AGGREGATION AND CORRELATION OF DATA FOR LIFE MANAGEMENT PURPOSES

• US 20150286929A1
• Filed: 04/04/2014
• Published: 10/08/2015
• Est. Priority Date: 04/04/2014
• Status: Abandoned Application

• Current Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Original Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Inventors: Aviles, Michael Jeffrey, Weekes, John Henry, Tohline, Abigail Rachel, Maurer, Jonathan Darius, Harr, Joseph, Phillips, Andrew Joseph, Hakimi, Daryoush, Hembra, Robert Louis
• Application Number:
  US14/245,839
• Publication Number:
  US 20150286929A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/0207 Discounts or incentives, e.g. coupons or rebates
• CPC Class Codes: G06Q 30/0631 Item recommendations

Abstract

A method and computer system aggregates and correlates various types of data from an individual for life management purposes. As an example, the various types of data may include home data, vehicle data, and personal health data associated with the individual. The method and system may receive the various types of data from the individual and analyze each type of data to determine patterns associated with each type of data. The method and system may then compare each of the determined patterns with one another to determine any underlying factors and correlations that may be affecting each of the determined patterns. Based on the determined underlying factors and correlations for each of the determined patterns, the method and system may provide the individual with various benefits such as personalized recommendations, insurance discounts, and other added values or services that the individual can use to better manage and improve his or her life.

Claims

1. A computer-implemented method of aggregating and correlating data, the method comprising:

receiving, via one or more processors, a set of data from an individual wherein the set of data includes at least two different types of data selected from the group of data types consisting essentially of;

home data, vehicle data, personal health data and finance data;

analyzing, via one or more processors, the set of data to determine patterns for each type of data in the set;

correlating, via one or more processors, each of the determined patterns for each type of data in the set with one another to determine one or more underlying factors associated with each of the determined patterns; and

providing, via one or more processors, recommendations to the individual for each of the determined patterns based on the determined one or more underlying factors associated with each of the determined patterns.

2. The computer-implemented method of claim 1, wherein analyzing each type of data in the set to determine patterns includes analyzing the home data to determine one or more home patterns, the one or more home patterns including patterns associated with physical conditions of a home, environmental and security settings of a home, energy or utility usages in a home, or control and automation of a home.

3. The computer-implemented method of claim 1, wherein analyzing each type of data in the set to determine patterns includes analyzing the vehicle data to determine one or more vehicle patterns, the one or more vehicle patterns including patterns associated with physical conditions of a vehicle, usage of a vehicle, physical and mental conditions of a driver of a vehicle, driving environments, or a driver'"'"'s interactions with a vehicle.

4. The computer-implemented method of claim 1, wherein analyzing each type of data in the set to determine patterns includes analyzing the personal health data to determine one or more personal health patterns, the one or more personal health patterns including patterns associated with physiological parameters of an individual including a heart rate, a blood pressure, a sleep pattern, a stress level, or a body weight, or fitness activities carried out by an individual including walking, running, biking, swimming or weight training.

6. The computer-implemented method of claim 1, further comprising:

extracting, via one or more processors, information associated with each type of data in the set; and

displaying, via one or more processors, the extracted information associated with each type of data in the set to the individual.

7. The computer-implemented method of claim 1, wherein providing recommendations to the individual for each of the determined patterns includes providing one or more of a reward, an alert, a coupon, a membership subscription, an insurance discount, a discount on goods and services, a means for home or vehicle automation, or a tutorial program.

8. The computer-implemented method of claim 1, further comprising displaying the recommendations to the individual.

5. (canceled)

9. A non-transitory computer-readable storage medium including computer-readable instructions to be executed on one or more processors of a system for aggregating and correlating data from an individual, the instructions when executed causing the one or more processors to:

receive a set of data from the individual wherein the set of data includes at least two different types of data selected from the group of data types consisting essential of;

home data, vehicle data, personal health data and finance data;

analyze the set of data to determine patterns for each type of data in the set;

correlate each of the determined patterns for each type of data in the set with one another to determine one or more underlying factors associated with each of the determined patterns; and

provide recommendations to the individual for each of the determined patterns based on the determined one or more underlying factors associated with each of the determined patterns.

10. The non-transitory computer-readable storage medium of claim 9, further including instructions that, when executed, cause the one or more processors to extract information associated with each type of data in the set;

and display the extracted information associated with each type of data in the set to the individual.

11. The non-transitory computer-readable storage medium of claim 9, wherein the instructions to provide recommendations to the individual for each of the determined patterns include providing one or more of a reward, an alert, a coupon, a membership subscription, an insurance discount, a discount on goods and services, a means for home or vehicle automation, or a tutorial program.

12. The non-transitory computer-readable storage medium of claim 9, further including instructions that, when executed, cause the one or more processors to display the recommendations to the individual.

13. A computer system for aggregating and correlating data from an individual, the system comprising:

a plurality of data repositories, each data repository storing a different type of data from the individual;

a data pattern repository; and

a data server, including a memory having instructions for execution on one or more processors, wherein the instructions, when executed by the one or more processors, cause the data server to;

receive a set of data from the plurality of data repositories wherein the set of data includes at least two different types of data selected from the group of data types consisting essentially of;

home data, vehicle data, personal health data and finance data;

analyze the set of data to determine patterns for each type of data in the set;

store each of the determined patterns in the data pattern repository;

correlate each of the determined patterns for each type of data in the set with one another to determine one or more underlying factors associated with each of the determined patterns;

provide, via a network connection, recommendations to the individual for each of the determined patterns based on the determined one or more underlying factors associated with each of the determined patterns; and

display, via a network connection, the recommendations to the individual.

14. The computer system of claim 13, wherein the instructions of the data server, when executed by the one or more processors, further cause the data server to extract information associated with each type of data in the set;

and display the extracted information associated with each type of data in the set to the individual.