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Overview
- Can you teach innovation?
- What are the different types of business model for innovation?
- Are there support services?
- Challenges for businesses
- Collaborate and protecting your IP
- AI as a spur to innovation
- Adapting your business to new thoughts, products and processes
- Getting a return on your investment
Product Details
| ISBN-13: | 9781789550283 |
|---|---|
| Publisher: | Legend Press |
| Publication date: | 05/01/2020 |
| Pages: | 256 |
| Product dimensions: | 9.00(w) x 6.00(h) x 0.60(d) |
About the Author
Read an Excerpt
CHAPTER 1
1.1 INNOVATION, INTENTION AND ARTIFICIAL INTELLIGENCE
Karren Whitely-Brooks Whitespace
While many of the chapters in this edition deal with specific details about Intellectual Property and the financial and tax implications of funding and protecting inventors, this opening chapter will deal with the more fundamental questions of the innovative process. The questions discussed will include, why a business should innovate? And how to make wise choices using the latest technological advances in artificial intelligence?
WHY INNOVATE? WHAT DOES IT REALLY MEAN?
If you take as a given that innovation is necessary for any business, and indeed society, to advance and flourish; then you may be reading this book to find an edge in your quest to improve your company. If however, you are an inventor, then you may not have the requisite mindset to be an innovator. Common wisdom would argue that if one can invent something then, surely, one is automatically, an innovator. The confusion begins when one reviews the popular definitions for the noun, innovation. Merriam-Webster Dictionary offers these definitions: 1. The introduction of something new. And goes on to state in its second most common definition: 2. A new method, idea or device.
When one explores other knowledge sources such as Wikipedia, they begin with the same definition for innovation as "something new" but go on to expand the idea by stating, "innovation takes place through the provision of more-effective products, processes ... that are made available to markets, governments and society". If however, the inventor, who builds a better mousetrap, does not require a- brand new-mousetrap and the inventor is able to commercialise and positively affect society with his mousetrap, then that inventor is truly an innovator. Innovations are thus, incremental improvements which satisfy our clients' needs and demands rather than just something new or different.
The futurist and author, Jacob Morgan gives a more easily understood example, which is important if you and your company want to innovate. He asks, "Is Google Glass an invention or an innovation?", and offers in contrast, "Is the Apple iPhone an innovation or an invention?". This is not just a discussion of semantics but goes to the core of the dilemma. If the invention of Google Glass, while novel and definitionally appearing to be an innovation, remains an oddity with little commercial acceptance or demand; then the invention of Google Glass, he argues, is not an innovation. The iPhone, Morgan states, is "both new and has had a profound influence in the way we communicate, store and access data" which makes it truly an innovation. It is as Wikipedia offers, "the more-effective" inventions which result in an innovation and the inventor and his company, to be classified as an innovator.
At a recent social gathering, I was asked, "what are you working on?" and I replied, "we are helping a business foment innovation." My friend immediately asked, "what have they invented?" My reply was, "they have invented a new intention, and indeed, a new mindset." I wasn't being glib or enigmatic but have found that the stated intention to innovate, which includes the goal of delivering something new or at least better and the mindset to commercialise the output of their work, are the two basic requisites for any company to be considered, truly innovative.
'INTENTIONS HAVE NO INTELLIGENCE'
Unfortunately, the word new and the promise of innovation have become ubiquitous in our lives, as any visit to the supermarket will attest. It is obvious in every aisle of our shopping that new and improved can apply to everything from apples to zucchinis. All too often, leaders of business have a mindset which says, 'innovation is just some business school hype or marketing speak.' The scepticism surrounding innovation is that it has little relevance to the day-to-day operation of the company. We strongly suggest the company "vision" or "mission statement" which is necessarily a broad generalisation to be delivered over a long period, should include the intelligent intention to innovate. The company's long view must be accompanied by the short-term deliverable projects, which will become commercially viable and make a difference to our clients and shareholders.
The following diagram demonstrates how the importance of long and short-term goals relate to a company's ability to innovate. We would argue that in all three areas of Mission, Strategic Goals and Operating Objectives there must be a stated and determined intention to innovate; not lip service, but a firm and communicated intention; not just a hope but an intelligent intention.
The requirement for specific and short-term projects to produce commercial success is the test, which will prove whether the new product or process is truly innovative.
