Contract analysis

I. Executive Summary You have asked us to analyze whether the outputs of Quorum Insurance Holdings Corp.'s ("Quorum") machine-learning models — trained in part on data licensed from Meridian Credit intelligence, Inc. ("Meridian") — constitute copyrightable "derivative works" under the Copyright Act or protectable trade secrets, and whether Meridian can assert intellectual property rights over such outputs. The Meridi

II. Scope and Limitations This memorandum addresses the ownership and protectability of Quorum's machine-learning model outputs under two bodies of law: (1) United States copyright law, specifically the Copyright Act of 1976, as amended, 17 U.S.C. §§ 101 et seq., and (2) federal and state trade secret law, including the Defend Trade Secrets Act of 2016 ("DTSA"), 18 U.S.C. §§ 1836 et seq., and applicable state trade s

III. Factual Background A. The Parties and the Transaction Quorum Insurance Holdings Corp. ("Quorum") is a Delaware corporation, publicly traded on the Nasdaq Stock Market under the ticker symbol QIHC. Quorum is headquartered at 1200 Atlantic Street, Suite 3400, Stamford, Connecticut 06901. Quorum operates as a multi-line insurance carrier and insurtech platform, offering products and services across property and cas

1. Ingestion and Preprocessing of Training Data. Raw data from multiple sources — including Meridian Data Products, Quorum's first-party data, and data from other third-party providers — is ingested into Quorum's data lake, cleaned, normalized, and prepared for use in model training.

2. Feature Engineering. Quorum's data scientists design and construct "features" — derived variables and statistical measures computed from the raw data that serve as inputs to the machine-learning model. Feature engineering is a highly proprietary process that reflects Quorum's domain expertise and data-science capabilities. Quorum's feature library for QuorumScore encompasses more than 1,400 engineered features.

3. Model Training. The machine-learning model is trained using the prepared training dataset. During training, the model iteratively processes the training data and adjusts its internal numerical parameters (commonly referred to as "weights") to minimize prediction error. The output of the training process is a trained model — a mathematical function defined by millions of numerical weight values that encode the stat

4. Model Validation and Testing. The trained model is evaluated against held-out validation and test datasets to assess its predictive accuracy, calibration, and fairness. This stage may involve iterative refinement of the model architecture, hyperparameters, and feature set.

5. Deployment for Live Inference. Once validated, the trained model is deployed into Quorum's production environment, where it processes new data inputs in real time (or near-real time) and generates predictions — including risk scores, propensity scores, and fraud probability indicators. These live inference predictions are the primary outputs that Quorum intends to use operationally and, through the QuorumScore B2B

IV. Copyright Analysis: Are ML Model Outputs "Derivative Works"? A. Statutory Framework — Derivative Works Under 17 U.S.C. § 101 The Copyright Act defines a "derivative work" as: a work based upon one or more preexisting works, such as a translation, musical arrangement, dramatization, fictionalization, motion picture version, sound recording, art reproduction, abridgment, condensation, or any other form in which a w

1. ML model weights and parameters are very unlikely to be "derivative works" of training data under current copyright doctrine. The training process is a statistical abstraction, not a reproductive process. Trained model weights do not incorporate copyrightable expression from any particular training example in recognizable form.

2. Live inference outputs (predictions, scores, and classifications) are even less likely to be derivative works, particularly when generated from non-Meridian inputs. These outputs are multiple layers of transformation removed from the training data and bear no resemblance to any element of that data.

3. Meridian's factual credit data has limited (if any) copyright protection. Under Feist, individual data records are uncopyrightable facts. Meridian's databases may receive thin compilation protection for original selection, coordination, and arrangement, but the underlying data elements are free for others to use.

4. Meridian's "derivative work" characterization in its proposed MDLA overreaches copyright law. There is no basis in the Copyright Act for the proposition that ML model outputs constitute derivative works of factual training data, and the "traceability" standard proposed by Meridian is wholly foreign to the derivative-works framework.

5. However, the unsettled nature of the law means contractual allocation is critical. Because no court has definitively ruled on these questions, the parties cannot rely on background copyright law to resolve their dispute with certainty. The MDLA will effectively define the parties' rights regardless of what copyright law provides, making the drafting of Article VII dispositive. Quorum must ensure that Article VII r

V. Trade Secret Analysis: Protection of Quorum's ML Model Outputs A. Trade Secret Framework Trade secret protection is available under both federal and state law. The Defend Trade Secrets Act of 2016 ("DTSA"), 18 U.S.C. §§ 1836 et seq., provides a federal civil cause of action for the misappropriation of trade secrets. At the state level, while New York has not adopted the Uniform Trade Secrets Act ("UTSA"), New York

1. Independent Economic Value. The information must derive independent economic value, actual or potential, from not being generally known to, and not being readily ascertainable through proper means by, other persons who can obtain economic value from the information's disclosure or use. 18 U.S.C. § 1839(3)(B). New York courts have described this element as requiring that the information provides a competitive advan

2. Reasonable Measures to Maintain Secrecy. The owner of the information must have taken reasonable measures to keep the information secret. 18 U.S.C. § 1839(3)(A). This includes physical and electronic security measures, access controls, confidentiality agreements, and other safeguards appropriate to the nature and value of the information. Under New York law, the reasonableness inquiry is fact-specific and consider

3. Not Generally Known or Readily Ascertainable. The information must not be generally known to, or readily ascertainable by proper means by, the relevant public or industry participants. Information that is publicly available, commonly known in the industry, or easily reverse-engineered does not qualify for trade secret protection. Trade secret protection is particularly well-suited to machine-learning models, train

VI. The Critical Distinction: Model Weights vs. Inference Outputs A. Technical Explanation To properly frame the legal analysis and inform the MDLA drafting, it is essential to understand the technical distinction between trained model weights and inference outputs. Model Weights/Parameters. When a machine-learning model is trained, the training algorithm processes the training dataset and iteratively adjusts the mod