See paragraph [0005] of McLaughlin.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Slonneger (US 2014/0364770) teaches systems and methods for sleep analysis using an accelerometer-based user device.
Freed (US 2018/0078733) teaches a sleep assistance device detects a user's sleep state and arranges a soundscape including individual sound records representing sounds associated with the selected soundscape based on the detected sleep state of a user.
Kim (US 2020/0163824) teaches an individually customized sleep massage chair using sleep state data and a control method that induces a user to enter the sleep state, and analyzes the sleep state of the user.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARIANA JOY LACAY DECASTRO whose telephone number is (571)272-8316. The examiner can normally be reached Monday - Friday 9:00 AM - 5:30.
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/A.L.D./Examiner, Art Unit 3791
/JACQUELINE CHENG/Supervisory Patent Examiner, Art Unit 3791