Vehicle electronic receptionist for communications management

• US 10,191,462 B2
• Filed: 06/06/2017
• Issued: 01/29/2019
• Est. Priority Date: 06/06/2017
• Status: Active Grant

• Current Assignee: Maplebear Inc.
• Original Assignee: International Business Machines Corporation
• Inventors: Brew, Kevin W., Gordon, Michael S., Kozloski, James R., Pickover, Clifford A.
• Primary Examiner(s): Bousono, Orlando
• Application Number:
  US15/614,809
• Publication Number:
  US 20180348723A1
• Time in Patent Office: 602 Days
• CPC Class Codes: G05B 13/042 in which a parameter or coefficient is automatically adjusted to optimise the performance
• CPC Class Codes: G05B 19/0425 Safety, monitoring
• CPC Class Codes: G06F 40/216 using statistical methods
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G10L 15/00 Speech recognition G10L17/00 takes precedence
• CPC Class Codes: G10L 15/1807 using prosody or stress
• CPC Class Codes: H04L 12/1895 for short real-time information, e.g. alarms, notifications, alerts, updates
• CPC Class Codes: H04L 2012/40273 the transportation system being a vehicle
• CPC Class Codes: H04L 51/046 Interoperability with other network applications or services

Abstract

A computer implemented method, system and computer program product for a receptionist function in vehicles for communications management, such as phone call and texting management, includes determining driving context of a moving vehicle, estimating a risk factor related to the operation of the vehicle based on the driving context, receiving an incoming communication directed to a mobile communication device within the vehicle, analyzing the incoming communication to determine a communication factor, and determining an electronic action in response to the incoming communication based on the driving context, the risk factor and the communication factor.

Claims

1. A system for communications management in driving vehicles, comprising:

one or more computer processors;

one or more computer-readable storage media;

program instructions, stored on the one or more computer-readable storage media, which when implemented by the one or more processors, cause the computer system to perform;

determining driving context of a moving vehicle;

estimating a risk factor related to the operation of the vehicle based on the driving context;

receiving an incoming communication directed to a mobile communication device within the vehicle;

analyzing the incoming communication to determine a communication factor; and

determining an electronic action in response to the incoming communication based on the driving context, the risk factor and the communication factor,

wherein determining the electronic action includes applying an adjustable weight factor to each of the driving context, the risk factor and the communication factor in determining the electronic action, the weight factors being adjustable based on driver preferences.

2. The system of claim 1, wherein determining the communication factor includes performing analytics on the incoming message, the analytics being selected from the group consisting of speech recognition, natural language processing and tone analysis, wherein determining the communication factor includes analyzing content of the incoming communication identified from the speech recognition and natural language processing analytics, and wherein determining the communication factor includes determining the cognitive state of a sender of the incoming communication.

3. The system of claim 1, wherein the driving context is determined from data selected from the group consisting of mode vehicle is driving, road conditions, traffic conditions, history of accidents, current destination, distractions in the vehicle, weather, cognitive state of a driver of the vehicle, cognitive state of a passenger in the vehicle and a name of a sender of the incoming communication, and wherein the risk factor is estimated from data selected from the group consisting on risk of accident, risk of cognitive overload of a driver of the vehicle, risk of waking a passenger sleeping in a self-driving vehicle.

4. The system of claim 1, wherein the electronic action is selected from the group consisting of taking a message, providing a silent or audible notification to a driver of the vehicle, providing an automated response to the sender of the incoming communication.

5. The system of claim 1, wherein the determining driving context of a moving vehicle is determined based on the internet of things and the determining an electronic action in response to the incoming communication based on the driving context, the risk factor and the communication factor is implemented in a cloud computing environment, and wherein determining the electronic action includes machine learning based on past driver performance correlated with the corresponding driving context, the risk factor and the communication factor.

6. A non-transitory computer program product comprising:

program instructions stored on a computer-readable storage medium, where execution of the program instructions using a computer causes the computer to perform a method for communications management in driving vehicles, comprising;

determining driving context of a moving vehicle;

estimating a risk factor related to the operation of the vehicle based on the driving context;

receiving an incoming communication directed to a mobile communication device within the vehicle;

analyzing the incoming communication to determine a communication factor; and

determining an electronic action in response to the incoming communication based on the driving context, the risk factor and the communication factor,

wherein determining the electronic action includes applying an adjustable weight factor to each of the driving context, the risk factor and the communication factor in determining the electronic action, the weight factors being adjustable based on driver preferences.

7. The non-transitory computer program product of claim 6, wherein determining the communication factor includes performing analytics on the incoming message, the analytics being selected from the group consisting of speech recognition, natural language processing and tone analysis, wherein determining the communication factor includes analyzing content of the incoming communication identified from the speech recognition and natural language processing analytics, and wherein determining the communication factor includes determining the cognitive state of a sender of the incoming communication.

