On Tuesday 24th October the US Patent & Trademark Office (USPTO) granted the personalized learning provider, Filtered, patent approval for its learning-content filtering algorithm.
The patent covers an algorithm that selects valuable content for an individual learner. It identifies other learners who share characteristics with them (e.g. a role, an objective, a competency) and uses detailed feedback from those similar learners to assess the likely usefulness of any given nugget of content. It’s a way of embedding subject matter, personalization and curation expertise in an algorithm, using insight drawn from real learners. Read more about it and the application process in the patent document.
In a move to inspire innovation for Filtered, its partners, and for the Learning and Development sector, patent approval allows the company to share with others the detail of how it filters content while protecting the algorithm from imitators. At a time when artificial intelligence and algorithms are commonly used as marketing glitz for run-of-the-mill products, this learning tech start-up is keen to continue making substantive progress, and use technology practically and pragmatically to make a real difference.
For more information on Filtered's journey to achieving patent approval read this blog post written by the company's Chief Scientific Officer, Chris Littlewood.