PatentNext Takeaway: In Applications in Internet Time, LLC v. Salesforce, Inc., the Federal Circuit provided a useful reminder for drafting AI-related patent claims: dependent claims can help preserve broader independent-claim scope where the dependent claims separately recite narrower AI or agent-based implementations.

Although Applications in Internet Time was not a generative AI case, its reasoning is directly relevant to modern AI patent practice. The disputed claim language concerned “automatically detecting” changes, while a dependent claim separately recited “one or more intelligent agents” that performed that detection. The Federal Circuit held that claim differentiation provided a strong reason not to read the “intelligent agents” limitation into the broader independent claim.

For AI patent practitioners, the lesson is practical. When drafting or amending claims directed to AI systems, machine-learning workflows, autonomous agents, model-based processing, or AI-assisted automation, dependent claims should not be treated merely as fallback positions. They can also help preserve the distinction between a broad AI-enabled function and a narrower implementation of that function.

That strategy has limits. As cases such as Cave Consulting Group, LLC v. OptumInsight, Inc. show, later-added dependent claims cannot overcome a specification that consistently and affirmatively limits the invention to a narrower implementation. Thus, dependent claims can support claim differentiation only where the specification already supports the broader claim scope. 

Thus, the best practice is to draft both the specification and the claim set to support a deliberate hierarchy: broader AI functionality in the independent claims, and narrower model-, agent-, data-, or architecture-specific implementations in dependent claims.

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Background: AI Claims Often Turn on the Level of Implementation Detail (Using Dependent Claims as More Than Fallback Positions)

AI patent claims frequently raise a familiar drafting problem: how much implementation detail should be included in the independent claim?

On one hand, claims that merely recite a desired AI result—such as “classifying data,” “generating a recommendation,” “detecting an anomaly,” or “automatically determining an output”—may face scrutiny under § 101, § 103, or § 112. Examiners may ask whether the claim recites a technological improvement, whether the claimed functionality is conventional, or whether the specification sufficiently describes how the AI functionality is actually performed.

On the other hand, overloading the independent claim with too much implementation detail can unnecessarily narrow the claim. For example, an independent claim that requires a particular type of model, a particular neural-network architecture, a particular training technique, or a particular agent-based workflow may be easier to distinguish during prosecution, but may also be easier for competitors to design around.

This is where dependent claims can play an important role.

A well-drafted AI claim set can separate the broader AI-enabled functionality from specific implementations. The independent claim may recite the broader technical operation, while dependent claims recite narrower embodiments such as:

  • a neural network;
  • a transformer-based model;
  • a large language model;
  • a reinforcement-learning policy;
  • a retrieval-augmented generation workflow;
  • one or more autonomous software agents;
  • a feature-extraction pipeline;
  • a vector database;
  • a prompt template;
  • a model-training workflow;
  • a post-processing rule set; or
  • a safety, validation, or guardrail process.

That structure can provide fallback positions. But it can also do something more: it can help show that the independent claim is not limited to the narrower implementation recited in the dependent claim.

The Prosecution Problem: Amendments Can Narrow Claims More Than Intended; but Claim-Differentiation can help redefine Scope

Software and AI-related patent applications often face multiple forms of scrutiny during examination. An examiner may reject the claims under § 101 as allegedly directed to an abstract idea. The examiner may also raise novelty or obviousness rejections under §§ 102 or 103, particularly where the claim recites broad software functionality. And, increasingly, examiners may raise § 112 issues where the claims recite technical functionality without enough supporting detail in the specification. See PatentNext: USPTO Examiners issue more Written Description (Section 112) rejections following Precedential Ex Parte Desjardins decision.

In response, applicants often amend independent claims to provide more technical specificity. That may be necessary and appropriate. For example, an applicant may amend a claim to recite a particular data structure, computer architecture, model-training workflow, communication sequence, or control process.

But such amendments raise a recurring strategic issue: how can the applicant distinguish the prior art or clarify the invention without unintentionally limiting the independent claim to a single implementation?

One answer is to use dependent claims deliberately. When an applicant amends an independent claim, the applicant should consider whether additional dependent claims should be added to capture narrower implementations. These dependent claims may later help demonstrate that the independent claim remains broader than those specific implementations.

For example, consider the following simplified claim set:

  1. “A computer-implemented method comprising automatically detecting a change in application data and updating a user interface based on the detected change.”
  2. “The method of claim 1, wherein automatically detecting the change comprises using one or more intelligent agents.”

