Automation and security application store suggestions based on usage data

• US 8,533,144 B1
• Filed: 11/12/2012
• Issued: 09/10/2013
• Est. Priority Date: 11/12/2012
• Status: Active Grant

• Current Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Original Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Inventors: Reeser, Andrew, Call, Shawn M., Kennedy, Stacy L., Drinan, Lee C., Payne, Kevin, Jacob, Michael, Frey, Lisa A.
• Primary Examiner(s): Chaki, Kakali
• Assistant Examiner(s): PELLETT, DANIEL T
• Application Number:
  US13/674,726
• Time in Patent Office: 302 Days
• Field of Search: None
• US Class Current: 706/47
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

A method, system, and computer-readable medium that facilitate the reception of usage data about the utilization of an intelligent home system and recommend intelligent home system products based on the usage data. The method, system, and computer-readable medium facilitate the analysis of the usage data to determine whether to recommend intelligent home system products and which, if any, intelligent home system products to recommend. Recommendations may be generated by comparing the usage data to an updated products list or an upgraded products list. Recommendations may be generated by comparing the usage data to a similar products list. The similar products list may be generated by analyzing previously received usage data to determine which products are often used together. Recommendations may be generated by comparing the usage data to a similar customers list. The similar customers list may be generated by analyzing previously received usage data to determine which products are used by other, similar users. Recommendations may be presented to a user if the intelligent home system.

Claims

1. A method for recommending intelligent home system products to a user of an intelligent home system comprising:

recording, with the intelligent home system using one or more processors, a first set of usage data about the utilization of the intelligent home system installed in a first building, wherein the first set of usage data is associated with the user;

receiving, with one or more processors, a second set of usage data about a plurality of intelligent home systems installed in a plurality of buildings, wherein each intelligent home system includes a plurality of installed devices, and wherein the sets of usage data include building-specific usage data about each of the plurality of installed devices installed in a particular building and is stored on a computer-readable medium;

analyzing, with one or more processors, the first and second sets of usage data to identify one or more intelligent home system devices to recommend to the user, wherein analyzing the first and second sets of usage data includes one or more of;

(1) using the second set of usage data to assemble a similar devices list to identify one or more intelligent home system devices that are often used in conjunction, and

comparing the similar devices list to the first set of usage data to identify one or more devices that are not already installed in the first building that are often used in conjunction with devices installed in the first building, or

(2) using the second set of usage data to assemble a similar customers list, wherein the similar customers list includes one or more of customers with similar usage histories or customers with similar demographics; and

comparing the usage data of the customers included in the similar customer list to the first set of usage data to identify one or more devices that are used by customers included in the similar customer list that are not already installed in the first building; and

presenting to the user, with one or more processors, the one or more identified intelligent home system devices.

2. The method of claim 1, wherein analyzing the first and second sets of usage data to identify one or more intelligent home system devices to recommend includes ranking the plurality of installed devices by usage.

3. The method of claim 1, wherein analyzing the first and second sets of usage data to identify one or more intelligent home system devices to recommend includes identifying one or more intelligent home system devices which are one or more of upgraded versions of installed devices or updated versions of installed devices.

4. The method of claim 1, wherein the similar devices list is assembled using one or more of collaborative filtering, a cluster model, or a search-based algorithm.

5. The method of claim 1, wherein the similar customers list is assembled using one or more of collaborative filtering, a cluster model, or a search-based algorithm.

6. The method of claim 1, further comprising analyzing, with one or more processors, the first set of usage data to determine whether to recommend one or more intelligent home system devices.