Ever since Peter Drucker argued in the Harvard Business review in 1996 that "Innovation is the specific function of entrepreneurship", business consultancies have been attempting to assist companies to change their mindset. Drucker's premise instructs companies to promote "wealth producing resources" as a fundamental goal for their employees. As the twenty-first century continues to make great advances in Artificial Intelligence, it appears that business leaders may now be hoping that technology will provide these much sought after resources and innovations.
WILL ARTIFICIAL INTELLIGENCE ASSIST INNOVATION?
Arvind Krishna, Director of IBM Research says, "Faced with a constant onslaught of data, we needed a new type of system that learns and adapts and we now have that with AI". Krishna further states, regarding the use of AI in business innovation, "What was deemed impossible a few years ago is not only becoming possible, it's very quickly becoming necessary and expected".
In just the last decade AI has exploded because of the combination of big data with powerful graphics processing units (GPUs), which has benefited from employing the well-known model of Deep Learning. We all remember the stories of Deep Blue which was programmed by the forefathers of IBM Research back in the 20th century and which culminated in a competition with the Russian Chess Master, Gary Kasparov in 1996. It was IBM's Deep Learning which allowed the computer to analyse the millions of chess moves and which revealed Artificial Intelligence as one of the most important technologies to be developed in the 21st century.
It is this Deep Learning popularised by Deep Blue, which will be educated by Deep Reasoning in order to create a new technology, enabling machines to conduct unsupervised learning. It is the human brain function of unsupervised learning, which continues to baffle the technologists. IBM Research is attempting to make machines curious and to seek answers. These machines will indeed produce new questions, as they learn from each other, but this is proving far more difficult than the much discussed language and image recognition applications.
The emotional side of facial recognition, for example, and the reasoning which humans develop from a young age, is much more difficult for Deep Learning to grasp. Computer scientists are working with neuroscientists and psychiatrists to help the advanced computer facial recognition of emotional indications. Dr. Fei Fei, Professor of Computer Science at Stanford University, says AI image recognition, struggles to identify and interpret emotions; the computer's cognition is similar to the human condition classified as Alexithymia. This is a condition which inhibits some humans from both expressing and recognising emotions, often accompanying Autism and other psychological disorders. IBM research, however, has made great strides in image recognition which is helping identify and diagnose a variety of illness and pathologies.
One example of AI image recognition is the computer application which assists the clinician in recognising melanoma lesions on human skin. While a clinician may be confident in recognising a certain type of lesion within a predominant racial skin colour, they dramatically increase their diagnostic ability with the AI database. The computer is able to compare, select and identify from a much greater range of images and skin tones and alert the doctor to possible areas of concern from patients they are not accustomed to treating. The use of AI in pathological recognition and diagnosis meets the societal impact criteria for innovation.
ARTIFICIAL INTELLIGENCE AND DECISIONS
Hollywood and science fiction authors may sensationalise the possibilities for unethical or humanity-threatening decisions from Artificial Intelligence; however, most computer scientists find this dystopic vision to be less than probable. The fascination with the human brain and the sensory inputs which we take for granted, underlines the complexity and difficulty in using Artificial Intelligence to actually reason. The intersection of Deep Learning with Deep Reasoning will produce innovative products and help business leaders make better and more informed decisions.
Aya Soffer, IBM Director of AI and Cognitive Analytics Research, says "computers don't have common sense". Humans grow up "understanding gravity" and inertia and the physical properties which allow us to function. A simple example of "objects staying where they are put", or moving, if influenced by wind or vibration or gravity is extremely complex for AI to comprehend on the mathematical level. It is the human, common sense, which is a mysterious combination of learned and inherent genetic wisdom, which is difficult for the computer models to simulate and understand.
Our life experience may however, make common sense decisions difficult. One popular test of common sense, which is easily passed by 4 and 5 year olds, is almost always failed by adult participants. It starts with the simple question of, "How do you put a Giraffe in a refrigerator?". Most adults struggle, consider size and cutting up pieces etc. while the child answers "You open the door and put in the Giraffe and close the door". The test continues with, "How do you put an Elephant in the refrigerator?" adults again, consider size and possibilities, animal cruelty and many permutations, while children, who learn and remember with greater focus on short term memory, answer correctly: "You open the refrigerator and take out the Giraffe and put in the Elephant". While humorous, the quiz does illustrate how complicated and layered our decision-making process can become after years of apparently logical conclusions.
