# US 2020/0375527 A1 · Method and system for optimizing short-term sleep

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- Number: US 2020/0375527 A1
- Document ID: US20200375527A1
- Status: Published application
- Kind: A1
- Application number: 16/425,946
- Filed: 2019-05-30
- Published: 2020-12-03
- Earliest claimed: 2019-05-30
- Assignees: Future World Holdings LLC
- Applicants: Future World Holdings LLC
- Inventors: Raghu Bathina; Sridhar Prathikanti
- Web page: https://patentgrep.com/patent/US20200375527A1

## CPC

- A61B 5/4809: {Sleep detection, i.e. determining whether a subject is asleep or not}
- A61B 5/4815: {Sleep quality}
- A61B 5/7435: {Displaying user selection data, e.g. icons in a graphical user interface}
- G02B 27/017: {Head mounted}
- G06F 3/011: {Arrangements for interaction with the human body, e.g. for user immersion in virtual reality (blind teaching G09B21/00)}
- G06N 3/044: Recurrent networks, e.g. Hopfield networks
- G06N 3/047: Probabilistic or stochastic networks
- G06N 3/08: Learning methods
- G06N 3/09: Supervised learning
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06N 20/20: Ensemble learning
- A61B 5/11: Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb {(A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721)}
- A61B 5/4836: {Diagnosis combined with treatment in closed-loop systems or methods (A61B5/0036 takes precedence)}
- A61B 5/6803: {Head-worn items, e.g. helmets, masks, headphones or goggles}
- A61B 5/6814: {Head}
- A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems}
- A61B 5/742: {using visual displays (displays for heart-related electrical signals, e.g. ECG, A61B5/339)}
- A61B 2562/0219: Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
- G06F 2203/011: Emotion or mood input determined on the basis of sensed human body parameters such as pulse, heart rate or beat, temperature of skin, facial expressions, iris, voice pitch, brain activity patterns
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G02B: OPTICAL ELEMENTS, SYSTEMS OR APPARATUS
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

A method for facilitating a sleep session is provided. The method comprises provisioning a virtual reality device with at least one sleep program configured to provide an immersive experience to facilitate a sleep session for a user; determining recommended sleep settings for the sleep program; configuring the sleep program with at least some of the recommended sleep settings; running the sleep program; and tracking sleep information for the user associated with the sleep session.

## Claims

### Claim 1

A method for facilitating a sleep session, the method comprising:
provisioning a virtual reality device with at least one sleep program configured to provide an immersive experience to facilitate a sleep session for a user;
determining recommended sleep settings for the sleep program;
configuring the sleep program with at least some of the recommended sleep settings;
running the sleep program; and
tracking sleep information for the user associated with the sleep session.

### Claim 2 (depends on 1)

The method of claim 1, wherein determining the recommended sleep settings for the sleep program comprises obtaining said recommended sleep settings based on a query to a server application.

### Claim 3 (depends on 2)

The method of claim 2, further comprising performing analysis by the server application to derive the recommended sleep settings.

### Claim 4 (depends on 3)

The method of claim 3, wherein the analysis comprises learning the recommended sleep settings for a user based on historical sleep quality data, and sleep session setting data.

### Claim 5 (depends on 4)

The method of claim 4, wherein said learning is based on a neural network configured to implement at least one of an Energy Based Model (EBM) and Restricted Boltzman Machine (RBM).

### Claim 6 (depends on 1)

The method of claim 1, wherein the recommended sleep settings comprise settings selected from the group consisting of sleep time, sleep position, sleep environment (corresponding to a virtual reality scene), environment volume, background music, background music volume, and theta waves (on/off).

### Claim 7 (depends on 4)

The method of claim 4, wherein the sleep quality data comprises a ratio of intended sleep time over actual sleep time for each sleep session.

### Claim 8 (depends on 1)

The method of claim 1, wherein configuring the sleep program comprises displaying the recommended sleep settings to the user; and allowing the user to accept or override said recommended sleep settings prior to the sleep session.

### Claim 9 (depends on 4)

The method of claim 4, wherein learning the recommended sleep settings comprises generating recommended sleep settings for the user based on sleep settings associated with a group into which the user is classified based on a metric of similarity.

### Claim 10 (depends on 9)

The method of claim 9, wherein the metric of similarity comprises demographic information.

### Claim 11

A system for facilitating a sleep session, the system comprising:
a virtual reality device provisioned with at least one sleep program configured to provide an immersive experience to facilitate a sleep session for a user;
a server device communicatively coupled to the virtual reality device and configured to provide recommended sleep settings for the sleep program.

### Claim 12 (depends on 11)

The system of claim 11, further comprising a plurality of virtual reality devices each associated with a user, wherein each virtual reality device tracks sleep session data for each sleep session and transmits same to the server device.

### Claim 13 (depends on 12)

The system of claim 12, wherein the server device is configured to learn recommended sleep settings for each user based on the sleep session data.

### Claim 14 (depends on 12)

The system of claim 12, wherein the server device is configured to learn the recommended sleep settings based on a neural network.

### Claim 15 (depends on 12)

The system of claim 12, wherein the neural network implements a probabilistic model to learn the sleep settings for each user corresponding to the best quality of sleep for that user.

### Claim 16 (depends on 15)

The system of claim 15, wherein the best quality of sleep for a user is learned based on historical sleep session data.

### Claim 17 (depends on 16)

The system of claim 16, wherein quality of sleep for each user is inferred based on a ratio of intended sleep time over actual sleep time for each sleep session.

### Claim 18 (depends on 14)

The system of claim 14, wherein the server device is further configured to classify each user into a cohort based on a metric of similarity.

### Claim 19 (depends on 18)

The system of claim 18, wherein the metric of similarity includes demographic information.

### Claim 20 (depends on 18)

The system of claim 18, wherein for selected instances sleep session recommended settings for a user are generated based on sleep session settings for the cohort which the user is classified