A model and tool proposal to understand and enhance collaboration-based innovations applying semantic

technologies, C-K Design Theory and TRIZ

Technology and Knowledge Transfer under the Open Innovation Paradigm

Problems to capitalize and apply the knowledge and

skills behind expensive publicly funded research in

universities and other R&D institutions

Missed collaboration potential and opportunities to

reduce duplicated efforts due to transactional costs to

identify partners in academia and the industry

The need and motivations

Difficulties to translate the potential of hundreds of

active technology needs (already in the public domain)

into value. Some reasons are

fragmentation, information overflow and an inexistent

integrative approach for aggregating and matching

them with technology offers

Lack of tools with a robust theoretical background to

help technology transfer officers and other innovation

agents to bring new technologies to the market

The need and motivations

Problem contextualization

UK is ranked as having the second-strongest research base in the world behind only the US. The UK also produces 8% of the world’s scientific papers and has a citation share of 12%, ranking second in the world, BUT in spite of that its commercialization results are very poor (as it happens in general in Europe)

The public UK R&D spending is over £3.0 billion in 2009-10 and is set to be 2.5% of GDP by 2014* meaning that the impact of that research and its ROI has to increase significantly to maintain the public support.

Source: http://www.rcuk.ac.uk

Areas of study

Technology and Innovation

Management

Management of Innovation processes

Models & Paradigms

Open Innovation

Technology & Knowledge

Transfer

Innovation/Design Theories

C-K Engineering Design Theory

Methods & Techniques

TRIZ

Knowledge & Information

Management

Information Technology Tools

Semantic Analysis

Information Aggregation and

Clustering

Data Mining

Context Domain Area Subject

Volume of publications per area and timeline

0

50

100

150

200

250

300

350

400

450

Volume of publications indexed in ISI Web of Knowledge per topic per year

Technology Transfer Knowledge Transfer Open Innovation

C-K Design Theory TRIZ

If it doesn’t

have commercial prospects

If there is no interest

in the offer

If it has

commercial

value

If there is

an interested

party

Research FundingResearch centre

infrastructure and

accumulated knowledge

Scientific Discovery

Evaluation of the

discovery/invention and

its potential applications

Scientific Publication

Application for a

patent or other

IP rights

Technology is

“packed” to be offered

in the market

Patent becomes part

of the passive portfolio

of IP

Negotiations to

licence, sell or create

an spin-off

Final transaction and

exchange of IP

Generation Evaluation and Selection Technology Push Transaction

TTO usually does not

get involved

TTO offers support and expertise

in commercial evaluation and IP

Usually TTO is fully

responsible for this process

Once IP is cleared

it is possible to publish

The traditional tech-transfer

Open innovation via brokers

If it doesn’t

have commercial prospectsIf there is no interest

in the offer

If it has

commercial

value

Scientific Publications

Technology is

“packed” to be

offered in the

market

Passive patents

Final transaction and

exchange of IP

Open innovation networksCompany with a need

Technology Push Technology Pull

Researchers

Classic university technology transfer model Open innovation through innov. intermediaries

Tech transfer meets open innovation

Final transactions

and exchanges of

IP

Technology Push Technology Pull

Researchers

Researchers

Researchers

Researchers

Company with a need

Company with a need

Company with a need

Company with a need

Company with a need

Open innovation networks

…Unfortunately the communication does not work properly

Can an integrated theoretical framework, composed by C-K design theory, open innovation and TRIZ help to understand and model a better approach to systematically match technology needs with technology offers?

Research Question

RESOURCES FOR THIS STUDY

Theories and models

Open Innovation Overall paradigm

The assumption is that closed models of innovation are very limited and thus is important to understand how to effectively incorporate external sources of knowledge/technologies to solve organizational problems (In addition to internal R&D)

The existence of the open innovation model for technology and knowledge transfer facilitates the identification of common barriers, implementation problems, best practices and existent tools

RESOURCES FOR THIS STUDY

Theories and models

C-K Theory Structure and framework

Open innovation lacks a robust theory and a higher level of abstraction that C-K theory can contribute with

In the context of technology transfer the concept space can be understood as the technology requirements, while the knowledge space represent technology offers (expressed for example in patents)

C K and K C “movements” are critical for technology transfer and they define the success (or not) of a process triggered by a new technology need

RESOURCES FOR THIS STUDY

Theories and models

TRIZ Model and tool for matching technology needs with technology offers

Facilitates using analogies for clustering and identifying potential areas of matching

It provides a good starting point to identify common problems (contradictions) and their solution principles

There are several available tools that make use of its principles to solve problems starting from an specific “technology need”

RESOURCES FOR THIS STUDY

Public Databases of Technology Needs

Hundreds of technology needs published every month in websites like www.innocentive.com, www.ninesigma.com and www.innovationexchange.com

Classic example:

“Damping Materials for Low-Frequency Vibrations: damping materials that can suppress low-frequency torque fluctuations and vibrations at a high-precision powertrain in electronic equipment.” Extract from ninesigma.com

RESOURCES FOR THIS STUDY

Public Databases of Technology Offers

Open scientific repositories of papers

Funding agencies such as research councils and other governmental organizations are rapidly implementing opendata as a way of operation. This releases important amounts of new information about research projects with high potential impact

Patent databases are by definition public and contain vast amounts of “solution principles”. More importantly some patents have already expired or do not apply in certain regions and they still contain valuable knowledge to use in a wide arrange of technology needs

RESOURCES FOR THIS STUDY

Technical Tools

Data Mining and Semantic Analysis

Web Mashups (data aggregation from different online sources using RSS and indexing techniques)

Searching and ranking algorithms to match needs with offers and provide an organize dashboard of alerts displaying areas of matching potential

THE DIFFICULT MIDDLE GROUND “between C and K”

One of the objectives is to explore the technical and social barriers in the technology transfer process. By doing so the proposed tool and model will incorporate those inputs in its design.

