Gen 2 Commerce




Shopping and purchasing items, whether it be online or in person, can be considered as a slow and tedious process at its current state (CNN). This can be seen through innovations such as the Amazon Go in person store that cut out the checkout process altogether in order to allow for a seamless shopping experience (Amazon) We wanted to create a system that would increase the speed of the checkout process, and even possibly bypassing the checkout process altogether. While following these guidelines, we also wanted to increase the security of these payments.



How can we promote efficiency and security through a new form of commerce?


Questions to consider:
How will we increase the security of the current purchasing methods?


How can we combat identity theft and fraud?


How can we improve efficiency of the current standard of shopping.


“An estimated 17.6 million persons, or about 7 percent of U.S. residents age 16 or older, were victims of at least one incident of identity theft in 2014” – the Bureau of Justice Statistics

“In the past six years identity thieves have stolen over $107 billion.”

-2017 Identity Fraud Study


User Journey:


Consumer Journey:



  • Go shopping
  • Go to a vendor and pick out all the items you want
  • Walk out of store (no lines)
  • The systems lets you know your amount before you leave so you can cancel or return items if you feel you are spending to much
  • If you feel the amount is within budget, proceed to leave
  • If desired set daily caps so you know exactly what your allocated for the day
  • Feedback from the system provides relevant information



  • If job is on an hourly base you can use system as a form of identification
  • Tracks the exact amount of time a worker is working, (must be placed on the host for it to work)
  • If salaried, Tracks if employees are coming in on time



Amazon Go:

Security Flaws:


Identity theft stats:


-Jake Tessier, Jacob Heuman, Alan Liu, Greg Borbon


Water Supply

Problem Statement:


Polyfluoroalkyl and Perfluoroalkyl (PFAS) are two widely used industrial chemicals. These two chemicals have been linked to cancer and other health problems. PFAS are man-made chemicals that have been used in industry and consumer products around the world since the 1950’s. Researchers from Harvard T.H. Chan School of Public Health and the Harvard John A Paulson School of Engineering and Applied Sciences found that PFAS exceed the recommended safety levels in public drinking water that has affected more than 6 million people in the United States. Since the industrial revolution, we have been allowing chemicals with unknown toxicities to be used and released into the environment. We now have cases where these chemicals end up in our water supply and start to affect those who drink that water.

We wanted to create a way to make it possible for homeowners to see what and how much of a substance is in their water. We also want to be able to do this in the easiest and most convenient way for the homeowner.

How can we make detecting hazardous substances in our water easy and convenient?
Questions to consider:

How will we go about informing the user about the chemicals in their water? Will it be a mobile app or some other mode of communicating with the user?

Will we focus on water that is used in people’s home or will it be designed for public places?

How do we want to warn the user if their water is unsafe to drink?

Is there any way to design this system so it is affordable for people who have low income?
– Andrew Walraven, Nathan Eggleston, Jad Sidi-yekhlef

The Issue


“Maintenance has a larger role in communication with the
resident than many realize. It’s important for technicians to
communicate in a way with residents so that nobody gets
offended, especially when it’s an issue created by resident error.”

-National Apartment Association

Problem Statement:

How might we improve communication between the landlord, maintenance, and apartment residents in order to ensure mutual responsibility and efficiency between them?

User Journey:

Resident’s Journey:

Search for house/apt
• Submit application/pay fees/deposit
• Meet and go over responsibilities with landlord
• Move in – fill out move-in inspection checklist
• Read over documents to list responsibilities
• Will any maintenance repairs cost money?
• Contemplate whether repair is a responsibility of the landlord
• Call/visit clubhouse, fill out online maintenance request
• Request allows maintenance workers access to residence
• Doesn’t have to be home
• Create a clean work space for maintenance workers
• Analyze repairment
• Leave a written review (doesn’t have to be a 1:1 relationship, review can
embody entire experience)
• Make payment if necessary

Landlord’s Journey:

