Multi-Agent based Automation of Inventorship Determination and Dispute Logging

Determining legal inventorship is a high-stakes bottleneck, directly impacting patent validity and ownership. Manual reconciliation of Git commits, lab notebooks, and meeting notes is slow, error-prone, and a source of legal disputes. This workflow automates evidence compilation and analysis using specialized agents, proposing an objective inventorship list and automatically logging discrepancies for legal review. It reduces administrative cycles from weeks to hours while creating a defensible audit trail, integrating with docketing systems like Anaqua or CPA Global.

Implementation requires orchestrating agents—built with LangGraph or CrewAI—to parse structured and unstructured data sources via APIs and document understanding models. The architecture must include configurable contribution scoring logic, human-in-the-loop approval gates for the final list, and immutable logging of all evidence and decisions to satisfy legal scrutiny. Exception routing to legal counsel within Enterprise Legal Management (ELM) systems like Onit is critical. This custom build transforms a contentious, manual process into a governed, scalable operational workflow that protects IP assets and reduces legal overhead.

This workflow eliminates the manual, contentious process of determining inventorship by deploying specialized agents to analyze contribution evidence from Git commits, lab notebooks (ELN), and meeting transcripts. It reduces administrative burden and legal risk by generating a proposed inventorship list with supporting evidence, directly addressing the operational bottleneck of delayed or inaccurate filings. The architecture integrates with source control (GitLab), document management, and IP docketing systems (Anaqua) to create a defensible, auditable trail.

Implementation involves deploying the orchestrator on a secure platform (e.g., Azure Container Instances) with LangGraph managing the agent state. Each evidence agent uses retrieval-augmented generation (RAG) over specific data sources. The system includes mandatory human review gates for dispute resolution and final approval before updating the system of record. Observability is built in via OpenTelemetry to track decision rationale and agent performance, ensuring governance and auditability for legal compliance.

This workflow directly addresses the costly administrative burden and legal exposure of manual inventorship determination. By automating the analysis of contribution evidence from Git commits, lab notebooks (e.g., Benchling), and meeting transcripts, it replaces weeks of manual reconciliation with a consistent, rules-based process. The operational upside is a 70-80% reduction in administrative time per disclosure and a defensible audit trail that mitigates future ownership disputes and potential patent invalidation. Implementation integrates with docketing systems like Anaqua or CPA Global and sources data from GitHub, Jira, and ELNs.

Phased delivery mitigates implementation risk. Phase 1 builds the evidence agent and a read-only dashboard for legal review, validating data connectors and scoring logic without altering live docketing. Phase 2 integrates the proposal agent and a dispute logging UI into the ELM system, with mandatory human-in-the-loop approval for all determinations. Phase 3 adds automated notifications and full audit trail generation. Governance requires configurable contribution thresholds, immutable logging for all agent decisions, and exception routing to specific legal counsel based on dispute severity and technical domain.

This workflow automates a high-stakes, manual bottleneck: determining who qualifies as an inventor on a patent. It analyzes contribution evidence from Git commits, lab notebooks (e.g., Benchling), and meeting notes (e.g., Microsoft Teams) using specialized agents. The operational upside is a 70-80% reduction in administrative time per disclosure and a defensible, auditable evidence trail that mitigates legal risk from incorrect inventorship, which can invalidate a patent. Savings come from replacing manual evidence compilation and contentious team meetings with a structured, data-driven proposal.

Implementation requires orchestrating agents (LangGraph, CrewAI) that query APIs from GitHub, SAP PLM, and electronic lab notebooks. The architecture must include an immutable audit trail and a human review gate before final docketing into systems like Anaqua or CPA Global. Critical controls involve configurable scoring thresholds, exception routing for edge cases, and integration with enterprise legal management for chain-of-custody. Rollout is phased, starting with a pilot R&D group to refine evidence weighting and dispute logic before scaling.

This workflow automates a high-stakes, legally sensitive bottleneck: determining who contributed inventively to a patentable concept. It replaces manual, spreadsheet-driven processes prone to error and bias, which can delay filings and create litigation risk. By deploying specialized agents to parse Git commits, ELN entries, and collaboration tools, it compiles an evidence-based contribution matrix. This reduces administrative burden on R&D and legal teams by 60-80%, ensures a defensible audit trail, and accelerates filing readiness by weeks.

Implementation requires integrating with source systems like GitHub, Benchling, and Microsoft Teams via APIs or RPA, then building agents for evidence weighting and scoring against legal criteria (conception vs. reduction to practice). A critical control is the dispute-logging agent, which flags discrepancies and routes them with full context to human counsel in docketing systems like Anaqua. The architecture must include an immutable explanation layer for each decision, supporting future litigation or audit requirements without manual reconstruction.