Evaluating conversation data based on risk factors

Patent metadata

Field	Value
Publication number	US-10545648-B2
Application number	US-201916242639-A
Country	US
Kind code	B2
Filing date	Jan 8, 2019
Priority date	Sep 9, 2014
Publication date	Jan 28, 2020
Grant date	Jan 28, 2020

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.

First claim

What is claimed is:

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;
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.

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

6. 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;
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.

7. A method comprising:
under control of a computing device configured with executable instructions,
identifying one or more risk factors to evaluate conversation data associated with one or more users, the conversation data representing at least one conversation;
processing the conversation data with a natural language processing system to identify an intent unit 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 unit is accurately identified for the conversation data; and
wherein the one or more risk factors include a plurality of risk factors; and the determining includes:
applying a weighting each of the plurality of risk factors; and
determining the confidence value based at least in part on the plurality of weighted risk factors;
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;
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.

8. The method of claim 7 , further comprising utilizing the confidence value by:
determining that multiple pieces of user input are each associated with a confidence value that is below a threshold amount, the conversation data inclu

Assignees

• Verint Americas Inc

Inventors

• Beaver Ian
• Brown Fred
• Gossard Casey

Classifications

• G10L15/183

  using context dependencies, e.g. language models · CPC title

• G10L15/18

  using natural language modelling · CPC title

• G10L25/66

  for extracting parameters related to health condition (detecting or measuring for diagnostic purposes A61B5/00) · CPC title

• G06F3/04842 Primary

  Selection of displayed objects or displayed text elements (G06F3/0482 takes precedence) · CPC title

• G10L15/22

  Procedures used during a speech recognition process, e.g. man-machine dialogue · CPC title