Search for house/apt
• Search for tenant
• Post opening of house/apt
• Show house/apt
• Background check
• Application /fee
• Get signed lease + make responsibilities clear, receive deposit
• Move-in
• Landlord contacted
• Phone evaluation / troubleshooting (can the solution to the problem be
handled by landlord over the phone?)
• Call help/maintenance
• Typically not handled by landlord unless they are the ones fixing it
• Confirmation of repair
• Pay bill for maintenance
• Check-up on tenant


Search for house/apt
• Inspect apartments
• File records of move-in checklist
• Reads maintenance request
• Prepares for repair with appropriate tools
• At the correct apartment
• Compares request description to the actual situation (entity?)
• Does repair in a timely manner without damaging anything else
• Report back to office of completion

Presentation: Problem-Statement_Colon-Tram-Green-Brizuela

Rachel Colon, Zach Green, Sarah Tram, Xavier Brizuela

The problem with grocery stores

Problem Statement:

A 2015 Consumer Reports study shows 12 of the most common grocery store shopper complaints include:

  • Confusing Layout
  • Out of stock on basic, non-sale items
  • Out of stock on advertised items

Design Consideration: 

How can the experience in a grocery store be more informative about the state of the store to assist customers and employees?


Why are they at the store and what do they need to accomplish?


  • Making shopping list, finding deals, locating items


  • Restocking, helping customers, cleaning/organizing, checking out

User Journeys:


Before going to the store:

Planning shopping list, recipe/item recommendation, check for sales/coupons, look up nearby stores

At the store:

Map locations for list (perishables last), find specific items, ping employee for problems, notify item needs restocking, notified of sales walking by, something to distract child?

After the store:

Found everything?, alerted when unstocked item is restocked, recommended recipes based on what bought?


Before going to the store:

Knowing about promotions or sales in the store, communicating with managers and other employees, suggest items to customers (weekly employee recommendation to encourage customers to try new products or recipes gives you a connection to the people working there)

At the store:

Getting called to different areas of the store (are the checking lines full?), restocking items, helping customers find/reach things, cleaning up messes (mopping floors, cleaning shelves and fridges), returning shopping carts, rearranging the store (moving sale items to the front), tracking breaks

After the store:

Track hours, employee incentives, they also are probably customers at the store and use the app in that way


Further Research:

Problems with retail:

According to statistics in 2017, 69% percent 1 of customers still prefer to shop in-store rather than online when buying products such as automobiles, phones, apparel, toys, kitchen appliances, tools etc. Since the interest in in-store shopping is still significantly high, it is important for retailers to create value in the experience of shopping to draw customers in their stores. The Vice President of Apple, Ron Johnson explains Apple Store’s success by pointing out to their improved customer journey and mentioning how retail shopping isn’t what is broken but “It’s their lack of imagination—about the products they carry, their store environments, the way they engage customers, how they embrace the digital future.” 2 Denis Ghys, the managing director of These Days, explains the importance of customer journey by stating “You need to design the store of the (near) future, define your proposition and beat out the competition in the process. And it all starts with shaping future customer expectations and paving the way to superior service” 3

With these ideas in mind, our goal looks towards a future where in-store experience is assisted through UX design to build a relationship between the customer and the employees that reduces frustration and makes the whole experience more efficient.


Grocery stores are an important retail situation considering everyone needs food. With this in mind, we decided it would be important to focus our efforts. Here are the main problems we hope discovered regarding the shopping experience:

  • According to the Time Use Institute, the average shopping trip takes 41 minutes. If you multiply that by the 1.5-trip per week average, that’s over 53 hours per year you’re spending in the grocery store.

  • 75% of supermarket shoppers do tend to visit the most conveniently located store, but 25% will go further to get to a store that offers better quality and variety, lower prices, better sales, and a clean location.

  • In 2014, grocery stores offered more than 42,200 items on average.

  • When asked, 30% of shoppers complained of uninformed or indifferent staff

  • 80% of shoppers make between 1 and 10  trips to the grocery store within two weeks. Only 39% of shoppers say they know the employees at the store they shop at.

  • 59% of consumers are motivated to shop at a new grocery because of in-store events and social media engagements. The majority would like the app to include coupons and 73% want current pricing to be available. Additionally shoppers want notifications for special events, product assortment, samples and recommendations from store associates.


