Introducing FluidityIQ
The way we search for information is broken.
Not that search engines and other tools don’t work. They do. It’s just that we’ve been conditioned to search for information in a particular way since the late nineties and we have become accustomed to receiving rendered results as a linear pile of documents or links to third party sites that have been mysteriously prioritized for our use by an algorithm whose workings, we do not have insight into.
If you are trying to find a restaurant or answer a crossword question, this is perfectly fine. But if you are an attorney helping a client manage infringement risk or an R&D professional trying to determine where the gaps are in your research area, then basic search engines and the tools that replicate them can create inefficiencies in your processes, whether you realize it or not. Sure, some solutions give you screening criteria to help narrow search results, such as jurisdiction or a key word, but wouldn’t a search that had your own context baked into it do a better job?
AI will help us and replace the standard search engine.
Likely not. Generative AI can answer questions, write poems and do other magical things that will undoubtedly improve productivity in certain industries. But it carries risks, especially in innovation research where hallucinations or unintended biases can result in costly business decisions being made. This is especially relevant in R&D of all kinds, medical technology research and business processes that are dependent on a deeper understanding of a technical field, such as patent research in the legal market.
In addition to the issues that off-the-shelf AI solutions can create for complicated research functions, data proliferation is a real problem for all kinds of researchers. According to an article published by Petroc Taylor in Nov. 2023, global data is forecast to increase by 180% from 64.2 zettabytes in 2020 to over 180 zettabytes by 2025. This data reflects a wild growth rate in available data before the impact of generative AI usage by individuals, institutions and industry. More data makes research harder and off-the-shelf AI solutions can create plausible, yet inaccurate results through unintended biases, hallucinations and other issues.
There must be a better way.
The FluidityIQ Private Innovation Library (PIL) is a persistently updated self-learning environment that is designed to connect existing research with innovation trends as represented by global patent filings. FluidityIQ helps researchers, R&D professionals and intellectual property practitioners reduce the cost and risk associated with their analyses by helping them leverage their existing knowledge to create contextually accurate search strategies that advance their research. We do this by leveraging our patent-pending technology and AI engine to align a researcher’s existing knowledge to claims within the global patent universe.
This ensures that a dynamically updated environment can be curated and maintained by the researcher to answer complicated business questions. No more Boolean searches. No more multiple searches to gather enough information to answer one question. No more reports that are out of date the next day. Search as a function of research is changing. To learn how FluidityIQ might help your business, reach out to us at info@fluidityiq.com.