Shihui’s Insight｜Legal Considerations in AI Sector M&A (2)

This article is Part II of a three-part series in M&A in the AI space. In this Part II we will explore intellectual property rights (“IPR”) related concerns in the AI sector and relevant considerations from an M&A perspective for AI deals.   

In AI M&A, potential IPR related risks for AI companies include ensuring clear ownership of AI patents and copyrights, trademark and trade secrets; assessing the freedom to operate without infringing on third-party rights; scrutinizing contractual obligations, particularly licensing agreements that could impact the use or transfer of IP assets post-acquisition etc.. Acquirors should also undertake due diligence to identify any undisclosed IP liabilities and consider the relevant risks, associated costs and solutions. 

Patent Portfolio Evaluation

AIGC applications and services rely on a complex interplay of technologies such as Deep Learning Models, Natural Language Processing, computer power (GPU, TPU etc.), algorithms etc. The origin of the underlying AIGC technologies is crucial to an AI company’s value, making it a potential risk in AI M&A deals and therefore requires strict scrutinization. Certain invention/utility patents can be secured for algorithms and models , often in combinationwith hardware or as in process patents, as well as for preprocessing and enhancements etc.¹ Therefore, strategic planning of patent portfolios will be important for AI businesses, such as in each of the following scenarios:

(1) If the target company develops its own proprietary AI, machine learning or generative AI solutions etc., it is crucial to confirm inventors' rights and the patentability of inventions. Addressing non-compete clauses and other potential rights issues is important, as is in investigating the R&D team's background, including academic-industry transfer agreements, professional restrictions on entrepreneurs, and the registration of university research outcomes.

(2) If the target company uses third-party technology, which can be categorized as Open Source, Outsourced, or Procured:

• For open source: Adhere to specific terms of the license agreements. For instance, the MIT requires license and copyright preservation; the BSD is similar but may include advertising clauses; the GPL is restrictive, demanding source code disclosure for modifications; the Apache License emphasizes patent rights retention.

• For outsourced : Scrutinize the agreements for clarity on patent ownership and the technical origins of the outsourcing team. The background of the outsourcing team,similar to an in-house R&D team, is critical, including their professional history and potential conflicts of interest.

• For procured: Focus on the traceability of the technology, the status of any prior technology, and the terms of the agreement, including registration procedures and dispute resolutions. Consider the differences and restrictions between technology sourced internationally versus domestically, and whether data traceability involves cross-border transfer.

  
  

Copyright Recordals

Copyright registration can be applied for certain data or datasets, for example, collective works. Also when an algorithm is expressed in computer software, the software copyright registration can be used to protect the entire set or a certain module of software code. In model training, it's essential to distinguish between dataset or corpus and potential third-party rights. While a dataset may be considered a collective work with its own rights, individual copyrights or other pre-existing rights within the dataset can still pose infringement risks. It is crucial to ensure that the use of such datasets does not violate the rights of original creators.

Confirming ownership of copyrights in software code, documentation, and creative content is vital. This can be achieved by ensuring proper assignment or licensing agreements are in place and by establishing a complete chain of rights. This includes the original proprietor, any transfers, and the nature of authorizations. Traceability is key to demonstrating the legal use of copyrighted materials in training datasets.

Trademark and Brand Protection

For the target company, trademark and brand protection can be a long-term concern and, especially where the acquiror intends to continue to use the brand and name of the target post-acquisition, as they form an important part of the long-term value of the acquisition. Throughout the due diligence process, the acquiror should carefully review the target’s trademark portfolio. This review should encompass whether the trademarks are fully registered, any pending trademark applications, existing trademark disputes and litigation, and the presence of  any global/regional trademark strategy.

Acquirors must assess the target company's proactive measures in safeguarding its trademarks and maintaining brand integrity. It is crucial to ensure registration and application for trademarks in key product and/or service categories, such as Classes 9, 35, 42, and 45 of the Nice Classification. Additionally, based on business needs, it may sometimes be necessary to arrange defensive registrations as a preemptive measure against potential squatters who could hinder future registrations. For AIGC companies with international operations, it is important to scrutinize the global trademark strategy to ensure consistent protection across all jurisdictions where the brand is active. In cases where the acquiring company already possesses established brands, it must also consider the impact of integrating  the new brand on the overall brand portfolio. Inconsistencies can lead to customer confusion and dilution of brand equity. 

Given the specific and rapidly evolving nature of the AI sector, M&A transactions in the AI sector need to take into account the specific issues and risks related to intellectual property that are pertinent to AI companies.

Specifically, the following issues in particular should be considered in the transaction process: 

• IPR review in due diligence: Verify the ownership and validity of IPRs including patents, trademarks, copyrights, and trade secrets. This action helps mitigate the risk of future infringement claims and ensures the protection of the technology being acquired.

  
  

• Legal disputes: During due diligence, investigate and assess the risks related to any unresolved legal disputes in which the target company may be involved. Pay  particular attention to disputes concerning intellectual property and data privacy.

  
  

• Transaction document provisions: The relevant provisions in the transaction documents which may concern IPRs including any IP transfer provisions, disclosure schedules listing the relevant IPRs etc. should be carefully and accurately prepared. 

  
  

• Representations and warranties: Tailor representations and warranties  concerning IPR to align withthe specific AI M&A transaction. They should coverthe relevant IPR-related risks associated with the transaction. 

  
  

• Covenants and conditions precedent: Include appropriate covenants or conditions precedent  to address any key IPR protection actions or continued right to use certain IPRs. This may involve pplications or completions of applications for key IPR registrations, obtaining any key IPR use rights to continue to use the relevant IPRs post-transaction which are needed for the target’s continued business operations.

Jeff Liu has a corporate and M&A practice specialized in cross border M&A and investment transactions as well as general corporate matters. He advises a wide range of clients including MNCs, private equity/venture capital funds and other corporate clients in various sectors including the financial, high-technology, biotechnology/pharmaceutical, TMT, energy, food & beverage and other consumer products sectors.

Jeff has extensive experience advising on transactions in China and globally (Europe, Asia and the US), often involving complex multijurisdictional issues. He also advises MNCs and other companies on a wide range of general corporate matters including cross border commercial transactions, IP licensing related matters and regulatory and compliance issues (antitrust, data compliance etc).

Before joining Shihui, Jeff worked at a number of leading international law firms in Beijing, Brussels, Hong Kong and Paris.

Liu Fan specializes in intellectual property (IP) litigation and non-contentious IP services, possessing extensive experience in the strategic management and protection of brand and IP portfolios, which encompass copyrights, trademarks, patents, and more. 

Fan has acted on behalf of numerous multinational companies in IP litigation, administrative proceedings, and various dispute resolution matters. She consistently achieves favorable outcomes in IP and brand protection cases. Her legal expertise is sought after across a diverse array of sectors, such as the internet, culture and entertainment, luxury goods, the food industry, medical products, hotel, and mechanical manufacturing etc.

In addition to her extensive practice, she has demonstrated particular expertise in the emerging field of AI, participating in multiple research projects focused on AIGC-related copyright and other compliance matters. This involvement aims to support clients in being well equipped to navigate the complexities of IP in the era of AI.