How WhyHow Uses Expert Reasoning Maps to Filter Millions of Signals

Summary

We see millions of data points each week. Most are noise. We use domain expert reasoning maps to turn raw signals into ranked, defensible leads.

Legal and regulatory intelligence demands scale and precision. Every week, millions of data points emerge from government agencies, court systems, industry databases, and the open web. The overwhelming majority is noise—irrelevant to any viable case theory, duplicative, or lacking evidentiary weight.

WhyHow solves this signal-to-noise problem with expert reasoning maps: structured graphs that encode how seasoned domain experts evaluate potential cases. These maps don't replace human judgment but focus on scaling it. 

By translating expert thinking into executable logic, we automate the filtering, scoring, and triage that would otherwise require armies of analysts. The result is a ranked pipeline of defensible leads, each with a transparent audit trail from final recommendation back to source documents.

What is a reasoning map

A reasoning map is a directed graph that models expert decision-making. Think of it as a blueprint of how an experienced attorney or investigator dissects a potential case. Each map captures three core components:

1. Claims and their constituent elements
Every legal theory has required elements. A product liability claim might require defect, causation, injury, and damages. The map explicitly defines these elements and their sub-components.

2. Evidence requirements for each element
For each element, the map specifies what evidence would confirm or refute it. This includes document types, data fields, thresholds, and corroborating sources. The map distinguishes between strong evidence (manufacturer admissions, regulatory citations) and weak evidence (isolated social media posts, anonymous complaints).

3. Decision logic and thresholds
The map defines scoring rules: how much weight each piece of evidence carries, how evidence combines, and what score triggers escalation versus dismissal. It also encodes expert priors—baseline expectations about prevalence, reporting patterns, and alternative explanations.

The output is not a black box. Every scored lead comes with a complete audit trail: which documents were reviewed, which tests passed or failed, and how the final score was calculated. Attorneys can reconstruct the entire reasoning chain and validate it against their own judgment.

Data we process

Our reasoning maps operate on a comprehensive intelligence layer built from:

• Government regulatory sources
  FDA warning letters and adverse event reports, MAUDE device complaints, product recalls, NHTSA consumer complaints, OSHA violation records, EPA enforcement actions, and FTC cease-and-desist orders.

• Court and litigation data
  Federal and state dockets, judicial orders and rulings, multidistrict litigation updates, corporate internal investigation disclosures, and settlement agreements.

• Industry filings and disclosures
  Adverse event summaries from pharmaceutical and device manufacturers, product instructions and safety bulletins, investor SEC filings (10-Ks, 8-Ks), and Freedom of Information Act request logs.

• Open web and social intelligence
  Social media posts discussing products and side effects, health forums and patient communities, news articles and investigative journalism, and consumer product reviews across retail platforms.

Every data point is deduplicated, timestamped, and entity-resolved. We link mentions of products, manufacturers, injury types, and individuals into a unified knowledge graph. This normalization is essential—without it, evidence fragments remain isolated and unscorable.

How the maps filter noise

1. Normalize

Raw data arrives in dozens of formats: PDFs, XML feeds, HTML scrapes, API responses. We extract and normalize key fields: clean text, resolve company and product entities, extract device models and lot numbers, identify body sites and medical outcomes, parse dates and timelines.

This normalization step is critical. A reasoning map can't evaluate evidence if it can't reliably identify "the same product" across a recall notice, a court docket, and a patient forum.

2. Hypothesis setup

Each reasoning map encodes the questions an expert would ask when evaluating a potential case. These questions are hypothesis-driven and legally grounded. Examples:

• Manufacturing and quality control: Is there evidence of a manufacturing deviation during the window when the plaintiff's device was produced? Are there FDA warning letters citing quality system failures at the relevant facility?

• Regulatory pathway and claims: Is this device approved via premarket approval (PMA) or 510(k) clearance? What claims does the manufacturer make, and which of those claims might survive preemption defenses?

