Transcript New Technologies Supporting Technical Intelligence

New Technologies
Supporting Technical
Intelligence
Anthony Trippe, 221st
ACS National Meeting
Aurigin Systems Inc.
Aurigin Consulting
Practice Director
IP Consulting Services
Introduction
 What is Technical Intelligence
– Definitions
– How Does it Fit with the Company’s Business
Strategy
• The Intelligence Cycle
• Actionable Intelligence
– What is it Not
Introduction (Cont.)
 Gatekeeper Approach to TI
– The Intelligence Cycle
 Ad-Hoc Team Approach to TI
– The Intelligence Cycle
Introduction (Cont.)
 Computer Assisted TI
– Data Mining
– Text Mining
 Available Methods
–
–
–
–
Concept Clustering
Self Organized Maps (SOMs)
Neural Networks
Decision Trees
What Is Technical Intelligence?
 Definitions:
– A tool to assist with long term strategic technical
planning
– Work processes for helping technical decision makers
make smarter decisions faster
– An analytical process that transforms disaggregated
technological information into relevant strategic
knowledge about your competitor’s technical position,
size of efforts and trends
What Is Technical Intelligence?
 How Does it Fit with the Company’s
Business Strategy
– Provides foresight into strategic activities
•
•
•
•
•
Entering new business areas
Acquiring new technologies
Evaluating competitor’s business moves
Project guidance
Developing partnerships
What Is Technical Intelligence?
 Actionable Intelligence
– Intelligence Cycle
•
•
•
•
Define needs and prepare a plan
Collect source materials
Analyze the results
Impact the business
– Information when analyzed becomes intelligence
– Intelligence directed towards a business decision
becomes actionable
– Must be used by the decision maker
What Is Technical Intelligence?
 What is it Not?
– For Patentability
– For Validity
– For Freedom to Practice
– Not about information its about intelligence
– It is about trends and forecasting not about
focused and specific information retrieval
Gatekeeper Networks and The
Intelligence Cycle
 Define Needs and Prepare a Plan
– Gatekeepers tend to be an expert in a specific
area and typically only work in that area
– TI is a part time job and involvement is often
reactive
– Tend to approach each problem the same way
(hammer and nail approach) and while excited
and interested in subject may not have time to
stay current with new intelligence methods
Gatekeeper Networks and The
Intelligence Cycle
 Collect Source Materials
– Limited conference attendance
– Personal journal reading
– Personal networking
– Heavy reliance on the “grapevine”
Gatekeeper Networks and The
Intelligence Cycle
 Analyze the Results
– Manual Mapping
• Involves reading each document one at a time
• Information is collected by using:
– Spreadsheets
– Word Processor tables
– Flow charts
– Butcher paper and sticky notes
• Difficult to see hidden trends in large data sets
• Does not scale well
Gatekeeper Networks and The
Intelligence Cycle
 Impact the Business
– Delivers message using:
• Handmade charts and graphs
• Memos
• Attendance at internal meetings
–
–
–
–
Knowledge is power
Potential silo creation
NIH
Potentially limited to specific projects
Ad-hoc Team Approach to TI
 Define Needs and Prepare a Plan
– Each project is done on a case by case basis using a
–
–
–
–
team approach involving subject matter experts
TI Facilitators can communicate in a technically
proficient manner and are trained in the field of TI
with frequent updates
TI people are often employed full-time in conducting
TI
Provides directed, actionable intelligence to the
specific business need
The Need Drives the Question
Ad-hoc Team Approach to TI
 Collect Source Materials
– Size doesn’t matter
– Any available electronic source is fair game
– Print resources can be scanned in
– Internal and external data
– Also use human intelligence
– The Question Drives the Data
Ad-hoc Team Approach to TI
 Analyze the Data
– The Data Drives the Tool
– Computer Generated Maps Can:
• Group similar documents together
• Build landscapes based on semantic concepts
• Discover trends and do statistical analysis
– Mining Activities
• Data
• Text
– Does not replace reading the source materials
Ad-hoc Team Approach to TI
 Impact the Business
– Delivers message using:
• Specific, focused charts, graphs and presentations
• Detailed visualizations
• Buy-in from subject matter experts
–
–
–
–
Focused on business need
Knowledge is shared
Collective effort of many experts
TI team is a corporate resource
Computer Assisted TI
 Data Mining
– Relies on fielded (structured) data and exact
string matches
– Involves numerically based statistical analysis
– Allows for temporal analysis
– Clustering based on coding
– Involves co-occurancy matrixes
• Examination of patent subject matter by Assignee
Co-code Clustering
Co-Occurancy Matrix
Graphical Representation
Computer Assisted TI
 Text Mining
– Relies on unstructured or semi-structured data
– Term extraction takes place based on semantic
based AI algorithms
– Documents containing similar concepts can be
organized together (Classification)
– Documents containing overlapping concepts
can be placed together geographically
(Clustering)
Text Mining
 Term Extraction
Linguistic
Preprocessing
Reader
Tokens
Part of
Speech
Stemming
Term
Generation
Candidate
Generation
Combination
of
Candidates
Linguistic
Patterns &
Association
Metrics
Term
Filtering
Information
Retrieval
Metrics
TFIDF
Text Mining
 Information Extraction
Term
Extraction
CoReference
Named Entity
Recognition
Domain
Knowledge
Taxonomies
Available Methods
 Concept Clustering
– A form of SOM
– Uses:
• Term extraction
• TFIDF
• Bootstrapping and generation of vectors based on
shared concepts
– Topographical representation
Themescape
Available Methods
 Self Organizing Maps (SOMs)
– WEBSOM a method for automatically
organizing collections of text documents and
for preparing visual maps of them to facilitate
the mining and retrieval of information
– Details on SOM algorithm can be found at:
http://www.cis.hut.fi/research/somresearch/som.shtml
WEBSOM
Available Methods
 Neural Networks
– Started as model of biological neural networks in the
–
–
–
–
–
brain
Start with a training set
Use a second known set to measure difference
between guess and known result
Computer makes adjustment, guesses again
Iterative process until within tolerance
Results visualized with standard methods (SOM, et…)
Available Methods
 Decision Trees
– Represents a set of rules
– Training set identifies rules based on defined
results and corresponding trends
– Can be used on new data to make business
decisions
– Also called expert systems
Decision Tree