Oracle Intelligent Bots: Key Components
The Oracle Intelligent Bots has 4 primary components:
Oracle Intelligent Bots
One integrated solution: Our solution has everything that customers need to build chatbots with channel integration, dialog flow, AI engine, integration with an Intelligent Bots Builder UI that brings this all together. Our competitors provide you with a menu of components / services that you have to put together that is unpredictable in cost, ease of use, level of effort and time to market.
One multichannel solution: Our Intelligent Bots Platform is an evolution from our mature and successful mobile platform. Chatbots are great at conversational interfaces but not designed for forms-based or structured data capture which requires a mobile app. To provide the best user experience it is essential that that the platform makes it easy, simple and rich to link user experiences across chatbots, mobile and web. Our competitors take a silo’d approach to this. Our solution is the only solution that provides the ability to combine structured data capture in a free form unstructured conversational UI. You can add powerful end user experiences to collect structured data like forms, checklists, wizard automatically into existing chatbot flows. Our competition does not provide this.
Enterprise Integration: The value of the Intelligent Bots Platform is to surface data intelligently to the end users. Oracle is the leader in integration to enterprise and cloud data sources and this is also provided out of the box, reducing the effort, time and cost to expose data to the chatbots. With the Oracle PaaS, we have a complete stack that includes Integration Cloud Service (ICS) that has integrations to over 75+ enterprise and non-enterprise back-end systems, Apiary cloud service that makes it easy to design and mock up APIs and API service to secure and configure API policies.
Real-time Insights: With Customer Experience Analytics (CxA), which is part of the platform, we provide one place for customers to get insights into multi-channel user adoption across mobile, chatbots and web. Customers can answer questions like – ‘Which channel is being used the most’, ‘What use cases are more popular as interactions in mobile vs. chatbots,’ ‘Do end users use multi channels and if so are there specific characteristics like time or preference when they use channels’. These channels provide insights to personalize the engagement with the end user − Intelligent Bots operational insights: The ability to know in real time how the chatbots are performing, where are the challenges and being able to path the conversation to train the model in real time.
Chatbot to Human to Chatbot: The ability to recognize when a chatbot is unable to respond appropriately to an end user and route the conversation to a human agent seamlessly, as well for a human agent to hand off to a chatbot.
Lifecycle Management: Integrated management of the life cycle of the chatbot from development, testing to production, managing version control and integration to the continuous integration / continuous development (CI/CD) appdev life cycle management.
Continuous Evolution: We are continuously adding additional algorithms to simplify chatbot development. We are adding algorithms to understand user sentiment, image analysis, language translation, self-learning, QnA, behavioral analysis etc. Chatbot developers can decide which additional algorithms they want to add to their chatbot app by simply including these algorithms in their pipeline without having to worry about the research, selection, development and fine tuning of the underlying models. The Intelligent Bots platform is built on a micro services platform using the Oracle PaaS and its underlying micro services capabilities. Developers can choose to use these specific machine learning services like NLP, or image recognition to build their own applications.
The Oracle Cloud: Delivered as a complete solution in Oracle Cloud, it reduces the cost, effort and complexity of managing patching, upgrades and provides a reliable and secure cloud that handles backups, high availability, fail over. The depth of the Oracle Cloud from the power of GPUs in the IaaS layer, to access to the widest and broadest range of PaaS capabilities and the built-in integration to Oracle SaaS applications that provide out of the box chatbots to the applications.
Sample Vertical Apps: Oracle has the broadest support for vertical solutions through the Industry business unit that have built vertical chatbot solutions.
Reference customers: We have several reference customers across B2C & B2E, across verticals who have compared our solution against our competitors and have chosen Oracle Intelligent Bots to develop their Intelligent Bots.
Artificial Intelligence – Built In AI Algorithms with Machine Learning, Context, Conversation Flow
Artificial Intelligence has the potential to significantly disrupt all industries – potentially empowering the global workforce and transforming customer engagement. Consider this: A chatbot never sleeps, never makes you wait, can personalize the customer experience and now chatbots powered by AI have the ability to learn and build relationships. So even if AI doesn’t disrupt every job or every industry, industry leaders will need to think about their own automation strategy and realize how best to use artificial intelligence – to increase engagement, efficiencies and lower costs.
Channel Configurator End users have a choice of messaging channels they prefer to use. Certain geographies have specific channels as their preferred messaging channel while other geographies prefer other messaging channels. There are different categories of channels as well. Broadly, these can be categorized as follows: • OTT Channels: Over the top (OTT) messaging channels such as Facebook Messenger, Facebook WhatsApp, WeChat, Line, Kik, Telegram, Talk, Skype, Slack, SMS. • VPA: Virtual Private Assistants such as Amazon (Dot, Echo, Show), Google Home, Apple HomePod. • Mobile & Web app extensions: Extending native or hybrid/responsive mobile apps or web applications with chat capabilities. • Voice Based Input: Custom devices or apps with interfaces that use Siri, Cortana, Google Voice or other speech input for interaction.