If the confluence of Deep Reasoning with Deep Learning can assist the innovator with decision making, then it stands to reason that more useful inventions and applications will be made available. If machine learning struggles with common sense and emotional components, it is important to understand the human decision making process. It is correct decision making which is critical for the creative employees and inventors to produce a viable product. Artificial intelligence is credited with the image and voice recognition which has produced innovative products like Amazon's Alexa and Apple's iPhone. The retrieval of libraries of information now resident on our smartphone is another example of machines assisting humans in decision making. The basic human cognitive function is now being studied using Quantum Theory, which will improve the Deep Reasoning technology and accelerate the human decision making process.
QUANTUM COGNITION
While computers are learning from the human capacity to increase the machine's perceptive abilities, the world of Quantum Theory is helping humans understand their own mental faculties. Artificial Intelligence can recognise faces and pathologies and can understand voice and a variety of human speech and languages, but it is the nuance of human perception which continues to be difficult. We can teach a computer to see and perceive a square but human vision can see and intuitively perceive three dimensions, as in the cube below, although it is obviously drawn in only two dimensions. Humans can see both the facial profiles, as well as the Urn, in the second image below and decide contextually which image is the more applicable. The human brain is being studied to understand what allows us to have these simultaneously efficient perceptions and intuitions.
The human brain is often said to rely on intuition or "gut" reactions and this Quantum Cognition is being understood on a molecular and chemical level. The mind, however, is not the brain and the distinction is important, if innovators are to develop the correct mindset and an accurate decision making process. Daniel Siegel, UCLA psychiatrist and author, defines the mind and distinguishes it from the brain as follows: "The mind is an embodied and relational process in relationship to other minds that regulates the flow of energy and information in the whole ecosystem." He further explains, "Your mind is listening as I am sharing thoughts and those thoughts are converted into electrical impulses".
The further development of Artificial Intelligence will increasingly understand the human mind's electrical impulses and become part of, and possibly linked directly to, the mind's ecosystem. A variety of recent studies have defined these mental information flows throughout the human nervous system. So the idea of an emotionally "heavy heart" or a "gut feeling" is a scientifically correct way to map our human thought process. If you are an inventor and an aspiring innovator, the important take-away from these discoveries is that one should trust their instincts and commit to decisions. Commitment to your invention and the passion to procure the investment funding is key to the commercial success of your ideas.
DECISIONS AND CHOICES
It is clear that Deep Learning can provide the data to help test your inventive theories and that machine intelligence can be a key component of the team you assemble to produce your innovation. Different academic disciplines including psychiatry, neurobiology, psychology and even philosophy are studying how we make choices and decide. Because of the human emotional component to our thought process, the lack of emotion in deep learning and deep reasoning, provided by Artificial Intelligence, can produce more objective conclusions, untainted by preconceived notions and human foibles. The importance of choice has been studied for thousands of years and the mathematician and discoverer of the geometrical theorem which carries his name said:
"Choices are the hinges of destiny." -Pythagoras (570 BC - 495 BC), Greek philosopher
If your destiny is to be an innovator and contribute to an innovative enterprise then your decision-making and the choices required will be fundamental to your success. We are often asked for an easy process to increase one's probability of reaching the correct conclusion. Artificial Intelligence can help in the information and analysis of the data to form an informed decision. Seeking the programmes which can test your theories in a time and economically efficient manner will help your project realise its objective. Our Whitespace team offers this innovative process, which describes the important components, leading to inspired decisions ...
Whitespace consultancy has coined the acronym RED-OP to assist with your important decisions:
R. retrieve all the data and pertinent information available (Artificial Intelligence will help).
E.evaluate the factors and outcomes (AI can test your probable outcomes).
D. deliberate and follow your intuition, your heart and indeed your gut.
O. opt for the best solution by the deadline which you have set. Time is definitely money.
P. passionately commit to your decision and communicate your firm intention to your team.
EXECUTIVE SUMMARY AND CONCLUSION
The definition of innovation must include the commercial viability of the product, process, or service; just being new, or better, is not sufficient to achieve innovation. In order to move an idea or desire to produce an innovation, the firm intention, communicated to the entire organisation, and indeed potential clients, is a fundamental requirement for success. While the intention to innovate is often found in long-term mission statements, it is even more important in short-term, time constrained, projects.
Artificial Intelligence can be an important contributor to the innovative process. AI will complement, supplement and verify the human's decisions and choices. The inventor must tune into and listen to the entire mind's ecosystem when making a decision. The most important element in the innovative process is one's passionate and communicated commitment. Good luck.
