This document discusses science, technology, and innovation processes and timelines for commercializing university research. It presents a linear model of identifying and matching university technologies and intellectual property to potential licensees from 2009 to 2015. It also discusses using ecosystem modeling to identify resources and intermediaries to strengthen commercialization, and modeling innovation networks to inform targeted economic development strategies in regions like regenerative medicine in Howard County, Maryland. This includes identifying key university labs and researchers for expansion, licensing opportunities, and accelerating student and faculty startups.
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STI processes, data sources & timeline for commercializing university research
1. Science, Technology & Innovation (STI) processes, data sources & timeline
(a simple linear model focused on commercializing university research)
2. How do we identify &
match technologies &
IP to likely licensees?
How do we identify &
accelerate promising
technologies & startups?
How do we incentivize more
commercialization without
compromising our educational
& land grant missions?
Strengthen engagement
with intermediaries &
expand resources
Identify & track
intermediaries &
resources
Innovation Ecosystem Modeling
Commercialization of Research & Ecosystem Modeling
3. Using Ecosystem Modeling for Research Portfolio Management
NCSG
Gerrit Knaap
Scott
Dempwolf
HISP
RE
URSP
NSF
EDA
EFC
COEUS Sponsored Research in ARCH
2009 - 2015
Image of interactive graph that allows visual
queries of all nodes and relationships
Neo4j Graph database of COEUS records
4. Using Ecosystem Modeling for Research Portfolio Management
EDA
NSF
ISTC
St. Mary’s
EDA Grant
Grant Details
5. Howard County, Maryland
Full Innovation Network & Innovation-Led Economic Development Strategies
Stem cell products group
• Commercialization, acceleration,
entrepreneurial support
• Business attraction
• Assistance with market
development & positioning
Delivery devices groups, ECM group
• Facilitate collaboration
• For ECM - assist with niche market
development around wound care
• Business attraction
Regenerative Medicine & Nutraceuticals
groups
• Develop ‘keystone companies’
• Promote local sourcing to help build
cluster around keystones
• Industry partnerships for workforce dev
• FDI / Business expansion
• Business attraction / supply chain
development
• Corporate sponsored research relations
with universities
University groups (JHU, UMCP, UMB)
• Follow-up leads for licensing (green ties)
• Identify key labs (dense subgroups) and
evaluate for expansion / enhancement
• Identify opportunities for faculty spin outs
• Identify / accelerate potential student
startups that can be seeded in this cluster
• Build long-term corporate sponsored
research relationships with keystone
companies
Targeted Economic
Development Strategies
At the Cluster Level
Regenerative Medicine Cluster – Howard County, MD
Innovation-Led Economic Development
Drill-down to Company Profiles
• Click to follow link
6. Implications for Universities: visualizing labs and research partnerships
Identify key labs (dense
subgroups) and evaluate for
expansion / enhancement
Identify opportunities for faculty
spin outs
Identify / accelerate potential
student startups that can be
seeded in emerging clusters
Link to Lab and researcher pages
(click to follow)
University of Maryland, College Park
Research labs, research partnerships,
and individual researchers
7. Implications for Universities: Leads for licensing & collaboration
Leads for licensing & research
collaboration (centered on UMCP; other
universities shown only if they also have
weak ties to target node)
Build long-term sponsored research
relationships with keystone companies
Shared patent classification
Notas do Editor
This is a simplified linear model of what is very often a complex, iterative and sometimes discontinuous process. It is focused primarily on showing the “pathways to commercialization” for University IP within a front-end research context and back end commercialization context. It should be recognized that the primary intended outcomes of University activities including research are graduates and publications; while the primary intended outcome of innovation is new products in the marketplace.
Our Innovation Ecosystem Modeling approach can help the University (and by extension USM and the State) in four specific areas. 1) connecting technologies and IP that is not being commercialized by faculty or students with likely candidates for licensing & tech transfer; Identify potential corporate research sponsors; 3) identify likely student and faculty startup candidates; and 4) identify & monitor intermediaries (accelerators, etc.) and resources for early stage startups and improve matches based on technologies and investment patterns. The University needs to figure out policies that facilitate this growth and balance it within the overall mission.