HIERARCHICAL VISUALIZATION FOR DECISION REVIEW SYSTEMS

• US 20170308836A1
• Filed: 04/22/2016
• Published: 10/26/2017
• Est. Priority Date: 04/22/2016
• Status: Abandoned Application

• Current Assignee: Accenture Global Solutions Limited (Accenture PLC)
• Original Assignee: Accenture Global Solutions Limited (Accenture PLC)
• Inventors: Kass, Alex M.
• Application Number:
  US15/136,182
• Publication Number:
  US 20170308836A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 3/0484 for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change

Abstract

Techniques are described for presenting a hierarchical arrangement of node evaluation results to facilitate the review of a decision. Machine learning (ML) and/or artificial intelligence (AI) techniques are employed to automatically determine an individual result for each of multiple decision nodes that are hierarchically arranged to contribute to an overall result of a decision. A user interface may present the decision nodes and individual results, in their hierarchical arrangement, to enable a reviewer to provide feedback regarding one or more of the individual results and/or the overall result. The reviewer feedback may be employed to further refine the model used to determine the individual results for the decision nodes.

Claims

1. A computer-implemented method for automated determination of an overall result for a decision, the method comprising:

receiving input data associated with the decision;

applying a machine learning (ML) algorithm to individually determine a node evaluation result for each of a plurality of decision nodes based on a portion of the input data, the plurality of decision nodes includes in a decision hierarchy for the decision;

determining an overall result based on traversing the decision hierarchy according to the node evaluation results;

presenting the overall result and the node evaluation results in a reviewer user interface (UI);

receiving reviewer feedback to modify at least one of the node evaluation results, the reviewer feedback provided through the reviewer UI; and

determining a modified overall result based on applying the decision hierarchy to the node evaluation results including the modified at least one node evaluation result.

2. The method of claim 1, wherein:

the ML algorithm further determines a confidence metric indicating a level of confidence in the node evaluation result for each of the decision nodes; and

the confidence metric for each of the node evaluation results is presented in the reviewer UI.

3. The method of claim 1, wherein the overall result is a binary result.

4. The method of claim 1, further comprising:

receiving training data including a plurality of labeled portions of the training data; and

applying the ML algorithm to the training data to generate a model for determining the node evaluation result for each of the decision nodes in the decision hierarchy;

wherein the model is employed by the ML algorithm to individually determine the node evaluation result for each of the decision nodes based on the portion of the input data.

5. The method of claim 4, further comprising:

adjusting the model based on the reviewer feedback.

6. The method of claim 1, wherein the reviewer feedback indicates approval or disapproval of at least one of the node evaluation results.

7. The method of claim 1, wherein:

the node evaluation results are presented in at least one of a graphical format or a textual format in a reviewer UI; and

the node evaluation results are presented in an arrangement according to the decision hierarchy.

8. The method of claim 1, wherein:

the reviewer UI presents the node evaluation results in an interactive session; and

the reviewer feedback is received during the interactive session.

9. A system, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor, the memory storing instructions which, when executed by the at least one processor, cause the at least one processor to perform operations comprising;

receiving input data associated with the decision;

applying a machine learning (ML) algorithm to individually determine a node evaluation result for each of a plurality of decision nodes based on a portion of the input data, the plurality of decision nodes includes in a decision hierarchy for the decision;

determining an overall result based on traversing the decision hierarchy according to the node evaluation results;

presenting the overall result and the node evaluation results in a reviewer user interface (UI);

receiving reviewer feedback to modify at least one of the node evaluation results, the reviewer feedback provided through the reviewer UI; and

determining a modified overall result based on applying the decision hierarchy to the node evaluation results including the modified at least one node evaluation result.

10. The system of claim 9, wherein:

the ML algorithm further determines a confidence metric indicating a level of confidence in the node evaluation result for each of the decision nodes; and

the confidence metric for each of the node evaluation results is presented in the reviewer UI.

11. The system of claim 9, wherein the overall result is a binary result.

12. The system of claim 9, the operations further comprising:

receiving training data including a plurality of labeled portions of the training data; and

applying the ML algorithm to the training data to generate a model for determining the node evaluation result for each of the decision nodes in the decision hierarchy;

wherein the model is employed by the ML algorithm to individually determine the node evaluation result for each of the decision nodes based on the portion of the input data.

13. The system of claim 12, further comprising:

adjusting the model based on the reviewer feedback.

14. The system of claim 9, wherein the reviewer feedback indicates approval or disapproval of at least one of the node evaluation results.

15. One or more computer-readable media storing instructions which, when executed by at least one processor, cause the at least one processor to perform operations comprising:

receiving input data associated with the decision;

applying a machine learning (ML) algorithm to individually determine a node evaluation result for each of a plurality of decision nodes based on a portion of the input data, the plurality of decision nodes includes in a decision hierarchy for the decision;

determining an overall result based on traversing the decision hierarchy according to the node evaluation results;

presenting the overall result and the node evaluation results in a reviewer user interface (UI);

receiving reviewer feedback to modify at least one of the node evaluation results, the reviewer feedback provided through the reviewer UI; and

determining a modified overall result based on applying the decision hierarchy to the node evaluation results including the modified at least one node evaluation result.

16. The one or more computer-readable media of claim 15, wherein:

the ML algorithm further determines a confidence metric indicating a level of confidence in the node evaluation result for each of the decision nodes; and

the confidence metric for each of the node evaluation results is presented in the reviewer UI.

17. The one or more computer-readable media of claim 15, wherein the overall result is a binary result.

18. The one or more computer-readable media of claim 15, the operations further comprising:

receiving training data including a plurality of labeled portions of the training data; and

applying the ML algorithm to the training data to generate a model for determining the node evaluation result for each of the decision nodes in the decision hierarchy;

wherein the model is employed by the ML algorithm to individually determine the node evaluation result for each of the decision nodes based on the portion of the input data.

19. The one or more computer-readable media of claim 15, wherein:

the node evaluation results are presented in at least one of a graphical format or a textual format in a reviewer UI; and

the node evaluation results are presented in an arrangement according to the decision hierarchy.

20. The one or more computer-readable media of claim 15, wherein:

the reviewer UI presents the node evaluation results in an interactive session; and

the reviewer feedback is received during the interactive session.