In that example, claim 2 can help support the argument that claim 1 is not limited to intelligent agents. Claim 2 adds that specific implementation. Therefore, reading claim 1 to already require intelligent agents would risk making the dependent claim redundant.

That is the basic claim-differentiation point. 

The doctrine of claim differentiation generally recognizes that different claims are presumed to have different scope. The doctrine is particularly powerful where a dependent claim adds a limitation that an accused infringer later seeks to import into the independent claim.

In practical terms, if independent claim 1 recites “automatically detecting an anomaly,” and dependent claim 2 recites that the detecting is performed “using a trained neural network,” there is a claim-differentiation argument that claim 1 should not be limited to neural-network-based detection.

Likewise, if independent claim 1 recites “generating a response based on input data,” and dependent claim 2 recites that the response is generated “using a transformer-based large language model,” there is an argument that claim 1 should not be construed to require a transformer-based LLM.

Of course, claim differentiation is not absolute. The specification and prosecution history still matter. But when the intrinsic record supports broader scope, dependent claims can become important claim-construction evidence.

That is the lesson illustrated by Applications in Internet Time. 

Applications in Internet Time v. Salesforce: A Recent AI / Software Example

In Applications in Internet Time, LLC v. Salesforce, Inc., the Federal Circuit addressed AI-based software patents directed to dynamically generated applications. Nos. 2024-1133, 2024-1685, 2024 WL 4487251 (Fed. Cir. Oct. 10, 2024) (nonprecedential). The claims involved computer systems including intelligent agents, server computers, client computers, browser applications, application layers, and a “change management layer” for automatically detecting changes that affect an application.

The district court construed “automatically detecting” to require “detecting without human intervention through the use of one or more intelligent agents.” That construction became case-dispositive. Because the accused Salesforce products allegedly did not use intelligent agents in the manner required by the district court’s construction, the district court granted summary judgment of non-infringement.

The Federal Circuit reversed that claim-construction ruling.

The key claim-differentiation issue was straightforward. Independent claim 1 recited a “change management layer for automatically detecting changes that affect an application.” But dependent claim 8 expressly recited that the change management layer comprised “one or more intelligent agents.” The Federal Circuit found that this claim structure provided a strong reason not to import the “intelligent agents” limitation into the independent claim.

That aspect of the decision is useful for software patent practitioners because it shows how a dependent claim can preserve broader independent-claim scope. The independent claim recited the broader software functionality: automatically detecting changes. The dependent claim recited a narrower way to perform that functionality: using intelligent agents. Because the narrower implementation appeared in the dependent claim, the Federal Circuit declined to read that implementation into the independent claim.

The court also rejected the argument that the specification limited the invention to intelligent agents. Although the specification discussed intelligent agents, the Federal Circuit viewed those disclosures as exemplary rather than limiting. The court explained that the repeated use of intelligent agents in the best-mode discussion showed that intelligent agents may be a preferred implementation, not that they were required by the claims.

That distinction is important. In software patents, specifications often describe one implementation in detail. But unless the specification or prosecution history clearly limits the invention to that implementation, the claims should not automatically be confined to it. Dependent claims can reinforce that point by expressly claiming the narrower implementation separately.

That distinction is critical for AI patent practice. 

Why AIT Matters for Software and AI Claim Drafting

The AIT decision is nonprecedential. Even so, it provides a useful and recent illustration of a practical drafting point: dependent claims can help separate broad AI and software functionality from specific implementation details.

That distinction is especially important for modern software and AI inventions. Consider the following examples:

  • an independent claim recites “classifying input data,” while a dependent claim recites that the classification uses a neural network;
  • an independent claim recites “generating a response using a machine-learning model,” while a dependent claim recites that the model is a transformer-based large language model;
  • an independent claim recites “retrieving context data,” while a dependent claim recites retrieval from a vector database;
  • an independent claim recites “automatically detecting an anomaly,” while a dependent claim recites detection using a trained anomaly-detection model;
  • an independent claim recites “modifying an interface based on user activity,” while a dependent claim recites a specific event-listener or agent-based implementation.

In each example, the dependent claim may help preserve the broader scope of the independent claim. If the specification supports multiple ways to perform the claimed function, dependent claims can make that breadth visible in the claim set.

This can be especially valuable after an amendment. For example, if an applicant amends an independent claim to overcome prior art by reciting “automatically detecting a change in a deployed application,” the applicant may also add dependent claims reciting specific detection mechanisms. Those dependent claims can serve as both fallback positions and claim-differentiation anchors.

