The Alice/Mayo test has fundamentally transformed the patent landscape for software inventions. Alice software refers to patents evaluated using this test, established by the US Supreme Court, to determine if a software invention is patentable or too abstract. This comprehensive guide explains how this test affects software patents and is applied under the framework of the Patent Act, providing the insights necessary to secure patents for software innovations.
Key Takeaways
- Understanding the Alice/Mayo test is essential for determining software patent eligibility (i.e., whether the invention is patent eligible subject matter), ensuring that patents foster innovation rather than protect abstract ideas.
- Demonstrating a specific technological improvement beyond generic implementations is crucial for passing the Alice test and securing patent protection for software inventions.
- Strategic drafting techniques for patent claims should focus on tangible outcomes and specific advancements to boost eligibility and combat rejections.
Understanding the Alice Software Test
The Alice/Mayo test employs a bifurcated approach to assess the viability of an invention for patent protection. This method, synthesizing multiple legal precedents, solidified its role as the bedrock for assessing patent eligibility following the Supreme Court’s seminal verdict in Alice Corp v. CLS Bank International. The high court delineated that certain software innovations could be classified as abstract ideas and thus fall outside the domain of patent protection. A thorough patent eligibility analysis is required to determine if an invention meets the criteria established by the Alice/Mayo test.
The profound ramifications of the Supreme Court’s ruling in Alice have dramatically limited patents based on abstract concepts merely applied using standard computer technology and diminished the issuance of ambiguous software patents. District courts have played a significant role in interpreting and applying the Alice framework, often conducting their own subject matter eligibility analysis independent of USPTO examiner reasoning. Consequently, this transition has equipped enterprises with stronger defenses against so-called “patent trolls” who previously leveraged such indistinct software patents for predatory litigation against burgeoning startups and smaller entities.
For AI creations to qualify for patent grants, they must conform to stipulations set forth by the Alice/Mayo examination framework. Such regulation ensures that issued patents contribute positively to technological progress and societal knowledge while preventing overly expansive claims from hampering innovation within industries dependent upon these advancements. The Manual of Patent Examining Procedure (MPEP), along with patent office guidance, informs the subject matter eligibility analysis performed by examiners, outlining the step-by-step process for determining whether claims are patent-eligible under the Alice framework.
Successfully navigating the complexities of software patent admissibility requires a thorough understanding of this essential scrutiny procedure, including the importance of determining subject matter eligibility.
Understanding the Three Categories of Abstract Ideas in AI Patent Claims
When working with AI patent applications, understanding how the USPTO categorizes abstract ideas that commonly appear in AI claims is crucial. The 2024 USPTO guidance provides enhanced clarity on this distinction, particularly important since AI technologies often incorporate mathematical concepts, algorithms, or computational techniques.
The USPTO identifies three primary categories of abstract ideas that may appear in AI patent claims.
Mathematical Concepts
These include mathematical relationships, formulas, equations, and calculations. Since many AI technologies involve complex algorithms and statistical methods, this category frequently applies to AI inventions. However, experienced practitioners have successfully argued that claims don’t recite mathematical concepts when they’re merely based on or involve mathematical principles without explicitly setting forth mathematical formulas or calculations in the claim language itself.
Methods of Organizing Human Activity
While less common in AI inventions, this category may arise in applications involving AI-driven financial systems, business methods, or workflow optimization. This commonly appears in claims related to AI systems that automate traditional business processes.
Mental Processes
These encompass concepts that could theoretically be performed in the human mind, such as observations, evaluations, judgments, and opinions. Significantly, the USPTO guidance specifies that claims don’t recite mental processes when they contain limitations that cannot practically be performed in the human mind. This exception frequently applies to AI inventions that process massive datasets or perform complex calculations beyond human cognitive capabilities—a distinction regularly leveraged in practice.
The critical insight is that when evaluating whether an AI invention recites an abstract idea, the USPTO carefully distinguishes between claims that explicitly “recite” an abstract idea (requiring further eligibility analysis) and those that merely involve or are based on an abstract idea without incorporating it into the actual claim language.
