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Expert sourcing rather than crowdsourcing
To solve highly critical and technological problems, a company needs experts, not random solvers. This is why MDES relies on highly skilled experts rather than solvers.
Dynamic (or On-Demand) rather than subscription-based
As previously explained, most Open Innovation intermediaries or network of experts rely on registration-based platforms: potential experts have to know about the platform and to register. This is not the right paradigm to address global expertise. Instead, we propose to build a worldwide automatic network of Experts that can be solicited on-demand and that allows automatic profiling of experts. In addition, this paradigm enhances confidentiality, since the visibility of the problems can be restricted to preselected experts (and not to any registered solver).
Millions of experts exist in the world; most of them leave tracks on the web through scientific literature, patents, research center corporate websites, blogs, forums etc. Based on Information Retrieval[1] and Machine Learning[2], PRESANS developed an Expert Search Engine (see Figure 2) allowing to discover and engage these expert in an automated manner:
- The Expert Search Engine build a fully structured map of expertise from unstructured data such as research center websites, scientific literature etc. (steps 1 and 3).
- The textual description of the seeker’s technological need is fed into the Expert Search Engine that, in return, suggest an exhaustive list of most relevant experts in the world (step 3).
- The Smart-Broadcast software then automatically contacts and invites a number of the most relevant experts, using automatically generated personalized emails.
- Interested experts can then join the Multistep Problem Solving Process.
Note that the complex algorithm of the Expert Search Engine is designed to improve automatic matching while maximizing cross-fertilization[3].
Figure 2: (1) The Expert Search Engine technology crawls tens of millions of scientific sources (scientific literature, patents, research center websites etc.). (2) The engine indexes and structure the information into expert profiles. (3) Sending a query (keywords or full-text description) from the search bar returns a list of potential most qualified experts.
Figure 2: (1) The Expert Search Engine technology crawls tens of millions of scientific sources (scientific literature, patents, research center websites etc.). (2) The engine indexes and structure the information into expert profiles. (3) Sending a query (keywords or full-text description) from the search bar returns a list of potential most qualified experts.
[1] Information Retrieval (IR) is the science of searching for documents, for information within documents, and for metadata about documents, as well as that of searching relational databases and the World Wide Web [http://en.wikipedia.org/wiki/Information_retrieval].
[2] Machine Learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases [http://en.wikipedia.org/wiki/Machine_learning].
[3] It supposes that the problem is well-formulated, which requires a smart methodology.
The present article is after the chapter we wrote in the book « A Guide to Open Innovation and Crowdsourcing: A Compendium of Best Practice, Advice and Case Studies from Leading Thinkers, Commentators and Practitioners« .
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