The claim set then communicates a hierarchy:

  • the independent claim covers the broader technical concept;
  • dependent claim 2 covers implementation A;
  • dependent claim 3 covers implementation B;
  • dependent claim 4 covers implementation C.

That hierarchy may later help rebut an accused infringer’s attempt to limit the independent claim to only one disclosed implementation.

Modern AI applications may involve software agents, autonomous agents, LLM agents, planning agents, monitoring agents, retrieval agents, classification agents, or model-based decision agents. In some applications, the agent is the invention. In others, the agent is one possible implementation of a broader automated process. 

Cave Consulting: The Important Cautionary Example

The usefulness of dependent claims has limits. The Federal Circuit’s decision in Cave Consulting Group, LLC v. OptumInsight, Inc. illustrates the other side of the doctrine. 725 F. App’x 988 (Fed. Cir. 2018). 

In Cave Consulting, the claims related to computer-implemented methods for analyzing physician efficiency using medical claims data. The dispute concerned whether the claimed “weighted episode of care statistics” covered direct standardization or was limited to indirect standardization.

The patentee relied on dependent claims that specifically recited direct standardization. The argument was that, under claim differentiation, the independent claims should be broad enough to cover direct standardization because the dependent claims expressly recited it.

The Federal Circuit disagreed. The problem was the specification. The court concluded that the specification repeatedly and consistently described the invention as using indirect standardization and distinguished that approach from direct standardization used in the prior art. In that context, the later-added dependent claims could not overcome the limiting description in the specification.

That is the key caution: claim differentiation is a tool of claim construction, not a substitute for written-description support. Dependent claims can help preserve broad claim scope only if the specification supports that breadth.

This point is particularly important when dependent claims are added during prosecution. If the original specification describes only one implementation as the invention, and disparages or excludes alternatives, later-added dependent claims may not be enough to broaden the independent claim. Worse, such dependent claims may invite written-description challenges if the specification does not reasonably convey possession of the later-claimed alternative.

Thus, the drafting lesson is twofold:

  1. Use dependent claims to preserve meaningful claim-scope distinctions.
  2. Draft the specification from the outset to support those distinctions.

Practice Tips for Patent Prosecutors

The practical takeaway is that dependent claims should be drafted with future claim construction in mind. When amending AI and software claims, practitioners should consider the following strategies.

1. Add dependent claims that expressly recite narrower implementations

If the independent claim recites broader functionality, consider dependent claims that recite specific implementations. For example:

  • “wherein the detecting is performed using one or more intelligent agents”;
  • “wherein the classifying is performed using a trained neural network”;
  • “wherein the retrieving comprises querying a vector database”;
  • “wherein the generating comprises applying a transformer-based language model”;
  • “wherein the updating comprises transmitting a synchronization message using a publish-subscribe protocol.”

These dependent claims may provide fallback positions and help show that the independent claim is not limited to those narrower implementations.

2. Avoid prosecution remarks that collapse the claim hierarchy

Dependent claims are less useful if the applicant’s remarks describe the independent claim as requiring the narrower dependent-claim feature. For example, if claim 1 recites “automatically detecting” and claim 2 recites “using intelligent agents,” the applicant should be careful before arguing that “the claimed invention requires intelligent agents” unless that is truly intended.

A better approach may be to distinguish the independent claim based on its actual amended language, while preserving dependent claims as additional, narrower implementations.

3. Draft the specification to support multiple implementations

Claim differentiation works best when the specification supports the broader genus. For software and AI inventions, the specification should identify alternative implementations where possible.

For example, if an invention involves automatic detection, the specification might describe detection using rules, event listeners, intelligent agents, machine-learning models, polling, message queues, or other mechanisms. If an invention involves classification, the specification might describe neural networks, decision trees, support vector machines, rules-based classifiers, or other models.

The goal is not to list alternatives without substance. The goal is to provide enough technical detail to show possession of the broader concept and its representative implementations.

4. Be careful with “the invention” language

Courts may treat repeated descriptions of “the invention” as limiting in some circumstances, particularly where the specification consistently characterizes the invention as requiring a particular feature. Patent drafters should therefore use care when describing embodiments.

Rather than stating that “the invention uses an intelligent agent,” it may be preferable to state that “in some embodiments, the system uses an intelligent agent,” or that “the automatic detection may be performed using one or more intelligent agents, event listeners, polling routines, rules-based engines, machine-learning models, or other detection mechanisms.”

That type of drafting can preserve flexibility and support later claim-differentiation arguments.