Step Two: Determining Inventive Concepts in Software Claims
In the second phase of the Alice/Mayo test, determining if an inventive concept that considerably enhances the abstract idea is present within a claim becomes the focus. This essential component has the potential to transform what might be recognized as a patent-ineligible abstract idea into an application fit for patent eligibility. The core purpose here is to establish whether additional elements, including any additional claim element, are included in the claimed invention that elevate its status beyond just executing an abstract notion.
During the evaluation process, practitioners must discern whether these claim elements are common and ordinary or considered conventional practices. An inventive concept can manifest through advancements that improve how computers operate or through developments applied to other technological fields. Particularly for AI innovations, showcasing specific technical progress rather than broad applications becomes pivotal in meeting the inventive concept criterion. Claims implemented with specific technical solutions are more likely to be considered patent eligible applications under the Alice/Mayo framework.
The documentation accompanying a patent—the patent specification—must concisely delineate both a distinct technical challenge and its solution with clarity. This serves as evidence supporting the inventive aspect of the proposal and is critical in supporting the inventive concept. Enhancements related to computer functionality or other tech areas remain focal points during examination by both USPTO examiners and under broader scrutiny within America’s patent framework. Ultimately, this step safeguards against granting patents merely on account of utilizing abstract ideas, but instead recognizes those inventions contributing genuine advances in technology as patent eligible inventions, with the claimed method playing a key role in demonstrating eligibility.
Insufficient Elements: Generic Computer Implementation
Simply adding a basic computer configuration to a patent claim does not confer patent eligibility on an abstract idea. Implementations using generic computers do not contain the requisite detail to satisfy the inventive concept criterion as per the Alice framework. Commonly, actions such as gathering data following finding a solution are not considered inventive. Routine data gathering steps and well-understood, routine, conventional activities are insufficient to make claims patent eligible.
The inability to show an inventive concept with non-specific computer implementations and commonplace procedures jeopardizes one’s chance of meeting patent-eligibility requirements. For instance, if a claim involves only utilizing a computer database for information storage without any particular technological enhancements, it is likely that this would fail under the Alice test by being categorized as an ineligible concept for patent protection.
Insignificant Post-Solution Activity
Patent claims that feature activities such as routine data gathering and other commonplace actions fail to contribute to the inventive quality of a software patent. These standard procedures, including basic computation or manipulation, do not meet the inventiveness criteria established by the Alice test. Such activities may be considered a recited judicial exception and do not contribute to patent eligibility.
To circumvent denial due to lack of significance, it is important for patent claims to highlight novel and creative components rather than ordinary or habitual tasks. It is essential for a claimed invention’s success under examination that it demonstrates how it leads to a distinct technological advancement.
Demonstrating Practical Application Integration for AI Inventions
Even when an AI invention is found to recite an abstract idea, it can still qualify for patent protection if it integrates that abstract idea into a practical application. This critical assessment evaluates whether the claim as a whole applies, relies on, or uses the abstract idea in a manner that imposes a meaningful limit on it.
Through analysis of numerous AI patent applications, one of the most effective strategies to demonstrate practical application integration is showing that the AI invention improves the functioning of a computer or another technology. The USPTO characterizes this as “a technological solution to a technological problem.”
For AI inventions specifically, practitioners focus on highlighting improvements such as:
- Enhanced computer vision capabilities that solve previously unsolvable technical problems
- More efficient data processing techniques that reduce computational overhead
- Increased speed or accuracy in machine learning operations beyond conventional approaches
- Novel neural network architectures that address specific technical limitations
- Reduced computational resource requirements for equivalent performance
- Improved ability to handle noisy, incomplete, or corrupted data sets
The key distinction emphasized in practice is between claims that reflect genuine improvements to technology (which are eligible) versus those that merely apply an abstract idea using generic computer components or simply link the abstract idea to a technological environment (which are ineligible). This distinction often determines success or failure in overcoming Alice rejections.
Practical Application Integration for AI Inventions
The USPTO is committed to ensuring clear and consistent assessments of AI-related patent claims, promoting innovation by confirming inventions satisfy the criteria for patent eligibility. The Patent Office evaluates whether claims constitute patent eligible applications and eligible subject matter by applying the standards established under 35 U.S.C. § 101 and the Alice framework. Even when a claim revolves around an abstract idea, it may qualify for patent protection if integrated into a practical application that reveals its concrete utility.
