System for optimizing detection of intent[s] by automated conversational bot[s] for providing human like responses

Patent metadata

Field	Value
Publication number	US-2020081939-A1
Application number	US-201916258680-A
Country	US
Kind code	A1
Filing date	Jan 28, 2019
Priority date	Sep 11, 2018
Publication date	Mar 12, 2020
Grant date	—

Abstract

Disclosed is a system for optimizing detection of an intent, pertaining to a query, by an automated conversational bot for providing human like responses to a user. An analyzer module builds an intent graph storing input dialogues, utterances, and output dialogues associated to an intent. A builder module fed training data, comprising the intent graph stored in the graph database to an automated conversational bot by enabling a bot builder to fill a bot template associated to each intent with a set of parameters indicating distinct utterances of an intent and output dialogues associated to the distinct utterances. A verification module trains the automated conversational bot through reinforcement learning by providing a feedback to the automated conversational bot. In one aspect, the automated conversational bot may be trained by validating an output dialogue against an input dialogue, received from the caller, with an expected response.

First claim

1 . A method for optimizing detection of an intent, pertaining to a query, by an automated conversational bot for providing human like responses to a user characterized by feeding a call recording archive to the automated conversational bot, the method comprising:
building, by a processor, an intent graph storing input dialogues, utterances, and output dialogues associated to an intent, wherein the intent indicates a context of a conversation between a caller and a call center representative, and wherein the intent graph is built by,
feeding each audio file indicating a call recording, present in a call recording archive, to a Natural Language Processing (NLP) engine in order to create a set of raw text transcripts pertaining to a category,
determining a plurality of intents from the set of raw text transcripts upon identifying one or more NLP entities from words present in the set of raw text transcripts, wherein each intent is associated to at least one category, and
mapping the input dialogues, the utterances, and the output dialogues with each intent, of the plurality of intents thereby building the intent graph pertaining to each intent;
feeding, by the processor, training data, comprising the intent graph stored in the graph database, to an automated conversational bot thereby enabling a bot builder to fill a bot template pertaining to each intent with a set of parameters indicating distinct utterances of an intent and output dialogues associated to the distinct utterances; and training, by the processor the automated conversational bot through reinforcement learning by providing a feedback to the automated conversational bot, wherein the automated conversational bot is trained by,
validating an output dialogue against an input dialogue, received from the caller, with an expected response, wherein the output dialogue is provided by the automated conversational bot based on the bot template and the training data
thereby optimizing detection of the intent of a query by the automated conversational bot for providing human like responses to the user based on the call recording archive.

2 . The method as claimed in claim 1 , wherein each call recording, present in the call recording archive, is fed to the NLP engine upon cleansing each audio file based on one or more filters, wherein the one or more filters comprises voice gender of the caller and the call center representative, language used in the call, the at least one category associated to the intent, and call duration.

3 . The method as claimed in claim 1 , wherein the intent graph is built by using a conceptual graph concept.

4 . The method as claimed in claim 1 , wherein the one or more NLP entities comprises noun, verbs, Question segment and Answer segment.

5 . The method as claimed in claim 1 , wherein the intent graph pertaining to each intent is stored in an intent graph database and wherein the set of parameters is filled in the bot template upon querying the intent graph database by the bot builder.

6 . A system for optimizing detection of an intent, pertaining to a query, by an automated conversational bot for providing human like responses to a user characterized by feeding a call recording archive to the automated conversational bot, the system comprising:
a processor and a memory coupled to the processor wherein the processor is capable of executing a plurality of modules stored in the memory and wherein the plurality of modules comprising:
an analyzer module for building an intent graph storing input dialogues, utterances, and output dialogues associated to an intent, wherein the intent indicates a context of a conversation between a caller and a call center representative, and wherein the intent graph is built by enabling an extraction module to
feed each audio file indicating a call recording, present in a call recording archive, to a Natural Language Processing (NLP) engine in order to create a set of raw text transcripts pertaining to a category,
determine a plurality of intents from the set of raw text transcripts upon identifying one or more NLP entities from words present in the set of raw text transcripts, wherein each intent is associated to at least one category, and
map the input dialogues, the utterances, and the output dialogues with each intent, of the plurality of intents thereby building the intent graph pertaining to each intent;

a builder module for feeding training data, comprising the intent graph stored in the graph database to an automated conversational bot by enabling a bot builder to fill a bot template associated to each intent with a set of parameters indicating distinct utterances of an intent and output dialogues associated to the distinct utterances; and a verification module for training the automated conversational bot through reinforcement learning by providing a feedback to the automated conversational bot, wherein the automated conversational bot is trained by,
validating an output dialogue against, an input dialogue received from the caller, with an expected response, wherein the output dialogue is provided by the automated conversational bot based on the bot template and the training data,
thereby optimizing detection of the intent of a query by the automated conversational bot for providing human like responses to the user based on the call recording archive.

7 . The system as claimed in claim 6 , wherein each call recording, present in the call recording archive, is fed to the NLP engine upon cleansing each audio file based on one or more filters, wherein the one or more filters comprises voice gender of the caller and the call center representative, language used in the call, the at least one category associated to the intent, and call duration.

8 . The system as claimed in claim 6 , wherein the intent graph is built by using a conceptual graph concept.

9 . The system as claimed in claim 6 , wherein the one or more NLP entities comprises noun, verbs, Question segment and Answer segment.

10 . The system as claimed in claim 6 , wherein the intent graph pertaining to each intent is stored in an intent graph database and wherein the set of parameters is filled in the bot template upon querying the intent graph database by the bot builder.

11 . A non-transitory computer readable medium embodying a program executable in a computing device for optimizing detection of an intent, pertaining to a query, by an automated conversational bot for providing human like responses to a user characterized by feeding a call recording archive to the automated conversational bot, the program comprising a program code:
a program code for building an intent graph storing input dialogues, utterances, and output dialogues associated to an intent, wherein the intent indicates a context of a conversation between a caller and a call center representative, and wherein the intent graph is built by,
feeding each audio file indicating a call recording, present in a call recording archive, to a Natural Language Processing (NLP) engine in order to create a set of raw text transcripts pertaining to a category,
determining a plurality of intents from the set of raw text transcripts upon identifying one or more NLP entities from words present in the set of raw text transcripts, wherein each intent is associated to at least one category, and
mapping the input dialogues, the utterances, and the output dialogues with each intent, of the plurality of intents thereby building the intent graph pertaining to each intent;
a program code for feeding training data, comprising the intent graph stored in the graph database to an automated conversational bot by enabling a bot builder to fill a bot template associat

Assignees

• Hcl Technologies Ltd

Inventors

• Subramaniam Senthil Kumar

Classifications

• G06F40/295 Primary

  Named entity recognition · CPC title

• G06F40/30

  Semantic analysis · CPC title

• G10L15/26

  Speech to text systems (G10L15/08 takes precedence) · CPC title

• G06F40/10 Primary

  Text processing (natural language analysis G06F40/20; semantic analysis G06F40/30; processing or translation of natural language G06F40/40) · CPC title

• G10L15/30

  Distributed recognition, e.g. in client-server systems, for mobile phones or network applications · CPC title