CHAPTER 21.2 THE INNOVATION CONUNDRUM
How large organisations can access fundamental research and innovation
Mike Herd, Executive Director, Sussex Innovation
There is a gap in our marketplace of ideas. Innovation in business tends to be driven by the biggest and the smallest organisations; the tech behemoths at one end of the scale, and the lean start-ups at the other. In between, true innovation is often difficult to achieve.
We need a new term to describe this kind of organisation – too large to be considered SMEs, but too small to bother the FTSE 500. Hundreds of employees, turning over tens of millions, but rarely a household name. Sometimes large public-sector bodies fit the description too. Let's call them LSEs: Large, Stable Enterprises.
What these LSEs all have in common is that they've built a structure that does a specific job consistently, but one that has almost never needed to change or adapt. This could be due to a lack of challengers or disruptors in their sector, or established relationships with reliable clients who help to insulate them from failure. That's not to say that they aren't open to innovative or disruptive methods; they simply haven't been able to make them happen.
"54% of innovating companies struggle to bridge the gap between innovation strategy and business strategy." – PwC, Reinventing Innovation
Why should this be the case? Well, through no fault of their own, many organisations of this type simply fall prey to short-term thinking. The pressures of focusing on sales and customers mean that there is often an inability to step outside of the immediate challenge and look at the wider picture.
The scale of the LSE also tends to lend itself to rigid budgetary cycles, which in turn lend themselves to inflexible R&D programmes. That's fine if you want to achieve incremental innovation within strict parameters – making an existing product work better over time, for example – but it isn't going to achieve a real step change in how you do business.
Many internal innovation programmes are also hampered by how the organisation defines success. For example, is their key performance indicator customer retention, rather than opening up new markets? Do they judge 'innovation' by counting the number of patents filed, rather than how their internal process has changed?
CORPORATE ACCELERATORS AND OPEN INNOVATION NETWORKS
The current solutions adopted by most organisations in this situation are either to launch a corporate accelerator, or to set an innovation challenge through an open innovation network. Both approaches have their flaws:
Corporate Accelerators are:
• A marketplace dominated by bigger brands
• Not managed or overseen by key stakeholders within the business
Open Innovation Networks are:
• Not particularly agile
• Constrained by narrow framing of challenges
Both are:
• Inward- rather than outward-facing
• Seeking ready-made solutions, rather than delivering collaborative innovation innovation
The corporate accelerator model involves inviting technology start-ups from the host's industry to pitch their products. From this group of start-ups, a select few are welcomed onto an accelerator programme and provided with financial support, expertise and resources to "hothouse" their development. It is often unclear whether the ultimate benefit to the business is intended to be a strategic advantage, or financial gain.
The corporate accelerator market is dominated by multinationals and bigger brands. Even if you have deep pockets, it's very difficult to garner the kind of publicity that will attract a queue of relevant start-ups without being backed by a household name.
Typically, these programmes are also completely siloed from the day-to-day operations of the business, managed by MBAs with no direct experience of the company's inner workings. This restricts the potential for collaboration and results in innovation being delivered in a prescriptive manner.
Open innovation networks can result in entrepreneurs having to operate under similar constraints. The standard model for these networks is that a problem is posed directly to the marketplace, in a similar approach to government-sponsored innovation challenges. However, where the likes of Innovate UK offer grant funding to achieve broad, society-changing goals, open innovation networks generally issue more narrow requests.
The problem is in how these challenges are framed – the organisation issuing the challenge tends to approach it with the same inward-facing mindset that hampers most internal R&D efforts. A simple example of this is the way the challenge is often phrased: "Company X has a requirement for product Y" rather than "Company X is trying to address problem Z". These kinds of narrow requests seek a specific, ready-made solution that fits within a marketplace the organisation already understands, rather than disrupting or challenging the status quo in any way.
Both these approaches can be seen as threatening the position of the LSE. A global corporate making a dramatic change to one of their products or their business model can very quickly change the whole industry supply chain, where many of the LSEs operate. On the other hand, an entrepreneurial start-up coming through an accelerator, or backed by VC funding, has a strong chance of disrupting and threatening the existing market place. Clearly, this is not necessarily the ideal model for collaboration.
AN INNOVATORS' EYE VIEW
"The need for innovation in medium to large companies has never been greater. The Business to Business market is increasingly taking the lead from the Consumer market in technological enhancement and the pace of change ever quickens. This means that larger companies need to partner with smaller start-ups to harness specialist technologies, innovation and expertise which it would be inequitable to have in house. This allows a lean model for innovation which benefits both sides." – David Lane, Head of Technology, Arcus Holdings
From our position at Sussex Innovation, a business incubation network owned by the University of Sussex, we get to see the flaws of these structures from the perspective of the innovators themselves. We work with two kinds – academic researchers, and early-stage companies.
