DISPLAYING A STATISTICALLY SIGNIFICANT RELATION
The present disclosure teaches techniques for aggregating observations across multiple sensor-data streams and for presenting the results to users in meaningful ways. Available data are analyzed using a variety of statistical techniques. Significant correlations are presented to users to help them to identify any underlying informative patterns. The presented results help people gain insight into their habits as those habits affect their health and wellness. Users can then make informed decisions about their health, wellness, and environment.
1 . A method for an analysis server to present a statistically significant relation, the method comprising:
receiving, by the analysis server, first data and second data, the first data distinct from the second data, the first and second data selected from the group consisting of: health-monitoring data associated with a person, home-monitoring data, and contextual data;
statistically analyzing, by the analysis server, the first and second data to find at least one statistically significant relation among the first data and the second data; and
presenting, by the analysis server, the at least one statistically significant relation.
2 . The method of claim 1 wherein the health-monitoring data comprise a measurement selected from the group consisting of: heart rate, blood pressure, blood-sugar level, steps taken, weight, mood, diet, calories expended, and amount of sleep.
3 . The method of claim 1 wherein the home-monitoring data comprises an element selected from the group consisting of: thermostat setting, indoor temperature, indoor humidity, appliance use, television use, water use, door status, and window status.
4 . The method of claim 1 wherein the contextual data comprise an element selected from the group consisting of: date, day of the week, weather, temperature, humidity, and physical location.
5 . The method of claim 1 wherein statistically analyzing comprises applying a technique selected from the group consisting of: correlation, t-test, standard deviation, erratic-pattern detection, acceleration-effect detection, binary-effect detection, presence-of-change-effect detection, and value-range-effect detection.
6 . The method of claim 1 wherein statically analyzing comprises analyzing behavioral data associated with the person.
7 . The method of claim 6 wherein the behavioral data comprise an element selected from the group consisting of: a preference explicitly stated by the person, a preference explicitly stated by something other than the person, passive usage data, passive contextual data, and a statistical aggregation of behavioral data.
8 . The method of claim 1 wherein presenting comprises rendering a message about the statistically significant relation to a display of an end-user device.
9 . The method of claim 1 wherein presenting comprises sending a message about the statistically significant relation to an end-user device.
10 . The method of claim 1 further comprising:
presenting an indication of a strength of the statistically significant relation.
11 . The method of claim 1 further comprising:
statistically analyzing the first and second data to find a plurality of statistically significant relations among the first data and the second data; and
filtering the plurality of statistically significant relations;
wherein presenting comprises presenting a filtered list of statistically significant relations.
12 . The method of claim 1 further comprising:
statistically analyzing the first and second data to find a plurality of statistically significant relations among the first data and the second data;
wherein presenting comprises presenting an aggregation of the plurality of statistically significant relations.
13 . An analysis server configured for presenting a statistically significant relation, the analysis server comprising:
a communications interface configured for receiving first data and second data, the first data distinct from the second data, the first and second data selected from the group consisting of: health-monitoring data associated with a person, home-monitoring data, and contextual data; and
a processor operatively connected to the communications interface and configured for:
statistically analyzing the first and second data to find at least one statistically significant relation among the first data and the second data; and
presenting the at least one statistically significant relation.
14 . The analysis server of claim 13 wherein the analysis server is selected from the group consisting of: a personal electronics device, a mobile telephone, a personal digital assistant, a tablet computer, a compute server, and a coordinated group of compute servers.
15 . A method for an analysis server to present a statistically significant relation, the method comprising:
receiving, by the analysis server, first data and second data, the first data distinct from the second data, the first and second data selected from the group consisting of: health-monitoring data associated with a person, home-monitoring data, and contextual data;
statistically analyzing, by the analysis server, the first and second data to find a plurality of statistically significant relations among the first data and the second data; and
presenting, by the analysis server, an aggregation of the plurality of statistically significant relations.
16 . The method of claim 15 wherein the health-monitoring data comprise a measurement selected from the group consisting of: heart rate, blood pressure, blood-sugar level, steps taken, weight, mood, diet, calories expended, and amount of sleep.
17 . The method of claim 15 wherein the home-monitoring data comprises an element selected from the group consisting of: thermostat setting, indoor temperature, indoor humidity, appliance use, television use, water use, door status, and window status.
18 . The method of claim 15 wherein the contextual data comprise an element selected from the group consisting of: date, day of the week, weather, temperature, humidity, and physical location.
19 . The method of claim 15 wherein statistically analyzing comprises applying a technique selected from the group consisting of: correlation, t-test, standard deviation, erratic-pattern detection, acceleration-effect detection, binary-effect detection, presence-of-change-effect detection, and value-range-effect detection.
20 . The method of claim 15 wherein statically analyzing comprises analyzing behavioral data associated with a person.
21 . The method of claim 20 wherein the behavioral data comprise an element selected from the group consisting of: a preference explicitly stated by the person, a preference explicitly stated by something other than the person, passive usage data, passive contextual data, and a statistical aggregation of behavioral data.
22 . The method of claim 15 wherein presenting comprises rendering a message about the aggregation of statistically significant relations to a display of an end-user device.
23 . The method of claim 15 wherein presenting comprises sending a message about the aggregation of statistically significant relations to an end-user device.
24 . The method of claim 15 further comprising:
presenting an indication of a strength of at least one statistically significant relation.
25 . An analysis server configured for presenting a statistically significant relation, the analysis server comprising:
a communications interface configured for receiving first data and second data, the first data distinct from the second data, the first and second data selected from the group consisting of: health-monitoring data associated with a person, home-monitoring data, and contextual data; and
a processor operatively connected to the communications interface and configured for:
statistically analyzing, by the analysis server, the first and second data to find a plurality of statistically significant relations among the first data and the second data; and
presenting, by the analysis server, an aggregation of the plurality of statistically significant relations.
26 . The analysis server of claim 25 wherein the analysis server is selected from the group consisting of: a personal electronics device, a mobile telephone, a personal digital assistant, a tablet computer, a compute server, and a coordinated group of compute servers.