What Is Patent Mining?
Patent mining is the systematic extraction of hidden value from technical work. The term means different things depending on who uses it.
To an IP service firm, it means searching patent databases. To a corporate IP department, it means facilitated sessions where engineers disclose inventions. To a developer with an AI scanner, it means something new entirely: letting computation find what you built but never thought to protect.
This guide covers all three definitions, explores the crypto mining parallel, and explains why AI is changing who gets to participate in the patent system.
This article is for educational purposes. It is not legal advice. Consult a patent attorney for your specific situation.
Three Definitions, One Term
The phrase "patent mining" carries three distinct meanings in practice. Each targets a different source of hidden value.
Database Mining
Searching existing patent databases for competitive intelligence, licensing targets, and prior art. This is the original definition used by IP service firms and law practices.
Invention Harvesting
Facilitated sessions where engineers and product teams meet with IP counsel to surface inventions that might otherwise go undisclosed. Sometimes called "mining sessions."
Codebase Mining
AI-powered scanning of source code to discover undisclosed patentable innovations. The "questions" are asked by AI. The "people" are replaced by code.
The Mining Metaphor
The "mining" metaphor for computational extraction traces back to data mining in the 1990s. The core idea: extracting value from large datasets that would be impossible to process manually.
| Domain | What's Mined | From Where |
|---|---|---|
| Data mining | Patterns, correlations | Structured databases |
| Text mining | Meaning, sentiment | Unstructured documents |
| Process mining | Workflows, bottlenecks | Event logs |
| Crypto mining | Coins, transaction validation | Blockchain data |
| Patent mining | Patentable inventions | Patent databases or source code |
All describe the same fundamental operation: using computation to discover hidden value in large datasets. Patent mining fits naturally into this lineage.
Traditional Patent Mining
The established definition focuses on searching existing patent databases. The goal is competitive intelligence: who owns what, what's expiring, where are the gaps.
Typical Process
Define objectives
Licensing targets, competitive intelligence, portfolio gaps, or acquisition due diligence.
Search patent databases
Keywords, classification codes, inventor names, assignee filters.
Filter and analyze
Narrow results, evaluate individual patent documents, assess commercial relevance.
Act on findings
License, acquire, abandon, monetize, or design around.
Key Activities
Infringer identification
Locating companies using patented technologies without a license.
Evidence of Use charts
Documenting how specific patents apply to real products in the market.
Patent valuation
Assessing financial worth of individual patents or entire portfolios.
Portfolio due diligence
Comprehensive evaluation for M&A, licensing deals, or investment rounds.
Mining vs. Landscaping
Patent mining and patent landscaping are related but different. Mining goes deep into individual patents using text analysis and statistical tools. Landscaping maps broad competitive terrain using visual representations and inventor networks. Mining is a scalpel. Landscaping is a map.
Patent Mining Sessions
The second definition: facilitated workshops where engineers meet IP counsel to surface inventions. The people building things know what's new. The challenge is getting them to disclose it.
Five Enterprise Approaches
Network with innovators
Spend time with developers, engineers, scientists, and product designers. The people building things are the ones who know what's new.
Tap into development checkpoints
Attend proposals, design reviews, and customer presentations. These are natural moments where patentable features surface.
Monitor product rollouts
Pay attention as products ship. Timeliness matters: U.S. patent rights expire one year after public disclosure.
Learn from marketing
When marketing says "20% faster" or "more user friendly," there's usually a technical innovation driving the claim.
Connect with technical leadership
CTOs and Chief Scientists see across product lines and can identify patent opportunities at the portfolio level.
What the best sessions have in common
- Fun environments (offsite locations, food, prizes). Fun is the most essential ingredient.
- Low structure. Excessive process inhibits innovation.
- Dedicated champions who plan and execute sessions across divisions.
- Tracked outcomes: sessions conducted, disclosures generated, applications filed, patents issued.
The Cost Problem
Traditional patent mining is expensive at every stage. The cost barrier isn't just filing. It's discovery.
Most developers never get to the filing step because they don't know they have something worth filing. Traditional mining sessions cost thousands in attorney time before a single application is drafted. The inventor has to articulate what they built, the attorney has to translate it into patent language, and both have to agree it's worth pursuing. This process is slow, expensive, and biased toward inventions the developer already recognizes as novel.
Who gets left out
Independent developers, small startups, and solo founders are effectively excluded from the discovery step entirely. They have no IP department, no attorney relationship, and no budget for facilitated mining sessions. The patent system requires disclosure, and disclosure requires awareness. Without a discovery mechanism, the inventions stay hidden.
The Crypto Mining Parallel
Cryptocurrency mining uses computational power to extract digital value from blockchain data. Every computer on the network races to solve a mathematical puzzle. The value is embedded in the data. The miner uses computation to surface it.
