Granted Patent B2

US 9,177,318 · App. 13/867,991 · Granted Nov 3, 2015

Abstract

Claims (101)

1. A computer-implemented method, comprising:

receiving, by a computing device, a user-statement from a user during a simulated conversation with the user;

determining a set of automatic-statements that each responds to the user-statement;

determining a set of behavior-characteristics associated with the user;

computing relevance scores for the automatic-statements based on the behavior-characteristics, wherein a respective relevance score indicates an outcome quality that the user is likely to perceive for the automatic-statement as a response to the user-statement, wherein computing the respective relevance score for a respective automatic-statement involves computing a template-relevance score using a response-prediction function that takes as input a response template and the user's behavior-characteristics, and wherein the template-relevance score indicates how well the response template matches a conversation style for a user having the behavior characteristics;

selecting an automatic-statement that has a highest relevance score from the set of automatic-statements; and

providing the selected automatic-statement to the user.

2. The method of claim 1 , wherein a respective behavior-characteristic indicates a personality-trait category, and indicates a numeric score for the corresponding personality-trait category.

3. The method of claim 2 , wherein the personality-trait categories include one or more of:

openness to experiences;

conscientiousness;

extraversion;

agreeableness;

self-esteem;

novelty-seeking;

perfectionism;

rigidity;

impulsivity;

harm-avoidance;

disinhibition;

alexithymia;

neuroticism;

psychoticism; and

obsessionality.

4. The method of claim 2 , further comprising:

identifying a personality-trait category to score for the user;

determining an automatic-response associated with the personality-trait category;

providing the automatic-response to the user;

responsive to receiving a response from the user for the automatic-response, computing a score for the personality-trait category for the user; and

updating the user's behavior-characteristics to assign the computed score to the personality-trait category.

5. The method of claim 1 , wherein determining the set of automatic-statements involves:

determining a query from the user's user-statement;

identifying, from a response-template repository, one or more response templates that match at least one query attribute of the query, wherein the response-template repository includes a set of pre-generated response templates that are each associated with at least one corresponding query attribute; and

generating the set of automatic-statements based on the identified response templates.

6. The method of claim 1 , further comprising:

providing a survey that indicates a set of conversation scripts to a plurality of users, and solicits a response rating for one or more conversation responses in the conversation script;

obtaining response ratings, from the users, for the conversation responses;

determining behavior characteristics for each of the plurality of users; and

training the response-prediction function based on the behavior preferences for the plurality of users, and the response ratings from each of the plurality of users.

7. A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method, the method comprising:

receiving a user-statement from a user during a simulated conversation with the user;

determining a set of automatic-statements that each responds to the user-statement;

determining a set of behavior-characteristics associated with the user;

computing relevance scores for the automatic-statements based on the behavior-characteristics, wherein a respective relevance score indicates an outcome quality that the user is likely to perceive for the automatic-statement as a response to the user-statement, wherein computing the respective relevance score for a respective automatic-statement involves computing a template-relevance score using a response-prediction function that takes as input a response template and the user's behavior-characteristics, and wherein the template-relevance score indicates how well the response template matches a conversation style for a user having the behavior characteristics;

selecting an automatic-statement that has a highest relevance score from the set of automatic-statements; and

providing the selected automatic-statement to the user.

8. The storage-medium of claim 7 , wherein a respective behavior-characteristic indicates a personality-trait category, and indicates a numeric score for the corresponding personality-trait category.

9. The storage-medium of claim 8 , wherein the personality-trait categories include one or more of:

openness to experiences;

conscientiousness;

extraversion;

agreeableness;

self-esteem;

novelty-seeking;

perfectionism;

rigidity;

impulsivity;

harm-avoidance;

disinhibition;

alexithymia;

neuroticism;

psychoticism; and

obsessionality.

10. The storage-medium of claim 8 , wherein the method further comprises:

identifying a personality-trait category to score for the user;

determining an automatic-response associated with the personality-trait category;

providing the automatic-response to the user;

responsive to receiving a response from the user for the automatic-response, computing a score for the personality-trait category for the user; and

updating the user's behavior-characteristics to assign the computed score to the personality-trait category.

11. The storage-medium of claim 7 , wherein determining the set of automatic-statements involves:

determining a query from the user's user-statement;

identifying, from a response-template repository, one or more response templates that match at least one query attribute of the query, wherein the response-template repository includes a set of pre-generated response templates that are each associated with at least one corresponding query attribute; and

generating the set of automatic-statements based on the identified response templates.

12. The storage-medium of claim 7 , wherein the method further comprises:

providing a survey that indicates a set of conversation scripts to a plurality of users, and solicits a response rating for one or more conversation responses in the conversation script;

obtaining response ratings, from the users, for the conversation responses;

determining behavior characteristics for each of the plurality of users; and

training the response-prediction function based on the behavior preferences for the plurality of users, and the response ratings from each of the plurality of users.

13. An apparatus, comprising:

a communication module to receive a user-statement from a user during a simulated conversation with the user;

a response-generating module to determine a set of automatic-statements that each responds to the user-statement;

a response-scoring module to compute relevance scores for the automatic-statements based on behavior-characteristics associated with the user, wherein a respective relevance score indicates an outcome quality that the user is likely to perceive for the automatic-statement as a response to the user-statement, wherein computing the respective relevance score for a respective automatic-statement involves computing a template-relevance score using a response-prediction function that takes as input a response template and the user's behavior-characteristics, and wherein the template-relevance score indicates how well the response template matches a conversation style for a user having the behavior characteristics; and

a response-selecting module to select an automatic-statement that has a highest relevance score from the set of automatic-statements;

wherein the communication module is further configured to provide the selected automatic-statement to the user.

14. The apparatus of claim 13 , wherein a respective behavior-characteristic indicates a personality-trait category, and indicates a numeric score for the corresponding personality-trait category.

15. The apparatus of claim 14 , further comprising a behavior-predicting module to:

identify a personality-trait category to score for the user;

determine an automatic-response associated with the personality-trait category;

provide the automatic-response to the user;

compute a score for the personality-trait category for the user, responsive to receiving a response from the user for the automatic-response; and

update the user's behavior-characteristics to assign the computed score to the personality-trait category.

16. The apparatus of claim 13 , wherein while determining the set of automatic-statements, the response-generating module is further configured to:

determine a query from the user's user-statement;

identify, from a response-template repository, one or more response templates that match at least one query attribute of the query, wherein the response-template repository includes a set of pre-generated response templates that are each associated with at least one corresponding query attribute; and

generate the set of automatic-statements based on the identified response templates.

17. The apparatus of claim 13 , wherein the communication module is further configured to:

provide a survey that indicates a set of conversation scripts to a plurality of users, and solicits a response rating for one or more conversation responses in the conversation script; and

obtain response ratings, from the users, for the conversation responses; and

wherein the apparatus further comprises a prediction-training module to:

determine behavior characteristics for each of the plurality of users; and

train the response-prediction function based on the behavior preferences for the plurality of users, and the response ratings from each of the plurality of users.

Assignments (10)