Detecting a User's Outlier Days Using Data Sensed by the User's Electronic Devices

A method for detecting an outlier time period of a user, comprising:

• receiving data corresponding to each of a plurality of features related to the user, wherein the data is acquired over a plurality of time periods by sensors on an electronic device of the user;
• labeling the data acquired on each of the plurality of time periods for each of the plurality of features as being regular or irregular by applying N labeling approaches, wherein N is a positive integer greater than one, and wherein at least one of the N labeling approaches involves comparing the data corresponding to each of the plurality of features with how values of previously acquired data for corresponding features are distributed;
• generating N labels for the data corresponding to each of the plurality of features for each of the plurality of time periods;
• for each of the N labeling approaches, using one of the N labels to train a machine learning classification model;
• selecting from among the N labeling approaches an optimal labeling approach for each of the plurality of features using the machine learning classification model;
• and
• determining for each of the plurality of features, using the labels obtained with the optimal labeling approach, whether each of the plurality of time periods is an outlier time period of the user.

A system for detecting an outlier time period of a user, comprising:

• a receiver that receives data through a communications network from an electronic device of the user, wherein the data corresponds to each of a plurality of features related to the user, and wherein the data is acquired during a plurality of time periods by sensors on the electronic device of the user;
• and
• a computer including a processor, wherein the processor is configured to:
• label the data acquired during each of the plurality of time periods for each of the plurality of features as being regular or irregular by applying N labeling approaches, wherein N is a positive integer greater than one, and wherein at least one of the N labeling approaches involves comparing the data corresponding to each of the plurality of features with how values of previously acquired data for corresponding features are distributed;
• generate N labels for the data corresponding to each of the plurality of features for each of the plurality of time periods;
• for each of the N labeling approaches, train a machine learning classification model using one of the N labels;
• select from among the N labeling approaches an optimal labeling approach for each of the plurality of features using the machine learning classification model;
• and
• determine for each of the plurality of features, using the labels obtained with the optimal labeling approach, whether each of the plurality of time periods is an outlier time period of the user.