Connected vehicle settings and cloud system management

• US 10,554,759 B2
• Filed: 03/03/2019
• Issued: 02/04/2020
• Est. Priority Date: 04/22/2011
• Status: Active Grant

• Current Assignee: Emerging Automotive, LLC
• Original Assignee: Emerging Automotive, LLC
• Inventors: Penilla, Angel A., Penilla, Albert S.
• Primary Examiner(s): Fantu, Yalkew
• Application Number:
  US16/290,936
• Publication Number:
  US 20190199800A1
• Time in Patent Office: 338 Days
• Field of Search: 709226, 320108, 320109
• CPC Class Codes: B60L 2240/70 Interactions with external data bases, e.g. traffic centres
• CPC Class Codes: B60L 2250/12 by confirmation, e.g. of the input
• CPC Class Codes: B60L 2250/16 by display
• CPC Class Codes: B60L 2250/20 by driver identification
• CPC Class Codes: B60L 53/65 involving identification of vehicles or their battery types
• CPC Class Codes: B60L 53/665 Methods related to measuring, billing or payment
• CPC Class Codes: B60L 53/80 Exchanging energy storage elements, e.g. removable batteries
• CPC Class Codes: B60L 58/12 responding to state of charge [SoC]
• CPC Class Codes: B60W 2050/0075 Automatic parameter input, automatic initialising or calibrating means
• CPC Class Codes: B60W 2556/10 Historical data
• CPC Class Codes: B60W 50/0098 Details of control systems ensuring comfort, safety or stability not otherwise provided for
• CPC Class Codes: B60W 50/10 Interpretation of driver requests or demands
• CPC Class Codes: G01C 21/3641 Personalized guidance, e.g. limited guidance on previously travelled routes
• CPC Class Codes: G06Q 10/02 Reservations, e.g. for tickets, services or events
• CPC Class Codes: G06Q 10/20 Administration of product repair or maintenance
• CPC Class Codes: G06Q 20/18 involving self-service terminals [SST], vending machines, kiosks or multimedia terminals
• CPC Class Codes: G06Q 20/308 using the Internet of Things
• CPC Class Codes: G06Q 30/00 Commerce
• CPC Class Codes: G06Q 30/0643 Graphical representation of items or shoppers
• CPC Class Codes: G06Q 30/0645 Rental transactions; Leasing transactions
• CPC Class Codes: G06Q 50/06 : Energy or water supply
• CPC Class Codes: G07F 15/005 : dispensed for the electrical charging of vehicles electrical charging as such, B60L50/00
• CPC Class Codes: H04B 1/3822 : specially adapted for use in vehicles H04B1/3827 takes precedence
• CPC Class Codes: H04L 67/10 : in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04L 67/125 : involving control of end-device applications over a network
• CPC Class Codes: H04L 67/141 : Setup of application sessions admission control or resource allocation in data switching networks H04L47/70
• CPC Class Codes: H04L 67/306 : User profiles
• CPC Class Codes: H04W 4/029 : Location-based management or tracking services
• CPC Class Codes: H04W 4/44 : for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
• CPC Class Codes: H04W 4/60 : Subscription-based services using application servers or record carriers, e.g. SIM application toolkits
• CPC Class Codes: Y02T 10/70 : Energy storage systems for electromobility, e.g. batteries
• CPC Class Codes: Y02T 10/7072 : Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
• CPC Class Codes: Y02T 10/72 : Electric energy management in electromobility
• CPC Class Codes: Y02T 90/12 : Electric charging stations
• CPC Class Codes: Y02T 90/14 : Plug-in electric vehicles
• CPC Class Codes: Y02T 90/16 : Information or communication technologies improving the operation of electric vehicles
• CPC Class Codes: Y02T 90/167 : Systems integrating technologies related to power network operation and communication or information technologies for supporting the interoperability of electric or hybrid vehicles, i.e. smartgrids as interface for battery charging of electric vehicles [EV] or hybrid vehicles [HEV]
• CPC Class Codes: Y04S 30/14 : Details associated with the interoperability, e.g. vehicle recognition, authentication, identification or billing

Abstract

A cloud system that includes one or more servers for communicating with vehicles and processing information received from vehicles and processing information sent to vehicles is disclosed. One method includes receiving data from a computing device associated with a vehicle. The data is for a user account, and the data is received over one or more sessions. The user account has a user profile with a plurality of settings and metadata captured from use of the vehicle. Processing, by a learning engine, the metadata captured from use of the vehicle during the one or more sessions of use of the vehicle to identify learned patterns sensed from the vehicle. Sending a recommendation to the user account associated with the vehicle. The recommendation is based in part on the learned patterns from the vehicle. The recommendation is configured to identify a setting or action for the vehicle. The setting or action is configured to be accepted via a user interface associated with the user account to apply said setting on the vehicle or confirm said action for the vehicle.

Claims

1. A computer implemented method processed by a server that communicates with vehicles and processes information received from vehicles and processes information sent to vehicles, comprising, receiving data from a computing device associated with a vehicle, the data being for a user account, the data being received over one or more sessions of use of the vehicle, the user account having a user profile associated with a plurality of settings and the user account is associated with metadata captured during use of the vehicle;

processing, by a learning engine, the metadata captured during use of the vehicle during the one or more sessions of use of the vehicle to identify learned patterns for the user profile using data sensed from system of the vehicle; and

sending a recommendation to the user account associated with the vehicle, the recommendation is based in part on the learned patterns identified by the learning engine based on the data sensed from systems of the vehicle during the one or more sessions of use, the recommendation is configured to identify a setting or action for the vehicle;

wherein the setting or action is configured to be accepted via a user interface associated with the user account to apply said setting on the vehicle or confirm said action to be taken by a system of the vehicle.

