Virtual Assistants Overview
Natural Language Processing (NLP)
Concepts and Terminology
Quick Start Guide
Accessing the Platform
Working with the Builder
Building a Virtual Assistant
Using Workspaces
Release Notes
Current Version
Previous Versions

Dialog Tasks
Dialog Builder
Node Types
Intent Node
Dialog Node
Entity Node
Form Node
Confirmation Node
Message Nodes
Logic Node
Bot Action Node
Service Node
Webhook Node
Script Node
Group Node
Agent Transfer
User Prompts
Voice Call Properties
Dialog Task Management
Connections & Transitions
Component Transition
Context Object
Event Handlers
Knowledge Graph
Knowledge Extraction
Build Knowledge Graph
Add Knowledge Graph to Bot
Create the Graph
Build Knowledge Graph
Add FAQs
Run a Task
Build FAQs from an Existing Source
Traits, Synonyms, and Stop Words
Manage Variable Namespaces
Move Question and Answers Between Nodes
Edit and Delete Terms
Edit Questions and Responses
Knowledge Graph Training
Knowledge Graph Analysis
Knowledge Graph Import and Export
Importing Knowledge Graph
Exporting Knowledge Graph
Creating a Knowledge Graph
From a CSV File
From a JSON file
Auto-Generate Knowledge Graph
Alert Tasks
Small Talk
Digital Skills
Digital Forms
ML Engine
Model Validation
FM Engine
KG Engine
Traits Engine
Ranking and Resolver
NLP Configurations
NLP Guidelines
Contextual Memory
Contextual Intents
Interruption Management
Multi-intent Detection
Amending Entities
Default Conversations
Sentinment Management
Tone Analysis
Test & Debug
Talk to Bot
Utterence Testing
Batch Testing
Conversation Testing
Conversations Dashboard
Performance Dashboard
Custom Dashboards
Meta Tags
Dashboards and Widgets
Conversation Flows
NLP Metrics
Containment Metrics
Usage Metrics
Smart Bots
Universal Bots
Universal Bot Definition
Universal Bot Creation
Training a Universal Bot
Universal Bot Customizations
Enabling Languages
Manage Assistant
Plan & Usage
Usage Plans
Support Plans
Multilingual Virtual Assistants
Masking PII Details
IVR Settings
General Settings
Assistant Management
Data Table
Table Views
App Definitions
Sharing Data Tables or Views

Build a Flight Status Assistant
Design Conversation Skills
Create a Sample Banking Assistant
Create a Transfer Funds Task
Create a Update Balance Task
Create a Knowledge Graph
Set Up a Smart Alert
Design Digital Skills
Configure Digital Forms
Configure Digital Views
Add Data to Data Tables
Update Data in Data Tables
Add Data from Digital Forms
Train the Assistant
Use Traits
Use Patterns for Intents & Entities
Manage Context Switching
Deploy the Assistant
Configure an Agent Transfer
Use Assistant Functions
Use Content Variables
Use Global Variables
Web SDK Tutorial
Widget SDK Tutorial
Analyze the Assistant
Create a Custom Dashboard
Use Custom Meta Tags in Filters

API Reference
API Introduction
API List
API Collection
koreUtil Libraries
SDK Reference
SDK Introduction
SDK Security
SDK Registration
Web Socket Connect and RTM
Using the BotKit SDK

Assistant Admin Console
Administration Dashboard
User Management
Add Users
Manage Groups
Manage Roles
Assistant Management
Invite Users
Send Bulk Invites
Import User Data
Synchronize Users from AD
Security & Compliance
Using Single-Sign On
Security Settings
Cloud Connector
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  5. Virtual Assistants Overview

Virtual Assistants Overview

Communication has been the essence of life from the beginning of time with the evolution of technology, mode, and style.

In the early days, conversations were restricted to verbal and textual interaction between humans. These interactions are usually guided by emotions, context, and awareness of previous conversations. Following the innovation of computers, interactions have now expanded to include machines i.e. human-machine interactions. The transitions from a command-based interface to a Graphical User Interface (GUI) to a Conversational User Interface (CUI) became natural and need-based making communication easier.

Further enhancements using Artificial Intelligence and NLP capabilities enabled Virtual Assistants to understand user utterances in the natural language; derive the task from the user utterance as well as extract the information required to successfully execute the task.

AI-driven, NLP-based chat, and voice virtual assistants are the latest in technology and a must for all businesses in this generation.

What are Conversational Virtual Assistants?

A Conversational Virtual Assistant acts as an intelligent intermediary between people, digital systems, and internet-enabled things. It replaces the traditional Graphical User Interfaces (GUIs) of an application or website with a Conversational User Interface. It is a paradigm shift from the earlier communications achieved either by entering syntax-specific commands or clicking icons.

Virtual Assistants are designed to converse with users through a combination of natural language-based conversations. Responses can come in the form of buttons, calendars, or other widgets that accelerate the speed with which a user can respond.

AI-powered messaging solutions or Conversational Virtual Assistants serve as the stepping stone to the future. It communicates through intelligent virtual agents, organizations’ apps and websites, social media platforms, and messenger platforms. Users can interact with such assistants using voice or text to access information, complete tasks, and execute transactions.

So what makes the Conversational Virtual Assistant so special? In a nutshell:

What are Intents & Entities?

A conversational virtual assistant faces three challenges:

  1. Intent Detection – Understanding what the user wants.
  2. Entity Extraction – Extracting the required information from the user to accomplish what the user wants.
  3. Dialog Flow/Conversation – Accomplishing the user wants.

Whatever the user says is considered as an Utterance. It is the task of the Conversational VA to extract the intent, and entities essential to carry a conversation from the user utterance. For example, let us consider the following user utterance: I want to fly to London this weekend.

An Intent is the user’s intention that usually comes in the form of a verb or noun within the user utterance. From the above user utterance, a Conversational VA understands the user intent as want to fly and triggers the corresponding dialog task.

Entities are a collection of data or information that the VA requires to complete the task which is identified in the user intent. There can be multiple entities in various formats that are required by the VA. These can either be a part of user utterance or the VA needs to prompt the user for the entity values. For example, in the above user utterance, London, and this weekend form the values for the entities representing Destination and Travel Date respectively. As you can notice, the Source entity value is missing and the Bot needs to prompt the user for the same.

As seen, an Entity can be of any type like location, date, time, person, etc,.

How to Build Intelligent Virtual Assistants?

Virtual Assistants are not smart by default. They are designed to show some level of artificial intelligence by leveraging technologies like machine learning, big data, natural language processing, and etc. A Virtual Assistant is intelligent when it is aware of user needs, understands the user’s perspective or context, and responds according to the user’s mood or emotion. Its intelligence gives the VA the ability to handle any scenario of a conversation with ease.

The key for a conversational virtual assistant to understand humans is; its ability to identify the human intentions, extract relevant information from the user utterance and map the relevant action/task against that utterance. Natural Language Processing (NLP ) is the science of extracting the intention (Intent) of text and relevant information (Entity) from the text.

Managing dialogs to keep track of multiple conversation threads, remembering the context, and responding to the user tone or sentiment provides the much-needed humane touch to the conversation and at the same time serving the user with accurate and appropriate responses.

Another aspect that helps build an intelligent virtual assistant is having a Knowledge Base. This gives the VA the ability to respond to frequently asked questions that return static responses. Building Knowledge Collection is an attempt to represent entities, ideas, and events with all their interdependent properties and relations according to a system of categories. This structured categorization of data helps the VA to answer user queries effectively and with ease.