生成AIがイノベーションと知財戦略強化にどう役立つか

「研究開発と知財で生成 AI を使用する 13 の方法（13 ways to use generative AI in R&D and IP）」という記事では、生成AIがイノベーションと知財戦略強化にどう役立つかを１３に分けて説明しています。
どういうレベル感、解像度で役立つかについては議論があるとしても、方向性としてはそれほど違和感はありません。
 
目的: 大規模なデータセットを簡単に確認および比較する 

1. SEP (標準必須特許):
2. 先行技術検索:
3. 特許出願の優先順位付け:
4. 発明開示分析:
5. テクノロジーランドスケープ:
6. 商標分析とブランド保護:
7. 競争力のあるインテリジェンス:

目的: 新しい取り組みを見つける

1. パートナー スカウティング:
2. 特許起草とクレーム生成:
3. ライセンスの機会:

目的: 次の大きな出来事を予測する

1. 技術予測:
2. 斬新なアイデア:
3. 予測的イノベーション:

 
 
13 ways to use generative AI in R&D and IP
https://www.evalueserve.com/blog/13-ways-to-use-generative-ai-in-rd-and-ip/
 
13 Ways Generative AI Can be The Game Changer in Research & Development in IP
https://ttconsultants.com/13-ways-generative-ai-can-be-the-game-changer-in-research-development-in-ip/
 
 
 
How Generative AI Can Contribute to Innovation and Strengthening IP Strategy
 
The article '13 Ways to Use Generative AI in R&D and IP' explains how generative AI can aid in innovation and the enhancement of intellectual property strategies across thirteen aspects. Even though there may be discussions about the level and resolution at which it can be useful, the general direction seems to be without much controversy.
 
Objective: Easily review and compare large data sets 

1. SEPs (Standard-Essential Patents): Generative AI can aid in the identification and analysis of SEPs in specific technological domains. By analyzing large literature of technical standards, meeting summaries, and industry publications, generative AI algorithms can identify whether a set of patents are likely to be essential for compliance with a particular standard. This can help IP professionals understand the SEP landscape, evaluate the strength of their own patents, and navigate licensing negotiations with confidence. 
2. Prior Art Search: Generative AI can be employed to enhance prior art searches. By leveraging natural language processing and machine learning algorithms, generative AI can analyze patent texts, research papers, and technical documents to identify relevant prior art more efficiently. This can save considerable time and resources for IP professionals involved in patent infringement assessments and patent validity studies. 
3. Prioritizing Patent Filings: Generative AI can analyze vast amounts of data, including existing patents, research papers, and market trends, to help IP professionals prioritize their patent filing strategies. By identifying white spaces and areas with high potential, generative AI can guide IP professionals in targeting their R&D efforts more effectively. 
4. Invention Disclosure Analysis: Generative AI can assist in analyzing invention disclosures submitted by employees or inventors within an organization. By automatically extracting key concepts, performing similarity analysis against existing patents, and identifying prior art, generative AI can help streamline the invention disclosure review process and provide insights for patentability assessments. This can significantly help reduce time and effort of assessments, especially at large companies. 
5. Technology Landscaping: Generative AI can generate visual representations and interactive maps of technology landscapes within specific domains. By analyzing patents, scientific literature, and market data, AI algorithms can identify emerging trends, clusters of related technologies, and potential cross-domain collaborations. This can aid R&D professionals in understanding the broader technological landscape and making informed decisions about future research directions. 
6. Trademark Analysis and Brand Protection: Generative AI can assist IP professionals in monitoring and protecting trademarks. By analyzing vast amounts of data from online sources, social media, and marketplaces, generative AI can identify potential trademark infringements, brand dilution, or counterfeit products. This can help IP professionals take proactive measures to protect their brand reputation. 
7. Competitive Intelligence: Generative AI can provide real-time competitive intelligence by monitoring competitors' patent activities, research publications, and technological advancements. By analyzing and summarizing relevant information, generative AI can enable IP professionals to stay updated on the latest developments in their industry, identify potential threats, and uncover new business opportunities. 

 
Objective: Finding new initiatives 

1. Partner Scouting: Generative AI can streamline the process of partner scouting by analyzing a wide range of data sources, including patent portfolios, research publications, company profiles, and market data. By employing machine learning techniques, generative AI algorithms can identify potential partners, KOLs, or collaborators based on specific criteria such as technical expertise, complementary patents, research capabilities, or market presence. This can help IP and R&D professionals identify suitable partners for joint ventures, licensing agreements, or research collaborations. 
2. Patent Drafting and Claim Generation: Generative AI can assist IP professionals in the process of patent drafting by generating initial drafts and suggesting potential claims based on input information. By analyzing existing patents and legal precedents, generative AI can provide valuable suggestions, improving the efficiency and quality of the drafting process. 
3. Licensing Opportunities: Generative AI can identify potential licensing opportunities by analyzing patent portfolios and market data. By leveraging machine learning techniques, AI algorithms can identify patents with high commercialization potential, match them with potential licensees or partners, and help IP professionals optimize their licensing strategies. 

 
Objective: Predict the next big thing 

1. Technology Forecasting: Generative AI can help R&D professionals predict future technological developments and identify potential disruptive innovations. By analyzing historical data, scientific publications, and market trends, AI algorithms can generate forecasts and scenarios, enabling R&D teams to anticipate emerging technologies and allocate resources accordingly. 
2. Novel Ideation: With it’s ability to analyze vast sums of data quickly, generative AI can find whitespaces within the information to generate novel ideas that are not being talked about frequently within the market. This gives companies an upper hand to go to market faster. 
3. Predictive Innovation: Generative AI can leverage its ability to analyze vast amounts of data, including patent databases, scientific literature, market trends, and consumer insights, to provide predictive analytics for innovation. By identifying patterns, correlations, and emerging technologies, generative AI algorithms can assist R&D professionals in identifying potential areas of innovation and predicting future trends. This can help organizations prioritize their R&D efforts, allocate resources effectively, and stay ahead of the competition. An added benefit is generative AI is well-versed in multiple domains so it can apply concepts prevalent in one area to another where is isn’t as widely used.