In class this week, we worked on the flow and product requirements of our solution. This helped us understand what the scope of the interaction with Dapi is, as well as to focus on the core of our product.
How is the flow of a product mapped out? First, we settle on an arbitrary number of frames. Oren chose 4, but later on we can change this to 3 or 5 if necessary. The first frame should be the beginning of the interaction with our product, and the final frame is the end of the interaction. In between the two we needed to pinpoint the most important aspects of our product.
The flow we came up with is:
After we came up with a flow that the four of us were happy with, we determined what the product requirement was for each frame:
Lastly we examined our list of four product requirements, and determined which one stands at the core of our product.
In our case, we concluded that voice recognition and comprehension (יכולת זיהוי אינטראקציה חיובית באמצעות מילות מפתח) is the core requirement of our product. Why? Because without this, our robotic object cannot react to the situations we want it to. We want Dapi to teach bus passengers new behavior. In order to do so, Dapi will need to differentiate 'good' behavior from the regular interactions between a bus driver and his passengers.
Most of the conversations between the driver and passengers is the passenger asking the driver about a bus stop; this is not the type of interaction that interests us, because it does not improve the atmosphere on the bus. What interests us is a passenger who says hello to the driver, or good morning, or how are you. Therefore our main product requirement is speech comprehension.
Market Research
The next step in our project is to research our competitors. We were instructed to choose two axes to measure our product by, and to place all of our competitors on a graph along with Dapi.
The axes we chose are:
Encourages behavioral change <> doesn't encourage behavioral change
Increases human interaction (HHRI) <> decreases human interaction (HRI)
Why did we choose these measures? Because the innovation of our prototype is it's ability to encourage interactions between bus drivers and passengers through positive enforcement learning. What interests us is to see what other people are doing to encourage learning (especially in the public sphere), and to encourage human interactions.
We have found several robots that are similar to ours, but they actually reduce human interaction because they replace it - such as Sophia. Talking to her replaces the human in these interactions.
We have also found technologies which increase interactions, but do not aim to teach behavior, such as GetTaxi. GetTaxi lets the passenger know what the driver's hobbies are, so that they can talk about it.
That's all for this week. In two weeks we will be meeting with mentors for speed-mentoring; until then we'll be working on our pitch.