To assist in discerning how abstract ideas might evolve into eligible patents, the USPTO has provided guidance featuring examples such as anomaly detection and personalized medical treatment. These hypothetical scenarios serve to exemplify compliance with the standards set forth in the 2019 Revised Patent Subject Matter Eligibility Guidance.
Inventions rooted in AI have the potential to gain patent protection when they infuse an abstract idea within a functional application. An AI invention’s ability to enhance how computers or other forms of technology operate substantiates this integration effectively. Adopting this pragmatic viewpoint secures not only theoretical inventive concepts, but also those applied within tangible contexts, encompassing software-based innovations.
Recent USPTO AI Guidance and Federal Circuit Decisions (2024-2025)
The patent eligibility landscape for AI and software has evolved significantly with recent developments. In July 2024, the USPTO released comprehensive AI-specific eligibility guidance, effective July 17, 2024, following a White House executive order directing agencies to adapt to AI innovations. This guidance provides clarity on how examiners should apply the Alice/Mayo test to AI-related claims while maintaining the existing legal framework.
Landmark Federal Circuit Decision: Recentive Analytics v. Fox (2025)
A pivotal case emerged in April 2025 with Recentive Analytics, Inc. v. Fox Corp., marking the Federal Circuit’s first major ruling on AI and machine learning in the Alice context. The court held all four patents on using machine learning models to optimize TV broadcast schedules patent-ineligible as merely implementing an abstract idea with generic technology.
Judge Dyk, writing for the court, framed the critical issue: “whether claims that do no more than apply established methods of machine learning to a new data environment are patent eligible. We hold that they are not.” This decision reinforces that AI algorithms don’t receive special treatment under §101—if an AI/ML claim lacks specific technological improvement, it remains vulnerable to abstract idea exceptions.
The Recentive decision exemplifies the challenges regularly encountered when securing AI technology patents under current judicial conditions. Through innovative techniques, inventors can tackle these hurdles more effectively, often resulting in significantly improved success rates for overcoming Alice rejections.
Survey of the 3 Key AI Patent Eligibility Examples from USPTO
The July 2024 USPTO guidance included three detailed Patent Eligibility Guidance (PEG) examples illustrating how the Alice/Mayo test applies to AI inventions across different domains. The USPTO designated these as “Examples 47-49” in their sequential numbering system, but there are only these 3 examples specifically focused on AI patent eligibility. These examples provide invaluable insights for practitioners drafting AI-related patent claims and represent the most comprehensive guidance available for AI patent eligibility.
AI Patent Example 1: Anomaly Detection in Network Security
This example demonstrates AI-based network security through an artificial neural network (ANN) designed to detect anomalies and malicious activities. The USPTO analyzed three distinct claims:
Claim 1 (Eligible): An apparatus claim describing an application-specific integrated circuit implementing the ANN. Because it recites a concrete machine without abstract processes, it qualifies as eligible subject matter.
Claim 2 (Ineligible): A method that simply “leverages an artificial neural network to identify anomalies from a data set” without specific field of use or further action. This claim is directed to an abstract idea (data analysis) and lacks integration into a practical application.
Claim 3 (Eligible): Adds crucial additional steps by situating anomaly detection within network monitoring context and including responsive actions—determining malicious activity and taking remedial blocking measures. These concrete post-detection steps demonstrate practical network-security improvement, integrating the abstract idea into a practical application.
The key takeaway: merely using AI is insufficient; claims must demonstrate technological application or improvement to satisfy Alice requirements.
AI Patent Example 2: Speech Separation Using Deep Neural Networks
This example involves AI-based audio signal processing to separate mixed audio signals into individual speakers’ voices using deep neural networks (DNNs).
Claim 1 (Ineligible): Broadly recites receiving mixed audio signals, converting them (e.g., into spectrograms), and applying DNNs for voice separation. This essentially describes mathematical data analysis without practical limitations, making it ineligible as it “recites a judicial exception (an abstract idea).”
Claim 2 (Eligible): Uses the same DNN-based analysis but specifies application context—improving speech-separation systems’ ability to isolate voices by generating enhanced audio outputs for transcription. This additional context demonstrates concrete improvement in speech-separation technology.
