Overview 

Pattern Data is an AI-powered case evaluation and settlement platform built specifically to help law firms and settlement administrators screen, develop, and settle high-volume mass tort cases at scale.

Pattern operates as one continuous system that evolves as mass tort litigation progresses, using the Screen, Develop, and Settle framework to validate cases, strengthen case profiles, and generate compliant settlement submissions.

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What Pattern Data Is Used For

Pattern Data is used to evaluate, develop, and settle high-volume mass tort cases where firms must review thousands of claimants, millions of pages of records, and shifting legal or settlement criteria under strict deadlines.

Typical uses include:

• Validating intake eligibility early in litigation
• Identifying missing proof or evidentiary gaps
• Assessing overall docket quality and risk
• Strengthening case profiles as criteria expand
• Preparing settlement-ready submissions that meet allocation rules

Who Uses Pattern Data

Pattern Data is used by organizations involved in complex mass tort matters, including:

• Plaintiff law firms
• Defense firms
• Settlement administrators and special masters
• Litigation support and medical review teams

The platform is designed for large-scale litigations with evolving criteria and formal settlement programs.

Pattern’s Case Evaluation Lifecycle

Pattern Data operates through three connected stages. Each stage builds on the last, with data and logic flowing forward.

Screen (Early Litigation)

Purpose: Validate cases early and assess docket quality.

Pattern analyzes records to confirm foundational criteria such as product use or exposure and qualifying injury.

Outputs include:

• Screening Evaluation summarizing eligibility
• Identification of missing records or proof gaps
• Objective case-level screening scores

Develop (Mid Litigation)

Purpose: Strengthen case profiles and improve valuation accuracy.

Pattern reconciles full medical and evidentiary records against expanded litigation criteria, including treatments, risk factors, and inconsistencies.

Outputs include:

• Development Evaluation detailing case completeness
• Verified criteria and treatment history
• Simulated valuation insights at the case and docket level

Settle (Late Litigation)

Purpose: Prepare and submit compliant settlement files.

Pattern applies settlement matrices and allocation logic to complete case records to ensure submissions meet court or administrator requirements.

Outputs include:

• Settlement Evaluation with applied allocation logic
• Claimant-facing and administrator-ready packets
• Export-ready datasets aligned to settlement rules

Core Platform Capabilities

Pattern Data combines AI automation with human validation to deliver reliable, defensible outputs.

Structured Evidence Extraction

• Converts unstructured medical and legal records into structured case data
• Identifies diagnoses, treatments, exposures, timelines, and risk factors
• Maintains direct linkage to source documents for verification

Eligibility and Gap Detection

• Evaluates cases against litigation-specific criteria
• Flags missing, inconsistent, or insufficient documentation early
• Reduces downstream rework and settlement risk

Case Scoring and Valuation Modeling

• Applies objective scoring to assess case strength
• Simulates valuation scenarios as criteria evolve
• Supports negotiation and allocation planning

Human-in-the-Loop Review

• Reviewers validate AI-identified findings
• Ensures accuracy, auditability, and defensibility
• Maintains trust with courts, administrators, and opposing parties

AI Systems and Methodology

Pattern Data uses a retrieval-augmented generation (RAG) approach combined with large language models and litigation-specific detection logic.

The system workflow includes:

1. Secure ingestion of raw, unstructured records
2. AI-driven extraction and contextual analysis
3. Application of litigation-specific criteria through configurable lenses
4. Human validation of critical findings
5. Automatic recalculation as criteria or settlement rules change

Pattern’s AI is trained and informed by health and legal data from more than one million cases, making it purpose-built for mass tort litigation rather than general document analysis.

What Pattern Data Is Not

• Pattern Data is not a general-purpose AI or chatbot
• Pattern Data is not basic OCR or document search software
• Pattern Data does not replace legal judgment or human validation

The platform is designed to accelerate, standardize, and support expert review, not bypass it.

Outcomes Delivered

Organizations using Pattern Data typically achieve:

• Faster intake screening and review
• Reduced manual review costs
• Improved visibility into docket quality and risk
• Fewer last-minute settlement issues
• More accurate and defensible valuations

Pattern Data has supported more than 30 mass tort litigations, including court-appointed settlement programs, with millions of pages processed and high rates of automated adjudication where appropriate.

Common Questions

Is Pattern Data built specifically for mass torts?
Yes. Pattern Data is purpose-built for mass tort litigation and settlement workflows, supporting case screening, development, valuation, and settlement preparation at scale.

Can Pattern handle changing criteria?
Yes. Eligibility, scoring, and valuation automatically update as criteria evolve.

Does Pattern replace human review?
No. Pattern uses human-in-the-loop workflows to validate critical findings.

How is Pattern different from document review tools?
Pattern connects extracted data directly to litigation logic, valuation models, and settlement outputs, rather than providing document summaries alone.

About Pattern Data

Pattern Data is an AI-powered case evaluation platform that evolves with litigation. From early screening through final settlement, Pattern helps legal teams organize evidence, assess case strength, and deliver compliant outcomes with speed and confidence.