Automated Workflow for Internal Research Repository Mining

This workflow converts sunk R&D costs into new, protectable assets. By systematically parsing years of lab notebooks, project reports, and engineering comments, it identifies novel concepts that were never formally disclosed. This directly recovers value from past research spend, turning 'data graveyards' into a pipeline of patentable ideas without new experimental investment.

Manual invention harvesting is sporadic and relies on scientist recall, often taking months. An automated mining agent continuously scans repositories like SharePoint, Confluence, and Git, flagging potential inventions in near real-time. This cuts the time from idea generation to formal disclosure from quarters to weeks, accelerating the entire IP pipeline and protecting against internal publications becoming prior art.

Unprotected internal publications, conference posters, and thesis documents can become prior art that blocks your own future patents. This workflow proactively identifies these 'self-defeating' publications by cross-referencing internal research against external patent databases. It alerts legal and R&D teams to file provisional applications before public disclosure deadlines are triggered, closing a critical IP leakage point.

Beyond recovering single inventions, the workflow builds a knowledge graph linking concepts, authors, and projects across the organization. This reveals whitespace in your technology landscape and identifies clusters of related innovations that can be protected as strategic patent families, creating a denser, more defensible portfolio that deters competitors and enhances licensing potential.

The initial triage and evidence gathering for a disclosure is labor-intensive. This workflow automates the assembly of a preliminary disclosure package: extracting key technical claims, identifying co-inventors via contribution tracking, and pulling relevant source documents. This reduces paralegal and administrative workload by 50-60%, allowing IP counsel to focus on high-value prosecution and strategy.

By quantifying the IP yield (inventions per dollar) of different projects, teams, and research domains, this workflow provides an evidence base for future R&D funding. Leadership can shift investment to areas with higher innovation throughput and stronger alignment with commercial IP strategy, creating a direct feedback loop between research spending and portfolio growth.

The workflow begins with a specialized ingestion agent that connects to data lakes, SharePoint, Confluence, and legacy file servers. It normalizes PDFs, presentations, code comments, and lab notes, using a combination of OCR (for scanned docs) and structured extraction (for slides, markdown). This agent handles versioning, deduplication, and metadata tagging (author, date, project) to create a clean, queryable corpus, which is the foundation for all downstream analysis.

Core analysis is performed by a pipeline that embeds document chunks into a vector store (e.g., using OpenAI or open-source models) and runs named entity recognition (NER) for technical concepts (e.g., chemical compounds, algorithms, material properties). A retrieval-augmented generation (RAG) agent answers natural language queries like "find all mentions of novel battery electrolyte formulations from 2020-2023." This replaces manual grep searches with contextual, concept-based discovery.

This agent performs the critical IP triage. It clusters extracted entities and technical descriptions, then cross-references them against internal prior disclosures and external patent databases via API (e.g., USPTO, Google Patents). Using similarity scoring, it surfaces clusters that are novel internally (i.e., never disclosed) but also assesses external novelty risk. It generates a confidence score and a preliminary "invention brief" for the highest-potential clusters.

Not every signal is a patent. This orchestrator manages the approval workflow, routing high-scoring concepts to the appropriate R&D lead or IP counsel for review via integrated systems like Anaqua, Jira, or ServiceNow. It presents the evidence packet (source docs, novelty score, external references) and captures feedback. Based on the decision ("File," "Further Research," "Reject"), it triggers the next phase—either kicking off a formal disclosure or archiving the finding.

The workflow's value is realized through integration. This component provides bi-directional APIs to Product Lifecycle Management (PLM) systems like Siemens Teamcenter and Enterprise Legal Management (ELM) tools like Anaqua or CPA Global. When a concept is approved for filing, it automatically creates a linked record in the docketing system, attaches source documents, and tags the relevant R&D project in the PLM for ROI tracking. This closes the loop between discovery and formal IP management.

A production workflow requires controls. This dashboard provides metrics on documents processed, concepts surfaced, review outcomes, and time-to-decision. It flags data quality issues (e.g., poor OCR yield) for retraining pipelines and logs all agent actions for compliance. Governance rules are enforced here, such as ensuring no document with PII is processed and that all analyst interactions are recorded for defensible audit trails in case of litigation.

The workflow begins with a multi-format ingestion pipeline that processes PDFs, presentations, code comments, and lab notebooks from sources like SharePoint, Confluence, and Git. LLM-powered agents perform named entity recognition (NER) to extract chemical compounds, algorithms, device components, and experimental methods, structuring unstructured data for downstream analysis. This pipeline must handle versioning, access controls, and data quality validation to ensure a clean, queryable knowledge base.

Extracted entities and concepts are embedded and indexed in a vector database (e.g., Pinecone, Weaviate). A knowledge graph is then constructed to map relationships between researchers, projects, techniques, and materials. This enables associative discovery—surfacing forgotten inventions by semantic similarity rather than keyword matching. The graph serves as the core reasoning layer, allowing agents to traverse connections and identify novel clusters of research that never culminated in a formal disclosure.

A specialized scoring agent evaluates surfaced concepts against external patent databases (USPTO, EPO, Derwent) via API integrations. It generates a novelty score and a preliminary prior art report. Concepts exceeding a confidence threshold are automatically routed to a human-in-the-loop triage queue in systems like Anaqua or CPA Global, while low-confidence signals are logged for future monitoring. This gate ensures legal and R&D resources focus only on high-potential IP.

For the workflow to be operational, it must integrate bidirectionally with Product Lifecycle Management (PLM) systems like Siemens Teamcenter and Enterprise Legal Management (ELM) software. Approved invention candidates trigger the creation of a disclosure record in the ELM, while relevant project data from the PLM enriches the invention context. This closed-loop integration embeds IP discovery directly into R&D and legal operations, preventing valuable signals from being lost between systems.

Every automated decision—from entity extraction to novelty scoring—is logged with a rationale in an immutable audit trail. A governance console allows IP managers to adjust scoring thresholds, review exception queues, and monitor workflow performance. Mandatory human review gates are placed before any formal disclosure is drafted, ensuring legal oversight and maintaining defensibility. This layer is critical for regulated industries and for building trust in the automated system.

The primary business value is recovering hidden IP asset value from sunk R&D investment and mitigating the risk of self-created prior art. By systematically mining years of research, companies can file on latent inventions before competitors or their own publications block them. The architecture directly translates to measurable outcomes: a 40-60% reduction in invention capture cycle time and a significant decrease in missed filing opportunities that otherwise result in lost licensing revenue or competitive disadvantage.