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Hello. 
Conversational Architecture on the Internet
Who’s Loren? 
• Founder / CEO of Axilent 
• Makes ACE - the Adaptive 
Context Engine 
• User Profiling and Dynamic, 
Personalized Content 
Targeting 
• Former Director of Technology 
at digital agencies HUGE and 
Alexander Interactive 
• Python hacker
Who’s Loren? 
• @LorenDavie on Twitter 
• loren@axilent.com
Phase 1: Internet in a Box 
www
Tipping Point: Introduction of 
the iPhone 
2007 
“Scrolls Like 
Butter”
Phase 2: Cloud + Devices
Another Tipping Point 
???
Phase 3: Ubiquitous Internet 
? ♫ 
www
Adaptive, Personalized, 
Contextual 
Here’s your coffee, 
just the way you like it. 
www
Five Forces 
• Mobile Devices 
• Social Media 
• Data 
• Sensors 
• Location
Problems
Problem 1: No Language 
?
Problem 2: Privacy Issues
Solving Problem #1 
Enter 
the 
Metaphor
The Conversation 
• Multi-directional 
• Multi-modal 
• Multi-channel
From Metaphor 
to Design Language 
Conversational 
Architecture 
Visual 
Expression
Metaphor to Design 
Language 
CAVE language 
cavelanguage.org
CAVE Language 
• Whiteboard / Napkin / Presentation -Friendly 
• Methodology Neutral 
• Scales Up, Scales Down 
• Useful Across Disciplines
Structure of CAVE language
Data 
The Foundation of Context
Data Origins: 
Devices and Sensors
Data Origins: 
External Data Sources
Data Processing
User Input
Data In a Contextual App
User Context 
PAGES Analysis
Personas
Affinity
Goals
Environment
Sentiment
Inferences 
Converts Data to User Context
Inferences 
An Inference is made from data
Inferences 
Usually there is a condition that must be met
Inferences 
If the condition is met, the user is 
associated with the context element.
Inferences in a 
Contextual App
Application Modes 
Dynamic Response to User Context
Switch
Modal Switch for a 
Contextual App
Modal Switch for a 
Contextual App
cavelanguage.org
Solving Problem #2 
• Contextual Apps require User Data 
• User Data is sensitive, and can be abused
Privacy Debate: 
All or Nothing 
Surrender all 
control of your 
personal data 
Completely opt 
out of contextual 
apps vs
Data Stewardship 
A Framework for Responsible Use of Personal Data
Most Problems Come From 
Third-Party Access to Data
Roles in the Data Ecosystem 
Data 
Data Producer Data Consumer 
Data Citizen 
Uses 
is the 
subject of 
Acquires or 
Creates
Data Policy 
The Citizen’s Rules for Their Data
Contents of Data Policies 
• A Default Rule 
• Rules Tied to Letter Grades 
• Rules About Specific Data Categories 
• Whitelists / Blacklists
How do you know data 
users will follow the rules?
telltrail.me 
• A kind of “Better Business Bureau” for data users 
• Holds repositories of citizen data policies 
• Provides certification marks for compliant data 
users (letter grades) to let citizens know they are 
trustworthy
Letter Grades 
• Like NYC Restaurant health letter grades 
• Indicates the level of compliance of the data user 
organization 
• Lets citizens know the data user organization is 
trustworthy
Letter Grades 
• A: Audited and Verified adherence to Data Polices 
for both internally created and externally sourced 
data. 
• B: Adherence to Data Policies for both internally 
created and externally sourced data. 
• C: Adherence to Data Policies for just externally 
sourced data.
TellTrail: A Data Policy 
Repository
Thanks! 
@LorenDavie 
loren@axilent.com 
cavelanguage.org 
telltrail.me 
www.axilent.com

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Conversational Architecture, CAVE Language, Data Stewardship