A New Age of Intelligent Conversational Interfaces

By Mitul Tiwari

Two trends are driving massive changes in the technology industry today: First, the rise of conversational mediums such as messaging platforms like Facebook Messenger and smart speakers like Amazon Echo. And second, the recent and disruptive advancements in Deep Learning and Artificial Intelligence.

Messaging Dominance in Mobile

There are more than 2.5 billion smart mobile devices in the world and people are spending more than 80% of their screen time on mobile devices. Messaging is the dominant activity on mobile and in fact, it’s the fastest growing activity on mobile devices. The numbers of users on some of the messaging platforms are staggering, for example, there are more than 1 billion users each on Facebook Messenger and WhatsApp. Very recently messaging has also surpassed social networks in usage.

Messaging as a Platform

Recently Apple announced that iMessage is opening up for developers, which follows last year’s trend where Facebook Messenger, Slack, Twitter Direct Messages, Skype, Kik, Telegram, etc. opened up for development of interactive applications or “bots”. Now bots have mobile native platform capabilities as well such as location, voice, camera and images. Now businesses have a way to build conversational interfaces and interact with their customers on a platform where the customers are spending most of their screen time.

Conversing with a device is a reality (picture from movie “Her”)

Rise of Voice Platforms

Smart speakers like Amazon Echo with Alexa came out a couple of years ago and are growing like wildfire. Google Home with Assistant, Microsoft Cortana, and Apple HomePod with Siri have also joined the market. In 2017, 35M people are expected to interact with smart speakers. Furthermore, LG is embedding Alexa in their refrigerators, Ford is putting Alexa in their cars, Ecobee is adding a voice interface in their thermostats and light switches. Very soon almost all homes and cars will have a smart speaker with a voice assistant. All these voice platforms are open for developing conversational applications such as Alexa skills or Google Home actions.

Conversational Artificial Intelligence

Building a bot on a messaging platform or voice platform is not that difficult but making the bot intelligent enough to understand natural language and to respond naturally is non-trivial. Some of the fundamental building blocks for conversational AI are natural language understanding, intent identification, information extraction, action triggers, query understanding and transformation, sentiment analysis, natural language response generation, speech processing, personalization, etc. Many of these conversational AI building blocks are feasible now because of groundbreaking advancements in Deep Learning.

Some of the building blocks of Conversation AI

Deep Learning and Natural Language Processing

In traditional machine learning, humans analyze data and design features, and machine learning optimizes a function to combine the features. On the other hand, in deep neural networks a.k.a. deep learning, the network learns multiple representations of the data and eliminates the need for complex feature engineering. In the last few years deep learning has been successfully applied in various Natural Language Processing (NLP) tasks such as language translation, text summarization, image captioning, information extraction, question-answering, speech recognition, etc.

At Passage AI, we are using the latest deep learning technologies to build an NLP engine that includes various conversational AI building blocks to understand natural language text and speech, to identify intents, to extract various useful information, to understand and transform query, to search over vast amount of data, and to create an intelligent conversational interface. To deliver that technology to businesses, we created a bot builder platform that anyone can use to build an intelligent bot without coding and in a simple drag-and-drop fashion. And most importantly, to ensure that the bots can reach the largest audience, we built a pipeline to deploy an intelligent bot using our bot builder across multiple messaging and voice platforms with ease, that is: build once, deploy anywhere.

Passage AI’s Bot Builder Platform

We are thrilled that Passage AI already offers the complete package for a conversational interface building platform: (1) a powerful NLP engine, (2) a bot building platform, and (3) a pipeline to deploy across multiple messaging and voice platforms.

Contact us to learn more and see a demo!

Clinical Trial Patient Experience and Recruitment Effectiveness with AI/NLP Chatbots

By Kurt MacDonald and MuckAI Girish

Biotechnology and pharmaceutical companies have seen a rapid growth in clinical trials, thanks to major advancements in drug discovery and novel ways of targeting and combining various technologies including immunotherapy and gene therapy. KPCB’s Mary Meeker’s Internet Trends 2017 reports that the number of clinical trials has grown by over 10 times between 2006 and 2016 and the number of publicly available clinical trial results has grown from 1.9K to 25.4K between 2009 and 2016. There is also a marked difference in how generations access the ever growing healthcare data. For example, millennials own wearables, go online to find a physician and select providers based on online reviews at a significantly higher rate than Gen X-ers and Baby Boomers. The newer generations’ behaviors with respect to devices, voice interfaces and messaging platforms are also becoming profoundly different.

Companies conducting clinical trials are finding it increasingly difficult to find and enroll patients, especially in the United States and other developed countries since drugs get approved first in these countries resulting in reduced wait times for patients and not having to resort to experimental drugs. Moreover, physicians tend to have limited time with patients and in order to describe the benefits and risks associated with an experimental drug limiting the opportunity for potential participants from getting all their questions answered.

AI/ML applications across the healthcare industry — NLP chatbots significantly improve patient convenience

We believe that an AI/NLP-based, well trained and trainable text and voice-based conversational chatbot would address these challenges. Support over multiple platforms such as the desktop or mobile web, messaging platforms such as Facebook Messenger, WeChat and voice assistants such as Amazon Echo and Google Home would enable vast coverage and frictionless and ubiquitous access for patients. Recently, several HIPAA compliant messaging platforms and applications have sprung up, which ensure regulatory compliance of healthcare data. Clinical trial sites can mandate the use of one or more such apps, for instance, and if the chatbot can work over these apps, while integrating seamlessly with other electronic medical records (EMR) systems through webhooks such as REST APIs (Representational State Transfer Application Programming Interfaces), the resulting solution becomes a very powerful and an effective communication tool.

