Using aggregated sensed data of individuals to predict the mental state of an individual
View Patent ↗The methods and systems described herein may involve determining at least one lifeotype of at least one individual, analyzing the at least one lifeotype, and delivering content to at least one individual based on the analysis. The methods and systems described herein may involve providing a game, determining at least one lifeotype of at least one player of the game, analyzing the at least one lifeotype, and affecting the game play based on the analysis. The methods and systems described herein may involve providing an interactive space, determining at least one lifeotype of at least one individual in the space, analyzing the at least one lifeotype, and modifying at least one attribute of the space based on the analysis.
1. A computer-system-implemented method of predicting the mental state for an individual based on the individual's traits, the computer system having at least one programmed processor to implement the method, the method comprising:
continuously collecting data components of an individual from a wearable sensor device;
collecting another set of data components of the individual from a source separate from the wearable device;
accessing a database by said computer system comprising data structures of other individuals, wherein said data structures of other individuals are related to known human traits;
the computer system—
(i) assembling a data structure for the individual that includes at least one component from the collected data components from the wearable sensor device and at least one of said another set of data components;
(ii) accessing said database to determine a similarity of the data structure for the individual with data structures of other individuals;
(iii) based on the determination, assigning a human trait to the individual; and
(iv) based on the trait of the individual using an engine to consider derived and sensed data from the wearable sensor device to predict the mental state of the individual based on the assigned trait.
2. The method of claim 1 , wherein the mental state is a healthy state.
3. The method of claim 1 , wherein the mental state is an unhealthy state.
4. The method of claim 3 , wherein the mental state is a disease state.
5. The method of claim 1 , wherein the mental state is a mood of the individual.
6. The method of claim 1 , wherein said another set of data components is selected from the group consisting of: derived data, analytical status data, contextual data, continuous data, discrete data, time series data, event data, raw data, processed data, metadata, third party data, physiological state data, psychological state data, survey data, medical data, genetic data, environmental data, transactional data, economic data, socioeconomic data, demographic data, psychographic data, sensed data, continuously monitored data, manually entered data, inputted data, continuous data and real-time data.
7. The method of claim 1 , wherein said set of data components from a wearable sensor device is selected from the group consisting of: derived data, analytical status data, contextual data, continuous data, discrete data, time series data, event data, raw data, processed data, metadata, third party data, physiological state data, psychological state data, survey data, medical data, genetic data, environmental data, transactional data, economic data, socioeconomic data, demographic data, psychographic data, sensed data, continuously monitored data, manually entered data, inputted data, continuous data and real-time data.
8. The method of claim 1 , wherein at least one data component collected from a wearable sensor device is a data component that is derived from a plurality of sensors that is distinct from the output of any single sensor.
9. The method of claim 1 , wherein the assembled data structure that includes at least one component from the collected data components from the wearable sensor device includes at least one data component collected from a wearable sensor device is a data component that is derived from a plurality of sensors that is distinct from the output of any single sensor.