Within a University, most academics are trained to talk about their research in a very specific way, highlighting the process and the insights it generates. To take the output of that research to a market requires a very different skillset – it needs to be framed around the applications of the research, and the commercial implications of the insight. To put it in business terminology, there is a tendency to talk about features rather than benefits.
Start-ups, meanwhile, will usually talk about what products they have already built, rather than what they're working on. There are also challenges around implementation when an early-stage company takes its first step into the corporate world. The time frames involved can be radically different, while there are often problems with integrating a new technology into existing systems architecture and processes.
Another common frustration comes with stress testing business models designed for the SME market in a corporate environment. To give just one example, many Software as a Service (SaaS) products are built on the assumption that every purchaser will operate like the start-up business that created the product. They don't have experience of the requirements of an enterprise-level solution, or the means to manage internal compliance. There won't be an individual executive who has the single credit card that they do business with.
Of course, all of these problems are solvable, and there are many systems integration businesses that can translate their business process innovation into an enterprise solution – but most small business developers don't have those skills.
Accelerator models are usually focused on the start-up having the right entrepreneurial management team to rapidly scale the business. Such management talent is both rare and expensive, and is not commonly found in the same business as the technical or creative innovator.
Understandably, Venture Capitalists also back management teams rather than focusing on ideas. The reason is that a good entrepreneur – as opposed to an inventor – is more likely to adapt, evolve and pivot an idea to meet market need.
This doesn't mean that a good idea or a novel innovation is any less good or less novel just because the inventor doesn't have the right business acumen and entrepreneurial mindset. Finding new ways to introduce new ideas into the LSE market, which is focused on delivery, can open up whole new areas of innovation and productivity growth.
THE SOLUTION: A NEW MODEL OF INNOVATION NETWORK
What the majority of LSEs need in order to break this cycle is access to a broad portfolio of innovations, curated outside of their R&D process. By creating clusters of different types of solution within this portfolio, we can start to present relevant innovation back to relevant individuals within the organisation. By working directly with a CTO, Finance Manager or HR Director, innovators can gain a much deeper understanding of the real challenges faced by the different functions of the business.
"Hyper-collaborative companies must make use of an eclectic group of partners to challenge senior management thinking and encourage a continuous process of unlearning and relearning." – Navi Radjou and Jaideep Prabhu, Frugal Innovation
Through its Business Research Academic Innovation Network (BRAIN) project, Sussex Innovation is making a direct intervention to help LSEs find innovation, and to help innovators find their market. There are several principles underpinning this:
• Real 'open innovation' must involve conversation and collaboration;
• It must involve a broad enough portfolio of ideas to challenge and prompt creative thought;
• LSEs need an "innovation champion" to assess ideas, prove their credibility and secure buy-in;
• The LSE should ideally act as a mentor as well as a customer in the relationship;
• Collaborations can lead to new commercial opportunities for all parties.
The model involves identifying early stage businesses and academics, who are selected on the criteria of having an innovation with potential and relevance to a particular market, rather than on the past experience of their management team.
From the other side of the equation, LSE businesses and organisations are selected on the criteria of being open to talking about their needs and opportunities in their market, rather than the contents of their latest accelerator call.
In short, the project has been designed to bridge the gap between real innovation and real needs.
This often means that the solutions involved don't come ready-made, and require a "customer-mentor" relationship on the part of the LSE. Sometimes the innovation turns out to be a solution for something completely different to its original purpose.
For example, one project involved a sensor that was originally built to monitor electric potential signals for taking electrocardiogram measurements of the human heart. A completely different opportunity was also identified, in monitoring the condition of electrical appliances.
The important lesson to take from this story is that a specialist within an LSE can often spot potential applications for an innovation that are well outside of the innovator's experience. Multi-sector experience – and exposure to new sectors outside of your experience – are hugely important to helping inventions find their market opportunity.
Many new technology solutions also have the potential to create deep and lasting impact across multiple sectors. For example, new applications built upon expertise in psychology can be used to better recruit, integrate and motivate teams – a product that clearly should be relevant to any employer. Or who would know that some of the biggest markets for installing solar power systems in the UK are universities, water companies and retail sites?
