A working proof of concept demonstrating how Chrisian can programmatically acquire, normalize, enrich, and structure public IP dispute and litigation data, with human review integrated where judgment or verification is required.
Built to explore where these capabilities could support enterprise legal-data operations.
Leading commercial IP litigation databases operate at significant global scale, combining automated data extraction with expert analysis to transform source material into normalized, verified, curated and searchable IP case data.
Chrisian has built a working proof of concept demonstrating that we can support this same class of data operation: programmatically acquiring data from multiple public sources, extracting and normalizing it into a common structure, enriching the resulting records, and routing cases that require verification or judgment into structured human-review workflows.
We are not proposing to recreate or replace an existing commercial IP litigation database. The purpose of this demonstration is to show your team what Chrisian can now do and start a conversation about where these capabilities — data automation combined with scalable human review — could provide additional capacity, support existing workflows, or enable new data initiatives.
The pipeline is designed around source-specific connectors that programmatically acquire data from authoritative or established public data services. For U.S. federal litigation, the proof of concept connects to CourtListener and its RECAP collection of PACER data, operated by the nonprofit Free Law Project. CourtListener provides structured access to federal dockets, filings, parties, attorneys and related metadata.
The same connector-based architecture has been extended to additional IP data sources and jurisdictions, demonstrating that the pipeline is not dependent on a single source or data structure.
Each source has its own data model, terminology and level of detail. The pipeline maps these disparate inputs into a common schema, creating consistent structured records that can be searched, enriched, reviewed and delivered regardless of the originating source.
| Field group | What we capture | Status |
|---|---|---|
| Case identity | Jurisdiction, court/board, case number, case name, source identifier | Programmatic |
| Dates | Filing, decision, termination and other available procedural dates | Programmatic |
| Parties & representation | Parties, attorneys and law firms where available from the source | Programmatic |
| IP classification | Patent, trademark, copyright or design | Programmatic |
| IP rights referenced | Patent, trademark or design identifiers detected in source data/documents | Programmatic + human review |
| Procedural status | Pending, terminated, decided or other source-supported status | Programmatic |
| Outcome & legal issues | Disposition, prevailing party and applicable points of law | Human review where required |
| Source material | Linked filings, decisions, descriptions, dates, page counts and available text | Programmatic |
| Enrichment | Registration, ownership, chain-of-title and related data where available | Programmatic + human review |
The result is a source-independent record structure: different jurisdictions and source systems can feed a common downstream workflow for search, enrichment, quality review and delivery.
Example of a normalized record generated from live source data:
{
"case_name": "The American Psychiatric Association v. EmblemHealth, Inc.",
"court": "District Court, S.D. New York",
"docket_number": "1:25-cv-10783",
"ip_type": "TRADEMARK",
"date_filed": "2025-12-30",
"disposition_status": "PENDING",
"assigned_judge": "John George Koeltl",
"parties": ["EmblemHealth Plan, Inc.", "The American Psychiatric Association"],
"firms": ["Zuckerman Spaeder LLP", "Janove PLLC"],
"documents": [ { "description": "NOTICE TO ATTORNEY REGARDING CIVIL...", "page_count": 2 } ]
}
The proof of concept automates the repeatable stages of the data pipeline — source acquisition, raw-data preservation, normalization, classification, enrichment and search — without manual data entry between source retrieval and the resulting structured record.
Where the source data alone is insufficient to make a reliable determination, the system routes the record into a structured Human Review Console rather than forcing an automated conclusion. An analyst can review the underlying source material, confirm or correct the extracted information, and submit a decision that updates the structured record with a visible verification status and audit trail.
Outcome & legal-issue review. Determining disposition, prevailing party, grounds for a decision, or applicable points of law may require reading the underlying order or judgment rather than relying solely on docket metadata. The review console presents the source material alongside structured review fields, so the analyst’s determination becomes part of the record.
The objective is not to automate every decision. It is to automate repeatable work and create an efficient, auditable path for the work that requires human judgment.
Chrisian combines the technology to acquire, structure and enrich data with the production resources to operate structured review workflows at scale. This allows the same pipeline to support programmatic processing, exception handling, verification and analyst review within a single managed workflow.
This proof of concept is intended to demonstrate a capability that can be adapted to existing data operations or new initiatives based on the requirements of each workflow.
The proof of concept includes eight additional review and enrichment workflows that demonstrate how programmatic processing and analyst review can work together. Each follows the same principle: automate the repeatable work, surface exceptions or ambiguity, and route decisions requiring verification to a reviewer.
Together, these workflows demonstrate Chrisian’s ability to combine data automation with structured human review across multiple sources, jurisdictions, languages and IP categories.
The same architecture can support different levels of human involvement depending on the source and task: from simple verification of programmatically generated data to document review, translation QA, entity resolution and legal-data classification.
This proof of concept is designed to demonstrate the underlying capability rather than prescribe a particular client workflow. The specific balance between automation and human review can be adapted to the requirements of the data operation being supported.
After establishing the workflow across U.S. federal courts, EPO and EUIPO, we extended the proof of concept to two substantially different sources: WIPO domain-name dispute decisions and Japan’s IP High Court.
The objective was to test whether the underlying architecture could accommodate new jurisdictions, languages, document formats and types of IP proceedings without redesigning the normalization and human-review framework.
Adding WIPO and Japan required source-specific connectors, but not a redesign of the underlying processing and review architecture.
The proof of concept now spans five live source systems, multiple jurisdictions and institutions, four languages, multiple document formats, and a growing range of IP and dispute types — all feeding a common framework for normalization, enrichment, translation, human review and delivery.
This demonstrates an architecture that can be extended to new data sources while preserving a consistent downstream workflow.
The normalized data is available through both a searchable demonstration interface and a queryable REST API, allowing records to be explored interactively or accessed programmatically:
GET /api/v1/cases/search?ipType=PATENT&court=txed&limit=10 GET /api/v1/stats
Because every record lives in one common schema, batch export formats such as JSONL could be added for bulk delivery or downstream processing without restructuring the pipeline — not yet built in this proof of concept, but a natural next step.
Because acquisition, normalization, enrichment and human review all feed the same underlying data model, verified information is reflected consistently across search and API responses.
This proof of concept is intended as a capability demonstration rather than a proposed replacement for an existing product or workflow.
It demonstrates Chrisian’s ability to combine data automation with scalable human review — acquiring information from disparate sources, normalizing and enriching it into structured data, routing exceptions and judgment-based tasks for analyst review, and delivering the resulting data through searchable or programmatic interfaces.
We would welcome the opportunity to explore where these capabilities could support your organization — whether by adding capacity to an existing data operation, taking on a defined acquisition, enrichment or review workflow, or helping develop a new data initiative.