[vc_row][vc_column][vc_row_inner css=”%7B%22default%22%3A%7B%22margin-bottom%22%3A%222rem%22%7D%7D”][vc_column_inner width=”1/2″][vc_column_text]Your company has an incredible AI innovation. The obvious question becomes, what is the best way to protect that innovation? There are generally two options: Patents or Trade Secrets. Which should you choose? For innovations related to machine learning, neural networks, related training models, algorithms and data, the answer, in many cases, is trade secrets.
A primer first. In the simplest terms, a patent provides its owner with the right to exclude others from making, using, selling, and importing the claimed invention for a number of years. To obtain this right granted by the US government, however, the inventor must disclose the invention to the public. Obtaining a patent requires this quid-pro-quo. Even the act of applying for a patent, generally, results in public disclosure of the innovation.[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/2″][us_image image=”935″ meta=”1″ link=”%7B%22type%22%3A%22popup_image%22%7D”][/vc_column_inner][/vc_row_inner][vc_row_inner css=”%7B%22default%22%3A%7B%22margin-bottom%22%3A%222rem%22%7D%7D”][vc_column_inner][vc_column_text]Trade secrets, on the other hand, protect information that is secret. Under both federal and state law, trade secrets protect any information that someone has taken reasonable efforts to keep secret so long as the information derives independent economic value from not being generally known. Such protection has been afforded to almost any information under the sun, including compilations of public data, source code, schematics, diagrams, and customer lists.
One final distinction is worth mentioning at the outset. Infringing a patent is, in essence, strict liability. A person who does not know of the existence of a patent is still liable for infringing the patent, if, for example, they make or use a device covered by the patent’s claims. Not so for trade secret misappropriation. To misappropriate a trade secret, a person must have known, or should have known, that the information was a trade secret. Given this requirement, someone can independently develop the subject matter of a trade secret and freely use that information without being liable for misappropriation. Reverse engineering from a legally acquired product also precludes liability for trade secret misappropriation.[/vc_column_text][/vc_column_inner][/vc_row_inner][vc_row_inner css=”%7B%22default%22%3A%7B%22margin-bottom%22%3A%222rem%22%7D%7D”][vc_column_inner width=”1/2″][vc_column_text]Back to the main query of this article. To decide whether to seek patent protection for an AI innovation, we must first consider Alice Corp.诉 CLS Bank International.[573 U.S. 208 (2014)]。最高法院裁定 爱丽丝 在 2014 年。 爱丽丝 对软件专利能力产生了巨大影响。该案导致专利被美国专利商标局（PTO）驳回，并被各级法院宣布无效。 