Evaluating conversation data based on risk factors

• US 10,175,865 B2
• Filed: 12/28/2017
• Issued: 01/08/2019
• Est. Priority Date: 09/09/2014
• Status: Active Grant

• Current Assignee: Verint Americas Incorporated (Verint Systems Incorporated)
• Original Assignee: Verint Americas Incorporated (Verint Systems Incorporated)
• Inventors: Beaver, Ian, Brown, Fred, Gossard, Casey
• Primary Examiner(s): Hang, Vu B
• Application Number:
  US15/857,160
• Publication Number:
  US 20180121062A1
• Time in Patent Office: 376 Days
• CPC Class Codes: G06F 3/04842 Selection of displayed objects or displayed text elements G06F3/0482 takes precedence
• CPC Class Codes: G06F 40/268 Morphological analysis
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06F 40/35 Discourse or dialogue representation
• CPC Class Codes: G10L 15/18 using natural language modelling
• CPC Class Codes: G10L 15/1822 Parsing for meaning understanding
• CPC Class Codes: G10L 15/183 using context dependencies, e.g. language models
• CPC Class Codes: G10L 15/22 Procedures used during a speech recognition process, e.g. man-machine dialogue
• CPC Class Codes: G10L 15/30 Distributed recognition, e.g. in client-server systems, for mobile phones or network applications
• 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

Abstract

This disclosure describes techniques and architectures for evaluating conversations. In some instances, conversations with users, virtual assistants, and others may be analyzed to identify potential risks within a language model that is employed by the virtual assistants and other entities. The potential risks may be evaluated by administrators, users, systems, and others to identify potential issues with the language model that need to be addressed. This may allow the language model to be improved and enhance user experience with the virtual assistants and others that employ the language model.

Claims

1. One or more non-transitory computer-readable media storing computer-readable instructions that, when executed, instruct one or more processors to perform acts comprising:

causing a conversation user interface to be displayed on a display device to enable a conversation between a user and a virtual assistant;

receiving, via the conversation user interface, user input;

processing the user input with one or more natural language processing techniques to identify an intent unit for the user input, the intent unit being associated with a language model for the natural language processing techniques;

determining, based at least in part on a presence of one or more risk indicators for the user input, a measure of confidence that the intent unit is correctly identified for the user input;

associating the measure of confidence with the user input;

determining a health status of the intent unit based at least in part on the measure of confidence, the health status indicating a level of risk associated with the intent unit;

presenting the health status of the intent unit via an output device associated with an administrator;

receiving a selection of the intent unit to obtain feedback regarding the intent unit;

presenting a feedback interface that enables a voter to provide feedback regarding matching of the user input to the intent unit;

receiving feedback for the voter regarding an accuracy of matching the user input to the intent unit; and

evaluating the intent unit based at least in part on the feedback, wherein the evaluating includes presenting a feedback results interface that indicates at least one of a number of votes that are associated with an accurate matching of the user input to the intent unit or a number of votes that are associated with an inaccurate matching of the user input to the intent unit.

2. The one or more non-transitory computer-readable media of claim 1, wherein the intent unit is associated with (i) an action to be performed at least partly by the virtual assistant and (ii) a pattern of components for triggering the intent unit.

3. The one or more non-transitory computer-readable media of claim 1, wherein the acts further comprise:

ranking the intent unit and another intent unit based at least in part on the health status of the intent unit and a health status of the other intent unit; and

presenting the ranking via the output device.

4. The one or more non-transitory computer-readable media of claim 1, wherein the one or more risk indicators include a plurality of risk indicators;

and the acts further comprise;

applying a weighting to each of the plurality of risk indicators to generate weighted risk indicators;

wherein the determining the measure of confidence includes determining the measure of confidence based at least in part on the weighted risk indicators.

5. The one or more non-transitory computer-readable media of claim 1, wherein the acts further comprise:

determining that the feedback indicates that the matching of the user input to the intent unit is accurate;

decreasing a weighting to be applied to the one or more risk indicators based at least in part the on determining that the feedback indicates that the matching of the user input to the intent unit is accurate; and

applying the weighting to the one or more risk indicators;

wherein the determining the measure of confidence includes determining the measure of confidence based at least in part on the presence of the one or more weighted risk indicators.

6. The one or more non-transitory computer-readable media of claim 1, wherein the acts further comprises:

determining that the feedback indicates that the matching of the user input to the intent unit is not accurate;

increasing a weighting to be applied to the one or more risk indicators based at least in part on the determining that the feedback indicates that the matching of the user input to the intent unit is not accurate; and

applying the weighting to the one or more risk indicators;

wherein the determining the measure of confidence includes determining the measure of confidence based at least in part on the presence of the one or more weighted risk indicators.

7. The one or more computer-readable media of claim 1, wherein the feedback includes a vote indicating whether or not the user input matches the intent unit.

8. A system comprising:

one or more computing devices used by voters;

one or more servers of a service provider that are in communication with the one or more computing devices and one or more smart devices of one or more users, wherein the servers comprise;

one or more processors;

a database storing (i) conversation data representing at least one conversation between the one or more users and virtual assistants running on the one or more smart devices and (ii) one or more risk factors;

memory communicatively coupled to the one or more processors and storing executable instructions that, when executed by the one or more processors, cause the one or more processors to perform acts comprising;

processing the conversation data with a natural language processing system to identify an intent for the conversation data;

determining a confidence value for the conversation data based at least in part on the one or more risk factors, the confidence value indicating a level of confidence that the intent is accurately identified for the conversation data; and

utilizing the confidence value, wherein the utilizing includes;

determining that the confidence value is below a threshold;

presenting a feedback interface at the one or more computing devices that enables each voter to provide feedback regarding matching of user input of the conversation data to the intent;

receiving feedback for the voter regarding an accuracy of matching the user input to the intent;

determining whether or not the feedback indicates that the matching of the user input to the intent is accurate; and

adjusting a weighting applied to the one or more risk factors based at least in part on determining whether or not the feedback indicates that the matching of the user input to the intent is accurate.

9. The system of claim 8, wherein the presenting a feedback interface at the one or more computing devices that enables each voter to provide feedback regarding matching of user input of the conversation data to the intent comprises:

presenting a feedback interface at the one or more computing devices that enables a voter to vote regarding matching of the user input to the intent;

determining a profile factor for the voter based at least in part on a comparison of a previous number of votes from the voter to votes from a majority of voters, the majority of voters including more than a predetermined number of voters; and

applying a weighting to the vote based at least in part on the profile factor of the voter.

10. The system of claim 8, wherein the acts further comprise:

applying a weighting to at least one of the one or more risk factors based at least in part on a maturity of intent units associated with the natural language processing system, or an amount of user inputs for the conversation data, or a combination thereof.