IPRally是芬兰新兴的初创企业，旨在解决专利搜索问题，已筹集了200万欧元的种子资金。
领先者是JOIN Capital和Spintop Ventures，现有的种子前支持者Icebreaker VC参与了此轮。这使这家成立于2018年的公司筹集的资金总额达到235万欧元。
IPRally由拥有15年专利律师经验的首席执行官Sakari Arvela共同创立，IPRally建立了知识图谱，以帮助机器更好地理解专利的技术细节，并使人类能够更有效地对现有患者进行拖网捕捞。前提是基于图的方法比简单的关键字或自由格式的文本搜索更适合专利搜索。
Arvela认为，这是因为，每个专利出版物都可以精简为一个更简单的知识图谱，从而与IP专业人员的思维方式“产生共鸣”，并且具有无限的机器可读性。
他告诉我：“在与我的联合创始人兼首席技术官Juho Kallio进行了自我引导和概念验证一年之后，我们于2018年4月成立了IPRally。” “在此之前，我已经消化了大约两年的图表方法，并鼓起勇气开始创业。”
Arvela说，专利检索是一个很难解决的问题，因为它涉及对技术的深刻理解以及详细比较不同技术的能力。
“这就是为什么只要专利制度存在就几乎完全手动完成的原因。即使是最新的现成的机器学习模型也太不准确，无法解决问题。这就是为什么我们为专利领域开发了一种特定的ML模型，该模型反映了人类专业人员处理搜索任务的方式，并使该问题对计算机也很敏感。”
这种方法似乎正在获得回报，Spotify和ABB等客户以及知识产权局已经在使用IPRally。目标客户指的是任何积极利用专利保护自己的研发并必须驾驭竞争对手的IP格局的公司。
同时，IPRally并非没有自己的竞争。 Arvela引用了像Clarivate和Questel这样的行业巨头，它们以传统的关键字搜索引擎主导了市场。
此外，还有其他一些基于AI的创业公司，例如Amplified和IPScreener。他补充说：“ IPRally的图形方法可以使搜索更加准确，可以进行详细级别的计算机分析，并且可以为用户提供可解释和可控制的非黑匣子解决方案。”
IPRally is building a knowledge graph-based search engine for patents
IPRally, a burgeoning startup out of Finland aiming to solve the patent-search problem, has raised €2 million in seed funding.
Leading the round is JOIN Capital and Spintop Ventures, with participation from existing pre-seed backer Icebreaker VC. It brings the total raised by the 2018-founded company to €2.35 million.
Co-founded by CEO Sakari Arvela, who has 15 years experience as a patent attorney, IPRally has built a knowledge graph to help machines better understand the technical details of patents and to enable humans to more efficiently trawl through existing patients. The premise is that a graph-based approach is more suited to patent search than simple keywords or freeform text search.
That’s because, argues Arvela, every patent publication can be distilled down to a simpler knowledge graph that “resonates” with the way IP professionals think and is infinitely more machine readable.
“We founded IPRally in April 2018, after one year of bootstrapping and proof-of-concepting with my co-founder and CTO Juho Kallio”, he tells me. “Before that, I had digested the graph approach myself for about two years and collected the courage to start the venture”.
Arvela says patent search is a hard problem to solve because it involves both deep understanding of technology and the capability to compare different technologies in detail.
“This is why this has been done almost entirely manually for as long as the patent system has existed. Even the most recent out-of-the-box machine learning models are way too inaccurate to solve the problem. This is why we have developed a specific ML model for the patent domain that reflects the way human professionals approach the search task and make the problem sensible for the computers too”.
That approach appears to be paying off, with IPRally already being used by customers such as Spotify and ABB, as well as intellectual property offices. Target customers are described as any corporation that actively protects its own R&D with patents and has to navigate the IPR landscape of competitors.
Meanwhile, IPRally is not without its own competition. Arvela cites industry giants like Clarivate and Questel that dominate the market with traditional keyword search engines.
In addition, there are a few other AI-based startups, like Amplified and IPScreener. “IPRally’s graph approach makes the searches much more accurate, allows detail-level computer analysis, and offer a non-black-box solution that is explainable for and controllable by the user,” he adds.