5. Use dependent claims to create a deliberate claim hierarchy

A strong software claim set often has a layered structure:

  • an independent claim directed to the broader architecture or technical process;
  • dependent claims directed to particular implementation mechanisms;
  • dependent claims directed to data structures, model types, communication protocols, user-interface behavior, or deployment environments;
  • dependent claims directed to performance improvements or technical effects.

This hierarchy can be valuable during prosecution, during continuation practice, and later during claim construction.

Conclusion: Dependent Claims as Claim-Construction Assets

Dependent claims should not be treated as an afterthought. In AI, software, and computing patents, they can be especially valuable because software inventions are often implemented in multiple ways. A claim set that captures those implementation differences can preserve flexibility during prosecution and provide important claim-construction support later.

Applications in Internet Time v. Salesforce illustrates the upside. A dependent claim reciting “intelligent agents” helped prevent that limitation from being imported into broader independent claims directed to automatic detection.

Cave Consulting illustrates the caution. Dependent claims cannot expand claim scope beyond what the specification supports, particularly where the specification consistently limits the invention to a narrower implementation.

The best practice is therefore clear: when amending software claims, practitioners should not only ask what language will overcome the rejection. They should also ask what dependent claims are needed to preserve the intended claim hierarchy.

In other words, dependent claims are not merely backup claims. Properly drafted, they can be claim-construction assets.

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PatentNext Takeaway: On April 30, 2026, the USPTO issued a memorandum titled “Best Practices for Submission of Rule 132 Subject Matter Eligibility Declarations (SMEDs),” updating the Office’s December 4, 2025 guidance to applicants and practitioners.  The new memorandum reinforces the Office’s SMED framework and characterizes it as a living body of guidance informed by practitioner experience.

The result is not a change in law or procedure, but a signal: the USPTO views SMEDs as an increasingly important evidentiary tool for resolving § 101 issues in emerging and fact‑intensive technologies, particularly artificial intelligence and diagnostics.

Continue Reading USPTO Updates SMED Best Practices

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Following the USPTO’s high-profile actions in Desjardins, the next question was how that guidance would play out in ordinary PTAB appeals. Ex parte Carmody provides an early answer.

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In our earlier posts, we discussed the significance of Ex Parte Desjardins: first when the Appeals Review Panel vacated the § 101 rejection, and again when Director Squires designated the decision as precedential.

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               On August 4, 2025, the USPTO issued a memo to patent examiners with the subject “Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101.” [1]
 Much has been made of these reminders and what they might signal in terms of a possible shift in how the Office treats rejections under § 101, in particular for applications involving software and artificial intelligence (AI). [2][3][4] However, it is worth considering that these reminders were not issued to the entire examining corps but instead only to three (out of eight) of the technology centers (TCs) which examine utility patents. While applicants and practitioners may be hopeful that these reminders are the beginning of a sea change in the world of § 101 rejections, especially as related to AI, they should also be concerned about the reminders’ exclusivity and what this may mean for consistency in examination standards across the USPTO.

               Examiners at the USPTO are divided into TCs which are then further subdivided into art units (AUs). Broadly speaking, each TC is directed to a particular type of technology, and each AU is directed to a subcategory of that technology. For example, TC 2100 provides examination for patent applications including “Computer Architecture, Software and Information Security,” and AUs 2121-2129 and 2141-2148 provide examination for patent applications including specifically “Artificial Intelligence.” For another example, TC 3700 provides examination for patent applications including “Mechanical Engineering, Manufacturing and Products,” and AUs 3771-3775 provide examination for patent applications including “Medical & Surgical Instruments, Treatment Device, Surgery and Surgical Supplies.” [5]

               The three TCs to which the August 2025 reminders were directed – TC 2100, TC 2600 (Communications), and TC 3600 (Transportation, Electronic Commerce, Construction, Agriculture, Licensing and Review) – examine most of the utility patent applications which involve AI. Consider, for example, the 163,565 utility patent applications which were filed between January 1, 2015, and December 31, 2023, which have been assigned to a TC and which include the phrase “artificial intelligence” or “machine learning” anywhere in the text of the application:

               This subset was chosen because newer applications may not have been assigned to a TC yet, and older applications which recite “artificial intelligence” or “machine learning” are likely not directed to AI as it is currently understood today. Data is provided by Juristat.

               As shown, 117,695 (71%) of the applications which recite AI or machine learning were examined in one of TC 2100, 2600, or 3600. But this still leaves 29% of applications unaccounted for. In particular, TC 2400 (Computer Networks, Multiplex, Cable and Cryptography/Security) received 22,586 (14%) of the applications mentioning AI which were filed during this period. And yet the August 2025 reminders were not directed to examiners in TC 2400.

