Mobile Conversational AI and Virtual Chatbot
Familiar.
AI Companion
Familiar is an AI chatbot built as a supportive companion. Our app helps users relieve stress and negative emotions built up throughout their day by being there to listen and talk to you. Your Familiar is a self-reflection of your own personal health, changing and growing with your own mood which it picks up through talking with you.
User Research
While investigating conversational support our team researched cases, symptoms, and treatments of depression and self-isolation from various papers and conducted interviews. During this process, we interviewed a mix of caretakers and those who have suffered from depression via self-isolation. Each set of interviews looks at the symptoms, effects, and the various treatments used to gauge their success. From this data, we developed the persona of Jessica, a stressed college student, who works very hard but causes her to have few friends and no one to talk to about her day-to-day stresses.
From our initial research, we found that conversations, in particular, listening provided the greatest benefit to suffering from self-isolative behavior. However, we found it was hard for people like Jessica to talk to people so instead we looked to animals. Emotional support animals as a proven benefit of providing a great outlet for stress, connection, and empathy.
Emotional support animals as a treatment for self-isolation behavior
Animal treatments currently in practice as emotional support pets
UX Solution's
Conversation
Familiar reaches out to you to talk when in need or when you most free. To feed and care for your familiar all you need to do is chat.
Goal Building
Your familiar is unique with specific needs: Food, Hygiene, and Mood. They are satisfied with your interactions and changes in their familiar’s status.
Engagement
Familiar is highly animated using voice, expression physicality, and emotes shows emotions/interest in your day.
Self-Reflection
Familiar reacts as a personal reflection. As your familiar improves in health our user’s social life improves by being open to conversations and others.
App Userflow
Our group focused on the user-to-pet interaction using multiple methods of engagement including machine learning, character design, and therapy techniques. Our familiarity grows with you and in turn, reflects your emotions. The diagram below shows the conversational loop that we built.
Visual Design
To make sure that people would want to engage initially with their familiar our visual style is very bright, colorful, and playful. We created an initial set of familiars from Pandas, Bears, Dogs, and Cat with various colors adding a sense of personalized companions. The semi-flat form also provides us the ability to animate multiple features easily with pre-built emotional states and emotes to provide context to the conversation adding another level of depth to the interaction mixing.
Familiar has a number of facial expressions to connect to their user in both positive and negative reactions.
Familiar uses a combination of emotional accessories to react to emotional triggers.
Familiar's come in a variety of characters to help personalize and connect to their users.
Creating Engaging Conversations
To create a realistic and engaging conversation with your familiar our machine learning system is a 4 step process. First engage the users by calling out to them at a set time or when pet needs are serious. Second, revealing the user’s task via the pet’s condition and emotions. Third, get user input via Watson’s speech-to-text analysis and respond accordingly based on text-to-speech API and condition matching in Waston Conversational mapping. Lastly recording data and feedback setting updated conditions for the next conversation.
This flow has multiple expansions detailed below explaining how the analysis works based on a database of set concerns and emotions related to them. Each of these concerns has related keywords of which their correlating emotional statements are run through a sentimental analysis logging concerns and reflecting back comfort, encouragement, or curiosity via the visual style based on the conditions meet. Thesis concerns can then later be referred back to on another day showing remembrance and interest to help improve localized concern categories.
Digital Prototype & Presentation
As part of the final project of the IBM Cognitive AI collaboration, we presented our research and prototype to various colleagues in the field of AI interfaces and UX design. Familiar was considered extremely successful and well regarded by the group, I hope to continue this project after reviewing with my other group members Shu and Yi expanding familiar into possibility as a physical toy-like interface and further detailing the system’s capabilities using IBM Watson’s conversational interface and node.