7. A computer system comprising:

a processor; and

a program memory storing executable instructions that when executed by the processor cause the computer system to;

record a first set of usage data about the utilization of the intelligent home system installed in a first building, wherein the first set of usage data is associated with a user;

receive, with one or more processors, a second set of usage data about a plurality of intelligent home systems installed in a plurality of buildings, wherein each intelligent home system includes a plurality of installed devices, and wherein the sets of usage data include building-specific usage data about each of the plurality of installed devices installed in a particular building;

analyze the first and second sets of usage data to identify one or more intelligent home system devices to recommend to the user, wherein the instructions to analyze the first and second sets of usage data includes one or more of;

(1) instructions to use the second set of usage data to assemble a similar devices list to identify one or more intelligent home system devices that are often used in conjunction, and

instructions to compare the similar devices list to the first set of usage data to identify one or more devices that are not already installed in the first building that are often used in conjunction with devices installed in the first building, or

(2) instructions to use the second set of usage data to assemble a similar customers list, wherein the similar customers list includes one or more of customers with similar usage histories or customers with similar demographics; and

instructions to compare the usage data of the customers included in the similar customer list to the first set of usage data to identify one or more devices that are used by customers included in the similar customer list that are not already installed in the first building; and

present to the user the one or more identified intelligent home system devices.

8. The computer system of claim 7, wherein the executable instructions that when executed by the processor cause the computer system to analyze the first and second sets of usage data to identify one or more intelligent home system devices to recommend include instructions to rank the plurality of installed devices by usage.

9. The computer system of claim 7, wherein the executable instructions that when executed by the processor cause the computer system to analyze the first and second sets of usage data to identify one or more intelligent home system devices to recommend include instructions to identify one or more intelligent home system devices which are one or more of upgraded versions of installed devices or updated versions of installed devices.

10. The computer system of claim 7, wherein the similar devices list is assembled using one or more of collaborative filtering, a cluster model, or a search-based algorithm.

11. The computer system of claim 7, wherein the similar customers list is assembled using one or more of collaborative filtering, a cluster model, or a search-based algorithm.

12. The computer system of claim 7, wherein the program memory further stores executable instructions that when executed by the processor cause the computer system to analyze the first set of usage data to determine whether to recommend one or more intelligent home system devices.

13. A non-transitory, computer-readable medium storing executable instructions that when executed by a processor of a computer system cause the computer system to:

record a first set of usage data about the utilization of the intelligent home system installed in a first building, wherein the first set of usage data is associated with a user;

receive, with one or more processors, a second set of usage data about a plurality of intelligent home systems installed in a plurality of buildings, wherein each intelligent home system includes a plurality of installed devices, and wherein the sets of usage data include building-specific usage data about each of the plurality of installed devices installed in a particular building;

analyze the first and second sets of usage data to identify one or more intelligent home system devices to recommend to the user, wherein the instructions to analyze the first and second sets of usage data includes one or more of;

(1) instructions to use the second set of usage data to assemble a similar devices list to identify one or more intelligent home system devices that are often used in conjunction, and

instructions to compare the similar devices list to the first set of usage data to identify one or more devices that are not already installed in the first building that are often used in conjunction with devices installed in the first building, or

(2) instructions to use the second set of usage data to assemble a similar customers list, wherein the similar customers list includes one or more of customers with similar usage histories or customers with similar demographics; and

instructions to compare the usage data of the customers included in the similar customer list to the first set of usage data to identify one or more devices that are used by customers included in the similar customer list that are not already installed in the first building; and

present to the user the one or more identified intelligent home system devices.

14. The non-transitory, computer-readable medium of claim 13, wherein the executable instructions that when executed by the processor cause the computer system to analyze the first and second sets of usage data to identify one or more intelligent home system devices to recommend include instructions to rank the plurality of installed devices by usage.

15. The non-transitory, computer-readable medium of claim 13, wherein the executable instructions that when executed by the processor cause the computer system to analyze the first and second sets of usage data to identify one or more intelligent home system devices to recommend include instructions to identify one or more intelligent home system devices which are one or more of upgraded versions of installed devices or updated versions of installed devices.

16. The non-transitory, computer-readable medium of claim 13, wherein the similar devices list is assembled using one or more of collaborative filtering, a cluster model, or a search-based algorithm.

17. The non-transitory, computer-readable medium of claim 13, wherein the similar customers list is assembled using one or more of collaborative filtering, a cluster model, or a search-based algorithm.

18. The non-transitory, computer-readable medium of claim 13, further storing executable instructions that when executed by the processor cause the computer system to analyze the first set of usage data to determine whether to recommend one or more intelligent home system devices.