Claim 3 (Eligible): Extends the process further by improving speech-to-text transcription accuracy after voice separation, perhaps by filtering commands versus background conversation. This claim ties AI processing to concrete transcription technology improvements.
The lesson: AI-based signal processing methods should link to specific technical outcomes (clearer audio, better transcription) rather than claiming underlying mathematical operations.
AI Patent Example 3: AI in Medical Diagnosis and Treatment
This scenario addresses using AI for diagnosing and treating pulmonary fibrosis, demonstrating the interplay between abstract ideas and natural law exceptions in biotech applications.
Claim 1 (Ineligible): Features an AI algorithm identifying fibrosis conditions from patient data, viewed as abstract data analysis implicating natural phenomena (correlations between biomarkers and disease).
Eligible Claims: Link AI diagnosis to specific treatment steps, such as administering tailored therapy based on AI determinations. By connecting discovery to concrete treatment protocols, these claims transform abstract ideas/natural laws into practical healthcare applications achieving real-world therapeutic outcomes.
This mirrors the Mayo precedent: pure diagnostic insights aren’t patentable, but methods applying insights in treatment can be. In practice with AI in medicine, including active treatment or intervention steps is crucial for passing Alice/Mayo scrutiny.
Takeaway from the 3 AI Patent Examples: Across these examples, the common thread is that specificity and technical application matter. AI or software-related claims must do more than recite an abstract algorithm or data processing; they should emphasize how the invention improves technology or achieves a tangible result. The USPTO’s 3 AI patent examples show that adding elements like particular devices, fields of use, or follow-up actions (e.g., remedial steps, outputting a concrete product, treating a patient) can make the difference between ineligible and eligible subject matter. In practice, patent applicants in AI/ML are learning to draft claims that highlight inventive technical contributions (such as improved accuracy, efficiency, or new functionality) to overcome Alice-based rejections.
Innovative Techniques for Overcoming Alice Rejections
At Thompson Patent Law (TPL), proprietary strategies have been developed that significantly enhance success rates in countering Alice rejections. These innovative techniques can improve the likelihood of overcoming such rejections by 25-50%, representing a substantial advantage for inventors facing these challenges.
These proven methodologies often result in time savings of one to two years and considerable reduction in prosecution expenses—often saving clients five figures in prosecution costs. This is particularly valuable given that patents obtained through prolonged prosecution typically emerge weaker than those allowed on first or second office actions, which is typically the case for software applications at TPL.
For inventors confronted with Alice rejections, strategic appeal considerations following rectification of other rejection categories may offer fresh perspectives. Should initial claims face rejection, filing continuation applications provides means to adjust responses to evolving legal benchmarks. Patent attorneys bring specialized expertise in navigating Alice rejections, analyzing claim elements, and formulating effective arguments for patent eligibility.
The latest modifications pertaining to patent subject matter eligibility encompass illustrative scenarios designed specifically for aiding patent professionals when assessing AI innovations during examination phases. Implementing these methodologies results in stronger patents that are more likely to withstand scrutiny and conform to criteria for being deemed patent-eligible.
Federal Circuit Decisions Impacting Software Patents
After the Alice verdict, the patent landscape for software inventions has become increasingly challenging. In 2020, a mere four out of twenty-seven software-related patent appeals passed through Section 101 scrutiny. The Federal Circuit has consistently applied stringent standards, recently adjudicating on machine learning patent status and upholding that patents pertaining to event scheduling optimization using standard machine learning techniques fell under judicial exceptions and were hence ineligible.
The Court has held that if a patent merely applies common machine learning algorithms to perform tasks manually achievable by humans, it does not qualify for patent protection. Federal circuit judges have underscored that utilizing typical machine learning models fails to satisfy Alice test criteria unless there are demonstrable technological enhancements. District court decisions have further reinforced this approach, often leading to patents being declared invalid when directed to abstract ideas without inventive concepts.
Despite these rigorous post-Alice decisions, investments in software R&D have surged, suggesting that such rulings haven’t stifled innovation within this sector. These judgments emphasize how critical it is for technological advancements to be precisely reflected in patent claims when seeking intellectual property protection.