CHAPTER 31.3 FILLING THE INNOVATION GAP BETWEEN CORPORATES AND SMES
Cliff Dennett, Innovation Birmingham
Innovation is very hard. Creativity is easier but turning those ideas into sustainable products and services is much harder. Our minds work at lightning speeds; just a single mind can easily create thousands of ideas in a single day (you need to keep the coffee on, clear your day and buy a lot of sticky notes) but when it comes to turning those ideas into businesses, that mind needs to engage with other minds and that's where the challenges start.
Minds inside large organisations have different world views to those inside start-up businesses. The vocabulary, corporate culture and ability to operate is very different to that in an early-stage business. Corporates have decision-hierarchies, rigid budget processes and employee reward mechanisms that can sit completely at odds with the flatter structures, flexible strategy and founder motivations of the start-up.
For smaller companies, speed is everything. It doesn't really have anything to do with enthusiasm or energy; there are plenty of corporate employees who can equal the verve and drive of the archetypal entrepreneur. Speed is a necessity for smaller companies because they are invariably always about to run out of money. To book a meeting in the calendars of most budget-holding, decision-wielding senior managers in large corporates might take three months. That's a single quarter for a corporate and a lifetime for most early stage businesses. If those businesses are lucky enough to have secured some early stage funding, it may give them a single year's runway at best, so waiting for a quarter of that time is tough (and stressful).
(Continues…)
Excerpted from "Growing Business Innovation"
by .
Copyright © 2019 Jonathan Reuvid.
Excerpted by permission of Legend Times Ltd.
All rights reserved. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher.
Excerpts are provided by Dial-A-Book Inc. solely for the personal use of visitors to this web site.
Table of Contents
Foreword 7
The Chartered Institute of Patent Attorneys
Preface 9
Jeremy Holmes, Head of IP, Imperial Innovations
List of Contributors 11
Introduction 21
The Editor
Part 1 Innovation for Tomorrow 23
1.1 Innovation, Intention and Artificial Intelligence Karren Whiteley-Brooks, Whitespace 25
1.2 The Innovation Conundrum - How Large Organisations Can Access Fundamental Research and Innovation Mike Herd, Executive Director, Sussex Innovation 33
1.4 Feeding China's Innovation Dragon Dominic Schiller, Equipped 4 (IP) Limited / Partner Investment 46
1.5 Appreciative Inquiry Charlie Wilson, Bosideon Consulting Ltd 54
Part 2 Innovation for Tomorrow 61
2.1 Options for Brand Protection and Dispute Resolution Gregor Kleinknecht, Hunters 63
2.2 Trolls… real or are they just in fairy tales? Melanie Mode, Miller Insurance Services LLP 70
2.3 Protecting Against IP Disputes Melanie Mode, Miller Insurance Services LLP 76
2.4 R&D Tax Credit Claims - an Overview of the SME and Large/RDEC Schemes Dr. Mark Graves Julia May, May Figures Ltd 81
2.5 The Operation of the UK Patent Box Graham Samuel-Gibbon, Taylor Wessing LLP 89
2.6 R&D Tax Credits - Totally Brilliant - but Not Just a Walk in the Park Terry Toms, RandD Tax 95
Part 3 Exploiting IP Opportunities 103
3.1 New Trends in the IP Industry Christian Bunke Natalia Korek, Basck 105
3.2 Patents are Not for Protecting Innovation: they are for doing business Michael Murray, Murray International Partners 112
3.3 The Impact of GDPR on IP work Margit Hoehne, Patentgate 117
3.4 Patent Landscape Study: pharmaceutical and engineering examples Steven Johnson Vedran Biondic, J&B Parttiers Ltd. 124
3.5 IP Considerations for IT Innovation John Collins, Creation IP 132
3.6 Start-up Business: Investing and Controlling Your IP Leah Grant, Impetus Limited 140
Part 4 Stimulating Commercial Innovation 147
4.1 R&D Tax Credit Claim Based Short-term Bridge Finance for High-Tec Start-ups Dr Mark Graves, 1851 Technology Group Julia May, May Figures Ltd 149
4.2 SME: University Collaboration - A Doorway into an Opportunity-Rich Innovation Environment Ian Ferris Dr. Averil Horton Eleftheria Ledaki, Brunel University London 156
4.3 Cross-disciplinary Innovation through Clusters Barbara Ghinelli, Harwell Science & Innovation Campus 163
4.4 Adapting Defence Technology to Business Enterprise Paddy Bradley, Swindon and Wiltshire Local Enterprise Partnership 170
4.5 Business Growth through Industry-Academia Interactions Dr. Brian More, Coventry University 178
Contributors' Contacts 188