Invention Retention – Matthew Osman, Co-founder, Legit – The Curious Case of Invention Support and How AI Apps Can Save Time, and Money
Finding Genius Podcast
Richard Jacobs
4.4 • 1K Ratings
🗓️ 11 July 2018
⏱️ 20 minutes
🧾️ Download transcript
Summary
Matthew Osman, Co-founder of Legit (legitpatents.com), discusses his company's advanced AI technology that allows inventors and developers, from every industry, access to critical information and knowledge that can save time and money.
A man of many accomplishments, Matthew was the youngest VP of a one billion dollar hedge fund in London, where he primarily focused on tax structuring and credit strategies. While working towards his degree from the University of Oxford, Osman studied philosophy, politics, and economics extensively. Osman's company, Legit, is a new brand of AI company that focuses on natural language processing and evolutionary computation. As Osman states, the goal of his company is to turn literally anyone into an actual inventor.
Legit builds applications that are powered by natural language processing for engineers, researchers, and scientists that aid in the identification of their work, to assess whether it is unique. Legit's engines look deeper into identification processes, going beyond the elementary keyword approach, delving into conceptual similarities. Legit can evaluate a project in real time, comparing it to as many as thirty million other documented projects and prototypes, thus giving the creator a true sense of their work's originality. But beyond the comparisons and real-time evaluation, Legit's technology can also suggest various means to increase an invention's uniqueness, offer suggestions of potential collaborators, and recommend possible buyers or licensors. Essentially, Legit can walk with inventors through their initial ideation phase all the way to patent.
Whether you're a solo inventor working out of your garage or a research and development professional employed by a billion-dollar corporation, some needs are the same for both, in that every inventor is benefited by knowledge of what's already in the market or in the pipeline. Legit's app saves time and decreases waste in R&D by gathering the important information necessary to suss an idea's originality and determine how valuable the invention could be in general, to maximize outcomes. With 2.2 trillion dollars expected to be spent in the coming year on R&D, it's clear that any inventor or company must be tapped into the development market to ascertain their inventions' particular characteristics of value and originality.
Osman explains how Legit's AI is trained on an extensive corpus, or collection, of technical literature such that their technology can make astute assessments based on the desired goals of similar projects, even if there are no shared keywords between them in their stated objectives or descriptions. As the push for new innovative products is certainly increasing, and technology is expanding, the need for specific knowledge is perhaps at an all-time high, and Legit's advanced AI can provide that knowledge.
Transcript
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| 0:00.0 | Welcome to Almost Here, Around the Corner of Future Technology Podcasts with Richard Jacobs. |
| 0:07.0 | Future Technologies is to transform our lives for better or worse or the focus of this podcast. |
| 0:13.0 | Almost here means these technologies are now here and starting to be used. |
| 0:17.0 | Or just around the corner, for Bitcoin to artificial intelligence, |
| 0:21.0 | 3D printing, blockchain, virtual reality, and more. |
| 0:25.0 | Hello, this is Richard Jacobs with a future deck podcast. |
| 0:30.0 | My guest today is Matthew Osmond, the CEO and founder of Legit, and the website is legit |
| 0:35.5 | LEG IT. A I. Matthew, how you doing? |
| 0:38.7 | Hey, rigid, how you doing? I'm very well, thanks. |
| 0:41.1 | Good, yeah. Can you tell listeners a little bit about your venture |
| 0:44.4 | what it's involved yeah absolutely so as you may guess from the URL we're an AI |
| 0:50.5 | company specifically focused on natural language processing and also a little bit of |
| 0:56.3 | evolutionary computation and what we are trying to do is to essentially turn anyone into an inventor. |
| 1:04.3 | So we build applications powered by natural language |
| 1:08.1 | processing for engineers, researchers and scientists |
| 1:12.0 | to identify whether what they're working on is new. |
| 1:15.9 | So we take a free text description of a new idea, a product or a service that's then entered into our web app. We then compare that for |
| 1:26.4 | conceptual similarity rather than keyword similarity against around 30 million |
| 1:32.3 | other inventions pieces of technical literature in close to real time. |
| 1:38.0 | So we're able to give the user real time feedback on how new and original what they're working on is, suggest ways that they |
| 1:47.1 | might increase its novelty, suggest potential people they could collaborate with or indeed sell their idea to. |
| 1:54.0 | So what we're trying to do is to lower the barrier to invention, democratize it a little bit, |
... |
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