Distinguishing Between Reciting and Involving Abstract Ideas
The United States Patent and Trademark Office (USPTO) makes a clear distinction between claims that explicitly include an abstract idea as a fundamental component, compared to those that simply utilize or are grounded in an abstract idea without integrating it within the actual language of the claim. This differentiation is essential for determining whether claims with mere references to abstract ideas can be considered for patent eligibility based on their practical implications and usages. Claims that are not properly integrated into a practical application may be considered patent ineligible subject matter under the standards set by the Alice decision and subsequent caselaw.
Claims do not account for mental processes if they incorporate limitations that cannot feasibly be executed within the human mind. The USPTO separates claims directly citing an “abstract idea” from those which merely associate with or derive from such an idea. This separation assists in evaluating both the inventive concept and establishing patent qualification, especially when examining claims aimed at specific implementations. These types of judicial exception-based claims cite additional elements contributing to its constraints.
Making this distinction plays a pivotal role in crafting applications capable of claiming patent-eligible status by allowing inventors to illustrate technological advancements and tangible uses, thereby fulfilling the requirements necessary.
Implications of the Supreme Court’s Alice Decision
The Supreme Court’s apprehension regarding the Alice/Mayo test centers on avoiding the monopolization of essential abstract ideas, which might impede innovation. The ruling from Alice emphasizes that patents ought to enhance public knowledge rather than limit it with excessively broad claims. The Alice decision has significantly influenced the patent system by shaping how courts interpret the patent statute, particularly regarding the eligibility criteria for software and computer-implemented inventions.
Since the decision in Alice, there has been a noticeable decrease in patent filings for business-method software. This trend hasn’t negatively affected venture capital investments within the software sector. Studies show that not having certain patents after Alice did not detrimentally affect positive business achievements such as acquisitions or initial public offerings (IPOs) in this field. Additionally, under the Alice framework, claims that are directed to a law of nature are often scrutinized and may be rejected if they merely apply a natural law without an inventive concept.
These insights imply that securing patents could have costs that surpass their advantages and necessitates reconsideration of their importance specifically concerning software. Meanwhile, efforts persist by some entities aiming to revise patent law potentially weakening the outcomes dictated by Alice and thereby increasing chances for more abstract concepts being patented, possibly escalating disputes over patent infringement within the world of patents.
Machine-or-Transformation Test for Software Claims
The machine-or-transformation test is a method used to determine the patentability of a process by checking whether it is tied to a specific machine or causes an alteration in an article’s condition. The Supreme Court has indicated that while this test serves as a valuable tool, it should not be considered the sole criterion for establishing process patent eligibility. Meeting the requirements of the machine-or-transformation test can help demonstrate that a claim is a patent eligible application, as it may provide the inventive concept needed to transform an abstract idea into patent-eligible subject matter under the Alice/Mayo framework.
Uncertainty remains over what exactly qualifies as ‘a particular machine’ and how much ‘transformation’ is required for something to be eligible for patent protection. Nevertheless, despite these ambiguities, the machine-or-transformation test offers an important guideline when assessing software-implemented inventions.
By comprehending this test, inventors are better equipped to craft claims that reflect technological innovation and real-world utility—enhancing their prospects of meeting the standards set out by Alice/Mayo and ultimately obtaining patent protection.
Strategies for Drafting Patent Eligible Software Claims
Crafting claims for software patents that meet eligibility requirements demands a focused methodology highlighting technological progress and practical use cases. Based on extensive experience, the recommendation is to aim for developing solutions with concrete effects, steering clear of proposals that simply rearrange current data. Such an approach guarantees deeper integration of claims with computer technologies, focusing on particular software functions or hardware capabilities.
When drafting, it is crucial to focus on patent eligible inventions by ensuring claims describe a patentable invention—one that constitutes a specific, technical improvement rather than an abstract idea, natural phenomenon, or routine application. Practitioners should stay away from framing overly broad or frequently rejected claim groups, especially those associated with Section 101 rejections by the USPTO.
Articulating specific technical benefits is essential in establishing software distinctiveness. Effective practice involves counteracting any ‘abstract idea’ objections by demonstrating how the invention improves upon existing computer functionality. To fortify patent applications, introducing various independent process and method claims covering diverse ranges of applicability while documenting incremental improvements as part of these additional claim elements within the submission proves beneficial.