The patients and potential patients would like to get trial questions answered quickly before recruitment, be made aware of their options, get questions answered including the Informed Consent Form (ICF) over the course of the trial and have the ability to access these from multiple platforms seamlessly and get reminders for appointments and tests. Using push notifications, the CRO/clinical trial site can send reminders to the patients such as medication schedule. In addition, patients can upload compliance information such as confirming medicine intake, what they ate during relevant meal times and recording any other pertinent information. By offering a universal and easy access, patients are more likely to provide compliance information, which would make the clinical trial results and analysis more accurate.

The clinical trial ecosystem consists of the pharmaceutical company, contract research organizations (CROs), trial sites and a potential pool of patients. The pharmaceutical companies are keen on increasing recruitment and coverage of trials, containing the growing cost of doing trials (especially in the US), educating Investigators and site staff about the research/experimental drug and improving patient experience (including healthy volunteer trials). The CROs are looking to increase recruitment effectiveness and reach, reduce the cost of doing trials and improve communication with site staff. The investigators and site coordinators want to solve the problem of investigators getting only limited time with patients, increase recruitment effectiveness, remind patients about upcoming appointments and make trial information more accessible for patients and track compliance. An AI-based conversational interface offers a sure remedy.

For more information about Passage AI, please look us up at: http://www.passage.ai

Next Generation Customer Service

By MuckAI Girish

Customer service, a key function of any successful business, can make or break a company. Customer dissatisfaction leads to defections and a tarnished brand. On the other hand, effective customer service helps companies differentiate themselves with a superior value proposition while increasing customer retention and improving metrics, such as their NPS (net promoter score), CSAT (customer satisfaction) and CES (customer effort score) scores.

Consumers are increasingly using voice (such as Amazon Echo and Google Home) and text-based (such as Facebook Messenger and Slack) communications as well as a variety of devices and applications to engage with companies and each other. Yet corporations by and large have not leveraged the full capabilities of modern conversational tools, like messaging platforms and voice assistants, to make it easier for their customers to interact with their customer support teams. Nor have customer support implementations tapped into the full benefits unleashed by machine learning (ML) and natural language processing (NLP) to improve customer experience at a reduced cost. Both large and small businesses can improve their customer experience and reduce costs by implementing next-generation of the customer support, which optimizes these ML and NLP-based conversational interfaces.

Many customer service queries typically include the same questions or concerns voiced repeatedly, which can easily be resolved by an automated interface powered by AI, eliminating the need for a phone or chat-based discussion with a person. In many cases, an AI system uses NLP to recognize user intent and is configured to seek answers to a set of questions based on a decision tree. These can often diagnose a problem and instantly provide a resolution — a welcome change for customers not in a position to use their phone or who are frustrated by long hold times that are so common on such a call.

For example, when Internet access goes down, often the solution is simply to switch off the modem and power it back on. However, many customers call their broadband provider, endure long wait times until they get connected, and then discover that all they had had to do was this simple step. With an automated conversational interface, this could have been accomplished immediately. When the system detects an unhappy customer, it automatically connects them to an agent. This system can also seamlessly hand off calls back to the automated interface, and vice versa, as needed. This reduces the load on call center staff, leading to lower wait times for all customers. Deploying NLP-based automated interfaces results in significantly lower support costs and improved customer satisfaction.

Another use case of AI-based automated interface is agent assist, which has applications in the contact center business in addition to enterprises. Businesses must not only cope with an ever-increasing volume of products, documents and information, but also adapt to the constant software updates, forcing support staff to stay current on the various releases, features, bugs and troubleshooting methods. An automated conversational interface can help the support staff answer questions accurately when a customer calls with a problem. With machine learning and integration with CRM and help desk systems, the system can be trained with customer and agent data, leaving agents being better equipped to more quickly resolve more issues. The company can hire higher quality personnel, train them on aspects that are harder to automate or program, leading to a more effective customer service staff and improving employee retention.

Let us examine the key attributes of such a system. It should work seamlessly in a range of messaging and voice-based platforms — something customers are used to — while being easy to use and configure, without the need for computer programming. It should have an intuitive way to specify intents, attributes and entities, it should be easy to input knowledge base documents, and it should allow web hooks to interface with various databases and systems. It should use the best and latest machine learning and deep learning algorithms to constantly learn, improve, and understand multiple languages. Moreover, it should seamlessly hand calls off to a human, as easily as it should pick up them back up, as it remains sensitive to sentiments of users. Finally, it should provide a rich set of analytics to help understand, train and improve the system.

At Passage AI, we are committed to helping you take customer service to the next level working across the myriad of text and voice-enabled platforms. The Passage AI solution offers an easy-to-use and intuitive UI, intent, entity and attribute-specification methods, API-based architecture — all while leveraging state-of-the-art deep learning algorithms to enable superior customer service. If you would like to learn more about Passage AI, please visit us at www.passage.ai.