Method and apparatus for facilitating voice user interface design

• US 10,656,908 B2
• Filed: 06/28/2017
• Issued: 05/19/2020
• Est. Priority Date: 07/02/2013
• Status: Active Grant

• Current Assignee: [24]7.ai, Inc.
• Original Assignee: [24]7.ai, Inc.
• Inventors: Brown, Kathy L., Srivastava, Vaibhav
• Primary Examiner(s): Ho, Ruay
• Application Number:
  US15/636,064
• Publication Number:
  US 20170300296A1
• Time in Patent Office: 1,056 Days
• Field of Search: 715728, 706 12, 717102, 1 1
• CPC Class Codes: G06F 3/167 Audio in a user interface, e.g. using voice commands for navigating, audio feedback
• CPC Class Codes: G06F 40/20 Natural language analysis semantic analysis of natural language G06F40/30
• CPC Class Codes: G06F 8/38 for implementing user interfaces
• CPC Class Codes: G10L 15/1815 Semantic context, e.g. disambiguation of the recognition hypotheses based on word meaning

Abstract

A computer implemented method and an apparatus for facilitating voice user interface (VUI) design are provided. The method comprises identifying a plurality of user intentions from user interaction data. The method further comprises associating each user intention with at least one feature from among a plurality of features. One or more features from among the plurality of features are extracted from natural language utterances associated with the user interaction data. Further, the method comprises computing a plurality of distance metrics corresponding to pairs of user intentions from among the plurality of user intentions. A distance metric is computed for each pair of user intentions from among the pairs of user intentions. Furthermore, the method comprises generating a plurality of clusters based on the plurality of distance metrics. Each cluster comprises a set of user intentions. The method further comprises provisioning a VUI design recommendation based on the plurality of clusters.

Claims

1. A computer-implemented method, comprising:

identifying, by a processor, a plurality of user intentions representing contextual reasons for seeking customer support from user interaction data;

associating, by the processor, each user intention from among the plurality of user intentions with at least one feature from among a plurality of features, the plurality of features representing words used when interacting with customer support;

computing, by the processor, a measure of similarity between user intentions in a corresponding pair of user intentions;

generating, by the processor, a plurality of clusters based on the measure of similarity between user intentions, wherein each cluster from among the plurality of clusters comprises a set of user intentions from among the plurality of user intentions;

wherein a value of the measure of similarity corresponding to each pair of user intentions in the set of user intentions is less than a first pre-defined threshold value;

identifying, by the processor, at least one user intention in the set of user intentions with associated values of measure of similarity between the at least one user intention and remaining user intentions in the set of user intentions to be less than a second pre-defined threshold value, the second pre-defined threshold value being less than the first pre-defined threshold value;

splitting the at least one user intention, by the apparatus, to generate two or more new user intentions; and

provisioning, by the processor, a voice user interface (VUI) design recommendation based on the plurality of clusters.

2. The method of claim 1, wherein the user interaction data comprises interaction data generated during interactions between a plurality of users and customer support representatives.

3. The method of claim 1, wherein a feature from among the one or more features is extracted from natural language utterances corresponding to one of a word, a bigram, a trigram, a n-gram, or a part of speech (POS) tag.

4. The method of claim 3, wherein a feature from among the plurality of features corresponds to a feature-based rule.

5. The method of claim 1, further comprising:

constructing, by the processor, a category-feature matrix comprising a plurality of categories and the plurality of features, wherein each category from among the plurality of categories corresponds to a user intention from among the plurality of user intentions, and wherein the category-feature matrix is configured to facilitate a visualization of an association of the at least one feature from among the plurality of features with each category.

6. The method of claim 1, wherein the measure of similarity corresponds to a correlation measure between the at least one feature associated with each user intention in the corresponding pair of user intentions.

7. The method of claim 1, wherein the measure of similarity is computed using at least one algorithm from among a cosine similarity based algorithm, a Pearson correlation based algorithm, a Spearman rank correlation based algorithm, a Kendall'"'"'s tau based algorithm, a city block distance based algorithm, a Euclidian distance based algorithm, or a custom algorithm derived from business logic and designer intuition.

8. The method of claim 7, wherein the measure of similarity for each pair of user intentions is based on using two or more algorithms from among the at least one algorithm.

9. The method of claim 1, wherein the plurality of clusters is generated using at least one clustering algorithm from among a K-means algorithm, a self-organizing map based algorithm, a self-organizing feature map based algorithm, a density-based spatial clustering algorithm, or an optics clustering based algorithm.

10. The method of claim 1, further comprising:

facilitating addition of new user intentions to the set of user intentions until a pre-determined stopping condition is met.

11. The method of claim 1, further comprising:

arranging, by the processor, user intentions corresponding to one or more clusters from among the plurality of clusters in a hierarchical structure to configure a plurality of levels of user intentions for the one or more clusters.