Engaging proactively with patent examiners to expound on technical specifics can lead to more precise implementation of claimed features during examination procedures. Through these concerted efforts, strong defensive positions for submissions can be created ensuring they withstand scrutiny and conform to criteria for being deemed patent-eligible.
However, patenting software remains challenging due to evolving legal standards, such as the Alice decision, which require applicants to clearly demonstrate that their invention is a patentable invention and meets the requirements for patent eligible inventions, rather than being an abstract idea or routine implementation.
Current USPTO Leadership Perspective on AI Patents
USPTO Director Kathi Vidal has emphasized the agency’s commitment to balancing innovation and clarity: “The USPTO remains committed to fostering and protecting innovation in critical and emerging technologies, including AI… [This] guidance update…will provide further clarity on evaluating subject matter eligibility of AI inventions while incentivizing innovations needed to solve world and community problems.”
Director Vidal has also stressed that AI-assisted inventions are welcome in the patent system, provided a human is in the loop. She has noted that using AI tools in invention is becoming ubiquitous and should be “encouraged” rather than feared. However, she has warned of potential challenges with AI-generated outputs creating a deluge of prior art, which could impact the ability to patent some inventions if novelty and non-obviousness become harder to establish.
Crucially, USPTO officials have reiterated that the Alice/Mayo test applies equally to AI—AI inventions aren’t excluded from patents, nor do they receive special treatment. The focus remains on what is claimed. The 2024 guidance explicitly states that the manner in which an invention is made (even if via AI) “does not impact subject matter eligibility”; an AI-assisted invention is evaluated like any other, based on the claim’s content and improvements it provides.
Summary
Grasping the intricacies of the Alice/Mayo test is pivotal for obtaining patent protection in the realms of software and AI. It’s imperative to comprehend this two-step analysis, discern what constitutes abstract ideas, and establish inventive concepts. Inventors can elevate their abstract ideas into inventions that are eligible for patents by concentrating on tangible applications and technological innovation.
The recent developments from 2024-2025, including the Federal Circuit’s Recentive decision and the USPTO’s comprehensive AI guidance with Examples 47-49, provide clearer roadmaps for navigating patent eligibility challenges. These examples demonstrate that specificity and technical application matter—AI or software-related claims must do more than recite abstract algorithms or data processing; they should emphasize how the invention improves technology or achieves tangible results.
In practice at Thompson Patent Law, proprietary techniques for preemptively addressing Alice rejections have proven effective in enhancing success rates by 25-50%, typically resulting in stronger patents obtained through first or second office actions rather than prolonged prosecution. This approach not only saves inventors significant time and resources but also produces more robust intellectual property protection.
In an era where both software and AI are rapidly advancing, keeping abreast with criteria for patent eligibility becomes increasingly essential. Employing strategic methods rooted in a thorough understanding of these guidelines will enable inventors not only to secure safeguards around their creative works, but also make meaningful contributions towards technological advancement.
Frequently Asked Questions
What is the Alice/Mayo test?
The Alice/Mayo test plays a crucial role in determining the patent eligibility of inventions, focusing on whether they possess an inventive concept that goes beyond a mere abstract idea. This examination helps safeguard the recognition and protection of creative endeavors.
Why is the Supreme Court’s Alice decision significant?
The Alice decision by the Supreme Court has had a profound impact on patent law, as it determined that specific software advancements are considered abstract ideas and thus do not qualify for patent protection. By doing so, this verdict promotes innovation and competition within the technology sector by ensuring that excessively general notions cannot be patented.
How can AI inventions qualify for patent protection?
Inventions involving artificial intelligence can be eligible for patent protection when they convert conceptual ideas into pragmatic applications that demonstrate concrete usefulness and distinct technological progress. By fulfilling these requirements, inventors can successfully safeguard their intellectual property rights.
What are some examples of technological improvements in AI inventions?
Technological improvements in AI inventions are revolutionizing the field with enhanced computer vision, more efficient data processing, and novel neural network architectures. These advancements unlock new possibilities and drive innovation.
What strategies can be used to draft patent-eligible software claims?
To effectively draft patent-eligible software claims, emphasis should be placed on inventions that deliver tangible results while avoiding generic descriptions. Clearly defining technical advantages, using multiple independent claims, and documenting improvement steps, while engaging with examiners, strengthens applications.
