System and method for determining and providing activity recommendations
Inventors
Offringa, Reid • Greenfield, Michael S.
Assignees
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Publication Number
US-10373519-B1
Publication Date
2019-08-06
Expiration Date
Abstract
A method and apparatus for determining and providing activity recommendations includes receiving glucose level data and activity data. The glucose level data is formed into two or more data sets, with each set representing a different time period. Each data set is evaluated and ranked against each other set according to one or more of several different individual factors and the individual ranking for each set are combined, resulting in an overall ranking for given data sets. A highest ranked data set is then determined, which is associated thereby with a highest ranked time period. Activities of the activity data which took place within the highest ranked time period are provided as recommendations to the user to encourage greater numbers of times those activities are undertaken.
Core Innovation
A computing system determines and provides activity recommendations by receiving, from at least one glucose collection device associated with an individual, glucose level data representing a plurality of glucose level readings taken from the individual over a plurality of time periods. The system forms a plurality of subsamples of glucose level data, where each subsample comprises glucose level data representing individual glucose levels taken within a given time period associated with the subsample, and receives activity data from an activity data source associated with the individual, with at least one activity being received with respect to each of the time periods.
For each subsample, the system determines an average area under the curve associated with the glucose levels of the subsample, determines a standard deviation of the glucose readings of the subsample, performs a biotransform algorithm on each individual glucose reading of the subsample, and determines a min-max total associated with the glucose readings of the subsample. The system then ranks each subsample against each other subsample, where the rankings are at least partly based on the average area under the curve, the standard deviation, the biotransform algorithm results, and the min-max total for each subsample.
A highest ranked time period is determined as associated with a highest ranked subsample. The system determines one or more activities of the activity data representing one or more activities performed by the individual that were performed within the determined highest ranked time period, and recommends that the individual increase a number of instances which the one or more determined activities are performed.
Claims Coverage
The document includes three independent claims. Across these independent claims, the inventive features include receiving glucose level readings, forming time-period subsamples, computing an average area under the curve, standard deviation, biotransform algorithm results, and a min-max total, ranking subsamples to select a highest ranked time period, and recommending increasing instances of activities performed during that time period.
Time-period glucose subsampling for activity recommendation
Receiving, from at least one glucose collection device associated with an individual, glucose level data representing a plurality of glucose level readings taken from the individual over a plurality of time periods; forming a plurality of subsamples of glucose levels data, each subsample comprising glucose levels data representing individual glucose levels taken within a given time period associated with the subsample.
Multi-metric glucose feature determination per subsample
Determining, for each subsample, an average area under the curve associated with the glucose levels of the subsample; determining a standard deviation of the glucose readings of the subsample; performing, for each subsample, a biotransform algorithm on each individual glucose reading of the subsample; determining, for each subsample, a min-max total associated with the glucose readings of the subsample.
Ranking subsamples to select a highest ranked time period
Ranking each subsample against each other subsample, the rankings being at least partly based on the average area under the curve, the standard deviation, the biotransform algorithm results, and the min-max total; determining a highest ranked time period associated with a highest ranked subsample.
Activity-based recommendation for increasing instances
Receiving activity data from an activity data source associated with the individual, with at least one activity being received with respect to each of the time periods; determining one or more activities performed within the highest ranked time period; recommending that the individual increase a number of instances which the one or more determined activities are performed.
All independent claims share the same core method and system: generate time-period glucose subsamples, compute multiple glucose-derived metrics per subsample, rank subsamples to identify a highest ranked time period, then recommend increasing instances of activities performed during that highest ranked time period.
Stated Advantages
Documented Applications
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