Personality-based chatbot and methods including non-text input

• US 10,748,534 B2
• Filed: 12/18/2017
• Issued: 08/18/2020
• Est. Priority Date: 06/19/2014
• Status: Active Grant

• Current Assignee: Mattersight Corporation (Nice Ltd)
• Original Assignee: Mattersight Corporation (Nice Ltd)
• Inventors: Gustafson, David, Danson, Christopher
• Primary Examiner(s): Shah, Paras D
• Application Number:
  US15/845,441
• Publication Number:
  US 20180108353A1
• Time in Patent Office: 974 Days
• CPC Class Codes: G06F 3/167 Audio in a user interface, e.g. using voice commands for navigating, audio feedback
• CPC Class Codes: G06F 40/20 Natural language analysis semantic analysis of natural language G06F40/30
• CPC Class Codes: G10L 13/00 Speech synthesis; Text to speech systems
• CPC Class Codes: G10L 15/19 Grammatical context, e.g. disambiguation of the recognition hypotheses based on word sequence rules
• CPC Class Codes: G10L 15/22 Procedures used during a speech recognition process, e.g. man-machine dialogue
• CPC Class Codes: G10L 15/26 Speech to text systems G10L15/08 takes precedence
• CPC Class Codes: G10L 15/30 Distributed recognition, e.g. in client-server systems, for mobile phones or network applications
• CPC Class Codes: G10L 2015/088 Word spotting
• CPC Class Codes: G10L 25/51 for comparison or discrimination
• CPC Class Codes: G10L 25/63 for estimating an emotional state
• CPC Class Codes: G10L 25/66 for extracting parameters related to health condition detecting or measuring for diagnostic purposes A61B5/00
• CPC Class Codes: H04M 3/493 Interactive information services, e.g. directory enquiries ; Arrangements therefor, e.g. interactive voice response [IVR] systems or voice portals

Abstract

The methods, apparatus, and systems described herein assist a user with a request. The methods in part receive input from a user that includes a voice input, a gesture input, a text input, biometric information, or a combination thereof; retrieve or determine a personality type of the user based on the input; determine a distress level or an engagement level of the user; determine a set of outputs responsive to the received input; rank the outputs in the set based on the retrieved or determined personality type and the determined distress level or engagement level; deliver a ranked output to the input in a modality based on the retrieved or determined personality type and a type of device configured to deliver the ranked output to the user, wherein the device comprises a navigation system, a car, a robot, or a combination thereof; and weigh the ranked output for future interactions.

Claims

1. A system adapted to communicate with a user, which comprises:

a node comprising a processor and a non-transitory computer readable medium operably coupled thereto, the non-transitory computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, where the plurality of instructions comprises;

instructions that, when executed, receive input from a user, wherein the input comprises;

a voice input, a gesture input, a text input, biometric information, or a combination thereof;

instructions that, when executed, apply a linguistic-based psychological behavioral model to the received input to determine a personality type of the user;

instructions that, when executed, determine a set of outputs based on the linguistic-based psychological behavioral model responsive to the received input, wherein the outputs comprise a suggested phone number for the user to call, a suggested webpage for the user to click, or a suggested contact for the user to email;

instructions that, when executed, rank the outputs in the set based on the determined personality type of the user;

instructions that, when executed, deliver a ranked output to the input in a modality based on the determined personality type of the user;

instructions that, when executed, determine that the ranked output is a success;

instructions that, when executed, aggregate determined successes based on repeated user input;

instructions that, when executed, determine a distress level or an engagement level of the user; and

instructions that, when executed, prioritize future ranked outputs for the user based on the determined distress level or engagement level of the user and the aggregated determined successes.

2. The system of claim 1, wherein the plurality of instructions further comprise instructions, that when executed, determine a mental state of the user based on the received input.

3. The system of claim 1, wherein the input further comprises context information.

4. The system of claim 3, wherein the context information comprises a user location, a local time, a positional orientation, a motion, a light level, a sound level, or a combination thereof.

5. The system of claim 3, wherein the plurality of instructions further comprise instructions, that when executed, predict input based on the context information.

6. The system of claim 5, wherein the plurality of instructions further comprise instructions that, when executed, rank the predicted input to prioritize predicted inputs for the user.

7. The system of claim 1, wherein the instructions that determine that the ranked output is a success comprise instructions, that when executed, determine whether the ranked output results in a decrease in the distress level of the user or an increase in the engagement level of the user.

8. The system of claim 7, wherein the plurality of instructions further comprise instructions, that when executed, store the evaluation of the success of the ranked output and update the stored evaluation with outputs that result in a decrease in the distress level of the user or an increase in the engagement level of the user.

