Use of AI-Generated Patent Documents: Maintaining Attorney-Client Privilege in Patent Application Preparation

Use of AI-Generated Patent Documents: Maintaining Attorney-Client Privilege in Patent Application Preparation

The use of large language models (LLMs) for preparing patent applications is top of mind for many in-house and law firm leaders. The rise is emblematic of the broader trend of technological integration in the legal domain. This trend not only enhances efficiency but also introduces a new set of questions surrounding traditional legal doctrines. One concern raised by practitioners and clients alike is whether deploying LLMs in patent application preparation might inadvertently breach attorney-client privilege. This privilege, fundamentally designed to ensure confidential interactions between a client and their attorney, is pivotal in fostering trust and ensuring a comprehensive understanding of the client’s invention.

Nature of Attorney-Client Privilege

The essence of the attorney-client privilege is to guarantee confidential exchanges between a client and their legal counsel. This assurance forms the bedrock of the attorney-client relationship. Without this foundational trust, clients might hesitate to fully disclose details vital for patent application preparation. A breach of this privilege primarily occurs when these confidential exchanges are shared with external entities, rendering the information no longer safeguarded.

LLMs as Tools, Not Third Parties

When LLMs are engaged, they function as instruments, akin to computational software or databases specifically designed for legal research. Viewing LLMs in this light offers clarity. They are advanced algorithms that can facilitate the patent application preparation process, not sentient beings privy to confidential data. Submitting queries or text to an LLM is not analogous to conveying confidential matters to a cognitive third party capable of independent understanding or action (e.g., a human). Instead, the LLM interprets input grounded in its programming and data lineage, delivering content devoid of human intervention or independent discernment. This mechanistic nature solidifies its role as a tool and not a conscious entity within the attorney-client dynamic.

No Public Dissemination

An indispensable aspect of preserving attorney-client privilege is the prevention of public exposure of privileged communication. This concept is foundational in legal protocols. Providing details to an LLM without subsequent public access or distribution should not be misconstrued as a public disclosure. The role of the LLM in this context is to be a facilitator in document creation, devoid of any publishing capacity.

Data Security & Confidentiality

Leading LLM providers prioritize data privacy and typically do not store individual queries or use them for future model training. This emphasis on data protection means that each query is treated as a unique interaction, without memory or bias. This practice mirrors that of dedicated legal databases not saving or sharing your research history. Such stringent protocols ensure that any sensitive information imparted remains safeguarded and inaccessible.

Conclusion

As patent law continuously adapts to technological advancements, understanding these new capabilities and their implications becomes paramount. Modern technology’s incorporation into patent portfolio development underscores the profession’s dynamic evolution in the digital era. When applied judiciously, LLMs in patent application preparation maintain the sanctity of attorney-client privilege. Embracing these technologies not only showcases the field’s progression but also harmoniously blends traditional legal acumen with cutting-edge technical prowess.

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??Ronelle Geldenhuys

??Principal & patent attorney at Foundry IP |??? Software, electronics & AI specialist |??Diversity champion |???Survivor

1 年

“LLMs as Tools, Not Third Parties” — well said! Of course you do need to read the fine print for the tool you use (or plan to use), if you have concerns about whether your input will be used to train the model. But still, even if it is, the model remains a statistical model, so it’s also worth thinking about how your particular words could impact a model learning from thousands of sources, looking for averages and medians (assuming yours would be the outlier).

Matt Rappaport

General Partner at Future Frontier Capital | Co-Founder UC Berkeley Deep Tech Innovation Lab |

1 年

Thanks for posting this, Ian Schick, PhD, Esq. I saw this article recently published by folks from McDonnell Boehnen Hulbert & Berghoff LLP discussing tangentially related issues.... particularly around patent drafting, points of novelty and LLMs. https://www.dhirubhai.net/posts/mcdonnell-boehnen-hulbert-%26-berghoff-llp_how-to-use-and-not-use-large-language-models-activity-7092156882416533505-GSWZ?utm_source=share&utm_medium=member_android

Robert Plotkin

25+yrs experience obtaining software patents for 100+clients understanding needs of tech companies & challenges faced; clients range, groundlevel startups, universities, MNCs trusting me to craft global patent portfolios

1 年

Thanks for the clarity on this. I've seen too many other people equating "LLM" with "ChatGPT," and drawing the false conclusion that the privacy/confidentiality concerns associated with ChatGPT are inherent in all LLMs. As you note, legal LLM providers are and will continue to respond to the demand for LLM products that preserve confidentiality and privilege for legal applications.

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