Audio assessment for analyzing sleep trends using machine learning techniques

Abstract

A computer system for assessing sound to analyze a user's sleep includes a processor configured to perform operations including: (i) storing sample sound data associated with a plurality of sample sleep events, the sample sound data including a plurality of sample characteristics each respectively associated with at least one sample sleep event of the plurality of sample sleep events; (ii) receiving, from a client device, subject sound data collected during a sleep interval; (iii) analyzing, using a machine learning algorithm, the subject sound data collected during the sleep interval; (iv) identifying, based upon the analyzing, a subject characteristic associated with the subject sound data; (v) comparing the subject characteristic with the plurality of sample characteristics; and (vi) determining, based upon the comparing, whether the subject characteristic substantially matches at least one sample characteristic to identify one or more subject sleep events occurring during the sleep interval.

1 - 20 . (canceled) 
     
     
         21 . A computer-based method for assessing sound to analyze a user's sleep, the method performed using one or more processors, the method comprising:
 receiving subject sound data collected during a sleep interval;   analyzing, using a machine learning algorithm, the subject sound data collected during the sleep interval;   identifying, based upon the analyzing, a subject characteristic associated with the subject sound data;   comparing the subject characteristic with a plurality of sample characteristics, the plurality of sample characteristics each being associated with at least one sample sleep event of a plurality of sample sleep events; and   identifying, based upon the comparing, to identify one or more subject sleep events occurring during the sleep interval.   
     
     
         22 . The method of  claim 21 , wherein the subject characteristic includes at least one selected from a group consisting of a frequency characteristic, an amplitude characteristic, a waveform characteristic, and a duration characteristic. 
     
     
         23 . The method of  claim 21 , further comprising:
 receiving driving behavior data of the user; and   correlating the driving behavior data with the one or more identified subject sleep events.   
     
     
         24 . The method of  claim 21 , further comprising:
 analyzing additional subject sound data collected during a time interval to determine that the user is falling asleep;   determining the user is performing a task during the time interval; and   generating an alarm to prevent the user from falling asleep.   
     
     
         25 . The method of  claim 21  further comprising:
 generating, based upon the one or more identified subject sleep events, a notification describing the one or more identified subject sleep events; and 
 transmitting the notification to a client device. 
 
     
     
         26 . The method of  claim 25  further comprising:
 determining a duration of one identified subject sleep event of the one or more identified subject sleep events; 
 wherein the notification includes an indication of a duration of the one identified subject sleep event. 
 
     
     
         27 . The method of  claim 25 , wherein the notification includes an indication of at least one selected from a group consisting of a date of the sleep interval, a start time of the sleep interval, an end time of the sleep interval, and environmental information of the sleep interval. 
     
     
         28 . The method of  claim 21  further comprising:
 determining a number of subject sleep events that occurred during the sleep interval; and 
 determining a level of sleep quality associated with the sleep interval based upon the number of subject sleep events. 
 
     
     
         29 . The method of  claim 21  further comprising:
 identifying one or more subject sleep events occurring over a plurality of sleep intervals based upon respective subject sound data for each sleep interval of the plurality of sleep intervals; 
 identifying at least one sleep trend associated with the plurality of sleep intervals; 
 generating a notification describing the at least one sleep trend; and 
 transmitting the notification to a client device. 
 
     
     
         30 . A computer system for assessing sound to analyze a user's sleep, the computer system comprising:
 one or more processors; and   one or more memories having instructions stored thereon that, in response to execution by the processor, cause the one or more processors to perform operations comprising:
 receiving subject sound data collected during a sleep interval; 
 analyzing, using a machine learning algorithm, the subject sound data collected during the sleep interval; 
 identifying, based upon the analyzing, a subject characteristic associated with the subject sound data; 
 comparing the subject characteristic with a plurality of sample characteristics, the plurality of sample characteristics each respectively associated with at least one sample sleep event of a plurality of sample sleep events; and 
 identifying, based upon the comparing, one or more subject sleep events occurring during the sleep interval. 
   
     
     
         31 . The computer system of  claim 30 , wherein the subject characteristic includes at least one selected from a group consisting of a frequency characteristic, an amplitude characteristic, a waveform characteristic, and a duration characteristic. 
     
     
         32 . The computer system of  claim 30 , wherein the operations further comprise:
 receiving driving behavior data of the user; and   correlating the driving behavior data with the one or more identified subject sleep events.   
     
     
         33 . The computer system of  claim 30 , wherein the operations further comprise:
 analyzing additional subject sound data collected during a time interval to determine that the user is falling asleep;   determining the user is performing a task during the time interval; and generating an alarm to prevent the user from falling asleep.   
     
     
         34 . The computer system of  claim 30 , wherein the operations further comprise:
 generating, based upon the one or more identified subject sleep events, a notification describing the one or more identified subject sleep events; and   transmitting the notification to a client device.   
     
     
         35 . The computer system of  claim 34 , wherein the operations further comprise:
 determining a duration of one identified subject sleep event of the one or more identified subject sleep events;   wherein the notification includes an indication of the duration of the one identified subject sleep event.   
     
     
         36 . The computer system of  claim 30 , wherein the operations further comprise:
 determining that a first subject sleep event recurs during the sleep interval; and   determining a number of occurrences of the first subject sleep event.   
     
     
         37 . The computer system of  claim 30 , wherein the processor is further configured to perform operations comprising:
 identifying one or more subject sleep events occurring over a plurality of sleep intervals based upon respective subject sound data for each sleep interval of the plurality of sleep intervals;   identifying at least one sleep trend associated with the plurality of sleep intervals;   generating a notification describing the at least one sleep trend; and   transmitting the notification to a client device.   
     
     
         38 . A non-transitory computer readable medium that includes executable instructions for assessing sound to analyze a user's sleep, wherein when executed by one or more processors, the executable instructions cause the one or more processors to:
 receive subject sound data collected during a sleep interval;   analyze, using a machine learning algorithm, the subject sound data collected during the sleep interval;   identify, based upon the analysis, a subject characteristic associated with the subject sound data;   compare the subject characteristic with a plurality of sample characteristics, the plurality of sample characteristics each respectively associated with at least one sample sleep event of a plurality of sample sleep events; and   identify, based upon the comparison, one or more subject sleep events occurring during the sleep interval.   
     
     
         39 . The non-transitory computer readable medium of  claim 38 , wherein the executable instructions further cause the one or more processors to:
 receive driving behavior data of the user; and   correlate the driving behavior data with the one or more identified subject sleep events.   
     
     
         40 . The non-transitory computer readable medium of  claim 38 , wherein the executable instructions further cause the one or more processors to:
 analyze additional subject sound data collected during a time interval to determine that the user is falling asleep;   determine the user is performing a task during the time interval; and   generate an alarm to prevent the user from falling asleep