9. The system of claim 1, wherein the plurality of instructions further comprise instructions, that when executed, determine that the received input comprises input from a group of users.

10. The system of claim 9, wherein the plurality of instructions further comprise instructions, that when executed, adjust the outputs based on a determination of which of the group of users is providing the received input.

11. The system of claim 1, wherein the linguistic-based psychological behavioral model identifies behavioral signifiers.

12. The system of claim 1, wherein the linguistic-based psychological behavioral model determines personality types selected from a thoughts-type personality, an opinions-type personality, a reactions-type personality, or an emotions-type personality.

13. The system of claim 12, wherein the personality type of the user is the reactions-type personality and the linguistic-based psychological behavioral model identifies emotional words.

14. The system of claim 1, wherein the psychological behavioral model identifies specific terms, keywords, or phrases that indicate a specific personality type.

15. The system of claim 1, wherein the personality type of the user determines a distress level or an engagement level of the user.

16. A method for communicating with a user, which comprises:

receiving input from a client device, wherein the input comprise a voice input, a gesture input, a text input, biometric information, or a combination thereof;

applying a linguistic-based psychological behavioral model to the received input to determine a personality type of the user from three or more personality types;

determining a set of outputs based on the linguistic-based psychological behavioral model responsive to the received input, wherein the outputs comprise a suggested phone number for the user to call, a suggested webpage for the user to click, or a suggested contact for the user to email;

ranking outputs based on the determined personality type of the user;

delivering a ranked output to the input in a modality based on the determined personality type of the user;

determining that the ranked output is a success;

aggregating determined successes based on repeated user input;

determining a distress level or an engagement level of a user; and

prioritizing future ranked outputs based on the determined distress level or engagement level of the user and the aggregated determined successes.

17. The method of claim 16, which further comprises determining a mental state of the user based on the received input.

18. The method of claim 16, wherein the input further comprises context information.

19. The method of claim 18, wherein the context information comprises a user location, a local time, a positional orientation, a motion, a light level, a sound level, or a combination thereof.

20. The method of claim 18, which further comprises predicting input based on the context information.

21. The method of claim 20, which further comprises ranking the predicted input to prioritize predicted inputs for the user.

22. The method of claim 16, which further comprises extracting one or more communication attributes from the input.

23. The method of claim 22, wherein each communication attribute is selected to comprise tone, tempo, pattern of speech, syntax, grammar, or any combination thereof.

24. A method for assisting a user, which comprises:

receiving, by one or more processors, at least one input from the user, wherein the at least one input comprise a voice input, a gesture input, a text input, biometric information, or a combination thereof;

applying, by one or more processors, a linguistic-based psychological behavioral model to the received input to determine a personality type of the user from three or more personality types;

determining, by one or more processors, a set of outputs based on the linguistic-based psychological behavioral model, the at least one input from the user and the determined personality type of the user, wherein the outputs comprise a suggested phone number for the user to call, a suggested webpage for the user to click, or a suggested contact for the user to email;

ranking, by one or more processors, the outputs based on the determined personality type of the user;

delivering, by one or more processors, a ranked output to the input in a modality based on the determined personality type of the user;

determining, by one or more processors, that the ranked output is a success;

aggregating, by one or more processors, determined successes based on repeated user input;

determining a distress level or an engagement level of the user; and

prioritizing future ranked outputs for the user based on the determined distress level or engagement level and the aggregated determined successes.

25. The method of claim 24, wherein the at least one input further comprises a user location, a local time, a positional orientation, a motion, a light level, a sound level, or a combination thereof.

26. A non-transitory computer readable medium comprising a plurality of instructions comprising:

instructions that, when executed, receive input from a client device of a user, wherein the input comprise a voice input, a gesture input, a text input, biometric information, or a combination thereof, and the client device comprises a wearable computer health monitoring device;

instructions that, when executed, apply a linguistic-based psychological behavioral model to the received input to identify a personality type of the user;

instructions that, when executed, determine a set of outputs based on the linguistic-based psychological behavioral model responsive to the received input, wherein the outputs comprise a suggested phone number for the user to call, a suggested webpage for the user to click, or a suggested contact for the user to email;

instructions that, when executed, rank the outputs in the set based on the identified personality type of the user;

instructions that, when executed, deliver a ranked output to the input in a modality based on the identified personality type of the user;

instructions that, when executed, determine that the ranked output is a success;

instructions that, when executed, aggregate determined successes based on repeated user input;

instructions that, when executed, determine a distress level or an engagement level of the user; and

instructions that, when executed, prioritize future ranked outputs for the user based on the determined distress level or engagement level of the user and the aggregated determined successes.