SMEs should be provided with appropriate support to enable them to access the knowledge they require from home and abroad. Government could map key global communities of practice for the benefit of SMEs.

Small firms should be helped to identify and use international agents.

A register of global university expertise should be compiled.

Firms need advice on effective network management.

Government must continue to fund existing network support.

Based on NESTA report “Sourcing knowledge for innovation” May 2010

Recommendations

The gaps between R, D and i

Research: usually in

Universities and

Research Centres.

Motivated by

scientific curiosity

and disruptive

discoveries.

Development:

Increasingly in high tech

SMEs (ex spin offs).

Sometimes in big

corporations and

universities.

Innovations: Due to the need of

market expertise and

commercialization players usually

successful mainly in global

companies.

needs

needs

needs

offers

offers

The full R&D + i potential is highly distributed

and requires collaboration and co-creation to be exploit

Science + Eng

Engineering

& design

marketing

Knowledge Sourcing Dynamics

Source: NESTA report “Sourcing knowledge for innovation” May 2010

Tools and Methods for Tech Transfer

Innovation intermediaries

Open Innovation

Technology transfer

Knowledge transfer

Creativity and innovation methods

Spin outs

New organizational structures

....Overall diagnosis is that they are not widely used

Innovation Intermediaries

In this context innovation intermediaries play an important role to smooth the relationships and create bridges. Some examples of them are:

Challenges and Opportunities Platforms

Technology needs brokers

Technology offers brokers

Technology Transfer Offices

Knowledge Transfer/Exchange Offices

Incubators and Innovation Centres

Science, Technology and Innovation Parks

Final transactions

and exchanges of

IP

Technology Push Technology Pull

Researchers

Researchers

Researchers

Researchers

Company with a need

Company with a need

Company with a need

Company with a need

Company with a need

Open innovation networks

Virtual hub for “discovery

and matching”

Company with a needNegotiations and

collaboration

Coordination helped by a neutral hub increases chances of discovery and matching

Integral view:

New theoretical model based on C-K design theory and TRIZ

K(Papers)

K(Patents)

K(g)

K(c)

K(f)

C2

Concept Space Knowledge Space

K(N1, N2,

N3) new

The visualization show Cs at two different stages. The

smaller nodes represent individual needs in T=1 while the

big nodes represent clustered groups of needs ready to

be matched with relevant K in T=2. The clusters “Speed”,

“Feedback” and “Segmentation” are only examples of

underlying common problems for those needs.

Aggregated level

K(h)

K(a) K(b)

K(d)K(e)

K(i)

K(β)

correlations

needs-K

K→C

C3

C1

C7

C6

C5

C10

C12

C11

C14

C17

C18

C9

C8

C4

C13

C16

C15

CN 1

CN 2

CN 3

CN

1:

Se

gm

en

tatio

nC

N2:

Fe

ed

ba

ck

CN

3:

Sp

ee

dC

luste

rs o

f n

ee

ds

(T=

2)

Tool Objectives

Describe alternative and more efficient ways to:

Aggregate and map needs, generating clusters of similar emerging problems.

Map knowledge and the experts behind it.

Create meaningful relationships between sets of needs and knowledge to provide clues about relevant technologies, methods or experts that could solve the problem

Tool Proposal

Systematically match technology needs with technology and knowledge sources and the experts behind the knowledge.

Aggregate technology needs into one common feed.

Group technology needs into clusters with common underlying solution principles.

Aggregate and index the different sources of knowledge and technologies in a relational database (including author, location, citations.

Scientific publications (ie papers), patents, explicit technology offers, governmentally funded research projects.

Cluster knowledge and technologies into common categories related with solution principles.

Match needs and offers into a dashboard with alerts and filters.

Potential Beneficiaries

Tool challenges and potential solutions

How to cluster groups of needs:

Via semantic data mining keywords are indentified. Relationships are established based on the proximity of the problems extracted from the analysis of knowledge trees from sources such as wikipedia. (Image shows example based on the keyword “nanotechnology”

Tool challenges and potential solutions

How to aggregate sources of technology and knowledge:

Using analogies based on known solution principles (as in TRIZ), sources of knowledge/technologies will be grouped under branches of K fitting similar patterns.

To expand and dynamically update solution principles, the dataset of K will be compared with patent claims to deduce known and new underlying solution principles present on the body of the patent and linking them back to groups of knowledge.

This process will be reinforced with the same technique explained in the case of technology needs.

Tool challenges and potential solutions

How to probabilistically match needs with offers:

Having the groups of K and C well defined and established using proximity filters to find nearest and cost effective sources of knowledge/technology will be possible to generate a dashboard with probabilistic alerts.