8. The non-transitory computer program product of claim 6, wherein the driving context is determined from data selected from the group consisting of mode vehicle is driving, road conditions, traffic conditions, history of accidents, current destination, distractions in the vehicle, weather, cognitive state of a driver of the vehicle, cognitive state of a passenger in the vehicle and a name of a sender of the incoming communication, and wherein the risk factor is estimated from data selected from the group consisting on risk of accident, risk of cognitive overload of a driver of the vehicle, risk of waking a passenger sleeping in a self-driving vehicle.

9. The non-transitory computer program product of claim 6, wherein the electronic action is selected from the group consisting of taking a message, providing a silent or audible notification to a driver of the vehicle, providing an automated response to the sender of the incoming communication.

10. The non-transitory computer program product of claim 6, wherein the determining driving context of a moving vehicle is determined based on the internet of things and the determining an electronic action in response to the incoming communication based on the driving context, the risk factor and the communication factor is implemented in a cloud computing environment, and wherein determining the electronic action includes machine learning based on past driver performance correlated with the corresponding driving context, the risk factor and the communication factor.

11. A system for communications management in driving vehicles, comprising:

one or more computer processors;

one or more computer-readable storage media;

program instructions, stored on the one or more computer-readable storage media, which when implemented by the one or more processors, cause the computer system to perform;

determining driving context of a moving vehicle;

estimating a risk factor related to the operation of the vehicle based on the driving context;

receiving an incoming communication directed to a mobile communication device within the vehicle;

analyzing the incoming communication to determine a communication factor; and

determining an electronic action in response to the incoming communication based on the driving context, the risk factor and the communication factor,

wherein determining the electronic action includes machine learning based on past driver performance correlated with the corresponding driving context, the risk factor and the communication factor.

12. The system of claim 11, wherein determining the communication factor includes performing analytics on the incoming message, the analytics being selected from the group consisting of speech recognition, natural language processing and tone analysis, wherein determining the communication factor includes analyzing content of the incoming communication identified from the speech recognition and natural language processing analytics, and wherein determining the communication factor includes determining the cognitive state of a sender of the incoming communication.

13. The system of claim 11, wherein the driving context is determined from data selected from the group consisting of mode vehicle is driving, road conditions, traffic conditions, history of accidents, current destination, distractions in the vehicle, weather, cognitive state of a driver of the vehicle, cognitive state of a passenger in the vehicle and a name of a sender of the incoming communication, and wherein the risk factor is estimated from data selected from the group consisting on risk of accident, risk of cognitive overload of a driver of the vehicle, risk of waking a passenger sleeping in a self-driving vehicle.

14. The system of claim 11, wherein the electronic action is selected from the group consisting of taking a message, providing a silent or audible notification to a driver of the vehicle, providing an automated response to the sender of the incoming communication, and wherein determining the electronic action includes applying an adjustable weight factor to each of the driving context, the risk factor and the communication factor in determining the electronic action, the weight factors being adjustable based on driver preferences.

15. The system of claim 11, wherein the determining driving context of a moving vehicle is determined based on the internet of things and the determining an electronic action in response to the incoming communication based on the driving context, the risk factor and the communication factor is implemented in a cloud computing environment.

16. A non-transitory computer program product comprising:

program instructions stored on a computer-readable storage medium, where execution of the program instructions using a computer causes the computer to perform a method for communications management in driving vehicles, comprising;

determining driving context of a moving vehicle;

estimating a risk factor related to the operation of the vehicle based on the driving context;

receiving an incoming communication directed to a mobile communication device within the vehicle;

analyzing the incoming communication to determine a communication factor; and

determining an electronic action in response to the incoming communication based on the driving context, the risk factor and the communication factor,

wherein determining the electronic action includes machine learning based on past driver performance correlated with the corresponding driving context, the risk factor and the communication factor.

17. The non-transitory computer program product of claim 16, wherein determining the communication factor includes performing analytics on the incoming message, the analytics being selected from the group consisting of speech recognition, natural language processing and tone analysis, wherein determining the communication factor includes analyzing content of the incoming communication identified from the speech recognition and natural language processing analytics, and wherein determining the communication factor includes determining the cognitive state of a sender of the incoming communication.

18. The non-transitory computer program product of claim 16, wherein the driving context is determined from data selected from the group consisting of mode vehicle is driving, road conditions, traffic conditions, history of accidents, current destination, distractions in the vehicle, weather, cognitive state of a driver of the vehicle, cognitive state of a passenger in the vehicle and a name of a sender of the incoming communication, and wherein the risk factor is estimated from data selected from the group consisting on risk of accident, risk of cognitive overload of a driver of the vehicle, risk of waking a passenger sleeping in a self-driving vehicle.

19. The non-transitory computer program product of claim 16, wherein the electronic action is selected from the group consisting of taking a message, providing a silent or audible notification to a driver of the vehicle, providing an automated response to the sender of the incoming communication, and wherein determining the electronic action includes applying an adjustable weight factor to each of the driving context, the risk factor and the communication factor in determining the electronic action, the weight factors being adjustable based on driver preferences.

20. The non-transitory computer program product of claim 16, wherein the determining driving context of a moving vehicle is determined based on the internet of things and the determining an electronic action in response to the incoming communication based on the driving context, the risk factor and the communication factor is implemented in a cloud computing environment.