IPRally正在构建一个基于知识图的专利搜索引擎

来自芬兰的一家新兴初创公司IPRally旨在解决专利检索问题，它已经筹集了200万欧元的种子基金。

领军企业是JOIN Capital和Spintop Ventures，现有的种子期前支持者Icebreaker VC将参与其中。这使得这家成立于2018年的公司募集的资金总额达到235万欧元。

IPRally由拥有15年专利律师经验的CEO Sakari Arvela共同创建，它构建了一个知识图表，帮助机器更好地了解专利的技术细节，并使人类能够更有效地搜索现有的患者。前提是基于图形的方法比简单的关键字或自由文本搜索更适合于专利搜索。

这是因为，Arvela认为，每一份专利出版物都可以提炼成一个更简单的知识图表，与知识产权专业人士的思维方式“产生共鸣”，并且具有无限的机器可读性。

他告诉我：“我们于2018年4月成立了IPRally，经过一年的引导和与我的联合创始人兼首席技术官Juho Kallio的概念验证。”。“在那之前，我自己已经消化了大约两年的图表方法，并鼓起勇气开始了这项事业”。

阿维拉说，专利检索是一个很难解决的问题，因为它既涉及到对技术的深刻理解，也涉及到对不同技术进行详细比较的能力。

“这就是为什么自从专利制度存在以来，这几乎完全是手工完成的。即使是最新的开箱即用的机器学习模型也太不准确，无法解决这个问题。这就是为什么我们为专利领域开发了一个特定的ML模型，它反映了人类专业人员处理搜索任务的方式，并使问题对计算机也有意义”。

这种方法似乎得到了回报，Spotify和ABB等客户以及知识产权办公室已经开始使用IPRally。目标客户被描述为任何公司，积极保护自己的研发与专利，并必须导航的知识产权景观的竞争对手。

与此同时，IPRally也并非没有自己的竞争对手。Arvela列举了行业巨头，如Clarivate和Questel，它们用传统的关键字搜索引擎主导市场。

此外，还有其他一些基于人工智能的初创公司，如Amplified和IPScreener。他补充说：“IPRally的图形方法使搜索更加精确，允许进行详细程度的计算机分析，并提供了一个非黑盒解决方案，用户可以解释和控制。”。

原文：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.