Presentation: Problem statement

Carpal Tunnel Syndrome

Maya, Joe, Milan, and LeighAnn



Carpal tunnel syndrome (CTS) is a medical condition due to the compression of the median nerve as it travels through the wrist at the carpal tunnel. The main symptoms are pain, numbness, and tingling, in the thumb, index finger, middle finger, and the thumb side of the ring fingers. Weak grip strength may occur and after a long period of time, the muscles at the base of the thumb may waste away. In more than half of cases, both sides are affected.


Carpal Tunnel Syndrome is the #1 reported medical problem, accounting for about 50% of all work-related injuries and is currently affecting over 8-million Americans

Problem Statement:

How can we make diagnosis and treatment of carpal tunnel syndrome convenient, accessible and affordable for those with milder symptoms of arm and shoulder pain?

Design Questions:

  • How can we determine the seriousness of symptoms through an app survey?
  • What is the best way to provide our users with molding equipment through the mail?
  • How can we use these mold to create custom cats that are durable, affordable, and are able to compete with those made by specialists?


Carpal Tunnel

Group Project Idea – Cosmetic Scanner

Retail 2.0

“The consumer has demonstrated a very high capacity to try and use multiple brands and that tendency seems to be growing over time.”

A recent poll found that the number of people who would rather reach for their smart phones for a quick check on a product’s reputation or price comparison rather than ask the salesperson has reached 58 percent.”

“Most purchases are planned — the buyer now goes in knowing what she wants at least 70 percent of the time.”

This DIY research boosts buying confidence and helps match product expectations with reality.

The Way We Buy Beauty Now

Problem Statement:

How do we improve a cosmetic retail experience while educating the user on their needs and the products available?

Design Prompts:

  • How can we personalize the process of buying cosmetic products?
  • How can we open consumers up to all of the products out there?
  • How can we bring product recommendations into the home?
  • How can we design a process that takes intimidation out of the cosmetic shopping experience?

Reflections on Machine Learning Readings

Machine Learning is something that scientists have been working with for years. Minimized concepts are already widely existent today and are used very frequently. Think of “recommended” features on YouTube, online stores, google searches, etc. I believe that much like any other materials we use to improve our standards of living, individuals need to get a better understanding of machine learning in order for it to be used for good.

Many people fear machine learning because they believe it to be a foreign concept. When cars first came out, I’m sure there was a lot of uproar on safety aspects of this new idea. Without any regulations, cars can be very dangerous. However, society slowly discovered the risks that come with driving a tin can at fast speeds. Seat belts, road signs, various laws, and constant regulation have been set in stone in order for cars to be used for good. In the same way,  for us to be able to regulate machine learning and for regular consumers to appreciate and respect such regulations, we as a whole need to be more familiar with the concept of machine learning, understand how it works and the risks that come with it if improperly utilized.

Personally, machine learning scares me a little, because of the lack of regulation out there and the lack of knowledge individuals have of this idea – think autonomous cars. Although it could be very useful in certain situations, it should not be a backbone for drivers to depend on 100% of the time. Essentially, all of the code and technology has been designed and created by us, imperfect humans. There is no way we could create a completely foolproof technology. Consumers need to understand the risks that come with this technology for them to avoid crashes and be able to utilize this feature to its best ability. Our best creations could also be our worst enemy. However, with proper monitoring, education, and use, I believe that machine learning could truly benefit our society as a whole.


Readings #2

Intelligence on Tap: Artificial Intelligence as a New Design Material

Artificial Intelligence, AI, is becoming more and more accessible to non-experts which is why it could be the next big utility for designers. I must question the desire for it, do designers want to use AI? Wouldn’t that take away from some of the creativity that designers love to incorporate into their products? Incorporating AI will redefine what constitutes a “unique” design in many ways. I am interested to see the rate at which design practices will shift when AI is widely implemented into new products. The article mentioned there was a big learning curve when designs went from paper to screen and I believe utilizing AI is a much larger jump.