• Defect patterns and injury mechanism: Is there a plausible defect mode that matches the injury narratives we're seeing? Do engineering analyses or expert opinions support this mechanism?

• Causation and competing explanations: Are there alternative causes—comorbidities, user error, natural disease progression—that better explain the observed pattern? How strong is the temporal and dose-response relationship?

3. Evidence tests

For every question, the map specifies concrete tests to run against our data. These tests are granular and evidence-focused:

• Match regulatory citations to failures: If an FDA warning letter cites specific deviations in sterilization procedures, do we see contemporaneous complaint clusters mentioning infections?

• Link failures to adverse events: Do the quality system failures cited in warning letters align temporally and geographically with spikes in medical device reports (MDRs) or MAUDE complaints?

• Track corrective actions: Has the manufacturer issued field corrections, safety alerts, or recall notices? Do these actions correlate with the timing of the alleged injuries?

• Cross-reference litigation signals: Are there similar allegations in existing MDL dockets or state court complaints? Have other plaintiffs' firms filed cases with overlapping fact patterns?

• Assess social and media indicators: Is there growing public awareness of the issue? Are patient advocacy groups or investigative journalists covering the problem?

4. Traceability

Every decision is explainable. For any ranked lead, we can generate a report showing:

• The exact source documents (with citations) that contributed evidence

• Which tests passed or failed and by what margin

• How the score was calculated, including individual contributions from each piece of evidence

• What alternative hypotheses were considered and why they were ruled out

• Which expert priors or backtested weights influenced the outcome

This transparency is essential for legal work. Attorneys need to verify reasoning, experts need to validate technical claims, and funders need to understand risk. A black-box recommendation is useless; a traceable one is actionable.

Why maps beat ad hoc rules

Traditional litigation intelligence relies on keyword alerts, manual review, and ad hoc analyst judgment. This approach doesn't scale. Reasoning maps offer decisive advantages:

Comprehensive coverage
The map structure forces completeness. To build a map, experts must enumerate all elements of a claim and all potential defenses. Nothing is forgotten in the heat of a deadline. Every lead is evaluated against the full checklist.

Speed and throughput
A senior attorney might personally review dozens of leads per week. A reasoning map evaluates thousands of hypotheses per minute. This speed isn't just about efficiency—it's about coverage. We can monitor emerging issues in real time, detect patterns across jurisdictions, and respond to regulatory actions the day they're published.

Continuous learning and improvement
Maps are not static. As cases progress, we track outcomes: which leads converted to filings, which theories survived motions to dismiss, which defenses proved dispositive. We feed this data back into the scoring models, refining weights and thresholds. The system gets smarter with every case.

What this means for partners

For law firms and litigation funders, reasoning maps translate to tangible operational benefits:

Higher lead quality
Fewer cold leads mean less wasted time. Attorneys spend their hours on cases with real evidentiary foundations, not chasing social media rumors or duplicative complaints.

Faster case development
The path from raw signal to legal theory is compressed. Instead of weeks of manual research, teams receive pre-scored leads with supporting documentation and preliminary memos. Intake becomes triage, not investigation.

Clear documentation and defensibility
Every recommendation comes with citations and reasoning. When presenting to experts, co-counsel, or funders, you have a transparent evidence package—not just an analyst's hunch.

Reduced risk of costly dead ends
Reasoning maps explicitly test for case-killers: preemption defenses, strong alternative causes, insufficient injury patterns. By surfacing these risks early, they prevent investment in cases likely to fail on legal or causation grounds.

Summary

The volume of legal and regulatory intelligence will only grow. More data sources, more filings, more complaints. Manual review cannot keep pace.

Expert reasoning maps offer a solution: they let us apply the rigor of seasoned legal thinking at machine scale. They filter the noise, preserve judgment, and surface the few signals that matter—complete with evidence you can verify.

This is not automation for its own sake. It's about ensuring that expertise, not just effort, scales. And in a world of millions of signals, that distinction makes all the difference.