Can I patent an algorithm?

Can I Patent an Algorithm?

Algorithm patentability represents one of the most complex and evolving areas of patent law. As computing advances reshape nearly every industry, the question of whether algorithms qualify for patent protection has significant implications for software developers, data scientists, and technology companies.

Understanding Algorithm Patentability

Basic Patent Requirements

For any innovation to qualify for patent protection, it must satisfy several fundamental requirements:

• Novelty - The invention must be new and not previously disclosed
• Non-obviousness - The invention must not be obvious to someone skilled in the relevant field
• Utility - The invention must serve a useful purpose
• Patent-eligible subject matter - The invention must fall within categories of patentable subject matter

The United States Patent and Trademark Office (USPTO) evaluates these criteria during the patent examination process.

The Abstract Idea Exception

Algorithms face particular scrutiny under the "abstract idea" exception to patent eligibility. The Supreme Court has consistently held that abstract ideas, mathematical formulas, and mental processes are not patentable on their own.

Key cases establishing these boundaries include:

• Gottschalk v. Benson (1972) - Rejected a patent for an algorithm converting binary-coded decimal numbers to pure binary
• Parker v. Flook (1978) - Denied protection for a mathematical formula used in alarm limits during catalytic conversion
• Diamond v. Diehr (1981) - Allowed a patent for a rubber-curing process that incorporated a mathematical formula as part of a larger process

These foundational cases established that mathematical algorithms themselves generally fall outside patent-eligible subject matter.

The Alice Framework and Algorithm Patents

The Two-Step Test

The modern approach to algorithm patentability was significantly shaped by the 2014 Supreme Court decision in Alice Corp. v. CLS Bank International, which established a two-step test:

1. Determine if the claims are directed to a patent-ineligible concept (abstract idea, natural phenomenon, or law of nature) 2. If so, determine if the claims add "significantly more" to transform the abstract idea into a patent-eligible application

This framework has substantially impacted how algorithm-based inventions are evaluated.

Post-Alice Developments

Since Alice, several developments have further clarified algorithm patentability:

• The Federal Circuit's decision in Enfish, LLC v. Microsoft Corp. recognized that improvements to computer functionality may be patentable
• McRO, Inc. v. Bandai Namco Games America Inc. established that algorithms automating previously manual processes may qualify for protection
• USPTO guidance has provided more detailed frameworks for examining abstract idea claims

These developments indicate that while pure mathematical algorithms remain difficult to patent, their practical applications in technology may qualify for protection.

Strategic Approaches to Algorithm Patents

Technical Implementation Focus

Successful algorithm patents typically focus on:

• Specific technical implementations rather than the underlying mathematical concepts
• Concrete applications solving particular technological problems
• Hardware integrations or system architectures
• Measurable improvements to computer functionality

IBM, one of the world's largest patent holders, regularly secures patents on algorithm implementations by focusing on specific technical solutions rather than abstract concepts.

Application Domain Considerations

Patentability often varies by application domain:

Machine Learning and AI Algorithms

Machine learning algorithms (G06N20) present unique considerations:

• Training methodologies may be more patentable than the underlying mathematical models
• Specific applications of AI to technical fields often fare better in patent examination
• System architectures implementing AI algorithms may qualify for protection
• Novel data preprocessing or feature extraction techniques may be patentable

Google has secured numerous patents related to machine learning implementations while focusing on specific applications and technical improvements.

Financial and Business Method Algorithms

Algorithms in financial technology (G06Q40) face heightened scrutiny:

• Must demonstrate technological improvements beyond business process optimization
• Should include specific technical implementations
• Often require hardware integration or system architecture components
• Face more stringent "significantly more" analysis under Alice

PayPal has navigated these challenges by focusing patent applications on specific security implementations and technical fraud prevention methods.

Image and Signal Processing Algorithms

Image processing algorithms (G06T) generally have stronger patentability prospects:

• Typically involve transformations of digital data
• Often solve specific technical problems in representation or analysis
• Frequently involve hardware considerations
• Usually produce concrete, tangible results

Adobe regularly patents specific implementations of image processing algorithms by focusing on the technical process rather than the underlying mathematical principles.

Claim Drafting Strategies

Effective patent claims for algorithms typically:

• Focus on systems or methods implementing the algorithm rather than the algorithm itself
• Include specific technical steps beyond mental processes
• Demonstrate technical improvements or problem-solving
• Avoid claiming mathematical formulas in isolation
• Incorporate hardware elements where relevant

Microsoft employs these strategies when developing patent applications for algorithm-based innovations.

Alternative Protection Strategies

Copyright Protection

While algorithms themselves may face patent challenges, related elements receive copyright protection:

• The specific code implementing an algorithm
• Documentation and explanatory materials
• User interfaces displaying algorithm results
• Compilations or unique selections of algorithms

Copyright protection is automatic upon creation but protects only the expression, not the underlying functionality or ideas.

Trade Secret Protection

For algorithms difficult to reverse-engineer:

• Maintain confidentiality through security measures
• Use access controls and encryption
• Implement confidentiality agreements
• Deploy obfuscation techniques in deployed software

NVIDIA protects some of its graphics processing algorithms as trade secrets while patenting other aspects of its technology.

Open Source Strategies

Some organizations strategically use open source approaches:

• Publishing algorithms to prevent others from patenting them
• Building community adoption and standards around algorithms
• Focusing monetization on implementation services or specialized applications
• Using open innovation while patenting specific improvements

TensorFlow, Google's machine learning framework, uses this hybrid approach with core algorithms published openly while specific implementations may be patented.

