METHOD AND APPARATUS OF TRACKING AND PREDICTING USAGE TREND OF IN-VEHICLE APPS

• US 20150262198A1
• Filed: 03/13/2014
• Published: 09/17/2015
• Est. Priority Date: 03/13/2014
• Status: Abandoned Application

• Current Assignee: GM Global Technology Operations LLC (General Motors Company)
• Original Assignee: GM Global Technology Operations LLC (General Motors Company)
• Inventors: BAI, FAN, GRIMM, DONALD K., OSELLA, MASSIMO, HRABAK, ROBERT A., NIEMAN, LEONARD C.
• Application Number:
  US14/210,158
• Publication Number:
  US 20150262198A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: B60K 2360/164 Infotainment
• CPC Class Codes: B60K 2360/592 Data transfer involving external databases
• CPC Class Codes: B60K 35/00 Instruments specially adapted for vehicles; Arrangement of instruments in or on vehicles
• CPC Class Codes: B60K 35/28 characterised by the type of the output information, e.g. video entertainment or vehicle dynamics information; characterised by the purpose of the output information, e.g. for attracting the attention of the driver
• CPC Class Codes: B60K 35/65 Instruments specially adapted for specific vehicle types or users, e.g. for left- or right-hand drive
• CPC Class Codes: B60K 35/85 Arrangements for transferring vehicle- or driver-related data
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

A method and system for tracking and predicting usage trends for in-vehicle infotainment system applications are disclosed. Application usage data are collected in the infotainment systems of many road vehicles. Vehicle context relevance indicators are also provided, using data from the vehicle CAN bus or other data bus. The context relevance indicators—which indicate vehicle contextual situations such as traffic and weather conditions, presence of back seat passengers, length of driving trip, etc.—are cross-referenced to the application usage data to determine which applications are likely to be used in which situations. Application usage data and application/context correlation data from many vehicles are collected on a central server and analyzed to provide various metrics which are indicative of application usage trends. The application usage trend data can be used by vehicle manufacturers to optimize future infotainment system designs.

Claims

1. A method for tracking and predicting usage trends for in-vehicle infotainment system applications, said method comprising:

collecting, in a processor onboard a vehicle, said processor including a microprocessor and a memory module, usage data for in-vehicle infotainment system applications;

computing user ratings for the applications from the usage data;

collecting, in the processor, vehicle operational data from a vehicle Controller Area Network bus (CAN bus);

computing context indicators from the vehicle operational data;

computing application/context correlations from the usage data, the user ratings and the context indicators;

uploading the usage data, the user ratings and the application/context correlations from the vehicle to a central server computer for aggregation; and

computing, on the central server computer, application usage trends for an entire population of users from the usage data, the user ratings and the application/context correlations which were uploaded from many road vehicles.

2. The method of claim 1 wherein computing user ratings includes computing implicit ratings which are calculated based on recency of a user viewing an application, recency of the user using the application, frequency of the user using the application, duration of the user'"'"'s usage of the application and monetary value of the application.

3. The method of claim 2 wherein the user ratings also include explicit ratings provided by the user.

4. The method of claim 1 wherein the vehicle operational data includes;

vehicle speed, whether a transmission is in park or a drive gear, time duration and distance traveled in a driving trip, navigation and GPS data, anti-lock brake system (ABS) usage data, traction control system data, whether windshield wipers are on or off, driver identity, and occupancy status of each seat in the vehicle.

5. The method of claim 1 wherein the context indicators include;

whether the vehicle is being driven or is parked, whether an application is being used before, during or after driving, whether the vehicle is being driven in a city or on a highway, traffic and weather conditions, and presence of passengers in the vehicle.

6. The method of claim 1 wherein uploading the usage data, the user ratings and the application/context correlations from the vehicle to a central server computer includes wirelessly uploading the usage data, the user ratings and the application/context correlations from the vehicle to the central server computer using a telematics service.

7. The method of claim 1 wherein computing application usage trends includes computing a popularity value of an application as a statistical average of the user ratings for the application.

8. The method of claim 7 wherein computing application usage trends includes computing a time-weighted activity of an application as a summation of the popularity value for the application for a set of past time intervals, with more recent popularity values given greater weight.

9. The method of claim 8 wherein computing application usage trends includes computing an uptrend value including a term which is a difference between a slope of the time-weighted activity for the application and an average slope of the time-weighted activity for all applications.

