Intelligent Car Finder

Justin Hinman, Stephanie Louraine, Sam Xia

Problem Space

Finding a car that fits you personally is difficult, time-consuming, and research-intensive.

There are many car review sites for both professional evaluators and user-created content. These sites are repositories of useful information, but even so, there is a lot of information about the car driving experience that cannot be searched through easily because it is lost within paragraphs of free text reviews that the user has to sift through.

This is where Watson can come in.

Research Summary

In addition to research into Watson’s capabilities, we also performed:

• Two interviews with car purchasers with differing needs

• Review of existing services, including:

• Several car review websites

• Other sites where the user can get personalized recommendations

*Please see Appendix for further details on our research

Key Insights

• It can take a long time to research cars

• Existing searches almost always require the user to know the make and model before they start searching

• Some criteria can’t be found by just looking at car specs

• Users can’t set their own criteria.

• Sometimes people have to compromise on what they want because urgency doesn’t allow them to research extensively

Our Design

Our concept leverages Watson’s natural language capabilities to provide the user with more personally relevant information when going through the automobile purchasing process.

It does this by looking through both more general information (e.g., price, fuel efficiency) and more personalized, individual information that is included in written user reviews.

Target Users

People who are looking for a car that fits their needs, but don’t yet know which specific car they’re looking for.

This might include:

• Busy people who don’t have time to visit dozens of sites to find a suitable vehicle

• People who need a car urgently and want to find out information fast

• People who are purchasing cars in bulk for a company and need to compare options

Potential Corpuses

We looked at a number of potential sources for our corpus of data. • Public data from vendors and car manufacturers • Public car reviews and customer comments (Cars.com,

Edmunds, etc.) • Review aggregators (alatest.com) • General information wikis (wikicars.org) • Various articles about vehicles (wikispeed.org) • Information about vehicles specific to travel and vacations

(wikitravel.org) • Brand- or model-specific forums (mr2oc.com)

Potential Corpuses

We also explored the possibility of adding information to our overall corpus from a Google search. The user’s natural language text entry, in addition to querying the Watson corpus, can also be used to search Google. • This could bring in results from sites that may not otherwise be

found in our corpus. For instance, if a user has a large body type, a car review may appear on a site like http://www.grandstyle.com/carslove.htm. This site is not necessarily about cars, but it offers general lifestyle tips for larger individuals.

• The results from these Google searches could be stored and later added into the corpus that Watson learns from. Over time, this would add more and more highly personalized and tailored information about cars.

Scenario

Let’s show this service in use.

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This is a standalone service provided by our startup. There is no signup process, and the service is funded by advertising revenue. This creates a low barrier of entry.

Meet Jane!

Jane, a nurse, takes the kids to the sitter each morning and then drives to the hospital for work.

She loves her current car because although she is short in height, she’s able to reach the pedals. However, the car is getting old and rusty. In addition, the heater has stopped working, which is bad news since winters in Columbus can get pretty cold.

Jane finally decides to buy a new more dependable one that she doesn’t have to take to the shop so often. However, she is very busy with work and kids and doesn’t have a lot of time to research.

She thinks about what’s important to her in a car. • Enough room for the family • Something she can sit in and still reach the pedals • A nice sound system where she can turn her music up loud and

sing along when going to work in the mornings

She goes to the Intelligent Car Finder service.

She sees the welcome screen describing the service and how it works.

She looks through the search criteria. She wants a sedan that can fit her family.

She can also search by a number of other specs, such as drive type (e.g., four wheel drive) or fuel type (e.g., diesel, electric).

It also has a text box asking “Tell us more about yourself.” She can use a free text field to enter other important information that wasn’t requested in other fields. She is sure to note that she’s short, and would like a car where she is able to reach the pedals easily. She also wants a nice sound system.

The search pulls some cars that fit her criteria.

Watson’s confidence ratings rank the cars for her and show her the evidence for its findings in the highlights.

After reading about the top cars in greater detail, she is able to make a well informed decision. She goes out to a local dealership to buy her chosen car, having saved lots of time on research!