UX Design Innovation: Challenges for Working with Machine Learning as a Design Material

Machines can learn from large datasets through reoccurring trends and explicitly separating what is “correct” for a given situation and what is “incorrect”. Will machine learning be able to take tendencies, likes, dislikes, and other characteristics of a person and cater to a specific person’s needs? I feel some people are afraid of machine learning because it doesn’t feel personal, in other words, they just like another line in a huge dataset. Every UX design has a certain demographic related to a persona created by the designer. Machine learning will be more welcoming to less technologically savvy users when it can take datasets from individuals and quickly create a comfortable environment for them. Sort of like an episode of Black Mirror where a company cloned the customer’s consciousness to provide the perfect home assistant. The assistant knew all the customer’s preferences because the assistant was an exact copy of the customer’s current state.

Machines Learning Culture

Art is a term that does not have a single definition. People who deem themselves as artists all specialize in a certain form of art and relay it to the world to express themselves. So why is the integration of technology and machine learning all of the sudden excluded from the idea of art? As a computer science (CS) student, I have realized that CS is an ambiguous topic to people who study it and those who do not. Someone could describe it as a science meanwhile someone else could describe it as an engineering practice. The interpretations are endless and now I can see why someone would deem CS as a form of art. I have seen beautiful code before and although I wouldn’t know what that looked like five years ago, it doesn’t make that code any less beautiful. Therefore, I think that machines built with beautiful code can and will create beautiful art. The art created by machines won’t be any less or any more artistic than an art piece created by a human.


Intelligence on Tap: Artificial Intelligence as a New Design Material

This article brought up some good points about AI’s uses and limitations.  It discussed how it is important for a designer to understand what AI is good and not good at, similar to use of materials.  I think understanding when not to use or limit AI is just as important as its use.  Every product does not need to predict what the user wants, and if it has that functionality, it becomes important for the user to understand why the machine made the decision that it did.


UX Design Innovation: Challenges for Working with Machine Learning as a Design Material

I think it was interesting how this article said how designers often see AI and ML as “black magic.”  As a designer this “black magic” often is in other complex systems as well (electronics, production techniques)  when a designer does not have a thorough knowledge of specifics of a process.  I know I definitely have made some projects where I was like “the circuit board will go in there” without actually knowing what exactly that entails.  AI and ML are very similar, in that designers need to gain an adequate understanding of their strengths, uses, and limitations in order to use them in their designs.  It is important that designers gain this understanding of AI as its uses broaden in the future.


Machines Learning Culture

I thought this was interesting how AI can evaluate art and culture, but does it in a very different way than humans.  As humans we usually look at art and culture with a context of emotion, connecting things with that human perspective. AI does not do this and evaluates things much more mathematically.  This could have interesting applications as a tool for artists to make possible decisions that they would not initially think about.

Second Readings

In response to these articles on Artificial Intelligence and Machine learning I will start by delving into this on a personal note. I play way too much of the video game Fifa 18, which is created by EA sports. There is a huge community around this game, and recently there has been mention and an uproar about how EA might be editing you gaming experience based off of data they collect from you. You can find the post here. Now there is a lot of skepticism on whether this is actually a well informed former employee of EA, or just a frustrated keyboard warrior.

The article basically states that EA collects data on how you game, when you game, how often you game, the last time you played the game and if you are on a winning streak or not. Recently there was an opportunity for players to connect their twitch (live streaming app/website) accounts to their EA accounts. And the article goes into a more tinfoil hat type argument that EA could be gaining information on you from Amazon. Amazon owns twitch so theoretically they could be selling your shopping habits and other bits of data to EA through twitch. What the post concludes is that based off of all the data that EA collects on you they can alter your drop rates in the packs (lootboxes) that you open.  This is one of those cautionary tales of how machine learning using big data can be dangerous and unethical.

The European Union passed a law called General Data Protection Regulation in 2016, which will go into effect on the 25th of May. Companies will be forced to disclose what information they have on you when you request it and you can also request that it be deleted. This will be a very interesting day for machine learning since it learns from big data.