The folks at Invention Machine believe that you can learn to innovate, regardless of the type of innovation. In fact, they believe that innovation can be injected everywhere in the product life cycle - from planning and research to design to preventing and fixing defects. The goal is to make innovation "repeatable and sustainable". They have crafted an interesting solution that blends best practices in innovation with text analytics software so that the software can actually act as a subject matter expert.

Best practices in innovation include text analytics

How does it work? The solution- called Goldfire- provides an innovation framework with a semantic engine that allows you to mine internal documents, patents, and external literature to find answers to questions. It supports the following innovation categories:

· Analyze a market: enables the user to cull through the literature, extracting relevant information in order to understand a technology better

· Develop a Product: enables the user to design a new or hybrid system, providing best practices frameworks for this along with the ability to analyze patents and other documents relevant to the functionality of the product.

· Improve a System: enables users to diagnose and fix a problem and resolve contradictions in an existing system.

· Risk Management: provides predictive failure analysis

· Leverage Intellectual property: enables users to cull through patents

Right now, the company is focused on the manufacturing sector only. For example, assume you are interested in developing a new product. You would log into the Invention Machine solution and select the task (on the left hand tool bar) design a system. You would see some steps involved with designing a new system. These are illustrated in the diagram below.

Here, you can see some best practices concerning what is involved in designing a system and then the steps you would take to do this. One step is to explore the opportunity space. The company uses Natural Language Processing techniques to rank concept retrieval requests relative to the question being asked. For example, say you're interested in designing a new coffee maker and you need a component to heat water. If you ask, "What heats water?" the result "The anode heats water" would be ranked higher than "Water is heated on a stove" because semantically, the first answer is more accurate. Note that here the user is interested in the function of heating water and the anode is a component that can help to do this. The text analytics engine will make use of selective substations for accurate extended concept retrieval. Selective means only the words that are precise substitutes are used for automatic rephrasing.

The order of words makes a difference in what would come back in a search query. Using the coffee maker example, assume the user is interested in getting power to the switch on the machine. If the user asks, "How to power the switch?" and "How to switch the power?" the software recognizes the difference in concepts expressed by the different word orders. This is because the semantic approach used by Invention Machine looks at the word roles and the relationships implied by the structure of language rather than a simple keyword or Bayesian approach.

Can you learn to innovate?

I have to say that I have not seen anything quite like this before. I've seen text analytics used in R&D to search the literature and patents, but not wrapped in best practices.

Invention Machine's semantic approach is interesting because it helps users focus on components and functions and, at one level, this is a part of what innovation is all about. You could argue that components and functions are relevant to any industry. If I am building something or even delivering a service, I am interested in what components I would need to meet a specific function. Right now Invention Machines is focused on manufacturing, but you could certainly see how the application could be extended to other domains.