爱丽丝的 analysis first asks whether the claims are directed to a patent-ineligible concept, such as an abstract idea in the context of software patents. Second, if the claims are directed to a patent-ineligible concept, the inquiry looks for an inventive concept—i.e., an element or combination of elements that is sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the ineligible concept itself.[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/2″][us_image image=”937″ meta=”1″ link=”%7B%22type%22%3A%22popup_image%22%7D”][/vc_column_inner][/vc_row_inner][vc_column_text]爱丽丝在实践中，《专利法》为软件实施的发明专利设立了很高的门槛。在 Docket Navigator 上进行的搜索显示，在 2015 年地区法院做出的 171 项不合格裁定中，有 137 项与计算机软件和硬件专利有关，而在 2008 年至 2014 年期间，年均仅有 19 项专利主题无效裁定。时至今日，基于主体资格的无效裁定仍在急剧增加：从2015年到2022年，地区法院平均每年作出217项无效裁定。一句话，如果软件是创新的核心，那么就很难获得专利保护。
鉴于本文讨论的人工智能创新几乎都是通过在计算机硬件上运行的软件进程来实现的，根据目前的 爱丽丝 在这一框架下，那些试图为此类人工智能创新申请专利的人面临着一场艰苦的战斗。美国专利商标局前局长大卫-卡波斯（David Kappos）在 2019 年也承认了这一点，他表示，有关主题资格的法律 "正在对人工智能专利申请产生重大负面影响"。[可查阅 https://www.judiciary.senate.gov/imo/media/doc/Kappos%20Testimony.pdf].事实上，专利商标局的立场是 "人工智能发明与任何其他软件技术并无不同，必须与任何其他发明一样，在主体资格方面受到同等对待"。[公众对人工智能和知识产权政策的看法，问题 5］
这也就难怪人工智能专利会受到怀疑，并根据《专利法》被宣告无效了。 爱丽丝人工智能专利被视为无效的最常见原因是：（1）权利要求使用了 "神经网络"、"机器学习 "甚至 "人工智能 "等通用语言，使其成为抽象概念；（2）权利要求模仿了人类行为；或（3）权利要求是在通用计算机上实现的。例如，在 Angel Technologies Group LLC 诉 Facebook Inc.在该案中，法院宣布几项与识别照片中的人有关的权利主张无效。[2022 WL 3093232 at *4 (C.D. Cal. Jun. 30, 2022)]。被宣告无效的权利要求描述了使用人工智能算法识别图像中的人物，但权利要求中并未提及人工智能算法如何工作。因此，这些权利要求针对的是无法获得专利的抽象概念。
IBM Corp. 诉 Zillow Group, Inc.该案是第二个无效理由的例子。[2022 WL 704137 at *12 (W.D. Wash. 2022)]。有争议的权利要求涉及一个机器学习系统。法院判定该权利要求的主题不具有专利性，因为权利要求中的 "过程可以用笔和纸来执行，尽管没有计算机的速度快......"[2022 WL 704137 at *12 (W Dash 2022)]。[同上。].
Quad City Pat., LLC 诉 Zoosk, Inc.该案涉及第三个无效理由。[498 F.Supp. 3d 1178 (N.D. Cal. 2020)]。有争议的权利要求使用人工智能来预测和模拟参与者的行为。这些权利要求是不能获得专利的主题，因为这些权利要求没有叙述任何技术实现；相反，它们要求的是通用的功能结果。因此，这些权利要求是无效的，因为没有一项权利要求的 "步骤需要的只是一般的计算机实现"。[同上。 第 1188 页]。
这并不是说，人工智能专利就不可能在 "互联网+"时代存活下来。 爱丽丝 攻击，[Ocado Innovation, Ltd. 等人诉 AutoStore AS, 561 F.Supp. 3d.36, 55 (D. N.H. 2021)； 帕洛阿尔托研究中心诉 Facebook Inc.2021 WL 1583906 at *7 (C.D. Cal. Mar. 16, 2021)]，这只是一个挑战。任何试图为人工智能创新申请专利的人都面临着一场艰苦的战斗。归根结底，无法保证一项已颁发的人工智能专利能在专利复审中存活下来。 爱丽丝 挑战。换句话说，一项人工智能创新可以通过向公众披露来换取专利，但后来法院却判定专利无效，从而允许任何人使用专利中披露的创新。
Given this playing field, the question arises, is seeking patent protection for AI innovations, specifically those related to machine learning, neural networks, related training models, algorithms and data, worth it?[/vc_column_text][vc_row_inner css=”%7B%22default%22%3A%7B%22margin-bottom%22%3A%222rem%22%7D%7D”][vc_column_inner width=”1/2″][vc_column_text]What about trade secret protection? Trade secrets, in general, are well suited to innovations that are difficult to reverse engineer or independently discover, as well as innovations that are replaced by new innovations at a very rapid pace. These factors exist in many AI innovations. Trade secret protection, MOREOVER, can be afforded to innovations that are not eligible for patent protection. [Kewanee 石油公司诉 Bicron 公司416 U.S. 470, 482-83 (1974)]。