A newly filed lawsuit in the Northern District of California centers on alleged trade secret misappropriation involving an AI powered patent analysis and management tool.
VideoLabs, Inc., a Silicon Valley based intellectual property licensing and patent aggregation company specializing in video technology, has accused two former consultants of stealing trade secrets related to its internal AI platform, Turing AI.
Background of the dispute
According to the complaint, VideoLabs previously acquired a portfolio of video related patents from Digital Keystone or related entities. The patents listed Paolo Siccardo and Luc Vantalon as inventors.
Following the acquisition, VideoLabs hired Siccardo and Vantalon as consultants to further develop and refine Turing AI, an internal system used for patent analysis and IP management.
The consulting agreements allegedly contained clear provisions assigning ownership of all work product to VideoLabs and prohibiting the disclosure or misuse of confidential information.
Allegations of misappropriation
Despite these agreements, VideoLabs alleges that Siccardo and Vantalon later claimed personal ownership of the Turing AI platform.
The complaint asserts that the consultants withheld access to the system, refused to cooperate unless VideoLabs granted them a substantial equity stake, and threatened to undermine VideoLabs’ business relationships if their demands were not met.
VideoLabs characterizes this conduct as outrageous misappropriation of trade secrets rather than a contractual disagreement or ownership dispute.
Why trade secret law is central to the case
The lawsuit does not focus on patent ownership. Instead, it centers on control and misuse of an internal AI system that allegedly derives its value from confidential technology, workflows, and proprietary know how.
According to the complaint, Turing AI was never intended to be publicly disclosed and was treated as confidential intellectual property owned by VideoLabs. As a result, the company is pursuing claims under trade secret law rather than patent law.
The case underscores how AI driven patent tools often fall outside traditional IP frameworks and depend heavily on trade secret protection.
Why this case matters
This dispute highlights a recurring risk in modern IP transactions.
Companies may properly acquire patents while simultaneously relying on consultants or inventors to develop internal AI systems that are not patented and never disclosed. When ownership, access, and confidentiality are not tightly governed, those systems can become leverage points in disputes.
The VideoLabs lawsuit illustrates how trade secret misappropriation claims are increasingly being used to protect AI platforms that support patent strategy, licensing, and portfolio management.
As courts continue to confront these issues, the case may offer important guidance on how companies should structure agreements and safeguard AI driven IP assets.
Case Reference
VideoLabs, Inc. v. Vantalon et al.
Case No. 5:25 cv 11001
U.S. District Court for the Northern District of California
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Why are programs and data considered trade secrets?
Because they contain algorithms, models, processes, and customer insights that provide competitive advantage. When exposed, the loss is immediate and irreversible.
What is the first step to protecting programs and data?
Create an inventory and identify which assets qualify as trade secrets, including code, models, datasets, prompts, and internal tools.
Why is classification important for trade secret protection?
Classification clarifies sensitivity, risk, and required controls. It strengthens governance and ensures teams understand how assets should be handled.
How does access control prevent trade secret loss?
Limiting access by role, tracking interactions, removing outdated permissions, and restricting repositories prevents internal misuse and accidental exposure.
How does AI increase the risk of trade secret leakage?
Employees often paste confidential information into public AI tools, causing it to become training data. Governance and private AI tools prevent this.
What legal agreements support trade secret protection?
NDAs, invention assignment agreements, contractor agreements, and offboarding processes ensure confidentiality obligations are clear and enforceable.
Why is employee training essential for protecting trade secrets?
Most exposure occurs through human behaviour. Training builds awareness and creates a culture of confidentiality across the organisation.
How do audits improve trade secret protection?
Audits identify weak points like outdated access, unsecured repositories, shadow AI use, and untracked datasets — key for legal defensibility.
How does Tangibly help protect programs and data?
Tangibly provides automated trade secret identification, classification, access tracking, governance workflows, and AI-driven risk detection.
