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C O S E E R
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Web/ XML Pages
Social Media
Third Party Research
Proprietary Databases
Internal Documents
Automated Workflows
(Tasks/ Processes/ Decisions)
E X A M P L E S• Building healthcare product database
from 35m documents
• Compiling 3m documents every day into
actionable stock insights
• Reading through complex legal/ technical
documents to answer questions2
T A C T I C A L C O G N I T I V E C O M P U T I N G
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About Cognitive Calibration
Implementation
Case Study
C O G N I T I V E C A L I B R A T I O N
Qualify data based on its value + Train differentially
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I N C R E A S E D A C C U R A C Y
L O W E R C O M P U T E I N T E N S I T Y
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A B I L I T Y T O M A N A G E O U T L I E R S
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Sky Color1200 PST San Francisco
Sky Color ??1200 LTST Mars Pathfinder
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About Cognitive Calibration
Implementation
Case Study
T I E R E D T R A I N I N G
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Constrain Solution Space
Constrain Solution Space TrainFilter
Filter
Reliability: High
Medium
Low
C A L I B R A T I O N O F I N P U T S
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Cognitive Calibration Classifier
Reliability: High
Medium
Low
M E T A D A T A C L A S S I F I E R
Map: m Reliability Tier
• Examples – Source, Author, Format (image/ text)
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D E T E R M I N I S T I C C L A S S I F I E R
Map: f(x) Reliability Tier
• Source order as per the context
• Completeness
• Grammatical accuracy
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P R O B A B I L I S T I C C L A S S I F I E R
f(x) p(R)
• Probabilistic distribution of the tier
• f(x) can be machine learnt independently e.g.
political correctness of statements
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P R O B A B I L I S T I C C L A S S I F I E R
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SculptSolution Space
SculptSolution Space TrainProbability
Distribution
Probability Distribution
Reliability: High
Medium
Low
C L O S E D L O O P C L A S S I F I E R
F(RO) F( f(x) p(R) )
• Probability distribution of a Probabilistic Classifier
is improved by results of the main machine
• Closed Loop Classifier Deterministic Classifier
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C L O S E D L O O P C L A S S I F I E R
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Probabilistic Classifier
Primary Trainer
Reliability: High
Medium
Low
Continuous Learning
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About Cognitive Calibration
Implementation
Case Study
P R O D U C T D A T A B A S EClient
Problem
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• One of the largest healthcare Companies
• 10m+ SKUs
• No standardized database comparing
attributes of products
• Previous human attempts to build
database unsuccessful
I N P U T• Product Brochures
• White Papers
• Surgical Protocols
• Sporadic human
entries by previous
attempts
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Drill Bit 4.5 mm Cannulated Jacobs Chuck
With 135 mm Stop 165 mm Humeral Nail-
EX Instrument 03.010.089 03.010.089
DRILLBIT 2.0 MM GUIDE WIRE 4.5MM
CANNULATED DRILL BIT JC/WITH 135MM
STOP/165MM 45mm cann BIT BIT DRILL
03.010.089* BIT DRILL CANN 4.5MM X
165MM BIT DRL CANN 4.5X135MM BIT
DRL CNLD 4.5X165MM DRILL Drill Bit 4.5
mm Cnltd Jacobs Chuck 135mm…
C H A L L E N G E S• Erroneous data e.g. human entries
• Different levels of detail e.g. Metal, CoCr, CoCr46
• Extensive use of context specific shorthand e.g.
OD = outer diameter for acetabular shells,
OD = optical density for intraocular lenses
• Incomplete or missing data
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M A N A G I N G C O N F L I C T I N G D A T A• Sources tiered based on accuracy and specificity
Product Brochures
White Papers/ Surgical Protocols
Authenticated Web Data
Human entries in the system
Non-authenticated web and other data
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I D E N T I F Y I N G M A T E R I A L S• Confusion of materials of the part itself and
corresponding parts.
– e.g. “Coated Tube Poly Silicone”
– Unclear if coating is Poly or Tube is Poly
• Constrained solution spaces using Deterministic
Classifiers help
– e.g. “parts use plastic tubes” tubes cannot be silicone.
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F I G U R I N G O U T S H O R T H A N D S
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Probabilistic Classifier
• Classifies docs if other info identifies part category
• Trainer uses high reliability data to identify more classifying features
Primary Trainer
Reliability: High
All corpus“outer diameter”“optical density”
Continuous Learning
“outer diameter”
“optical density”
Reliability: High
Reliability: Low“outer diameter”“optical density”
O U T P U T• Learnt attributes and their
corresponding values for al l parts
• Impossible without multiple cognitive calibration frameworks deployed in models
• Diff icult to implement without other strengths in tactical cognitive computing
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CategorySub-Category
DiameterStop
LengthGuide Wire Dia
CannulatedRadiolucent
CouplingPlatformReusable
Drill BitHumeral Nail4.5 mm135.0 mm165.0 mm2.0 mmYesNoJacobs ChuckSynthes (Estd.)Yes
