1. Contextual Intelligence as a Driver of Services Innovation
Teresa Lunt, VP PARC
© 2013 PARC

2. Harnessing Context
Content and context analytics infer relationships among information to construct a Personal Semantic Network for each individual
Explicit Tags & Preferences …
People
Content
Tasks
Events
Places
Topics
Extracted content & meta-data
Sensed Location, Proximity, …
Inferred activity & inter-relationships….
User model:
Patterns, Interests, Behavior, Relationships, Personality…
Use it to infer:
Current Situation (Venue, Who I’m with, What I’m doing), and Likely Future 2
© 2013 PARC

3. Example: Psychological Profiles
Online behavior is highly correlated with personality variables
Extract and classify textual features from online behavior to build psychological profiles as indicators for future behavior
Personality Traits
Neuroticism
Agreeableness
Conscientiousness
Extraversion
Openness
Perceived Stress
Self-Assurance
Overall Mood/Emotion
Extraversion
© 2013 PARC

4. Contextual Recommendations combine multiple factors to drive decisions*
Distance w1
Personal Utility Score
GPS +
NFC *
Purchase History w2
Calendar
The next step is to take these models of past behavior, along with an estimate of current and likely future situations, and to recommend a course of action. (or take the action directly, if the confidence of the recommendation is high enough).
We’ve developed an extensible, general-purpose hybrid recommendation engine for decision support.
Given any set of alternatives (coupons, movies, stores, activities, health choices, financial options, etc.), there is a set of scoring functions that query the user behavior model and current situation to score each of the alternatives based on what it knows. E.g., the distance function simply uses current and likely near-future distance to score the alternatives; the purchase history makes its score based on your history of purchasing in different product categories; etc. This can also call out to third party databases, like say a credit card company.
The scores are combined in a linear weighted combination to generate the overall utility score – the weights are adjusted per individual based on the observance of the user outcome and the success/fail of the scoring function’s prediction in the observed outcomes. The weights can change dynamically.
(incidentally, this hybrid recommendation is a patented technique. Parc also invented collaborative filtering, a common recommendation algorithm)
Ranked Alternatives *
Predicted Activity w3
Option 25
0.77
Option 16
0.64
Option 3
0.60
Option 49
0.57
Option 8
0.39 *
Others … w4
Alternatives
© 2013 PARC

5. Lean Service Innovation
Digital Nurse Assistant:
Contextual information
Improve clinical decisions
Reduce errors
Increase patient time
Improper Payment Amounts
Fraud, Waste & Abuse:
Health insurance
Transportation
Credit cards
Social services
Police Operations
Enable evidence-based decisions
Parking Enforcement:
Shared real-time context for coordinating & documenting activities
Sentiment Analysis & Topic Detection in Social Media:
Customer intelligence
Emerging event detection
© 2013 PARC

6. Lean Service Innovation
Call centers
Mining audio & transcripts for learning
Real-time analysis & intervention
Intelligent call routing
QA, training, call center analytics
Transportation services
Personalized rider services in metro area transit systems
Participating vendors create offers
Riders get real-time contextual recommendations
Personalized services for the car
Proactively infer user’s intent, anticipate needs, take action
Smart information feeds & prioritized , Facebook, Twitter, texts, etc
Interactive calendar assistant
Understands when driver’s attention is available (e.g., at red light)
© 2013 PARC

7. Thank You!