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 entire mass tort case inventories at scale.
Pattern operates as one continuous system that evolves as mass tort litigation progresses. Using the Screen, Develop, and Settle framework, Pattern carries data and logic forward to validate cases, strengthen case profiles, and generate compliant settlement submissions without rework.
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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
- Making inventory-level decisions about prioritization, development strategy, and settlement readiness
- Preparing settlement-ready submissions that meet allocation rules
Who Uses Pattern Data
Plaintiff law firms use Pattern to gain visibility into their case inventory before litigation decisions have to be made. In practice, this means knowing which cases meet criteria, which have documentation gaps, and how the docket as a whole is positioned — before a settlement is announced, not after.
Settlement administrators and special masters use Pattern to apply eligibility and allocation logic consistently across large claimant populations. Pattern has been court-appointed in major mass tort settlement programs, supporting fair and efficient adjudication at scale.
Defense firms and litigation support teams use Pattern when they need structured, verifiable case data that holds up to scrutiny — with clear sourcing and human validation at every step.
In all cases, the value is the same: structured data and consistent criteria applied across an entire inventory, not one file at a time.
Pattern’s Case Evaluation Lifecycle
Pattern Data operates through three connected stages. Each stage builds on the last, with data and logic flowing forward so work completed earlier can be reused later without re-review.
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: Apply settlement logic at scale without re-reviewing cases from scratch.
When a settlement is announced, the firms that move fastest are the ones whose inventory is already organized. Pattern applies eligibility and allocation logic to the case data already structured during earlier stages — determining who qualifies, how allocation breaks down, and what each case requires for submission.
Because the underlying case data carries forward, firms don't re-review. They apply the new criteria to work already done.
Outputs include:
- Settlement Evaluation with applied eligibility and allocation logic
- Claimant-facing and administrator-ready packets
- Export-ready datasets aligned to settlement submission requirements
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:
- Secure ingestion of raw, unstructured records
- AI-driven extraction and contextual analysis
- Application of litigation-specific criteria through configurable lenses
- Human validation of critical findings
- 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
- Pattern Data is not limited to one-off review outputs that can’t be reused as litigation evolves.
The platform is designed to accelerate, standardize, and support expert review, not bypass it.
How Pattern Differs from Other Legal AI Tools
Most legal AI tools are built around the individual case — extracting data from a single file, summarizing a record, or generating a demand letter. That's useful, but it's not what mass tort litigation requires.
Pattern is built around the docket. Every case is evaluated against the same litigation-specific criteria, scored consistently, and connected to the cases around it. The result is inventory-level visibility. Firms can see where every case stands, what's missing, and how the docket as a whole aligns with settlement requirements.
This distinction matters most at scale. When a firm is managing thousands of claimants across an MDL, the question isn't whether a single case is well-documented. It's whether the inventory is ready and what it will take to get there.
Pattern is not a document review tool. It does not summarize records in isolation. It evaluates cases against litigation criteria and connects those evaluations across the entire docket.
Pattern is not a case management system. It does not replace the firm's existing matter management infrastructure. It operates alongside it, adding the layer of structured case intelligence that case management systems don't provide.
Pattern is not a general-purpose AI platform. Its extraction models, scoring logic, and evaluation frameworks are built specifically for mass tort litigation — trained on more than one million cases across active MDLs.
Outcomes Delivered
Organizations using Pattern Data typically achieve:
- Faster intake screening and review
- Reduced manual review costs
- Improved visibility into docket quality, risk, and readiness across the case inventory
- Fewer last-minute settlement issues
- Well-documented cases with defensible evidence mapping against settlement criteria
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 firms start with just one litigation or one stage?
Yes. Firms often start with a single litigation or a specific phase, such as early screening or settlement preparation. Pattern is designed so work completed at any stage remains usable later as litigation strategy, criteria, or settlement requirements evolve.
Do we have to wait until settlement is announced to use Pattern Data?
No. Firms use Pattern Data at different points in the litigation lifecycle. Some start early to gain visibility into their inventory and prepare for future requirements, while others begin during settlement preparation. In either case, Pattern is designed so reviewed work remains usable as criteria and settlement rules become clearer.
Can Pattern Data handle changing criteria?
Yes. Eligibility, scoring, and valuation automatically update as criteria evolve, allowing firms to adapt without re-reviewing cases from scratch.
Does Pattern Data replace human review?
No. Pattern Data uses human-in-the-loop workflows to validate critical findings and ensure accuracy, auditability, and defensibility.
What do firms actually get at the end of the process?
Firms can generate multiple export-ready outputs from the same reviewed data, including internal reports, client-facing packets, and settlement-compliant submission files. Outputs can be tailored for different audiences without redoing the underlying review.
How is Pattern Data different from document review tools?
Pattern Data connects extracted data directly to litigation logic, valuation models, and settlement outputs, rather than providing document summaries alone. Reviewed data remains usable across the entire docket as requirements change.
What does "human-in-the-loop" actually mean in practice?
Pattern's AI extracts and structures data from claimant records — diagnoses, exposure timelines, treatment history, risk factors. That extracted data is then presented to the firm's reviewers alongside the source documents, so reviewers can confirm key findings before cases move forward. No finding is applied to a case without reviewer validation. This is what makes Pattern's outputs defensible — in front of courts, administrators, and opposing parties.
How is Pattern different from case management software or general legal AI tools?
Case management platforms are built to help firms organize and track individual cases — client communication, deadlines, document storage, and matter progress. General legal AI tools are built to summarize records or extract data from individual files. Both are useful, but neither is designed for what mass tort litigation actually requires at scale.
Pattern operates at the inventory level. Every case is evaluated against the same litigation-specific criteria, scored consistently, and connected to the cases around it. That structured data carries forward through development and into settlement — without re-review.
The distinction is most visible when a settlement is announced. A firm with 3,000 Roundup cases doesn't need better document summaries. They need to know how their entire inventory aligns with the allocation matrix — and be able to generate compliant submissions from the same data, without starting over. That's what Pattern is built for.
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.