               This data can be further broken down. AI has become integrated into a wide variety of disciplines over the last decade; the scope of patent applications which recite AI looked very different in 2023 than it did in 2015. If we break down the data into applications filed from 2015-2019 and applications filed from 2020-2023, we see a corresponding shift in the percentage of applications examined by each TC:

               These charts suggest that, as time goes on and AI becomes integrated into more fields, the percentage of AI applications examined in TC 2100 (where, as noted above, there is an AU specifically dedicated to AI) will decrease, and the percentage of applications examined in other TCs will correspondingly increase. Between 2015 and 2019, 30% of all applications which mentioned AI or machine learning were examined in TC 2100. Between 2020 and 2023, that number was only 24%. Another data point worth noting: the total number of applications mentioning AI has increased dramatically, with about twice as many being filed from 2020-2023 as from 2015-2019. If the trend continues, we might expect both more applications directed to AI and applications directed to AI being examined in a broader range of TCs and AUs.

               This trend begs the question: is the USPTO equipped to handle such a rapidly evolving field? There are two important and interrelated issues at the USPTO that are relevant to this question: 1) application routing and 2) consistency in examination guidelines.

               Application routing at the USPTO

               Any evolution in patent application subject matter is likely to encounter some friction at the USPTO. The organization of TCs at the USPTO and the way application routing is handled are particularly relevant for newer fields like AI, which did not exist in their current form at the time that relevant procedures were put in place.

               Application routing at the USPTO is determined by patent classification. Each application is classified according to a particular scheme, and then the application is routed to a relevant AU based on that classification. There are two classification schemes used for utility patents before the USPTO: the US Patent Classification (USPC) scheme and the Cooperative Patent Classification (CPC) scheme. [6] The USPC scheme has largely been replaced by the CPC scheme, and patents no longer have their USPC class printed on them, only their CPC symbol(s).

               According to the USPTO website, documentation about the USPC scheme exists today only for historical purposes. [6] It is clear from the update record (called “classification orders”) that the USPC scheme has not been updated since 2013. [7] Consider what the landscape of AI looked like in 2013 compared to what it looks like today. Along with other newer fields, like quantum computing (QC), AI is not adequately captured by the USPC scheme, and because the USPC scheme is no longer updated, this will never be fixed.

               One may reasonably wonder why this is a problem. The USPTO website says the USPC scheme is purely historic – why would we expect a “historic” classification scheme to adequately capture AI? Who cares?

               This is a problem because the USPC scheme is still in active use at the USPTO for one key purpose: application routing. Utility patent applications are still, as of 2025, routed to the AU where they will be examined based on USPC class, not CPC symbol. A USPC class is assigned to each application specifically for this purpose. For a brief trial period in 2022, the USPTO attempted to route applications by CPC symbol. This resulted in so many misrouted applications that, rather than fix the problem, they simply reverted to the old procedure of routing by USPC class. As of 2025, there was no indication that the USPTO was planning to move away from USPC-based routing anytime soon, despite incorrect and out-of-date information still listed in MPEP § 909.

               To continue to classify and route applications based on a classification scheme that has not been updated in more than a decade is, to say the least, not best practice.

               Any classification scheme needs to be updated regularly in order to remain relevant, but in particular a classification scheme for patents, where the whole point is that something new is disclosed in every application, cannot effectively serve its purpose if it has not been updated in twelve years. The way that application routing is handled at the USPTO means it is difficult to determine whether the trend observed in the charts above is an intentional shift in how applicants’ and the Office are viewing and handling AI or simply a quirk of the routing system. Has the Office intended to spread AI-related applications across more TCs? Does the evolution of the field mean that applications which integrate AI into specific technologies should be considered differently than applications which are directed to AI in a purely computer science context? Or has any distinction arisen accidentally as it has become harder and harder to classify AI using a classification scheme that is more than a decade out of date?

               As a specific example, should training machine learning models for use in medical or surgical procedures be routed to an AU that handles training machine learning models? Or should it be routed to an AU that handles medical and surgical procedures? Or either? Or both? The answer to this question should not hinge on how a classification scheme last updated in 2013 classifies AI. If the USPC scheme does not adequately capture the technology an application is directed to, there is no real way to assess whether the application has been routed “correctly.” This brings us to another relevant issue at the USPTO: consistency.

