This is the age of Artificial Intelligence, Cobot, and Chatbot. Mostly chatbot is used or the previous chatbot, which was generally used to be rule-based, but the quest to make them intelligent has begun. The Integration of Chatbot with cognitive technologies is a step toward this direction. Consider a scenario that an employee requested HR to make a file of the employees who come late at work with the timing at which they came. This is not a typical task when the company's count is somewhere, let’s say 50 employees, but what if the number is near about 500 employees, and what if this request can be passed to a bot through text/voice.
Chatbots are the computer program you can talk to through messaging apps, chat windows, or voice calling apps. Source: What are Chatbots?
Cobot can fulfill the request irrespective of the size and number of employees. This article will give an overview of Cobot's architecture and applications. This can be done by giving cognitive power to the bot. The bot also requires a conversational framework. This conversational framework should address the following key points -
Personalization - Includes knowing the user, knowing the user's sentiments and responding according to it, and making recommendations.
Knowledge Representation - Knowing the domain of the problem to be addressed and acknowledging using the backend model should have reasoning power.
Dialog Authoring - Generation of the dialogs in a semi-automated manner using various support artifacts.
So before diving deep into the concepts of Cobot. Let’s understand some terminologies related to it. The main requirements of Cobot are cognitive analytics and Chatbot.
What are Chatbots?
A chatbot is a program that recreates a genuine discussion that you would usually have with a customer service representative. The utilization of bots has many possibilities, from practical to fun, and beneficial in any real-time messaging platform, such as Facebook Messenger, Telegram, Slack, instant messages, and so forth. There are three types of chatbots -
Bot as Introducer -
For Email Marketing
For Social Media
It is also for Video
Bot as Influencer - For a bot to be a successful Influencer, you must always consider how it can be helpful and add value.
Bot as closer -Leverage bots to remove any friction and make the experience super relevant and Empathetic.
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What is Cognitive technology?
Cognitive Automation is an intelligent and smart automation method based on Artificial Intelligence used to replicate human capabilities, including simpler mental processes like thinking, reasoning, decision making, and other activities, providing a bridge for linking human consciousness and the static logic of computing. The collaboration of these two technologies gives birth to Cobots. Master Architecture - Description
The architecture contains three main parts Introducer bot, Influencer bot, and Closer Bot.
Introducer bot will act like a prime interface that will take a query and has a task to respond accordingly.
Influencer bot will serve as the front-end to the Introducer bot or to a user for making and handling their queries.
Closer bots will act as domain experts. So each domain will have a bot representation. These bots can handle their domain-specific query using a defined ontology graph, and they will provide a relevant solution.
Trigger the whole framework when a client puts the request of an error encounter, and he/she summons the introducer bot.
If a query comes beyond the scope of these bots combined, the query will be passed to the human expert of that specific domain.
What is the working architecture of Cobot?
The explained below is the working architecture of Cobot
Registration of the user
If the user is not registered with the framework, the system will ask to register. If the user is already logged, then the structure will move toward the Identification of the Domain and understand the problem accordingly.
Identification of the Domain
The influencer bot starts the identification of the Domain using user query or the data of the past encounter saved in the database, enabling the personal touch.
Problem and its related Entity Extraction
The next step is to identify the key and intent's problem belong to the user query identified.
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Routing the Query generated by the problem
The query and its attributes are passed to the domain-specific bot or the Information Retrieval (IR) engine. If the query belongs to which requires general troubleshooting, it will be handled by Influencer Bot using its IR engine and provide the relevant solution URLs. If the query belongs to a specific domain, it will pass to the corresponding closer bot, an expert in that particular domain.
Extraction of the solution to the query for providing a solution a domain expert also use the query received by the Influencer bot and use it predefined ontology in which it checks if more equation is required on the query and asks the question using child node and node match procedure the user requests this question and based on it solution or response is passed to the Influencer bot and then to the user through user bot.
Handling the Query beyond the scope of the framework
If a query is beyond the scope of the Influencer bot and the closer bot, then the query will pass to the human expert of the domain of query.
Passing the Response
Influencer bot collects the response from its IR or Closer bot or the Human-agent and forwards it to the user.
What is the architecture of the Key Bots?
The below mentioned is the architecture of bots:
Key components of Introducer Bot -
Speech to text module
Processing Model of the Bot
Text to speech module
Key elements Influencer Bot-
Question Analysing Unit
Information Retrieval Engine
Key components -
Knowledge Representation using Ontology
Dialog Authoring module
A conversational User Interface is an interface that allows users to interact with either real humans or bots using text and voice/speech. Source: Conversational User Interface
Description of Cobot's Architecture
The Architecture of Introducer Bot
The main task of this bot is to interact with the user, taking the problem and providing the solution. In simple words, it acts as the front-end to both the remaining bots. Although it also has its front end (conversational UI) and it also has four different units. These units are Conversational Interface, Speech to the text module, Processing Model of the Bot, Text to speech module. It can take the problem from the user and problem-related data and pass it to the influencer bot. Influencer bot then takes further actions (whether to solve its influencer bot level or pass it to the closer bot (domain expert). It then receives the response on the influencer bot (whether it a failure or success) and passes it to the user.
The Architecture of Influencer Bot
It should have the capability to understand the Natural Language query to understand the question from the user and pass the response either from the user or from the bot itself. Firstly it divides the query into the user’s intent, key entities, and question domain. It identifies using the Machine learning trained model. Once the question is divided, it tries to solve the query using its IR engine or fails. It passes the query to its domain expert closer bot. If the closer bot requires further information to solve the query, it passes that requirement to the user bot and the user and handles the response.
It is the process of analyzing the query in detail to determine the query’s intent, keywords and words from the user query, and the domain of the query.
Personalization is the prime concern to be obtained while implementing a chatbot. Influencer bot achieves it by saving the query and its domain with the user's data so that it helps to give suggestions further. An area may have multiple systems, or a user has asked queries related to multiple domains. In such, it shows the suggestions containing multiple queries and multiple systems.
Closer bots are the domain expert bots. Each registered domain must have a bot under closer bots.
The functionality of the closer bot has two main building blocks -Knowledge Representation and Dialog Authoring.
Knowledge Representation - Each closer bot depends on the knowledge graph known as ontology. It is from its domain knowledge source and historical ticket data to establish the relationship between the query and its solution using domain and entities. The figure below showing the example of ontology.
Dialog Authoring - To solve a query closer bot needs multiple interactions with the user sometimes. For that Dialog, Authoring is using work by the ontology and handle the conversations.
What are the applications of Cobot?
It can provide Technical Support at an enterprise level.
A cobot can handle the Initial level filtering in the Recruitment System of a company.
It helps to provide a better-personalized experience to the customers/ clients online.
It helps to handle online shopping customers (with a recommendation engine and sentiment analysis). To provide a better shopping experience and recommendations.
A Holistic Strategy
To know more about how conversational interfaces help increase customer engagement, cut down operational costs, and Deliver 24×7 support, we advise talking to our expert.