COLLABORATIVE DECISION ENGINE FOR QUALITY FUNCTION DEPLOYMENT

• US 20170091633A1
• Filed: 09/28/2015
• Published: 03/30/2017
• Est. Priority Date: 09/28/2015
• Status: Active Grant

• Current Assignee: Bank of America Corp.
• Original Assignee: Bank of America Corp.
• Inventors: Vemula, Srikanth, Gaddam, Sunil Reddy, Purushothaman, Sasidhar
• Granted Patent: US 10,438,143 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Abstract

Disclosed is systems, methods, and computer program products that provide for a technique for reducing computing resources, storage space needs, and network bandwidth associated with collaborative decision making. More particularly, this disclosure relates to a system for performing automatic predictive decision making using predictive fit models derived from previous user responses and the user characteristics of each responding user, and using the results to reduce the amount of computing and operational resources needed to operate a collaborative decision engine.

Claims

1. A system for reducing computing resources associated with collaborative decision-making, said system comprising:

one or more memory devices having computer readable program code stored thereon; and

one or more processing devices operatively coupled to the one or more memory devices, wherein the one or more processing devices are configured to execute the computer readable program code to;

identify a plurality of users that use a feature of an application, wherein the application is a virtual or physical process or task associated with an entity, and wherein the feature is at least a component of the application;

retrieve, from a database, sets of user characteristics for each of the plurality of users;

identify criteria associated with the feature, wherein the criteria are aspects of the feature that affect how the plurality of users perceives the feature or makes a decision regarding the feature;

determine predictive responses for each criteria of the feature based on stored prediction fit models for each criteria and each set of user characteristics represented in the identified plurality of users;

determine confidence scores for each predictive response based on the predictive response, the stored fit models for each criteria, and the sets of user characteristics;

determine that a first predictive response from the predictive responses for each criteria does not meet a threshold confidence score;

provide a survey to the plurality of users comprising at least one question related to the predictive response that does not meet the threshold confidence score;

receive an actual response as user input from the plurality of users;

replace the first predictive response with the actual response; and

display the actual response, along with the predictive responses that do meet their respective threshold probability of accuracy.

2. The system of claim 1, wherein the one or more processing devices are configured to execute the computer readable program code to:

identify a plurality of features;

identify a plurality of users that use one or more of the features;

receive, from a database, a set of user characteristics for each of the plurality of users;

provide feature surveys to users for each of the plurality of features, wherein the surveys request user input for criteria associated with each feature;

receive the user input for the criteria of each feature;

receive user weighting of each criteria for each feature;

apply the user weighting to each criteria;

determine user group weighting for each user input for a criteria based on a user group associated with each user;

associate and store the user input, the user weighting, the user group weighting, and the set of user characteristics for each user in a relational database;

determine a criteria response distribution for each user characteristic by analyzing the relational database; and

create a fit model for each criteria response distribution, wherein the fit model provides a predictive analysis of how a future user would respond to a criteria question based on a user characteristic.

3. The system of claim 1, wherein the set of user characteristics are characteristics of each responding user and include at least one of user group, age, gender, geographical location, time zone, work experience, education, and technical background.

4. The system of claim 1, wherein the criteria associated with each feature include at least one of ease of use, intuitiveness, speed, maintainability, effectiveness, amount of support required, usefulness, whether the feature should be retained, and the extent to which a responding user utilizes the feature.

5. The system of claim 1, wherein the one or more processing devices are configured to execute the computer readable program code to display the confidence scores to an application manager.

6. The system of claim 1, wherein providing a confidence score further comprises:

determining a goodness of fit for the predictive fit model based on a correlation between the predictive fit model and its respective criteria distribution; and

calculating a confidence score based on the goodness of fit.

7. The system of claim 2, wherein the criteria response distribution for each user characteristic is analyzed by a pattern analyzer that determines a linear or nonlinear regression fit model for the response distribution.

8. A computer program product for reducing computing resources associated with collaborative decision-making, the computer program product comprising a non-transitory computer readable medium comprising computer readable instructions, the instructions comprising instructions for:

identifying a plurality of users that use a feature of an application, wherein the application is a virtual or physical process or task associated with an entity, and wherein the feature is at least a component of the application;

retrieving, from a database, sets of user characteristics for each of the plurality of users;

identifying criteria associated with the feature, wherein the criteria are aspects of the feature that affect how the plurality of users perceives the feature or makes a decision regarding the feature;

determining predictive responses for each criteria of the feature based on stored prediction fit models for each criteria and each set of user characteristics represented in the identified plurality of users;

determining confidence scores for each predictive response based on the predictive response, the stored fit models for each criteria, and the sets of user characteristics;

determining that a first predictive response from the predictive responses for each criteria does not meet a threshold confidence score;

providing a survey to the plurality of users comprising at least one question related to the predictive response that does not meet the threshold confidence score;

receiving an actual response as user input from the plurality of users;

replacing the first predictive response with the actual response; and

displaying the actual response, along with the predictive responses that do meet their respective threshold probability of accuracy.