Dialog Flow Execution
Dialog Flow Execution Users interact with the Intelligent Bots Platform through a conversational interaction. This interaction, also called the conversational user interface (UI), is a dialog between the end user and the chatbot, just as between two human beings. It could be as simple as the end user saying “Hello” to the chatbot and the chatbot responding with a “Hi” and asking the user how it can help, or it could be a transactional interaction in a banking chatbot, such as transferring money from one account to the other, or an informational interaction in a HR chatbot, such as checking for vacation balance, or asking an FAQ in a retail chatbot, such as how to handle returns – essentially the Chatbot becomes the first line assistant to provide immediate answers to questions, 24/7. The Oracle Intelligent Bots dialog flow editor and the execution engine makes it simple to model these conversational flows while providing a high level of control over the conversation logical paths. The dialog flow includes several different pieces, including
Steps or States – Decisions – Intents – Entities – Variables
A conversation with a chatbot goes through a specific flow with the conversation having different states and context. This flow defines what should happen next based on an input and the flow itself is implemented in the platform as a state machine, which can be thought of as a workflow or process flow. The steps are the states in the workflow which the system guides the end user through as part of the conversation. The system that guides you through those states is called a state machine. To define your flow in the chatbot, you will define specific states and assign actions (called Components) to do work in that state. These steps or states take different paths based on the user input that impacts the decision the chatbot makes for the flow. Intents and Entities are the user-configurable components of the machine learning (ML) based natural language processing (NLP) algorithm that is used in the chatbot flow to manage the state or the steps. Variables store values that can change, depending on conditions or on information passed to the system. The Oracle Intelligent Bots Process Flow in chatbots is a ‘dialog flow’, as you will be creating applications that interact with users in the form of a dialog. The process itself is implemented as a state machine via a set of rules and it is defined in the Intelligent Bots Platform console in a format known as Oracle Intelligent Bots Markup Language, Intelligent BotsML for short. Intelligent BotsML is a form of YAML. YAML is a markup language that is, fundamentally, a way of expressing data and data relationships in a format that is highly focused on human readability. One of the differentiated value of this dialog designer and execution engine is that the dialog designer and execution engine is tightly integrated with the ML based NLP engine. Human conversations are often non-linear in nature. End users could potentially branch into different states / context in course of a conversation. For example, let’s say I want to transfer funds from account A to someone. Let’s say I start by asking the chatbot – pay Tom for dinner. The chatbot responds with “from which account?”. Let’s say the user picks checking but realizes he is not sure of how much he has in the account; he switches context to ask for balance and further wonders about and asks for recent transactions, and so on. In other words, the user triggers changes in the state, from transferring money, to checking balance, and then to recent transactions. At some point, he decided to pay Tom, which is the original state he started with. The Oracle Intelligent Bots platform makes it very easy to model this with built in state management so the developer does not have code and maintain the complexities of state management, reducing the time to deploy and the cost of ownership in managing the code.
Artificial Intelligence Engine
The Oracle Intelligent Bots expose multiple natural language understanding (NLU) models to predict user-intent from incoming chatbot requests and accurately execute the required dialog flow. To achieve this, we utilize multiple natural language processing (NLP) and machine learning (ML) algorithms combined with other approaches to classify end user intent.
Custom Components is another name for ‘3rd party integrations’. Custom Components can consume any integration service (REST) for almost any ERP, CRM, E-Commerce, Warehouse Management Software, Transportation Management Software or Bot-to-Live Agent hand off to any ACD platform. For chatbots to add value to a conversation, it must be able to integrate to the system of record that holds information required for the targeted use cases. These are typically enterprise backend systems that own customer’s banking information, or employee information in a HCM system, or sales information in a Sales CRM system. Oracle is the leader in integration to enterprise and cloud data sources and this is also provided out of the box, reducing the effort, time and cost to expose data to the chatbot. With built in integration to Oracle Mobile Cloud, Enterprise (OMCe), the Intelligent Bots Platform can leverage the shaped mobile APIs, and in addition, making use of mobile-first services, like Push Notifications to send asynchronous notifications from the backend systems to the chatbot.
Intelligent Bot Analytics
The Oracle Mobile Cloud Enterprise (OMCe) includes Customer Experience Analytics (CxA) that provides deep insights into multichannel user adoption across mobile, chatbots and web. Customers can answer questions like, “Which channel is being used the most”, “What use cases are more popular as interactions in mobile vs. chatbots”, “Do end users use multi channels and if so are there specific characteristics like time or preference when they use channels”. These channels provide insights to personalize the engagement with the end user. With CxA, businesses can create segments of user, perform cohort analysis and deep funnels, to get deeper insights, and integrate with marketing applications to create personalized promotions that engage the user within the most appropriate channel.
Intelligent Bots Operational Insights:
CxA provides real time insights on how the Intelligent Bots are performing, where the usability challenges lie, and the ability to test the paths of conversation to train the model in real time. Specific to the Intelligent Bots, CxA provides out of the box reports and tools to optimize Intelligent Bots performance and end user journey. With CxA, business users can determine:
How is my Intelligent Bots being used?
What’s my Intelligent Bots’s engagement / utilization metrics?
Intent resolution accuracy over time
Popular phrases of user input
Popular conversational paths and choices being selected
User Reports: Active, New and Inactive by Geo, Device, and Channel
Message Reports: # of Sessions by Intelligent Bots by Channel & Message load (# of Messages) / Intelligent Bots / Channel