Future Strategies

In addition to car purchasing, our strategy of parsing reviews could be applied to making decisions on other reviewed goods such as clothing or appliances. !

Though for the purposes of this design we are acting as a startup, we also explored a number of other possibilities for the service provider. • Existing car review site like cars.com or Edmunds • Consumer advocacy organizations like Consumer Reports

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We have also explored a few possible funding models: • Value-add to existing service (either paid service or funded by ads) • Users pay directly to search • Display advertising pays for services

Special Thanks

Tarun Gangwani Brandon Le Mark Marrara Professor Marty Siegel Chung-Ching Huang Our colleagues in the HCI/d program

Appendix: Persona

Jane Samson Age: 28 Hometown: Columbus, OH Occupation: Hospital Nurse Husband Charlie Two children: Annie, 5; Jack, 3 Height: 5’0” !

Pain point: Current car breaks down; heat doesn’t work Goal: Buy a new car with as little hassle as possible, then get on with her life

Appendix: Process

Following are slides detailing our design process.

Process: Exploring Watson

To begin, we looked through white papers and researched Watson and its capabilities. !

• The Era of Cognitive Systems: An inside look at IBM Watson and how it works

• IBM Watson Ecosystem Program

Process: Narrowing the problem space

We explored a number of spaces where someone might need to make a decision: • Food • Fashion • Cars • Education • Legal • Academia • Medicine • Farming

We decided to focus on car reviews because there are tons of car reviews in many disparate sites, yet it’s difficult and time-consuming for a human to parse through these. In addition, we recognized a lot of potential value in adding Watson’s natural language processing capabilities to help people in making what is generally a very important and pricey decision with long-lasting ramifications.

Process: Exploring the space

Exploring the space of car purchasing: • What do buyers need to know? • What information can’t buyers find easily with current methods? • What corpus might we look at?

Process: Primary interview resultsInterview 1: Bought a car last year. Search lasted a month.

Most important factors: • Number of seats • Fuel economy • Reliability • Experience with driving the car • Is it comfortable for taller people?

(This person is 6 foot 4 inches tall)

Interview 2: Bought a car five years ago. Search lasted a day.

Most important factors: • Cost • Fuel economy • Wanted an auxiliary jack to plug in

phone and listen to music • Needed to buy a car in one day

• The combination of factors meant this interviewee had to compromise

• Didn’t get the auxiliary jack because he was in a hurry, and the only car he found was out of his price range

Process: Secondary research

Review of existing car review sites (a few are presented in greater detail in the next slides)

• Edmunds http://www.edmunds.com/chevrolet/cruze/2014/

• cars.com reviews http://www.cars.com/reviews/ • Kelley Blue Book http://www.kbb.com/ • Autoweek http://www.autoweek.com/section/

carreviews • Yahoo! Autos http://autos.yahoo.com/newcars/

reviews.html

Process: Secondary research

Edmunds.com allows search for make/model and a number of categories spread along the bottom of the page. !

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Screen capture from http://www.edmunds.com

Process: Secondary research

Cars.com has several ways to look for a car: by make/model, price range, body style, and others. !

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Screen captures from http://www.cars.com

Process: Secondary researchThe Carmax recommendation tool asks users to rate their car usage on a number of criteria using slider bars. This can help users get much more personalized recommendations, but still relies on preset criteria. !

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Screen capture from http://www.carmax.com/ENUS/car-recommender/default.html

Process: Secondary research

Reviews of other sites that offer personalized information, such as in healthcare

Process: Analyzing written car reviews!

We looked at what sort of things people write in reviews. The highlighted items in this screen capture include parts of the car experience that we realized aren’t captured outside of text reviews, including the body size of the user, previous cars owned, and details about the sound system and quietness inside while driving. !

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Screen capture from http://www.cars.com/buick/encore/2013/consumer-reviews

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We put our research and ideas in a Google Doc.

Process: Exploring possible corpuses

Process: Outlining features

Process: Sketching layouts

Process: Creating a scenario