SCaNspect™ (commercially marketed as Search Incite by Minetech) is the "next step" beyond the searching process.  SCaNspect™ has recognized that to properly absorb the information that people retrieve from search engines, it is often necessary to manually read through each page. SCaNspect™ solves this problem by automatically generating an intelligent index to help expedite the "reading" process, which is arguably the most tedious and time-consuming factor contributing to Information Overload. 

One highly noticeable outcome of the IT revolution is the transition of document format from basic hard copy to digital.  

Digital format brought with it the "Keyword Search", a compelling feature that helped users quickly navigate through documents. This feature was so dynamic that it soon eclipsed the "index in the back of the book" approach to locating desired information.  

In fact, the advantages of an "Index" over a "Keyword Search" have been effectively forgotten, since in their quest to solve the "Information Overload" problem, IT researchers have been concerned with other issues such as improving search engine efficiency and relevancy of results.  

Nowadays, however, despite having achieved remarkable levels of search engine efficiency, we seem to have come full circle with regard to the problem of Information Overload. People are realizing that there is little benefit in just finding highly relevant pages since they still need to read them all. In this context, the old-fashioned "index in the back of the book" approach (albeit with a modern tweak or two) proves to be an indispensable tool that allows the user to better absorb the information from a set of search results. For this purpose the index outperforms its keyword-searching counterpart.  

By definition, keyword searching is extremely insular. You can only find precisely what you specify, which results in both limitation and weakness in your ability to control information. For example, if you want to use a keyword search to determine whether a document contains information about Asia, what keywords would you use? If you use "Asia" you will of course miss references to "Japan", "China", "Tokyo", "Korean" etc. To quickly construct a sophisticated keyword search to cover all these references is clearly a huge task that is both cumbersome and impractical.  

SCaNspect's document analysis technology has a predetermined ontology of all references to Asia. So at the click of a button all references to Asia in your document are instantly grouped into an index.  

This idea is so rudimentary and necessary you may wonder why nobody has thought of it before. Perhaps people have considered this solution, but creating the massive ontology covering virtually every subject matter, and an intelligent technology to drive it, is a complex problem that took SCaNspect over thirty man-years to solve.  

The problem is not only about building a comprehensive and ubiquitous ontology covering every subject domain, it is also about making intelligent decisions when allocating words and phrases found in a document into their appropriate index categories.  

For example, with ambiguous words like "mouse", which could refer to either animals or computers, SCaNspect can accurately determine which meaning is correct from the context. A word like "bank", which can also be used in various contexts, like "money in the bank", "car hits a bank turn", "airplane banks left", "river bank" etc., is always correctly understood.  

Therefore, SCaNspect not only broadens your information universe, it also gives you a powerful knowledge management tool that streamlines that universe.  

The intelligent indexes can also serve as digital "fingerprints" of each document's content to accurately sort large quantities of documents (such as data within a corporate intranet). Naturally, sorting through document indexes is significantly more efficient than opening each document manually to see what's inside.  

SCaNspect is easily customizable for any project or vertical market. For example, it can be customized for biotechnology companies to understand gene structure and chemical formulas, allowing researchers to mine data much more efficiently. The technology can be similarly applied to medicine, law, education and virtually any research or enterprise.