10. The method of claim 1 wherein computing application usage trends includes computing a diversity value of an application by dividing a user community into a number of groups, calculating a penetration of the application into each of the groups, and calculating the diversity value as a function of the penetration of the application into all of the groups.

11. The method of claim 10 wherein the groups used in computing the diversity value include demographic groups and geographic groups.

12. A method for tracking and predicting usage trends for in-vehicle infotainment system applications, said method comprising:

collecting, in a processor onboard a vehicle, said processor including a microprocessor and a memory module, usage data for in-vehicle infotainment system applications;

computing user ratings for the applications from the usage data, including computing implicit ratings which are calculated based on recency of a user viewing an application, recency of the user using the application, frequency of the user using the application, duration of the user'"'"'s usage of the application and monetary value of the application;

collecting, in the processor, vehicle operational data from a vehicle Controller Area Network bus (CAN bus), where the vehicle operational data includes;

vehicle speed, whether a transmission is in park or a drive gear, time duration and distance traveled in a driving trip, navigation and GPS data, anti-lock brake system (ABS) usage data, traction control system data, whether windshield wipers are on or off, and occupancy status of each seat in the vehicle;

computing context indicators from the vehicle operational data, where the context indicators include;

whether the vehicle is being driven or is parked, whether an application is being used before, during or after driving, whether the vehicle is being driven in a city or on a highway, traffic and weather conditions, and presence of passengers in the vehicle;

computing application/context correlations from the usage data, the user ratings and the context indicators;

wirelessly uploading the usage data, the user ratings and the application/context correlations from the vehicle to a central server computer for aggregation; and

computing, on the central server computer, application usage trends for an entire population of users from the usage data, the user ratings and the application/context correlations which were uploaded from many road vehicles.

13. The method of claim 12 wherein computing application usage trends includes:

computing a popularity value of an application as a statistical average of the user ratings for the application, and computing a time-weighted activity of the application as a summation of the popularity value for the application for a set of past time intervals, with more recent popularity values given greater weight; and

computing a diversity value of an application by dividing a user community into a number of groups, calculating a penetration of the application into each of the groups, and calculating the diversity value as a function of the penetration of the application into all of the groups, where the groups used in computing the diversity value include demographic groups and geographic groups.

14. A system for tracking and predicting usage trends for in-vehicle infotainment system applications, said system comprising:

a processor onboard a vehicle, said processor including a microprocessor and a memory module, where the processor is configured with an algorithm for tracking usage of infotainment system applications including;

an application usage collection module configured to collect usage data for in-vehicle infotainment system applications and compute user ratings for the applications from the usage data,

a vehicle operational information collection module configured to collect vehicle operational data from a vehicle Controller Area Network bus (CAN bus),

a context relevance identification module configured to compute context indicators from the vehicle operational data, and

a cross-reference module configured to compute application/context correlations from the usage data, the user ratings and the context indicators,

where the processor is also configured to wirelessly upload the usage data, the user ratings and the application/context correlations from the vehicle for aggregation; and

a central server computer including a processor, a memory module and a network connection, where the central server computer is configured to compute application usage trends for an entire population of users from the usage data, the user ratings and the application/context correlations which were uploaded from the vehicle and many other vehicles.

15. The system of claim 14 wherein the user ratings include implicit ratings which are calculated based on recency of a user viewing an application, recency of the user using the application, frequency of the user using the application, duration of the user'"'"'s usage of the application and monetary value of the application.

16. The system of claim 14 wherein the vehicle operational data includes;

vehicle speed, whether a transmission is in park or a drive gear, time duration and distance traveled in a driving trip, navigation and GPS data, anti-lock brake system (ABS) usage data, traction control system data, whether windshield wipers are on or off, driver identity, and occupancy status of each seat in the vehicle.

17. The system of claim 14 wherein the context indicators include;

whether the vehicle is being driven or is parked, whether an application is being used before, during or after driving, whether the vehicle is being driven in a city or on a highway, traffic and weather conditions, and presence of passengers in the vehicle.

18. The system of claim 14 wherein the application usage trends include a popularity value of an application computed as a statistical average of the user ratings for the application,

19. The system of claim 18 wherein the application usage trends include a time-weighted activity of the application computed as a summation of the popularity value for the application for a set of past time intervals, with more recent popularity values given greater weight.

20. The system of claim 14 wherein the application usage trends include a diversity value of an application computed by dividing a user community into a number of groups, calculating a penetration of the application into each of the groups, and calculating the diversity value as a function of the penetration of the application into all of the groups, where the groups used in computing the diversity value include demographic groups and geographic groups.