Patent Forecast

1. A system comprising: (2) (4)

a user interface having a display and a receiving means for receiving input from the user;

at least one motion detection sensor capable of detecting movement by an individual wearing said at least one motion detection sensor,

at least one memory configured to store an alertness model and data associated with the individual; and

a processor in electrical communication with said user interface said at least one motion detection sensor, and said memory; said processor configured to receive a signal from said at least one motion detection sensor to monitor a level of activity of the user,

store data obtained from the signal in said memory,

when caffeine consumption information is received from said receiving means, store the caffeine consumption information in said memory as caffeine consumption data,

determine a cognitive level for the user with the alertness model based on the stored data, the signal from said at least one motion detection sensor, and caffeine consumption data, and

display the cognitive level on said display.

2. The system according to claim 1, further comprising upon request, a predetermined schedule, when activity is detected or a combination of these, performing a response time test with said processor and said user interface; and (7) (1)

wherein said processor is configured to perform the response time test by displaying a visual cue on said display,

receiving a user response to the visual cue through said receiving means,

calculating a response time, and

repeating said displaying, receiving and calculating a plurality of times to determine a tested cognitive level;

determine an offset between the model-determined cognitive level and the tested cognitive level;

adjust at least one parameter weight in the alertness model based on the determined offset.

3. The system according to claim 2, wherein said processor uses a Bayesian model and a plurality of offsets to improve the alertness model fit to the individual. (0)

4. The system according to claim 2, further comprising a server; and (1) (2)

wherein said processor transmits the alertness model weights to a server;

said server capable of electrical communication with said processor, said server configured to receive alertness model weights from said processor,

store received alertness model weights in a database associated with the user of said computing device that sent the alertness model weights,

analyze user data including user activity history, cognitive levels, caffeine consumption and weights to develop a new set of weights for future users matching a profile of current users, and

provide a planning interface to model different timing and amounts of sleep and caffeine consumption to provide a forecast for future cognitive levels or a regression for past cognitive levels for the user associated with the alertness model weights.

18. The system according to claim 4, further comprising a communications module in communication with said processorh, said communications module configured to communicate with a server for further processing of the data stored in said memory. (0)

8. The system according to claim 2, wherein said server configured to (0) (4)

receive a future time that an optimal cognitive level is desired,

receive at least one restriction regarding sleep or caffeine consumption,

run a plurality of models with different caffeine consumptions and sleep using the at least one received restriction, and

display the plurality of results from the model runs.

9. The system according to claim 2, wherein said server configured to receive projected sleep time and/or caffeine consumption for a particular user and project a future cognitive level using the project sleep time and/or caffeine consumption for the user. (0)

12. The system according to claim 2, wherein said alertness model includes (0) (3)

14. The system according to claim 2, wherein said receiving means is configured to receive a future time from the user, and (0) (1)

21. The system according to claim 2, wherein said (0) (1)

10. The system according to claim 1, wherein said processor displays an alertness gauge on said display to show the user a graphical representation of their current cognitive level relative to at least one benchmark cognitive levels. (0)

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30. A system comprising: (3) (2)

a plurality of computing devices where each computing device is assigned to an individual, each of said computing devices having a user interface having a display and means for receiving input from the user;

at least one motion detection sensor capable of detecting movement by an individual wearing said at least one motion detection sensor;

at least one memory configured to store a sleep model and data associated with the individual;

a communications module; and

a processor in electrical communication with said user interface, said motion detection sensor, said communications module, and said memory; said processor configured to receive a signal from said at least one motion detection sensor to monitor a level of activity of the user,

store data obtained from the signal in said memory,

when caffeine consumption information is received from said receiving means, store the caffeine consumption information in said memory as caffeine consumption data,

determine an cognitive level for the user with the alertness model based on the stored data, the signal from said at least one motion detection sensor, and caffeine consumption data, and

display the cognitive level on said display; and

a server capable of at least intermittent communication with each of said plurality of computing devices, said server configured to receive alertness model weights from said processors through respective communications modules,

store received alertness model weights in a database associated with the individual associated with said computing device that sent the alertness model weights, and

provide a planning interface to model different timing and amounts of sleep and caffeine consumption to provide a forecast for future cognitive levels or a regression for past cognitive levels for the user associated with the alertness model weights.

31. The system according to claim 30, wherein said server is configured to analyze user data including user activity history, cognitive levels, caffeine consumption and weights to develop a new set of weights for future users matching a profile of current users. (0)

33. The system according to claim 30, wherein said server configured to (0) (4)

receive a future time that an optimal cognitive level is desired,

receive at least one restriction regarding sleep or caffeine consumption,

run a plurality of models with different caffeine consumptions and sleep using the at least one received restriction, and

display the plurality of results from the model runs.

34. The system according to claim 30, wherein said server configured to receive projected sleep time and/or caffeine consumption for a particular user and project a future cognitive level using the project sleep time and/or caffeine consumption for the user. (0)

32. (canceled) (0)

35. (canceled) (1) (0)

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38. A method for determining an individual's cognitive state using a computing device having a processor, a memory, a motion detection sensor, a display, and a receiving means for receiving input from a user about the individual, the method comprising: (2) (5)

receiving a signal from the at least one motion detection sensor configured to monitor a level of activity of the user,

storing data obtained from the signal in the memory,

when caffeine consumption information is received from the receiving means, storing the caffeine consumption information in the memory as caffeine consumption data,

determining a cognitive level for the user with the alertness model based on the stored data, the signal from the at least one motion detection sensor, and caffeine consumption data, and

displaying the cognitive level on the display.

39. The method according to claim 38, further comprising: (0) (3)

42. The method according to claim 38, further comprising providing a future cognitive level by using a sleep history provided by the user, a projected sleep history based on recent sleeping patterns such that an average bedtime and an average wake time are used to determine when sleep will occur, and/or assumption that no sleep will occur between a current time and the time for the future cognitive level. (0)

40. (canceled) (0)

Application US20180289314

Method and System for Measuring, Predicting, and Optimizing Human Cognitive Performance

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