Analyzing conversations to automatically identify product feature requests

• US 10,367,940 B2
• Filed: 02/22/2018
• Issued: 07/30/2019
• Est. Priority Date: 06/01/2015
• Status: Active Grant

• Current Assignee: Zoominfo Converse LLC
• Original Assignee: AffectLayer, Inc. (ZoomInfo Technologies, Inc.)
• Inventors: Raanani, Roy, Levy, Russell, Breakstone, Micha Yochanan, Facher, Dominik
• Primary Examiner(s): Addy, Thjuan K
• Application Number:
  US15/902,751
• Publication Number:
  US 20180183930A1
• Time in Patent Office: 523 Days
• Field of Search: 37926507, 37926505, 37926506, 3792661, 37926503
• CPC Class Codes: G06F 15/76 Architectures of general purpose stored program computers with program plugboard G06F15/08; multicomputers G06F15/16
• CPC Class Codes: G06F 16/3329 Natural language query formulation or dialogue systems
• CPC Class Codes: G06F 16/3344 using natural language analysis
• CPC Class Codes: G06F 40/10 Text processing natural language analysis G06F40/20; semantic analysis G06F40/30; processing or translation of natural language G06F40/40
• CPC Class Codes: G06F 40/284 Lexical analysis, e.g. tokenisation or collocates
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06F 40/40 Processing or translation of natural language natural language analysis G06F40/20; semantic analysis G06F40/30
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G10L 15/183 using context dependencies, e.g. language models
• CPC Class Codes: G10L 2015/088 Word spotting
• CPC Class Codes: H04M 2201/40 using speech recognition speech recognition per se G10L15/00
• CPC Class Codes: H04M 3/5175 Call or contact centers supervision arrangements

Abstract

A feedback identification system to automatically determine product feature requests by analyzing conversations of representatives with customers. The feedback identification system retrieves recordings of various conversations, extracts features of the conversations, and analyzes the features to determine a set of features that is indicative of a feature request. A feature request is a request for adding a specified functionality to a product. The set of features is analyzed to generate a feedback manifest, which includes the feature request (a) as a summary of what is discussed in the conversations or (b) verbatim from the conversations.

Claims

1. A computer-implemented method, comprising:

retrieving multiple recordings of conversations associated with multiple representatives, wherein each of the conversations is between at least one of the representatives and at least one of multiple customers;

extracting multiple features from each of the recordings, wherein the multiple features indicate characteristics of any of (a) a customer of multiple customers in the corresponding conversation, (b) a representative of multiple representatives in the corresponding conversation, (c) the corresponding conversation;

analyzing features associated with a first conversation of the multiple conversations to determine a set of features that is indicative of a feature request for a product, wherein the feature request is a request made by a first customer of the multiple customers associated with the first conversation for adding one or more functionalities to the product; and

generating, based on the set of features, information regarding the feature request.

2. The computer-implemented method of claim 1, wherein analyzing the features includes determining the set of features based on a usage of one or more words or phrases in the first conversation that are indicative of the feature request.

3. The computer-implemented method of claim 2, wherein determining the set of features includes determining a specified word or a specified phrase in the first conversation which is explicitly indicative of the feature request.

4. The computer-implemented method of claim 2, wherein determining the set of features includes determining a set of words or phrases in the first conversation based on which the feature request is implicit.

5. The computer-implemented method of claim 4, wherein determining the set of words or phrases based on which the feature request is implicit includes analyzing a portion of the first conversation prior to or after the set of words or phrases to determine the feature request that is implied.

6. The computer-implemented method of claim 1, wherein generating the information regarding the feature request includes generating the set of features, the set of features including one or more words or phrases in the first conversation that are indicative of the feature request.

7. The computer-implemented method of claim 1, wherein generating the information regarding the feature request includes generating a summary of the feature request.

8. The computer-implemented method of claim 7, wherein generating the summary of the feature request includes generating the summary by performing a semantic analysis of the set of features, the set of features including one or more words or phrases in the first conversation that are indicative of the feature request.

9. The computer-implemented method of claim 7, wherein generating the summary of the feature request includes generating the summary to include a context of the first conversation, the context including identification of the first customer or a first representative associated with the first conversation.

10. The computer-implemented method of claim 7, wherein the summary includes any information obtained using metadata associated with the first conversation.

11. The computer-implemented method of claim 7, wherein generating the summary includes generating the summary using any of a rule-based, artificial intelligence, machine-learning, or natural language processing techniques.

12. The computer-implemented method of claim 1, wherein retrieving the multiple recordings include retrieving a data stream associated with a real-time conversation between the first representative and the first customer.

13. The computer-implemented method of claim 12 further comprising:

extracting one or more features from the real-time conversation that are indicative of the feature request associated with the product.

14. The computer-implemented method of claim 1, wherein extracting the features includes:

generating features that include a transcription, vocabulary and a language model of the conversations as a first output.

15. The computer-implemented method of claim 14, wherein extracting the features includes:

generating, using the first output, features that include semantic information from the conversations.

16. The computer-implemented method of claim 1, wherein extracting the features includes extracting a visual feature associated with a conversation of the conversations.

17. The computer-implemented method of claim 1, wherein extracting the features includes extracting the features using any of an artificial intelligence, a machine learning, or natural language processing technique.

18. The computer-implemented method of claim 1, wherein at least one of the recordings includes a recording of a video call between one of the customers and one of the representatives.

19. The computer-implemented method of claim 1, wherein at least one of the recordings includes an online meeting between one of the customers and one of the representatives.

20. The computer-implemented method of claim 1, wherein at least one of the recordings includes a recording of a virtual reality-based conversation between one of the customers and one of the representatives.

21. The computer-implemented method of claim 1, wherein at least one of the recordings includes a recording of an augmented reality-based conversation between one of the customers and one of the representatives.

22. The computer-implemented method of claim 1, wherein at least one of the multiple recordings includes an e-mail conversation between one of the customers and one of the multiple representatives.

23. A non-transitory computer-readable storage medium storing computer-readable instructions, comprising:

instructions for retrieving a recording of a conversation associated with a representative and a customer;

instructions for extracting multiple features from the recording, wherein the multiple features indicate characteristics of any of (a) the customer, (b) the representative, (c) the conversation;

instructions for analyzing features associated with the conversation to determine a set of features that is indicative of a feature request for a product, wherein the feature request is a request made by the customer for adding one or more functionalities to the product; and

instructions for generating information regarding the feature request, the information including one or more words or phrases in the conversation that are indicative of the feature request.

24. The non-transitory computer-readable storage medium of claim 23, wherein the instructions for generating the information include:

instructions for generating a summary of the feature request, the summary generated based on the one or more words or phrases in the conversation that are indicative of the feature request.

25. The non-transitory computer-readable storage medium of claim 23, wherein the instructions for determining the set of features include:

instructions for determining a set of words or phrases in the conversation based on which the feature request is implicit.

26. A system, comprising:

a processor;

a first component that is configured to extract multiple features from each of multiple recordings of conversations, wherein each conversation is associated with at least one of multiple customers and at least one of multiple representatives;

a second component that is configured to analyze the features of each of the conversations to determine a first set of features that is indicative of a feature request for a product, wherein the feature request is a request made by a first customer of the multiple customers associated with the corresponding conversation for adding one or more functionalities to the product; and

a third component that is configured to generate information regarding the feature request based on the set of features.

27. The system of claim 26, wherein the third component is further configured to generate the information by determining a set of words or phrases in one or more of the conversations that is indicative of the feature request implicitly.