The parallel with AI patent mining is surprisingly precise.
| Dimension | Crypto Mining | AI Patent Mining |
|---|---|---|
| Value source | Blockchain data | Source code |
| Extraction tool | Computational power (ASICs, GPUs) | AI analysis (LLMs, code understanding) |
| Output | Cryptocurrency (coins) | Patent disclosures and applications |
| Hidden value | Coins exist in the chain, require computation to extract | Inventions exist in the code, require analysis to surface |
| Democratization | Individuals can mine, not just banks | Individuals can discover patents, not just enterprises |
| Infrastructure | Mining rigs, electricity | Local-first AI tools, developer machines |
| Key shift | Physical gold to digital extraction | Manual attorney sessions to automated scanning |
Where the parallel holds
- Democratization. Crypto mining let anyone with hardware participate in financial systems previously controlled by institutions. AI patent mining lets any developer discover their own inventions without a $50K IP budget.
- Hidden value. The blockchain already contains the coins. The codebase already contains the inventions. Neither creates something new. Both surface something hidden.
- Local-first. Crypto miners run rigs at home. Developers run patent scanners on their laptops. Both are privacy-preserving and individually operated.
Where it breaks down
- Competition. Crypto mining is competitive (one miner wins the block). Patent mining is not (every invention found is independently valuable).
- Energy. Crypto mining consumes enormous energy. Patent mining is computationally lightweight by comparison.
- Certainty. A mined Bitcoin has a known market price. A discovered invention has uncertain value until validated.
Patent Mining in the AI Era
The third definition is emerging: AI computationally extracts undisclosed inventions from source code. This is distinct from both database mining and invention harvesting.
The shift: from "tell the attorney what you built" to "let the tool find what you built."
| Traditional Mining | AI Codebase Mining |
|---|---|
| Surfaces what people already know they built | Finds innovations developers don't recognize as novel |
| Depends on developer memory and articulation | Scans every file systematically |
| Takes months to schedule and conduct sessions | Produces results in minutes |
| Costs thousands in attorney time | Fraction of the cost |
| Requires sharing code with third parties | Local-first, code never leaves the machine |
The vast majority of software innovations go unprotected. Not because they aren't novel, but because developers don't know they have patentable inventions. The patent system requires disclosure, and disclosure requires awareness. AI patent mining creates that awareness automatically.
AI Patent Tools (2026)
Every major AI patent tool today focuses on searching existing patent databases. They operate downstream, after inventions have already been filed.
| Tool | Focus | Scans Code? |
|---|---|---|
| Patsnap | Patent search + drafting + analytics | No |
| IPRally | Patent search + classification | No |
| Cypris | R&D intelligence, 500M+ patents | No |
| Minesoft Origin | AI search for IP professionals | No |
| The Lens | Open-access patent + scholarly search | No |
| DeepIP | Patent drafting with generative AI | No |
| PQAI | Open-source patent quality | No |
The gap
None of these tools scan source code. They all start from existing patent data. This means they help with searching, analyzing, and drafting, but not with the fundamental question: what should become a patent in the first place?
AI codebase mining operates upstream. It discovers what should become a patent before the invention enters the patent system.
Who Benefits
Independent developers
No IP department, no attorney relationship, no budget for mining sessions. AI patent mining is their only practical entry point into the patent system.
Startups
Building fast, shipping code daily, raising funding. No time for facilitated workshops. Automated scanning runs alongside development, not instead of it.
Enterprise teams
Supplement existing mining sessions with automated coverage. Human sessions catch what developers remember. AI catches what they don't.
Patent attorneys
Get structured technical disclosures from clients faster. A structured disclosure with evidence from actual code compresses the hardest part of drafting.
Universities
Faculty inventions often go undisclosed due to process friction. Technology transfer offices are understaffed. Automated scanning could surface inventions that never make it to the disclosure form.
Frequently Asked Questions
What is patent mining?
Patent mining is the systematic process of extracting patentable inventions and IP insights from data sources. It has three meanings depending on context: searching patent databases for competitive intelligence, facilitated invention harvesting sessions where engineers disclose inventions to IP counsel, and AI-powered scanning of source code to discover undisclosed innovations.
How is patent mining different from patent landscaping?
Patent mining goes deep into individual patents using text analysis and data visualization to extract specific technical insights. Patent landscaping maps broad competitive terrain using visual representations, inventor networks, and technology area analysis. Mining is a scalpel. Landscaping is a map.
How does the crypto mining analogy work?
Both crypto mining and AI patent mining use computation to extract hidden value from data. Crypto miners use GPUs to find coins embedded in blockchain data. Patent mining tools use AI to find inventions embedded in source code. Both democratize access to systems that were previously restricted to well-funded institutions.
Can independent developers do patent mining?
Yes. AI codebase mining tools make patent discovery accessible without an IP department, attorney relationship, or enterprise budget. Local-first tools scan your code on your machine and surface potentially patentable innovations in minutes. The output feeds directly into the patent filing process, whether self-filed as a provisional or handed to an attorney with the technical disclosure already done.
Do any AI patent tools scan source code?
Most AI patent tools (Patsnap, IPRally, Cypris, Minesoft Origin) search existing patent databases. They help with prior art searches, patent analytics, and classification. AI codebase mining operates upstream, discovering what should become a patent before the invention enters the patent system. This is a fundamentally different problem: not "what patents exist?" but "what patents should exist?"