Questions about Algorithm Patents

How Do I Distinguish Between an Abstract Algorithm and a Patentable Implementation?

The critical distinction between an abstract algorithm and a patentable implementation lies in specificity and technical application. Abstract algorithms—mathematical formulas, general computational procedures, or data manipulation principles—typically remain unpatentable. However, specific implementations that apply these algorithms to solve particular technical problems may qualify for protection. Focus on concrete technical elements: hardware configurations that enable the algorithm, specific data structures optimized for performance, memory management techniques, or architectural designs that improve efficiency. A patentable implementation typically includes technical details beyond the mathematical steps—like how the algorithm interacts with specific computer components, transforms data in technically advantageous ways, or overcomes identified limitations in existing systems. Courts often look for a "technical effect" that improves computer functionality itself or solves a technical problem in an unconventional way. Document benchmark comparisons showing measurable performance improvements over conventional approaches. The more your implementation focuses on specific technical solutions rather than the underlying mathematical principles, the stronger your patent position becomes.

What Types of Algorithms Have the Strongest Patent Potential?

Algorithms with the strongest patent potential typically solve specific technical problems in non-obvious ways with measurable improvements. Computer vision algorithms that enable machines to identify objects or patterns more accurately or efficiently than previous methods often secure patent protection, particularly when tied to specific applications like medical imaging or autonomous vehicle navigation. Algorithms improving fundamental computer operations—such as compression techniques that reduce storage requirements or transmission bandwidth, encryption methods that enhance security while minimizing computational overhead, or cache optimization approaches that demonstrably improve system performance—generally have strong patent potential. Data processing algorithms that transform information in ways that produce specific technical benefits, especially when integrated with hardware components, fare better than purely mathematical approaches. Machine learning algorithms typically gain stronger protection when claiming specific training methodologies or architectures rather than underlying mathematical principles. The key factor is technical implementation—algorithms solving recognized technical problems with specific, non-obvious implementations that produce measurable improvements over existing solutions have significantly higher success rates during patent examination than abstract computational approaches.

How Have Recent Court Decisions Changed Algorithm Patentability?

Recent court decisions have significantly refined algorithm patentability standards, creating both challenges and opportunities. The Federal Circuit's decision in Enfish recognized that software improvements to computer functionality themselves are not abstract ideas, opening pathways for algorithm patents that enhance computing performance or capabilities. In McRO, the court found that algorithms automating previously manual processes using specific rules different from human approaches may be patentable, benefiting algorithms with rule-based implementations that differ from conventional human methods. The Berkheimer decision established that whether a claim element is "well-understood, routine, and conventional" is a factual question, allowing patent applicants to submit evidence demonstrating their algorithm's unconventional nature. These positive developments contrast with continuing restrictions from decisions like Electric Power Group, which found that merely gathering, analyzing, and displaying data remains abstract and unpatentable. These evolving standards have shifted patent strategies toward emphasizing specific technical improvements, unconventional approaches, and concrete implementation details rather than general computational principles. Successful algorithm patents now typically focus on how the implementation improves computer functionality itself, solves specific technical problems, or achieves results in technically distinctive ways, rather than claiming broad computational concepts.

How Should I Document My Algorithm Development for Patent Purposes?

Comprehensive documentation during algorithm development significantly strengthens patent applications. Maintain detailed development logs identifying the specific technical problem your algorithm addresses, previous approaches and their limitations, and how your solution overcomes these challenges. Document performance benchmarks comparing your algorithm against existing solutions, with quantitative measurements showing specific improvements in speed, accuracy, resource utilization, or other technical metrics. Record all design alternatives considered and why particular approaches were selected, highlighting non-obvious design choices. Keep dated records of key insights or breakthroughs that led to your solution, especially those demonstrating inventive conceptualization beyond routine development. Document hardware considerations, optimization techniques, and implementation specifics beyond the mathematical formulation. Include flowcharts, pseudocode, and architectural diagrams illustrating your specific implementation approach. For machine learning algorithms, document training methodologies, dataset characteristics, and hyperparameter optimization techniques. Preserve evidence showing your algorithm solves problems that previous solutions couldn't address effectively. This comprehensive documentation helps patent attorneys identify patentable aspects of your work, focus claims on technical implementations rather than abstract concepts, and provide evidence of non-obviousness and specific technical contributions—all crucial for overcoming subject matter eligibility challenges.

What International Considerations Apply to Algorithm Patents?

Algorithm patentability varies significantly across major jurisdictions, requiring territory-specific strategies. The European Patent Office (EPO) applies a "technical effect" approach—algorithms must produce a technical effect beyond normal computer operation, focusing on specific applications rather than the algorithm itself. Japan's patent office has relatively favorable views toward computer-implemented inventions, typically requiring that algorithms solve specific technical problems. China has become increasingly receptive to algorithm patents, particularly those with industrial applications, though enforcement remains challenging. South Korea follows an approach similar to the United States but generally maintains a more favorable view of software patents. India applies particularly strict standards, generally excluding mathematical methods and algorithms unless they demonstrate hardware integration. When pursuing international protection, consider filing a Patent Cooperation Treaty (PCT) application to preserve rights while developing jurisdiction-specific strategies. Draft your primary application with these international variations in mind, including claims of varying scope that address different jurisdictional requirements. Consider starting with filings in jurisdictions with more favorable treatment of algorithm patents to establish priority dates. For some algorithms, geographic patent strategy should reflect regional industry strengths—financial algorithm patents in New York and London, manufacturing algorithm applications in East Asia, or telecommunications algorithms in markets with advanced network infrastructure.