Be My Guest
Product Design
COURSE NAME
48-734 Possibilistic Design
We believe dining experience is not only about food and ritual but also the personal memory, including a regular meal with our family, friends, or significant others. What if we could document the moment physically and share our memories with those willing to listen?
COLLABORATORS
Chua Yin Li (Brielyn), Thomas Youn, Eliseea Faur
PROJECT YEAR
2023
Ideation
The dining experience is shaped not only by the food or the rituals around it, but also by the personal memories it evokes. Even an ordinary meal with family, friends, or loved ones holds deeper meaning. We wonder: What if we could capture these moments in a tangible form and share them with others?
Dining connects us to memories and sparks ideas of food, culture, and the stories that unfold around the table. Inspired by the art pieces shown, a tablecloth could serve as a canvas for documenting our meals —where we sit, what we eat, what we do, and who we share these moments with. This medium, simple to fold, pack, and gift, becomes a meaningful invitation. Our friends who receive it are invited to join us as guests to celebrate these intimate and cherished moments of our lives.
Dining connects us to memories and sparks ideas of food, culture, and the stories that unfold around the table. Inspired by the art pieces shown, a tablecloth could serve as a canvas for documenting our meals —where we sit, what we eat, what we do, and who we share these moments with. This medium, simple to fold, pack, and gift, becomes a meaningful invitation. Our friends who receive it are invited to join us as guests to celebrate these intimate and cherished moments of our lives.
PROCESS & DEVELOPMENT
We began by narrowing down to three ideas from our precedent study.
- On-the-go fine dining experience
- Using projection to enhance dining experience
- Designing utensils to simulate conditions that hinder eating to build empathy.
The final idea ended being a combination of aspects from the first two ideas: design and create a tablecloth that would transform to fit different table sizes and arrangements. We wanted to bring in elements of cultural exchange that we explored in the second idea, by drawing out how we eat back home with our family.
- On-the-go fine dining experience
- Using projection to enhance dining experience
- Designing utensils to simulate conditions that hinder eating to build empathy.
The final idea ended being a combination of aspects from the first two ideas: design and create a tablecloth that would transform to fit different table sizes and arrangements. We wanted to bring in elements of cultural exchange that we explored in the second idea, by drawing out how we eat back home with our family.
Our first prototype was a pill-shaped table linen that would cover the entire surface of the dining table. Placements for plates are marked on the linen to dictate the position of the plates on the mat. We sew on buttons and draw fold lines, which allows the user to fold the linen to serve variety of arrangements and numbers of guests. The paper prototype aimed to achieve a similar result, but in a rectangular form.
We discovered however, that in the process of creating these foldable linens, we lost our original vision for the project: To share my own personal dining experience to other people. The demarcations of plate placements made it sterile and typical. We explored using different types of fabric to allow varied arrangements, but we ended up backtracking to our very first idea. Instead of creating a table mat that can conform to any table size, we decided to simply create 1 table mat, designed for 1 person of 1 particular dining experience.
There are areas demarcated by different colors of clothes representing the family characteristics, with names attached to each plate. We also added placements for phones and tablets, with QR code to the show or app that they would often watch/use while eating. The compass in the corner is less about exact position of the table mat in reference to the Earth’s magnetic field, but rather to indicate where the TV, the center of family dinner ritual, should be in reference to the table. There is a flexible area for shared dishes defined by cute illustration.
Additionally, we made a manual to illustrate the dining experience on the tablecloth, including an introduction to the family, the legend for dishes, utensils, and accessories, and the scenario of everyday dining routines as storyboards. We encourage those who receive this gift to experience life on the cloth immersive by imitating the indirect dining settings.
The final product is a true collaboration, with every team member contributing to each stage—from sewing and pattern design to painting. Together, we have worked to preserve these memories, as if we ourselves were part of the family.
FINAL PRODUCT
The final tablecloth mat has different colored fabric stitched on, representing different family members. This particular mat represents Brie’s family. The Southeast corner is reserved for her brother, who often eats away from their family and in his own room. The corner is therefore detachable and reattached via buttons. Those receiving this can also scan the QR Code on the iPad area and cloth edge to see what the gift senders see during the meal and get the digital version of the manual.
Coming soon :)
THE PROJECT
An AI-driven web-based plugin that provides better communication between users and generative AI tools
Discover & UNDERSTAND
Our journey starts from the question below: As a designer, have you ever used Generative AI tools in design process? And have you ever felt misunderstood by Generative AI tools in design process?
