A new partnership between SIM IP and Tangibly signals a targeted expansion of litigation finance into the trade secret enforcement space, combining capital with technology designed to assess early stage risk. The collaboration reflects growing interest among funders in data driven approaches to underwriting complex intellectual property claims, particularly those that are traditionally viewed as expensive and uncertain.

A press release reports that the two companies have launched a joint offering aimed at financing trade secret litigation while leveraging Tangibly’s technology platform to help identify, value, and monitor trade secret assets. The partnership is positioned around an AI driven model that evaluates the strength of potential claims earlier in the lifecycle, with the goal of reducing uncertainty for both claimholders and funders before significant legal costs are incurred.

According to the announcement, SIM IP will provide litigation financing for qualifying matters, while Tangibly’s platform will support due diligence by mapping trade secret assets, tracking misappropriation risks, and generating data that can inform enforcement strategies. Trade secret claims often present unique challenges compared to patents, including evidentiary complexity and difficulties around valuation. By combining funding with structured analytics, the partners argue that more meritorious claims can move forward that might otherwise stall due to cost or risk concerns.

The launch also comes against a backdrop of heightened scrutiny of litigation funding disclosures in the United States, particularly in intellectual property disputes. While the partnership announcement does not focus on regulatory issues, it highlights how funders are refining their models to emphasize selectivity, transparency, and risk management rather than broad based capital deployment.

For the legal funding industry, the collaboration underscores a broader trend toward specialization and technology integration. As competition among funders increases, partnerships that blend capital with proprietary tools may become more common, especially in niche areas like trade secrets where early insight can materially affect case outcomes and investment performance.

A federal judge has rejected an effort by Apple to force disclosure of litigation funding materials in a patent infringement dispute, reinforcing judicial reluctance to open third-party funding arrangements to routine discovery. The decision comes amid ongoing debates over transparency in litigation finance and marks another instance where courts have declined to equate outside funding with improper influence.

As reported in Bloomberg Law, the dispute arises from Apple’s defense against patent infringement claims tied to its iPhone “Back Tap” functionality. As part of its litigation strategy, Apple argued that communications between the plaintiff and its litigation funders could reveal undue control over the case or otherwise undermine claims of independence by counsel. According to the court, those arguments fell short of the threshold required to justify disclosure.

In denying the request, the judge emphasized that generalized concerns about litigation funding do not override long-standing protections such as the work-product doctrine. Materials prepared in anticipation of litigation remain shielded absent a clear showing of substantial need, and the court found no evidence that the funder’s involvement compromised legal strategy or decision-making. The ruling also rejected the notion that the mere presence of third-party funding creates a presumption of relevance or discoverability.

The decision aligns with a growing body of case law in federal courts that treats litigation funding as a legitimate commercial arrangement rather than an automatic basis for expanded discovery. While some judges have ordered limited disclosures in narrow circumstances, particularly in class actions or conflicts analyses, courts have generally resisted defendant attempts to use funding as a backdoor to privileged materials.