               Consistency in examination guidelines at the USPTO

               Contrary to the trends suggested by the charts above, the USPTO does not seem to take into account how AI-related applications will permeate the entire Office. The USPTO further seems to be short-sighted in the applicability of 35 USC § 101 to its entire examining corps, otherwise, why were the August 2025 guidelines issued only to TCS 2100, 2600, and 3600? These three TCs examine most of the AI-related applications that the Office receives, but not all of them. For one example, TC 2400 examines a large number, and for another example, applications which mention “artificial intelligence” or “machine learning” but also include the words “medical,” “surgery,” or “surgical,” anywhere in the text, are very likely to be examined in TC 3700:

               One would expect a similar trend for TCs and AUs in other disciplines which have been quick to adopt AI methods.

               Why is the USPTO issuing guidelines that should be relevant to the entire examining corps only to a subset of examiners? Examiners at the USPTO issue Office actions, not examiner actions, because there is meant to be consistency across the entire Office in how rules and regulations are applied. Providing guidance and reminders to only a subset of the examining corps does not help the Office achieve consistency in these issues, and it creates headaches for practitioners and applicants looking for clarity and consistency across Office actions. As things are, a § 101 rejection from an examiner in TC 3700, who has not received much guidance on this topic, looks different than a § 101 rejection from an examiner in TC 2600. This may make it harder to convince the examiner to withdraw a rejection because, in addition to being unfamiliar with best practices regarding writing a rejection, they may also not be familiar with distinguishing between persuasive and non-persuasive arguments for overcoming it. Conversely, this may lead to further prosecution and litigation headaches down the road if the examiner does not make a § 101 rejection when they should have due to lack of experience and guidance. Meanwhile, an examiner in TC 2100 or TC 2600 may not be familiar with the medical terms they need to understand in order to examine an application about machine learning in medical devices effectively, but as a consequence of the Office’s outdated routing procedures, they are stuck doing the best they can.

               Conclusions

               Many applicants and practitioners are hoping for changes in how the USPTO addresses § 101 rejections, especially as they relate to AI, and soon. Recent developments, such as the August 2025 reminders, have given those in the field renewed optimism that change is on the horizon. But the changes that practitioners and applicants see do not necessarily match what is going on behind the scenes at the USPTO. The way application routing is currently handled at the Office and the lack of consistency in guidelines issued to examiners suggests that the USPTO is not currently equipped to adapt to a field evolving as quickly as AI.

               In addition to hoping for changes directed specifically to § 101, practitioners and applicants should also be looking for the USPTO to move away from USPC-based routing and to improve consistency in examination procedures if any real, intentional change is to be effected. The USPTO website and MPEP are misleading, and in some cases flat-out incorrect, about how the USPC scheme is used at the USPTO and how application routing is handled. The USPTO should provide more transparency in this regard. But at the end of the day, regardless of whether the Office is transparent with applicants about how applications are routed, it is troubling that the Office continues to rely on a classification scheme that has not been updated in more than a decade to classify patent applications. If routing by CPC symbol was so inaccurate, the reason for this needed to be investigated and corrected. The USPC scheme is not suitable for use anymore, and this will only become more of an issue as time goes on and technology evolves even further.

               In the meantime, the USPTO must provide consistent guidance across the whole examining corps regarding § 101 and other statutes. Although the August 2025 guidance was provided to only three TCs, it is clear that many other TCs examine AI-related applications and deal with the associated § 101 analysis.

               Perhaps a sea change in § 101 is coming, but a change in the Office’s general approach to § 101 is not enough. Real change is going to require specific modernization in practices related to application routing and examination guidelines at the USPTO. For that, applicants and practitioners may have to wait a bit longer.

               References

[1] C. Kim, “Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101,” 4 August 2025. [Online]. Available: https://www.uspto.gov/sites/default/files/documents/memo-101-20250804.pdf. [Accessed 2023 October 2025].
[2] M. Lew, “How USPTO Examiner Memo Informs Software Patent Drafting,” 8 September 2025. [Online]. Available: https://www.law360.com/articles/2383638/how-uspto-examiner-memo-informs-software-patent-drafting.
[3] R. N. Phelan, “USPTO Issues “Reminders” Supporting AI and Software Patenting and Instructing Patent Examiners on the Limits of Section 101 Patent Eligibility,” 29 September 2025. [Online]. Available: https://www.patentnext.com/2025/09/uspto-issues-reminders-supporting-ai-and-software-patenting-and-instructing-patent-examiners-on-the-limits-of-section-101-patent-eligibility/.
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