We collected opinions from designers across different disciplines and integrated them into an affinity diagram. The takeaways from this status quo guided us to our design space: human-AI communication.
We collected opinions from designers across different disciplines and integrated them into an affinity diagram. The takeaways from this status quo guided us to our design space: human-AI communication.
define question
How might we ease the process of communication between designers and generative AI tools by fostering mutual understanding?
IMAGINE
Our proposal is to leverage a web-based plugin with language process ability. Users label the results after comparing the current outcome with their mind and iterate the suggestion on the plugin. The human-in-the-loop method will provide users preferred results by achieving consensus.
RAPID PROTOTYPING
Feature 1: User Domain Settings (web-based plugin)
Once the plugin is installed and logged in, the interface will prompt user to select their corresponding domain in design professions, which parameters will be applied on image generation and suggestion-making processes of the plugin.
Once the plugin is installed and logged in, the interface will prompt user to select their corresponding domain in design professions, which parameters will be applied on image generation and suggestion-making processes of the plugin.
Feature 2: User-oriented Image Classification / Generation (web-based plugin)
Once user begin to execute text-based image generation processes, the plugin will provide users with tailored user-friendly image labels; which will allow the user to revise the labels based on one’s interpretation of content.
Once user begin to execute text-based image generation processes, the plugin will provide users with tailored user-friendly image labels; which will allow the user to revise the labels based on one’s interpretation of content.
Feature 2: User-oriented Image Classification / Generation(web-based plugin)
Image re-generation based on revised image classification labels will help users acquire updated contents more precisely; while helping the algorithm to learn user’s domain knowledge and pattern - providing data for the algorithm to improve performance overtime.
Image re-generation based on revised image classification labels will help users acquire updated contents more precisely; while helping the algorithm to learn user’s domain knowledge and pattern - providing data for the algorithm to improve performance overtime.
Feature 3: User Profile Settings (dedicated web interface)
The web-based plugin also provides a standalone website that allows the user to retrospectively review domains and labels associated with their profile, recycling previously removed parameters while gaining more insights about their interaction patterns.
The web-based plugin also provides a standalone website that allows the user to retrospectively review domains and labels associated with their profile, recycling previously removed parameters while gaining more insights about their interaction patterns.
PRECEDENT STUDY
We wanted to build an object and experience that would embody what it would be like to be underground. We explored diorama as a possible final product as we wanted to design an experience, but also build a physical prototype. We took heavy inspiration from underground, ant colony dioramas.
PROCESS & DEVELOPMENT
Our initial sketch consisted of three levels: underground, ground, and canopy. Both the canopy and the ground level would have holes cut through the base, which would allow us to drip water through to simulate rainfall. The trees we build would play a vital part, not just in terms of aesthetics but also in terms of the experience itself. We wanted to design it so that the water that falls from the canopy would drip down, saturate the tree and its roots, carrying the water all the way down to the underground level. We also designed a staircase inviting guests to imagine what it would be like to go down into a cave, covered with roots, soil, and the countless creatures hidden underneath.
For the underground level, we built the ‘cave’ with black aluminum foil, poured soil over around the edges, and punched holes on the ceiling through which we snaked through tube cleaners. This would carry the water from above ground to below ground, once saturated. We also cut a large hole in the front panel, inviting guests to reach in and touch the soil as the water drops from the cave’s ceiling. We wrapped the black foil over and around the opening in the center, to help keep the soil contained and sealed. The cotton was threaded through thin copper wires which hung over the two side walls.
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FINAL PRODUCT
The final model is completed with organic soil and manmade trees and clouds. The environment comes to life in our film through sprinkling water over the clouds and seeping into the soil through the tube cleaners. A play on both the Wildcard and Synthesis, this experience allows one to feel the senses of the Earth 6 ft under.
IDEATION
For this workshop, I chose my conversation history with my Alexa as the small data here. As a poor graduate student, I lived in an old house with three roommates. There are no smart home devices installed. So, most of my communication with such devices is playing music and waking me up in the morning. Although I've spent nearly a year with it, there is not much data inside. This session gives me a good chance to reflect on my relationship with it - a companion spending endless days and nights with me.
I downloaded the data from Amazon the day before the workshop and processed them into the proper format: content, data, and time. In the working session, I began by going through the latest date, then I realized many interesting and also intriguing conversations happened without me even noticing.
I'm not an English native (or you can say my English is ... not good enough), and Alexa is not as smart as the most advanced LLM, either. These misunderstandings and relation-building inspired me about what should I do for this zine. I categorized all conversations into three categories:
Type 1: Correct recognition: lucky huh?
