TechCrunch Disrupt 2016 Submission

Status: Incomplete, discontinued.

DebtChecker Alexa Skill

"A Lannister always pays his debts."
"Be a man, do the right thing."

The idea was to use Splitwise API to retrieve existing expenses owed to friends. Followed by using a payment transaction API invoked over a server through Alexa to pay those debts, just because you can.

I decided to stop pursuing this project submission because the use case was not carefully researched: Braintree payments API is used for customer to merchant payments, which is a completely different use case from Venmo, which can be used to transfer cash/credits to personal groups.

However, there are some learning points from this short foray into the AWS developer documentation and console:

  • AWS Lambda is awesome: you could dive directly into the application without worrying about any of the bare metal container related setup.
  • AWS CloudFormation is pretty cool as well: defining rules to bring up a stack for any AWS cloud based application is pretty handy, especially in a hackathon setting.
  • Alexa Skills Kit does not allow for dynamic slots, which makes it harder for Alexa to 'learn' new information from API calls for reuse in subsequent sessions

Use cases considered:

APIs that were chosen (albeit with little consideration or research) include the Braintree API, Alexa Skills Kit, and the use of Domain.com's .CLUB domain registration, to which a joke was to be played on a Game of Throne trope.

  1. Pay money (Braintree/Paypal/Venmo)
  2. Request for money (Braintree/Paypal/Venmo)
  3. See outstanding debts (Splitwise)
  4. Resolve debts
    • All debts
    • Only debts to person X

Details considered for the Voice UX:

Language processing:

Intents have to be manually enumerated in a list of 'Utterances', which is matched through Alexa/AWS's NLP processing.

For the 'magic' to work in creating a smooth voice user experience, there were several key points of consideration as to how money should be referred to:

(dollars, bucks) AND (cent(s), dime, nickel, quarter, half dollar):

  • parse ten dollars -> $10.00
  • "ten fifty" -> $10.50
  • "hundred bucks" -> $100
  • "two quarters" -> $0.50
  • "three pence" -> $0.30

User Experience (Voice) considerations:

Also, other considerations on how to follow up (actionable intents) on the newly retrieved information for outstanding debts include:

  • Does the user want to pay a user that was queried?
  • Does the user want to find out about another person that he owes?
  • Does the user want to resolve all debts without actually knowing the debt?
  • How do we ensure that this is a secure operation?

Although a server could have been easily set up to act as another layer of abstraction for the APIs provided, as the primary use case of resolving the debts could not be solved with the prevailing APIs to be used, I decided to stop work after implementing the following features:

  1. Retrieving a list of all friends to whom I owed money to.
  2. Retrieving information on how much money I owed a particular friend.

Development

I started off with the Alexa tutorial as viewed on bit.ly/alexafactvid. Since this was a hackathon, I used cookies to skip the much necessary OAuth callbacks for proper RESTful API calls to get the results I want quickly.

My workflow for developing the Alexa skill was as such:

  1. Develop off a text editor for the *.js files which were relevant to the project
  2. Ran make to zip the files required (because using the UI is slow)
  3. Upload the zip file manually to run the test code to inspect errors
  4. Used echoism.io to invoke the utterances that called the function handlers
  5. Used CloudWatch to inspect the requests as they streamed in

by Jonathan Tan,
September 11 2016
@jhwtan
LinkedIn

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