Using individuals predicted type to aid in content selection

Abstract

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.

What is claimed is: 
     
         1 . A computer-system-implemented method, the computer system having at least one programmed processor to implement the method, the method comprising:
 continuously collecting data components with respect to an individual from a wearable sensor device; and   collecting another set of data components with respect to the individual from a source separate from the wearable device;   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) determining a type for the individual based on at least one of a match and a similarity between the data structure for the individual and data components for at least one other individual; 
 (iii) determining the context of the individual based on at least one of data from the wearable sensor device and data from a contextual sensor, wherein the context is a life-cycle stage; and 
 (iv) providing an input to a content selection process based on the determined type and context. 
   
     
     
         2 . The method of  claim 1 , wherein the context is determined based on the output of a plurality of sensors of the wearable sensor device. 
     
     
         3 . The method of  claim 1 , wherein the data structure includes data components 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. 
     
     
         4 . 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. 
     
     
         5 . 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. 
     
     
         6 . The method of  claim 1 , wherein the life-cycle stage is a college life-cycle stage. 
     
     
         7 . The method of  claim 1 , wherein the life-cycle stage is a workforce life-cycle stage. 
     
     
         8 . The method of  claim 1 , wherein the life-cycle stage is a married life-cycle stage.