Type 2: Alexa's fault: Due to limited speech recognition ability, Alexa can't read ambiguous commands beyond its skill set and learning algorithm. In this case, it can't parse the command and show:
"Audio could not be understood."
But what happens more frequently is that I can parse what I say but wrong.
Type 3: My fault: Voice recording only works when I press the recording button, but my English skills can't help me figure out what to say in such a short period, so it often captures nothing, which is shown as:
"No text stored."
Sometimes, I would also overestimate its ability, so when I said something that it apparently can't recognize, this is what it thought:
"Audio was not intended for this device."
By inspecting the data from the categories above, it's natural to be curious about when these conversations happen how the frequency changes over time, and how the attitude and impression in Alexa and me change after our conversation.
So, I decided to focus on two things in my data visualization:
1. The timeline of each conversation under the categories.
2. The trend map of these categories.
But jumping into the quantitative analysis too early will curb my creativity, so I tried to image these trends in my mind by going through the dataset and making some side notes. In this process, I found myself behaving like a detective, the conversation content and time brought a piece of memory about what I did at that time to life. The emerging vivid stories hit me so hard that I decided to interpret the experience as an interaction with the zine.
I downloaded the data from Amazon the day before the workshop and processed them into the proper format: content, data, and time. In the working session, I began by going through the latest date, then I realized many interesting and also intriguing conversations happened without me even noticing.
I'm not an English native (or you can say my English is ... not good enough), and Alexa is not as smart as the most advanced LLM, either. These misunderstandings and relation-building inspired me about what should I do for this zine. I categorized all conversations into three categories:
Type 1: Correct recognition: lucky huh?
Type 2: Alexa's fault: Due to limited speech recognition ability, Alexa can't read ambiguous commands beyond its skill set and learning algorithm. In this case, it can't parse the command and show:
"Audio could not be understood."
But what happens more frequently is that I can parse what I say but wrong.
Type 3: My fault: Voice recording only works when I press the recording button, but my English skills can't help me figure out what to say in such a short period, so it often captures nothing, which is shown as:
"No text stored."
Sometimes, I would also overestimate its ability, so when I said something that it apparently can't recognize, this is what it thought:
"Audio was not intended for this device."
By inspecting the data from the categories above, it's natural to be curious about when these conversations happen how the frequency changes over time, and how the attitude and impression in Alexa and me change after our conversation.
So, I decided to focus on two things in my data visualization:
1. The timeline of each conversation under the categories.
2. The trend map of these categories.
But jumping into the quantitative analysis too early will curb my creativity, so I tried to image these trends in my mind by going through the dataset and making some side notes. In this process, I found myself behaving like a detective, the conversation content and time brought a piece of memory about what I did at that time to life. The emerging vivid stories hit me so hard that I decided to interpret the experience as an interaction with the zine.
PROGRESS
With a clear purpose, I began to prototype. The best way to present a timeline is through a long diagram, so the best physical representation is a multi-folded map. A set of memory cards function as keys here leading people to find when the story on the card happened. A two-direction slider is a good medium to inspect a chart for two entities - myself and Alexa, it's also a cover for the fragile map. With the purpose in mind, the physical form comes easily and smoothly.
Then I started to visualize the data in charts with Python. To improve readability, I spent some time on legend design color choice, and also layout.
Then I started to visualize the data in charts with Python. To improve readability, I spent some time on legend design color choice, and also layout.
First Functional Prototype
Data Visualization and Processing
Second Prototype: Test for Iteration
FINAL PRODUCT
The final product is a hi-fi prototype of the zine above, the colors represent two entitles here, and they also imply the difference in time in a day (day and night). The cover is designed as a ruler, the important attitude switch happens in the marker. Readers just need to find them on the map by themselves.
REFLECTION
Due to the time limit, I don't have time to iterate on it. It's a little pity that I didn't get enough feedback from the audience on the showcase. For myself. I think it might be meaningless to find a whole story in the chart since the chart didn't offer any extra information. Also, the trend map doesn't reflect on the change I anticipated before visualizing them. It seems that I'm not getting familiar with Alexa because it doesn't understand me better and I'm not more sophisticated in operating it. Lastly, the dataset from Alexa doesn't store its own feedback. So, the conversation is just about my questions. At least I remembered some interesting answers from Alexa, but I can't express them because of a lack of data. In general, the workshop is a good chance for me to reflect on my relationship with my personal artifacts and think about them in an interactive mindset.
Copyright © 2024
Hongfei Ji
Hongfei Ji