9. The computer program product of claim 8, wherein the computer readable instructions further comprise instructions for:

identifying a plurality of features;

identifying a plurality of users that use one or more of the features;

receiving, from a database, a set of user characteristics for each of the plurality of users;

providing feature surveys to users for each of the plurality of features, wherein the surveys request user input for criteria associated with each feature;

receiving the user input for the criteria of each feature;

receiving user weighting of each criteria for each feature;

applying the user weighting to each criteria;

determining user group weighting for each user input for a criteria based on a user group associated with each user;

associating and storing the user input, the user weighting, the user group weighting, and the set of user characteristics for each user in a relational database;

determining a criteria response distribution for each user characteristic by analyzing the relational database; and

creating a fit model for each criteria response distribution, wherein the fit model provides a predictive analysis of how a future user would respond to a criteria question based on a user characteristic.

10. The computer program product of claim 8, wherein the set of user characteristics are characteristics of each responding user and include at least one of user group, age, gender, geographical location, time zone, work experience, education, and technical background.

11. The computer program product of claim 8, wherein the criteria associated with each feature include at least one of ease of use, intuitiveness, speed, maintainability, effectiveness, amount of support required, usefulness, whether the feature should be retained, and the extent to which a responding user utilizes the feature.

12. The computer program product of claim 8, wherein the computer readable instructions further comprise instructions for displaying the confidence scores to an application manager.

13. The computer program product of claim 8, wherein providing a confidence score further comprises:

determining a goodness of fit for the predictive fit model based on a correlation between the predictive fit model and its respective criteria distribution; and

calculating a confidence score based on the goodness of fit.

14. The computer program product of claim 9, wherein the criteria response distribution for each user characteristic is analyzed by a pattern analyzer that determines a linear or nonlinear regression fit model for the response distribution.

15. A computer implemented method for reducing computing resources associated with collaborative decision-making, said computer implemented method comprising:

identifying, via a processing device, a plurality of users that use a feature of an application, wherein the application is a virtual or physical process or task associated with an entity, and wherein the feature is at least a component of an application;

retrieving, via a processing device, from a database, sets of user characteristics for each of the plurality of users;

identifying, via a processing device, criteria associated with the feature, wherein the criteria are aspects of the feature that affect how the plurality of users perceives the feature or makes a decision regarding the feature;

determining, via a processing device, predictive responses for each criteria of the feature based on stored prediction fit models for each criteria and each set of user characteristics represented in the identified plurality of users;

determining, via a processing device, confidence scores for each predictive response based on the predictive response, the stored fit models for each criteria, and the sets of user characteristics;

determining, via a processing device, that a first predictive response from the predictive responses for each criteria does not meet a threshold confidence score;

providing, via a processing device, a survey to the plurality of users comprising at least one question related to the predictive response that does not meet the threshold confidence score;

receiving, via a processing device, an actual response as user input from the plurality of users;

replacing, via a processing device, the first predictive response with the actual response; and

displaying, via a processing device, the actual response, along with the predictive responses that do meet their respective threshold probability of accuracy.

16. The computer implemented method of claim 15, further comprising:

identifying, via a processing device, a plurality of features;

identifying, via a processing device, a plurality of users that use one or more of the features;

receiving, via a processing device, from a database, a set of user characteristics for each of the plurality of users;

providing, via a processing device, feature surveys to users for each of the plurality of features, wherein the surveys request user input for criteria associated with each feature;

receiving, via a processing device, the user input for the criteria of each feature;

receiving, via a processing device, user weighting of each criteria for each feature;

applying, via a processing device, the user weighting to each criteria;

determining, via a processing device, user group weighting for each user input for a criteria based on a user group associated with each user;

associating, via a processing device, and storing the user input, the user weighting, the user group weighting, and the set of user characteristics for each user in a relational database;

determining, via a processing device, a criteria response distribution for each user characteristic by analyzing the relational database; and

creating a fit model for each criteria response distribution, wherein the fit model provides a predictive analysis of how a future user would respond to a criteria question based on a user characteristic.

17. The computer implemented method of claim 15, wherein the set of user characteristics are characteristics of each responding user and include at least one of user group, age, gender, geographical location, time zone, work experience, education, and technical background.

18. The computer implemented method of claim 15, wherein the criteria associated with each feature include at least one of ease of use, intuitiveness, speed, maintainability, effectiveness, amount of support required, usefulness, whether the feature should be retained, and the extent to which a responding user utilizes the feature.

19. The computer implemented method of claim 15, wherein providing a confidence score further comprises:

determining a goodness of fit for the predictive fit model based on a correlation between the predictive fit model and its respective criteria distribution; and

calculating a confidence score based on the goodness of fit.

20. The computer implemented method of claim 16, wherein the criteria response distribution for each user characteristic is analyzed by a pattern analyzer that determines a linear or nonlinear regression fit model for the response distribution.