2. The computer implemented method of claim 1, wherein processing the metadata captured from use of the vehicle is based on a monitoring of systems of the vehicle over time, and the learned patterns are analyzed for satisfying a condition for said recommendation.

3. The computer implemented method of claim 1, wherein user profile for the user account includes biometric data, or an image of a face of a person, or a fingerprint of a person, or combinations of two or more thereof.

4. The computer implemented method of claim 3, wherein the image of the face of a person is used at least in part as credential of a user that is associated with the user account.

5. The computer implemented method of claim 1, wherein the metadata captured from the vehicle relate to one or more of use of APPS, or system components of the vehicle, or environment conditions, or historical actions taken by the vehicle, or input/output controls of the vehicle, or use of buttons of the vehicle, or use of levers of the vehicle, or use of keys or key fobs of the vehicle, or use of a display selections of the vehicle, or use of a display interface, or use of a communication action of the vehicle.

6. The computer implemented method of claim 1, wherein the metadata captured from use of the vehicle includes captured systems data, captured user input settings, or identification of said user account or another user account, wherein another user account is for another user profile to be used with the vehicle.

7. The computer implemented method of claim 1, wherein the identified setting by said recommendation relates to a component or use mode of the vehicle.

8. The computer implemented method of claim 1, wherein the identified action by said recommendation relates to service of the vehicle or reservation for service of the vehicle.

9. The computer implemented method of claim 1, wherein the server is part of a cloud services system, the cloud services system is provided by one or more of a manufacturer of the vehicle or a third party provider, the cloud services system is configured with access to storage for holding said metadata received from the vehicle and from other vehicles.

10. The computer implemented method of claim 1, wherein the user profile being one of an administrator profile, a temporary profile, a shared vehicle profile, a valet profile, a child profile, or a custom profile.

11. The computer implemented method of claim 10, wherein the shared vehicle profile is for the vehicle when the vehicle is used via two or more user accounts having associated user profiles.

12. The computer implemented method of claim 2, wherein said settings or actions relate from one or more of a setting a temperature of the vehicle, a setting a window defrost mode in the vehicle, a heating a seat or seats of the vehicle, a setting an emergency signal of the vehicle, a setting an emergency signal for help, a setting a restriction or enabling a feature for the vehicle, a disabling the vehicle, a selecting an audio setting, making a reservation for service, reporting an error in the vehicle, setting a position of mirrors of the vehicle, setting to open or crack windows upon reaching a temperature, setting notifications for recharging a battery if the vehicle is an electric vehicle, setting reminders to charge upon reaching a predefined battery state if an electric vehicle, setting parameters to identify nearby charge stations if the vehicle is an electric vehicle, setting advertisement settings allowed to be displayed via a screen of the electric vehicle, setting maps data, setting communications data, setting navigation data, setting display settings data, setting customization of display applications on the screen of the vehicle, setting suspension parameters for the vehicle, setting auto lock/unlock controls of the vehicle, activating an alarm, setting instructions to receive video feedback from the vehicle upon detection of alarm conditions, setting to call for help when an accident is detected, setting time of day on vehicle clocks based on local time;

setting navigation parameters in the vehicle, setting preferred radio stations, setting sensitivity of collision detection sensors, setting sensor programming, or combinations of two or more thereof.

13. The method of claim 1, wherein the recommendation is one of a plurality of recommendations, and said plurality of recommendations are associated with a confidence score.

14. The computer implemented method of claim 1, wherein the metadata is associated with sensor data from the vehicle, the sensor data includes one or more of sensed temperature data, geo-location data, speed data, vehicle condition data, occupant identification data, number of occupant data, biometric detection data of an occupant in the vehicle, proximity of surrounding vehicles or obstacles data, voice data, video data, touch data, gesture data, surrounding vehicle shared sensor data, or at combination of two or more thereof.

15. The computer implemented method of claim 1, wherein learning engine processes metadata associated with third-party history profiles of other user accounts.

16. The computer implemented method of claim 15, further comprising examining actions of one or more of the third-party history profiles to identify similarity in actions or patterns to those of a history profile of the user account.

17. The computer implemented method of claim 1, wherein the learned patterns sensed from the vehicle relate to use of settings of the vehicle while the vehicle is used via the user account or relate to use of the vehicle causing sensor data output from system of the vehicle to produce said metadata.

18. The computer implemented method of claim 17, wherein said server is one of a plurality of servers associated with a cloud services system that executes said learning engine to process said metadata for said vehicle and other user accounts managed for other user accounts and other vehicles, the cloud services system is configured to manage said recommendation and other recommendations sent to said vehicle and said other vehicles.

19. The computer implemented method of claim 18, wherein the metadata is associated with sensor data from the vehicle, the sensor data includes one or more of sensed temperature data, geo-location data, speed data, vehicle condition data, occupant identification data, number of occupant data, biometric detection data of an occupant in the vehicle, proximity of surrounding vehicles or obstacles data, voice data, video data, touch data, gesture data, surrounding vehicle shared sensor data, or at combination of two or more thereof.

20. The computer implemented method of claim 1, wherein the user profile is identified and used for the period of time during which said vehicle is used with the user account, and enabling a second user account to be used with the vehicle;

wherein the second user account is configured to transfer settings to the vehicle consistent with at least some settings associated with a second profile of the second user account, the transfer of settings causing the vehicle to implement said at least some settings automatically for use of the vehicle via the second user account, and processing learning of patterns for said second user account for providing recommendations to the second user account.