Systems and techniques for tracking sleep consistency and sleep goals

Patent metadata

Field	Value
Publication number	US-2017352287-A1
Application number	US-201615171049-A
Country	US
Kind code	A1
Filing date	Jun 2, 2016
Priority date	Jun 2, 2016
Publication date	Dec 7, 2017
Grant date	—

Abstract

Methods, techniques, apparatuses, and systems for setting up and tracking sleep consistency goals of users are provided. In one example, a computing system for setting a sleep schedule of a user of a biometric monitoring device may obtain sleep data derived from sensor data generated by the biometric monitoring device, store the sleep data in a sleep log data store as one or more sleep logs associated with an account assigned to the user, and calculate a target bedtime based on a scheduled waketime of the user and a sleep efficiency derived, at least in part, from the sleep data for one or more users stored in the sleep log data store. The computing system may also be configured to provide a number of personalized user interfaces to an individual for the purposes of setting a sleep schedule. Such interfaces may include parameters that are tailored to the individual sleep needs and/or characteristics of the individual's sleep.

First claim

1 . A system for setting a sleep schedule of a user of a biometric monitoring device, the biometric monitoring device comprising one or more sensors, the system comprising:
one or more processors; and a memory, wherein:
the one or more processors are communicatively connected with the memory, and
the memory stores instructions that, when executed, cause the one or more processors to:
obtain sleep data derived from sensor data generated by the one or more sensors in the biometric monitoring device, the sensor data including data generated by at least one of the one or more sensors in the biometric monitoring device responsive to movement of the user of the biometric monitoring device, the sleep data including data regarding a plurality of sleep sessions and specifying various sleep states of the user for the respective sleep sessions,
store the sleep data in a sleep log data store as one or more sleep logs associated with an account assigned to the user, the sleep log data store also storing other sleep logs including other sleep data derived from other sensor data generated by one or more other sensors in other biometric monitoring devices of other users, the other sensor data including other data generated by at least one of the one or more other sensors in the other biometric monitoring devices responsive to movements by the other users of the other biometric monitoring devices, and
calculate a target bedtime based on a scheduled waketime of the user and a sleep efficiency derived, at least in part, from the sleep data for one or more users stored in the sleep log data store.

2 . The system of claim 1 , wherein the target bedtime is based on the sleep efficiency of the other users of the other biometric monitoring devices.

3 . The system of claim 1 , wherein the target bedtime is based on the sleep efficiency of the user of the biometric monitoring device.

4 . The system of claim 1 , wherein:
the memory further stores instructions that, when executed, cause the one or more processors to:
obtain sleep state duration data for a sleep session stored in the sleep log data store, the sleep state duration data being representative of the total amount of time the user that is associated with the sleep session spent in one or more sleep states during the sleep session, and
determine sleep session duration data for the sleep session stored in the sleep log data store, the sleep session duration data being representative of the total time of that sleep session; and
the sleep efficiency is representative, at least in part, of a correlation between sleep state duration data for one or more sleep sessions and sleep session duration data for the corresponding one or more sleep sessions.

6 . The system of claim 4 , wherein the calculation of the target bedtime is further based on the sleep efficiency of the user.

7 . The system of claim 4 , wherein the calculation of the target bedtime is further based on a selected sleep duration of the user for a sleep session.

8 . The system of claim 7 , wherein the memory further stores instructions that, when executed, cause the one or more processors to obtain the selected sleep duration of the user for the sleep session.

9 . The system of claim 7 , wherein:
the memory further stores instructions that, when executed, cause the one or more processors to determine a regression model that relates the sleep session duration data for a plurality of sleep sessions stored in the sleep log data store to corresponding sleep state duration data for the plurality of sleep sessions, and the calculation of the target bedtime is further based on the regression model.

10 . The system of claim 9 , wherein the sleep efficiency for each sleep session is calculated, at least in part, by dividing the sleep state duration data for that sleep session by the sleep session duration data for that sleep session.

11 . The system of claim 9 , wherein the plurality of sleep sessions are for the other users of the other biometric monitoring devices.

12 . The system of claim 9 , wherein the regression model is a regression model selected from the group consisting of: a linear regression model, a nonlinear regression model, a parametric regression model, a nonparametric regression model, a semiparametric regression model, and a multivariate linear regression model.

13 . The system of claim 9 , wherein the sleep session duration data and corresponding sleep state duration data for the plurality of sleep sessions included in the regression model are associated with sleep sessions with waketimes that are within a first threshold amount of the scheduled waketime.

14 . The system of claim 9 , wherein the sleep session duration data and corresponding sleep state duration data for the plurality of sleep sessions included in the regression model are associated with sleep state duration data that are within a second threshold amount of the selected sleep duration.

15 . The system of claim 9 , wherein the sleep session duration data and corresponding sleep state duration data for the plurality of sleep sessions included in the regression model are associated with sleep sessions with waketimes that have occurred on the same day of the week as the day of the week on which the scheduled waketime occurs.

16 . The system of claim 9 , wherein the regression model accounts for one or more of: one or more specific days of the week, a specific time of year, holidays, workdays of the user, non-workdays of the user, a seasonal time change, a geographic location, travel by the user between at least two time zones, exercise of the user, and a duration of daylight in a day.

21 . The system of claim 1 , further comprising the biometric monitoring device that includes:
the one or more sensors, wherein the sensors are configured to generate the sensor data, and a communications interface to communicate the sensor data to the one or more processors.

22 - 47 . (canceled)

48 . The system of claim 4 , wherein the calculation of the target bedtime is further based on the sleep efficiency of other users of the other biometric monitoring devices.

49 . The system of claim 1 , wherein the calculation of the target bedtime is further based on one or more of: one or more specific days of the week, a specific time of year, holidays, workdays of the user, non-workdays of the user, a seasonal time change, a geographic location, travel by the user between at least two time zones, exercise of the user, and a duration of daylight in a day.

5 . The system of claim 4 , wherein the memory further stores instructions for controlling the one or more processors to obtain the scheduled waketime of the user from the user via a graphical user interface.

17 . The system of claim 7 , wherein:
the sleep efficiency for each sleep session is calculated, at least in part, by dividing the sleep state duration data for each sleep session by the sleep session duration data for each corresponding sleep session, respectively, and the sleep efficiency is accounted for, at least in part, by multiplying the selected sleep duration by a factor that is based on the sleep efficiencies for the plurality of sleep sessions to determine a predicted sleep session duration.

18 . The system of claim 7 , wherein the memory stores instructions for further controlling the one or more processors to cause a notification mechanism to produce a notification relating to a comparison of sleep state duration data for one or

Assignees

• Fitbit Inc

Inventors

• Arnold Jacob Antony
• Cheng Yeqing
• Baiani Yasaman
• Russell Allison Maya

Classifications

• A61B5/486

  Biofeedback (using electroencephalography [EEG] A61B5/375) · CPC title

• A61B5/743

  Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots · CPC title

• G06F3/0484

  for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range · CPC title

• A61B5/4815

  Sleep quality · CPC title

• G06F3/0481

  based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance · CPC title