Rye:
Making most efficient use of your grocery inventory

MHCI+D Ideation Studio 2017
8 Weeks
In collaboration With Lan Vu and Kelly Hwu
Product Design, User Interface Design, User Testing, VUI/CUI Design

Rye is an application for the Amazon Echo Show and uses both a touch screen interface and Amazon’s virtual assistant, Alexa, to communicate.

Rye suggests recipes based on optimal use of the user’s food inventory.

By using machine learning, your cooking preferences and food purchasing behaviors are tracked by Rye. This information is used to manage your food inventory.

Problem

Smart Cities:
The theme for the studio involved finding solutions to problems within a "smart city." We decided to address food waste within the concept of smart cities. Rather than addressing the problem of food waste on a systemic, infrastructural level, we decided to pursue a solution to be implemented at the consumer level.

Challenge
How might we develop a smart system that helps consumers efficiently manage food consumption?

Our solution

Why Rye?
We found that people have trouble managing their personal and household food consumption. Despite these wasteful habits, there is not a widely used, or effective solution to tackle this.

Rye addresses this habitual behavior by not changing the way people purchase food, but rather how they use the food they purchased.

Considering the increasing expansiveness of machine learning, we believe the Amazon Echo Show is the right platform to initiate widespread change in food consumption and reduce food waste.

How it works

Rye connects to your Amazon account and creates a virtual food inventory from Amazon and Whole Foods grocery purchases. By understanding each item’s expiration or estimated expiration date along with your food preferences, Rye suggests recipes based on the most efficient usage of your inventory— preventing foods from being forgotten, spoiled, or thrown away.

Secondary Research + Assumptions

Through our secondary research, we found that in most Americans cities, waste is sorted into three categories: landfill, recycle, and compost. However, this isn't always practiced on the consumer level.

This led us to question how people viewed their own consumption and waste habits. We developed the following assumptions:

Assumption #1

People prefer not to waste their food and desire a useful alternative that requires minimal effort.

Assumotion #2

People are willing to receive other people’s food that would otherwise be thrown away.

Assumption #3

People are willing to exchange or trade their unwanted foods with community members.

Testing Assumptions

In-City Intervention

We executed an "in-city intervention" to test our assumptions about exchanging unwanted foods. We installed a "neighborhood food pantry" in a residential area and in a community park. The neighborhood food pantry contained an assortment of opened, unopened, cooked, and uncooked foods.

As we passively observed from afar, we wanted to see if people would be willing to take or contribute their unwanted foods to the pantry.

We quickly learned through this experiment and user interviews that our food sharing network did not fit with most people’s mental models. Instead of a food sharing network, many people saw the Neighborhood Food Pantry as another form of food donation for the needy.

“I actually would like to, but I have the means to afford what I need.”

We also learned that most people found exchanging opened or cooked foods undesirable.

“I would just be dumping my problem somewhere else...I mean who wants half-eaten food? I don’t think anyone.”

Concept Exploration

After synthesizing our findings and insights from the intervention, we developed storyboards to address different scenarios. Our goal was not to address food waste directly— instead, we wanted to capitalize on peoples' existing behaviors and implement a solution that extended the life of purchased foods.

Our hi-fi storyboard focused on a VUI device to assist people with meal prepping based on their schedule, grocery purchases, and food inventory.

Prototype exploration

While we found through our "in-city intervention" that the food sharing network was rather ineffective, we did not want to completely dismiss the idea. We proceeded with three paper prototype concepts:

FoodMatch
A mobile app that encourages neighbors to exchange unwanted foods within their community.

Meal Prep Assistant
A mobile app that encourages neighbors to exchange unwanted foods within their community.

Meal Prep GUI + CUI
A personal assistant that uses a graphical user interface and a conversational user interface to help people efficiently meal prep.

Insight #1

Users felt comfortable with FoodMatch since some of the features reminded them of Craigslist or dating apps.

Insight #2

For the Meal Prep prototypes, users did not understand that the recipes were based on their food inventory or in order of efficient food usage.

Insight #3

Users preferred to make efficient use of their food before it goes bad instead of giving away unwanted or near expiring foods.

Down Selection + Iteration

We found that all of our participants participated in wasteful habits with their food. Additionally, each participant expressed feelings of “sorry” or “guilt” for their forgotten, spoiled, or thrown away food items. 

"I wish there was something else I can do, but what do I do with bad food?"

With these insights from testing, we decided to pivot from a food sharing network to a food management system. 

Hi-Fi Prototypes

As smart systems rapidly grow and integrate into our home environment, we felt that the Amazon Echo Show was the right platform to initiate widespread change in food consumption and food waste.

We designed our HiFi prototypes for the Echo Show and conducted a second round of user testing. During our user testings, all of our users stressed the importance of seeing the ingredients for each recipe step. They also wanted the flexibility to see previous and future steps. We took their feedback into consideration and created the final design for Rye.

Interaction Flows

Logic is important. Things need to make sense. Here are the interaction flows I developed for our three use cases.

Onboarding

This flow outlines Rye’s onboarding scenario for a first-time user. The onboarding flow is designed to familiarize the user through key features such as the home screen, the sidebar navigation, the inventory, and the recipe steps and overview.

View Prototype

Low Confidence

This flow outlines an instance when a user chooses a recipe outside of the recommended recipes. Therefore, Alexa has “low confidence” in this ingredient because it was either purchased a long time ago, has not been used in awhile, or has expired. 

View Prototype

Recipe + Feedback

This flow depicts the user’s journey when realizing they do not have an ingredient after starting a recipe. The flow begins at the start of the recipe and examines the ingredient substitution suggestion, recipe steps, and recipe feedback.

View Prototype

Style Guides

Amazon's color and typography influenced Rye's design. We were also inspired by our interaction with an Amazon Echo Show and its AllRecipes application. For the conversation interactions, we explored Amazon Alexa Voice Design Guide and tailored it to Rye.

Next Steps

Beyond Amazon

Future iterations of Rye would ideally include grocery purchases from other stores. While creating Rye, we experimented with the idea of tracking non-Amazon purchases through store rewards cards or credit cards. However, we need to further explore these tracking techniques as well as other methods to find an effective solution.

Takeaways

I learned that it is important to quickly adapt to change and proactively learn new skills.