Lifeotypes

Abstract

The present invention provides a method and a system for assembling data from at least one data source into at least one life bit, assembling the at least one life bit into at least one life byte, and defining a lifeotype based on the attributes of at least one life byte.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application claims the benefit of the following provisional application, which is hereby incorporated by reference in its entirety:[0002]Ser. No. 60 / 901,952, SYSTEMS AND METHODS FOR UNDERSTANDING AND APPLYING THE PHYSIOLOGICAL AND CONTEXTUAL LIFE PATTERNS OF AN INDIVIDUAL OR SET OF INDIVIDUALS, filed Feb. 16, 2007.BACKGROUND[0003]1. Field[0004]The invention relates to the field of data informatics, and more specifically to systems and methods for analyzing and parsing information relating to information monitored about subjects, including human lifestyle information.[0005]2. Description of the Related Art[0006]Vast resources have been devoted to the sequencing of the human genetic code and to cataloging the influence of genes and other physiological traits. However, a major component of health and wellness can be attributed to the interactions of subjects with their environment, including their lifestyles. Despite the widely accepted...

AI Technical Summary

Benefits of technology

Enables the classification of individuals into lifeotypes, allowing for informed medical decision-making, disease management, and personalized recommendations based on lifestyle patterns, thereby improving health and wellness insights.

Problems solved by technology

Despite the widely accepted view that lifestyle activities, such as those related to diet, exercise, sleep habits and the like, affect health and wellness, efforts to catalog those effects to date have been limited.

However, this wealth of data has not yet led to a better overall understanding of the influence of particular lifestyles; instead, the wealth of data has overwhelmed existing systems and methods.

Further, the derived data may be a status parameter that cannot be directly measured with any of the first and second sensors alone.

Further, the sensed health data may consist of yearly blood pressure readings administered at a doctor's office and extracted from a personal health record, the residence data may indicate that the individual lives in an urban area may not be conducive to year-round exercise and may be characterized by long commute time.

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