12. The method of claim 11, wherein provisioning the VUI design recommendation comprises generating a visual representation of the plurality of clusters, wherein the one or more clusters from among the plurality of clusters are associated with a plurality of levels of user intentions.

13. The method of claim 12, wherein the VUI design recommendation is configured to support refinement in structure arrangement based on at least one of business logic and designer variables.

14. The method of claim 12, wherein the VUI design recommendation is configured to facilitate a design of a VUI for an interactive voice response (IVR) system.

15. The method of claim 14, further comprising:

provisioning, by the processor, appropriate dialog with prompts corresponding to the VUI design recommendation for facilitating interaction with a plurality of users.

16. The method of claim 15, further comprising:

receiving, by the processor, a request for interaction by a user from among the plurality of users over a communication network;

querying the user, by the apparatus, for determining a corresponding user intention; and

providing, by the processor, an optimized interaction journey experience based on the VUI design recommendation and the determined user intention.

17. An apparatus comprising:

at least one processor; and

a memory having stored therein machine executable instructions, that when executed by the at least one processor, cause the apparatus to;

identify a plurality of user intentions representing contextual reasons for seeking customer support from user interaction data;

associate each user intention from among the plurality of user intentions with at least one feature from among a plurality of features, the plurality of features representing words used when interacting with customer support;

compute a measure of similarity between user intentions in a corresponding pair of user intentions;

generate a plurality of clusters based on the measure of similarity between user intentions, wherein each cluster from among the plurality of clusters comprises a set of user intentions from among the plurality of user intentions;

wherein a value of the measure of similarity corresponding to each pair of user intentions in the set of user intentions is less than a first pre-defined threshold value; and

wherein the apparatus is further caused to;

identify at least one user intention in the set of user intentions with associated values of the measure of similarity between the at least one user intention and remaining user intentions in the set of user intentions to be less than a second pre-defined threshold value, the second pre-defined threshold value being less than the first pre-defined threshold value;

split the at least one user intention to generate two or more new user intentions;

facilitate addition of new user intentions to the set of user intentions till a pre-determined stopping condition is met; and

provision a VUI design recommendation based on the plurality of clusters.

18. The apparatus of claim 17, wherein the user interaction data comprises interaction data generated during interaction between a plurality of users and customer support representatives.

19. The apparatus of claim 17, wherein a feature from among the one or more features is extracted from natural language utterances corresponding to one of a word, a bigram, a trigram, a n-gram, or a POS tag.

20. The apparatus of claim 19, wherein a feature from among the plurality of features corresponds to a feature-based rule.

21. The apparatus of claim 17, wherein the apparatus is further caused to:

construct a category-feature matrix comprising a plurality of categories and the plurality of features, wherein each category from among the plurality of categories corresponds to a user intention from among the plurality of user intentions, and wherein the category-feature matrix is configured to facilitate a visualization of an association of the at least one feature from among the plurality of features with each category.

22. The apparatus of claim 17, wherein the measure of similarity corresponds to a correlation measure between the at least one feature associated with each user intention in the corresponding pair of user intentions.

23. The apparatus of claim 17, wherein the measure of similarity is computed using at least one algorithm from among a cosine similarity based algorithm, a Pearson correlation based algorithm, a Spearman rank correlation based algorithm, a Kendall'"'"'s tau based algorithm, a city block distance based algorithm, a Euclidian distance based algorithm, or a custom algorithm derived from business logic and designer intuition.

24. The apparatus of claim 23, wherein the measure of similarity is computed for each pair of user intentions using two or more algorithms from among the at least one algorithm.

25. The apparatus of claim 17, wherein the plurality of clusters is generated using at least one clustering algorithm from among K-means algorithm, a self-organizing map based algorithm, a self-organizing feature map based algorithm, a density-based spatial clustering algorithm, or an optics clustering based algorithm.

26. The apparatus of claim 17, wherein the apparatus is further caused to:

arrange user intentions corresponding to one or more clusters from among the plurality of clusters in a hierarchical structure to configure a plurality of levels of user intentions for the one or more clusters.

27. The apparatus of claim 26, wherein provisioning the VUI design recommendation comprises generating a visual representation of the plurality of clusters, wherein the one or more clusters from among the plurality of clusters are associated with a plurality of levels of user intentions.

28. The apparatus of claim 27, wherein the VUI design recommendation is configured to support refinement in structure arrangement based on at least one of business logic and designer intuition.

29. The apparatus of claim 27, wherein the VUI design recommendation is configured to facilitate an unambiguous design of a VUI for an IVR system.

30. The apparatus of claim 29, wherein the apparatus is further caused to:

provision appropriate dialog with prompts corresponding to the VUI design recommendation for facilitating interaction with a plurality of users.

31. The apparatus of claim 30, wherein the apparatus is further caused to:

receive a request for interaction by a user from among the plurality of users over a communication network;

query the user for determining a corresponding user intention; and

provide an optimized interaction journey experience based on the VUI design recommendation and the determined user intention.