METHODS AND SYSTEMS FOR CIRCADIAN PHYSIOLOGY PREDICTIONS
First Claim
1. A system for predicting a circadian state of an individual, the system comprising:
- a processor connected to receive light stimulus information related to light stimulus to which the individual is exposed;
wherein the processor is configured to;
provide a model representative of circadian response to the light stimulus information, the model comprising one or more model variables and at least one model variable representative of a probability distribution function (PDF) of a phase offset of the circadian state of the individual; and
use the model and the light stimulus information to estimate an updated PDF of the phase offset, wherein using the model and the light stimulus information to estimate the updated PDF of the phase offset comprises performing a Bayesian estimation process commencing with an initial PDF of the phase offset and iterating toward the updated PDF of the phase offset.
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Accused Products
Abstract
Systems and methods are provided for predicting a circadian state of an individual. The methods comprise: providing a model representative of the response of the circadian state to light stimulus, the model comprising at least one model variable representative of a probability distribution function (PDF) of a phase offset of the circadian state of the individual; and using the model to estimate an updated PDF of the phase offset, wherein using the model to estimate the updated PDF of the phase offset comprises performing a Bayesian estimation process commencing with an initial PDF of the phase offset and iterating toward the updated PDF of the phase offset.
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Citations
40 Claims
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1. A system for predicting a circadian state of an individual, the system comprising:
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a processor connected to receive light stimulus information related to light stimulus to which the individual is exposed; wherein the processor is configured to; provide a model representative of circadian response to the light stimulus information, the model comprising one or more model variables and at least one model variable representative of a probability distribution function (PDF) of a phase offset of the circadian state of the individual; and use the model and the light stimulus information to estimate an updated PDF of the phase offset, wherein using the model and the light stimulus information to estimate the updated PDF of the phase offset comprises performing a Bayesian estimation process commencing with an initial PDF of the phase offset and iterating toward the updated PDF of the phase offset. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21)
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22. A method for predicting a circadian state of an individual, the method comprising:
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receiving light stimulus information related to light stimulus to which the individual is exposed; providing a model representative of circadian response to the light stimulus information, the model comprising one or more model variables and at least one model variable representative of a probability distribution function (PDF) of a phase offset of the circadian state of the individual; and using the model to transform the light stimulus information into an estimate of an updated PDF of the phase offset, wherein using the model to transform the light stimulus information into an estimate of the updated PDF of the phase offset comprises performing a Bayesian estimation process commencing with an initial PDF of the phase offset and iterating toward the updated PDF of the phase offset. - View Dependent Claims (23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39)
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40. A computer program product comprising computer-readable instructions, which when executed by a suitably configured processor, cause the processor to perform a method for predicting a circadian state of an individual, the method comprising:
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receiving light stimulus information related to light stimulus to which the individual is exposed; providing a model representative of circadian response to the light stimulus information, the model comprising one or more model variables and at least one model variable representative of a probability distribution function (PDF) of a phase offset of the circadian state of the individual; and using the model to estimate an updated PDF of the phase offset, wherein using the model to estimate the updated PDF of the phase offset comprises performing a Bayesian estimation process commencing with an initial PDF of the phase offset and iterating toward the updated PDF of the phase offset.
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Specification