AI イネーブルメント
人工知能は、企業のイノベーション、知的財産の保護、戦略的意思決定の方法を変革しています。私たちは2000年の設立以来、新しいテクノロジーを採用してきました。数年前から、特許サービス向けのジェネレーティブAIの探求と統合に取り組んできました。私たちはまず、サービス内の AI ツールを活用して、より速く、よりスマートで、より効果的なソリューションを提供してきました。そして今では、お客様も支援できるようになりました。
この急速に進化する環境の中で、戦略の展開から起草プロセスのスピードアップやデータへのより効率的なアクセスなどの結果に至るまで、お客様の特許ワークフローにAIを組み込むことについて、専門家によるアドバイスを提供します。
“Mark Lubbock is a high-level operator and tremendously impressive in negotiations.”
Services
We offer joined-up thinking across a comprehensive range of services:
Accelerated patent analysis
Smarter prior art searches
Data-driven insights
Generative AI workflows
Enhanced collaboration
Legal Layer®: guiding you to the best outcome
Our team's fully-connected approach can help you to understand how your dealings with data and algorithms engage different aspects of IP and related areas of law. In this way, we serve as a “Legal Layer” to help you optimize outcomes for your business.
Paths through the neural network represent how certain features of data and algorithms can engage different legal considerations in the Legal Layer. Our Codiphy team considers these all together to optimise the outcomes for your business.
Click the buttons to show some examples of the many paths through the network.
Some AI models are trained on data scraped from publicly-available sources. This may have legal implications. For example, training an AI model using copyright works without obtaining a licence from the rightsholder could result in litigation and/or reputational damage. AI regulations will also play a role. For example, the EU AI Act will oblige General Purpose AI providers to publish summaries of training data and to respect opt-outs from data scraping. Otherwise, they could face penalties under the Act.
Proprietary algorithms may involve IP that can be protected, for example using trade secrets or patents. Competing factors need to be considered to optimise protection of that IP. For example, trade secret protection may extend an early-mover advantage, but will be lost if technical information is publicly leaked. Patents can provide protection even if the technical information is later published or if someone else develops the same idea independently, but filing a patent application for an idea that isn’t patentable may result in a loss of IP. An effective IP strategy will consider these factors and others to maximise the value in the IP.
Original code is protected by copyright, which may be licensed for others to use, for example under an open-source licence. Non-compliance with the obligations of an open-source licence may result in litigation and/or reputational damage. If you are considering releasing code under an open-source licence, it is important that this decision fits in with your business model and wider IP strategy, and that the licence you use supports these. Attempting to change licence terms after release can be difficult or impossible, and can cause reputational damage in the eyes of the open-source community.
AI models may be trained on, or may subsequently process, personal data that relates to an identified or identifiable natural person (including personal data that is publicly accessible). This may have legal implications if the training/processing is not compliant with data protection obligations. The party controlling the processing could face regulatory penalties including orders to stop the processing or using the AI model, litigation between the user and the controller, and/or reputational damage. Providers of AI systems should consider the principle of ‘privacy by design’, as well as ensuring contractual provisions are in place to mitigate the legal risk.
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Strategic licensing: Turning IP into commercial value
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私たちは、経営幹部から知財チームまで、クライアントと緊密に連携して、知的財産の可能性を最大限に引き出します。訴訟担当者、弁理士、商事知財の専門家から成る当社の統合チームは、特許を保護し、戦略的に活用することでビジネスの成長を促進します。
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