Writing Voice Mashups with Amazon Turks

Writing Voice Mashups Thomas Howe - http://www.thomashowe.com

Agenda
Introduce the After Hours Doctor’s Office
Scenario
The goals of the Mashup
Introduce the architecture
Go over every component
Functionality, interface, code
Give you some ingredients, ideas
What this isn’t
A complete, and coherent, application
Utterly scalable, but surprisingly close
What is this?
Think of Lewis and Clark
Today’s telephony is very far from this. Very far.
With a bit of luck, this is tomorrow’s telephony

After Hours Doctor’s Office
Mashup written specifically for the Contest
I quit my job because I was technically bothered
Goals
Show you the money
Explore the use of Amazon Turks in Telephony Mashups
Demonstrate the effects of modern Web technologies in telephony applications
Resources
Me, 40 hours and a PowerMac G5 Server
TellMe Studio - Voice XML hosting
Strike-Iron - SMS Messaging
Amazon Web Services - Amazon Mechanical Turks
Web Hosting Provider - PHP and Ruby on Rails
Source and commentary available at thomashowe.com

Application Scenario
Mr. Kraus feels sick, calls Dr. McCarthy
After hours - no one’s there.
Decision to make - emergency room? Tomorrow?
Application takes voice mail message
Forwards into pool of Amazon Turk Nurses
SMS messages increase reaction time for small service
Immediately responds to patient - constant feedback
Turk Nurse determines urgency
Listens to the message, summarizes and forwards
Application gives direction to patient
Routine? A message from nurse reassuring patient
Urgent? A call from the Doctor, right away
Benefits?
Faster, cheaper with a higher quality of care

The Money
Where is the money?
50 million unnecessary emergency room visits in the US
Each costs about $900.00 more than a doctor’s office visit
Where isn’t the money?
Customer education that the service exists
Repeated marketing to habituate the service
Costly market trials
Why?
Integration of real time communications into the business process makes it more efficient, saving money for the enterprise and increasing customer satisfaction

Amazon Turks
Internet Scale Workforce
One doctor needs 1/10 of a nurse
One thousand doctors needs 100.
More use, more turks, faster service
Practical? Absolutely
Nurses get $3.00 for three minutes work == $60/hour
Down time, family time, sick time, whatever
Off-shoring?
Comprehensive Quality
Amazon Turks automatically
Record urgency accuracy, compare history
Transcriptions

Bringing a knife to a gun fight…
What did I use?
40 hours, web services and a rapidly aging geek
Ruby on Rails, SOA, PHP
$25.00 to Strike Iron, and I’d probably have to pay x cents a minute to TellMe, Angel, or Voxeo
What did I not use?
Big Iron ==> NO LARGE INVESTMENT
A Contract ==> NO CARRIER INVOLVEMENT
Marketing ==> NO CUSTOMER EDUCATION
Risk ==> NO LONG DEVELOPMENT CYCLES
Traditional carriers will get their clocks cleaned.

After Hours Mashup Architecture

VoiceXML
Accepts inbound phone calls with rich media experience
Voice detection, ASR, IVR, recording audio
Use CCXML from Voxeo for outdial operations
May be blended with other voice applications
Standards Compliant Operation
I can choose among hosted options, local platform
Little lock in
Records audio and posts it to Web Server
Store it, record it, play it
Web Server can produce VoiceXML files to play
Stateful processing
Many choices for hosted providers
TellMe, Voxeo
Some have platform/hosted options

Uploader.php
PHP file for accepting inbound request
Called by VoiceXML file
Handles uploading voice files, data
Calls shell scripts to issue SMS requests
Returns VoiceXML files back
Could be dynamic; template procesing
Complete integration with other Web Tech
Database integration
Instant Messaging and Presence Information

Strike Iron SMS
Web Service to send text messages
One of many online, paid services
Reverse phone number lookup
Address verification, currency rate, background check
Pay per click model
First 100 clicks are free, small fee thereafter
168 characters per message
User interface issues
URL issues (TinyURL)
Extraordinarily Powerful
Ubiquitous
Immediate

Amazon Web Services
Amazon Mechanical Turks
Human Intelligence Tasks
Things that humans can do easily, difficult for computers
A9 Search Engine
Artificial Artificial Intelligence
It’s now OK to cheat
Humans become subroutines
S3, EC2 have unique telephony implications
Mr. Erlang never worked for Amazon
True defense against DDOS
S3 == linear scalability == holy grail of telephony

Extensions
Call Centers
Expert based call centers, makes response even faster
Better for larger scale installations
Integrates well with Amazon Turks
Geolocation
Give the data right to the nurse, showing hospitals, medical centers, etc.
Combines well with text messages (text directions)
Get location from reverse phone number lookup, zip code from VxML, GPS inside phone, or all of the above for fraud detection
Medical Transcriptions to Testing
Use medical transcriptions to check for consistency, accuracy, training.

Call Center : Sylantro
Integrating with Sylantro switch
Accepts inbound calls into expert queue
Nurses login to the system from soft client or PSTN phone
Turks are used to manage larger schedules
Even better response for patient
Nurse is right there to answer questions
Use VoiceXML to do pre-processing, skills based routing
SOAP based interface using Java
Better choice for larger setups
Dedicated equipment, staff
Other functionality
Conferencing in Doctor or other expert
Past history integration with CRM for accuracy
Click to dial for post care support through e-mail

Yahoo! Local
Nurses page shows location of caller
Location derived from inbound calling info
Alternatives are GPS, asked
Gives nurse immediate information to share with patient
Nearest clinic, hospital, etc.
Directions; call ahead; 911 conference
Flash based API
Many APIs available… this was wicked simple to use

Thank you!!!
For more information, source code, and this presentation, see my site at
thomashowe.com

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Writing Voice Mashups with Amazon Turks

by howethomas on Apr 17, 2007

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Writing Voice Mashups with Amazon Turks — Presentation Transcript