Deep learning techniques based multi-purpose conversational agents for processing natural language queries

A system for Deep Learning techniques based multi-purpose conversational agents for processing natural language queries, the system comprising:

• a memory storing instructions;
• one or more communication interfaces;
• and
• one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to:
• define a plurality of components comprising of a Dialogue State Manager (DSM), a Multi-level Intent Identification Component, an Agents Manager, a plurality of Primary Agents, an Intent-Action-Dialogue (IAD) Framework, a Query-UpdateEngage (QUE) Framework, a Knowledge Graph Update-Natural Language (KGU-NL) Agent, a Knowledge Graph Engage Agent, a plurality of Auxiliary Agents, and a Knowledge Graph Update Agent, wherein each component amongst the plurality of components comprises one or more multi-purpose conversational agents;
• logically integrate, based upon a set of anticipated natural language user queries, the plurality of components by one or more application programming interfaces (APIs);
• receive, by the plurality of components logically integrated, a set of natural language queries from a plurality of sources;
• perform, based upon the set of natural language queries, a plurality of steps, wherein the plurality of steps comprise:
• (i) identify at least one multi-purpose conversational agent amongst the one or more multi-purpose conversational agents by using the DSM, wherein the identified multipurpose conversational agent corresponds to either the IAD Framework or the QUE Framework;
• and
• (ii) predict, by using one or more Deep Learning techniques, a probable user intent against a user query amongst the set of non-classified natural language queries;
• perform, based upon the predicted user intent and the identified multi-purpose conversational agent, one of:
• (i) select one or more pre-defined set of responses amongst a plurality of pre-defined set of responses or engaging a user for extracting in-depth information or calling external APIs for communicating the in-depth information to one or more external services upon determining the identified multi-purpose conversational agent to be corresponding to the IAD Framework, wherein the one or more pre-defined set of responses and the in-depth information correspond to the set of natural language queries;
• or
• (ii) classify a query amongst the set of natural language queries to identify one or more categories of conversations by implementing a recurrent neural network technique upon determining the identified multi-purpose conversational agent to be corresponding to the QUE Framework;
• and
• (iii) perform, based upon the classified query, one of:
• (a) query one or more knowledge graphs to generate a first set of responses corresponding to the set of natural language queries;
• or
• (b) update, by the KGU-NL Agent, the one or more knowledge graphs to generate a second set of responses corresponding to the set of natural language queries.

A non-transitory computer readable medium having a computer readable program embodied therein, wherein the computer readable program, when executed on a computing device, causes the computing device to:

• define, by one or more hardware processors, a plurality of components comprising of a Dialogue State Manager (DSM), a Multi-level Intent Identification Component, an Agents Manager, a plurality of Primary Agents, an Intent-Action-Dialogue (IAD) Framework, a Query-Update-Engage (QUE) Framework, a Knowledge Graph Update-Natural Language (KGU-NL) Agent, a Knowledge Graph Engage Agent, a plurality of Auxiliary Agents, and a Knowledge Graph Update Agent, wherein each component amongst the plurality of components comprises one or more multi-purpose conversational agents;
• logically integrate, based upon a set of anticipated natural language user queries, the plurality of components by one or more application programming interfaces (APIs);
• receive, by the plurality of components logically integrated, a set of natural language queries from a plurality of sources;
• perform, based upon the set of natural language queries, a plurality of steps, wherein the plurality of steps comprise:
• (iii) identifying at least one multi-purpose conversational agent amongst the one or more multi-purpose conversational agents by using the DSM, wherein the identified multi-purpose conversational agent corresponds to either the IAD Framework or the QUE Framework;
• and
• (iv) predicting, by using one or more Deep Learning techniques, a probable user intent against a user query amongst the set of non-classified natural language queries;
• perform, based upon the predicted user intent and the identified multi-purpose conversational agent, one of:
• (iv) selecting one or more pre-defined set of responses amongst a plurality of pre-defined set of responses or engaging a user for extracting in-depth information or calling external APIs for communicating the in-depth information to one or more external services upon determining the identified multi-purpose conversational agent to be corresponding to the IAD Framework, wherein the one or more pre-defined set of responses and the in-depth information correspond to the set of natural language queries;
• or
• (v) classifying a query amongst the set of natural language queries to identify one or more categories of conversations by implementing a recurrent neural network technique upon determining the identified multi-purpose conversational agent to be corresponding to the QUE Framework;
• and
• (vi) performing, based upon the classified query, one of:
• (c) querying one or more knowledge graphs to generate a first set of responses corresponding to the set of natural language queries;
• or
• (d) updating, by the KGU-NL Agent, the one or more knowledge graphs to generate a second set of responses corresponding to the set of natural language queries.