Increase the users ability to decide which data is stored and the interactions it’s used for
AMOEBA has automation levels, from manual to autonomous. The default setting is fully autonomous, which can be turned off by the user in the detailed menu.
The personalization of the settings are based on repeated interactions. In a general example:
If the user sets AMOEBA to fully autonomous in the navigation category but, the user continuously ignores all suggestions made by the system of what destination to drive to (that it has learned the user drives to at those specific times), AMOEBA recognizes this behavior and understands that giving this kind of suggestions is an unwanted behavior from the system. In response AMOEBA lowers the level of automation of navigation, making the system stop collecting user data of potential destinations and thus reduces the number of suggestions the user gets.
In reverse a different user may have set the navigation to manual but as the user starts using suggestions when using it, AMOEBA will slightly increase the automation to also give suggestions and save favorites. If AMOEBA then recognizes that the user keeps using these features it may also ask for permission to allow the system to start collecting data of potential destinations, to further be able to increase personalization.
We were 3 people in the design team for AMOEBA. I was part of the creation of the concept by using a combination of different ideation methods, later we defined the functionality of the concept. I was in charge of the look and feel. I used Illustrator CC in order to create the amoeba.
The coding for the prototype was made by my team mate in Xcode, with the GUI that was created by me in Illustrator CC then imported to Sketch where he adapted it for touchscreen.
Look & Feel
Shape- An amoeba as principal shape allowed the transmission of adaptability and constant flux that was intended to convey to the user.
Levels- The closer an “arm” is to the outer circle the more automated that specific category is.
Color- The level is also mediated through a gradient.
Six interviews were performed in a car where driving was simulated so the user could get the feeling of being in a real case scenario while using the prototype. The aim of these tests were to evaluate the feel and perception the user gets when interacting with AMOEBA. The lead for this interviews were taken by my third team mate, while I took notes and recorded the interviews.