为人工智能创新寻求商业秘密保护而非专利保护有几个好处：（1）可作为商业秘密保护的范围更广、 例如(2) 由于商业秘密保护可以自动附加，商业秘密保护可以跟上人工智能创新的速度；(3) 不存在为了获得专利而将创新披露给竞争对手，结果根据《专利法》该创新被视为无效的风险；(4) 不存在为了获得专利而将创新披露给竞争对手，结果根据《专利法》该创新被视为无效的风险；(5) 不存在为了获得专利而将创新披露给竞争对手，结果根据《专利法》该创新被视为无效的风险。 爱丽丝.[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/2″][us_image image=”934″ meta=”1″ link=”%7B%22type%22%3A%22popup_image%22%7D”][/vc_column_inner][/vc_row_inner][vc_column_text]To assess whether you should use trade secrets to protect an AI innovation, there are several questions to consider. 第一人工智能创新是可以保密的，还是可以从公开的产品或服务（如果有的话）中轻易确定的。如果你的人工智能创新在公众向使用该创新的网站输入查询时是显而易见的，那么商业秘密保护就不太可能是一个可行的选择。 第二如果创新与主题有关，则很难获得可实施的专利。如果创新涉及的是培训数据或消极的技术诀窍，专利实际上并不是一种选择。 第三届如果从实际情况来看，很难确定竞争对手是否在使用创新，那么商业秘密保护就是一个不错的选择。在这种情况下，如果你获得了专利保护，就很难根据公开信息确定竞争对手是否侵犯了你的专利。这就降低了专利的价值，因为你首先无法确定它是否侵犯了专利。 第四届如果你想获得专利，那么首先要考虑你为什么要获得知识产权保护。如果你希望获得专利，而不是为了阻止竞争对手实践所宣称的创新，例如，为了向市场发出你是该领域领先创新者的信号，那么对你的创新保密将无法实现这一目标。
与生活中的所有事情一样，商业秘密保护也有缺点。首先，如上所述，作为商业秘密保护的信息只能防止盗用--不当获取、使用或披露。你无法阻止竞争对手独立创造创新或对创新进行逆向工程。而且，一旦商业秘密信息被公开披露，无论是有意还是无意，该信息通常就失去了商业秘密的保护。其次，由于必须采取合理措施对相关信息进行保密，因此你必须采取合理措施保护信息的机密性。采取合理措施不是免费的。因此，采用商业秘密保护信息会带来财务和人事负担。
Given the head winds faced by AI innovations in obtaining enforceable patent protection and the nature of many AI innovations, trade secret protection is something individuals and companies innovating in the AI space should seriously consider in lieu of patent protection.[/vc_column_text][/vc_column][/vc_row][vc_row color_scheme=”footer-bottom”][vc_column][vc_row_inner content_placement=”middle” columns_type=”1″][vc_column_inner width=”1/3″][us_image image=”939″ align=”center” style=”circle” size=”thumbnail” link=”%7B%22url%22%3A%22https%3A%2F%2Fmillerbarondess.com%2Fpersonnel%2Fben-herbert%2F%22%2C%22target%22%3A%22_blank%22%7D”][/vc_column_inner][vc_column_inner width=”2/3″][vc_column_text]
作者简介
本-赫伯特 是一名出色的出庭律师 米勒-巴伦迪斯 specializing in patent infringement and trade secret misappropriation litigation, co-leading his firm’s Intellectual Property practice. Recognized among the 2024 Best Lawyers in America: Ones to Watch® for Patent Law and Litigation, he has been instrumental in securing over $1.5 billion in jury verdicts within two years. Ben has extensive experience in complex patent and trade secret cases across diverse technologies and significant expertise in Patent Office proceedings and the U.S. Court of Appeals for the Federal Circuit. He is a frequent author and speaker on IP topics and holds a J.D. from the Sandra Day O’Connor College of Law and a B.A. in Molecular Cellular Developmental Biology from the University of Colorado. His prior roles include partner at Kirkland & Ellis and operations leader for Law Finance Group in Los Angeles, along with clerking for Judge Kathleen O’Malley.[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row]
