Demonstrating Technical Effect for AI at the EPO: How to Write Technical Effect Statements that Convince Examiners

1) Anchor: What counts as “technical effect” for AI at the EPO—and what does not

The European Patent Office (EPO) accepts that artificial intelligence and machine learning can contribute to inventive subject-matter—but only where the claimed features credibly produce a technical effect. In EPO practice, a technical effect is an objectively verifiable change in a technical system or process, tied to how the computer is operated or how data is processed at a technical level. By contrast, aims like “improving user engagement,” “increasing conversions,” or “providing better content relevance” are considered non-technical because they relate to business, presentation of information, or purely cognitive outcomes.

The key is to show that your AI implementation solves a technical problem using technical means. For example, reductions in memory footprint, latency, energy usage, bandwidth, or error rate in a signal processing pipeline are technical effects. Likewise, improved robustness against sensor noise or better numerical stability in training can be technical when they change how a computer processes or stores data. The EPO’s core concern is whether the claimed features move the needle in the behavior of the machine: Is the processor, memory, network stack, or sensor system made to work differently or more efficiently because of specific technical features of your AI method?

EPO examiners also require that the technical effect be credibly demonstrated at the time of filing and during prosecution. “Credible” means that a skilled person would accept, on the basis of the disclosure, that the claimed features actually achieve the asserted effect across the full scope of the claim. Mere aspirational statements are insufficient. The specification and post-filing submissions should align to present a coherent, reproducible story: the effect arises because of identifiable technical features, is measurable, and is supported by either data or well-founded reasoning.

Use this quick diagnostic to test whether an asserted effect is technical and credibly supported:

• Does the effect change how a computer or device operates at a technical level (e.g., fewer cache misses, lower inference latency on specified hardware, reduced packet loss due to error-correcting decisions by the model)? If yes, you are in technical territory.
• Can you measure the effect with objective metrics (ms latency, Joules, FLOPs, cache hit rate, throughput, accuracy on a defined signal-processing benchmark)? If yes, it’s more credible.
• Is there a traceable causal link between the claimed features (e.g., a sparsity-inducing pruning strategy, a quantization schedule, a memory layout) and the measured outcome? If yes, it supports inventive step and sufficiency.
• Would the effect hold across the claim scope or only in a cherry-picked setting? If it risks being too narrow, qualify the claim or bolster the disclosure so the effect is plausible under the breadth you seek.

Non-technical aims often look appealing but are risky. Statements like “improves user satisfaction,” “streamlines workflow decisions,” or “produces more appealing content” are typically excluded. If your invention touches such aims, reframe them through technical mechanisms and outputs: show how the AI model’s architecture reduces compute load, minimizes stalls, improves cache locality, or mitigates numerical instability, and provide concrete measurements.

2) Construct: The three-part structure of a convincing technical effect statement

A strong technical effect statement at the EPO usually follows a three-part logic: 1) Technical Purpose → 2) Quantified Outcome → 3) Claimed Feature Causality.

Each part plays a distinct role in persuading an examiner that the invention provides a technical solution to a technical problem.

1) Technical Purpose

• Identify the concrete technical problem the invention addresses (e.g., reducing inference latency on edge hardware, stabilizing training under limited precision arithmetic, compressing model parameters without accuracy loss beyond X%).
• Keep the purpose grounded in the operation of computing resources, communications, sensors, or control systems. Avoid framing around business goals or user preferences. A technical purpose clearly signals that the contribution is to the functioning of the machine or data processing pipeline.

2) Quantified Outcome

• Provide measurable results with explicit metrics: milliseconds, Watts, megabytes, FLOPs, throughput, error rate on a designated dataset, or number of cache misses. Where possible, give exact figures or ranges and specify the evaluation conditions (hardware, batch size, sequence length, network bandwidth, dataset version).
• Quantification matters for both inventive step and sufficiency. It shows the effect is not speculative and allows a skilled person to verify the benefit. It also narrows ambiguity: examiners can see whether the improvement is non-trivial and consistent with the claimed scope.

3) Claimed Feature Causality

• Trace the effect to concrete claim features (e.g., “a layer-wise quantization schedule comprising steps S1–S3,” “a memory layout that aligns parameters to cache lines,” “a pruning mask learned under constraint C and applied at export-time”).
• Explain the mechanism: why the feature produces the performance change. This causal explanation can be qualitative but should be credible and linked to known system behavior (e.g., reduced memory transfers due to contiguous layouts, fewer multiplications due to structured sparsity, stabilized gradients due to adaptive scaling under low precision).

When these three parts are integrated, the statement helps examiners apply the problem–solution approach: the purpose sets the problem, the quantified outcome substantiates the effect, and the causality ties the effect to the distinguishing claim features. If any part is missing, the argument weakens. A purpose without numbers looks aspirational; numbers without feature causality look accidental; causality without a recognizable technical problem looks abstract.

Counterproductive patterns to avoid include:

• Vague outcomes (“faster,” “better”) without metrics or test context.
• Effects framed as user perception or business KPIs rather than machine operation.
• Causal attributions to high-level goals (“because it personalizes content”) rather than to technical means (“because the quantization schedule reduces memory reads by N%”).
• Claim features so broad that the measured effect is not credible across the scope—this invites sufficiency and inventive-step objections.

To operationalize this structure in your drafting and prosecution submissions, write technical effect statements that read as a compact syllogism: “For the technical purpose P, the invention achieves measurable outcome M under conditions C, because specific claimed features F alter system behavior B.” This format resonates with EPO reasoning and makes it easier to fold your argument into the problem–solution framework.

3) Evidence: Aligning effect statements with robust evidence packages

Examiners assess credibility and sufficiency by asking whether a skilled person would accept that the claimed features achieve the asserted effect. The most persuasive path is to align the effect statement with evidence that is appropriate for the field and reproducible.

Key evidence categories include:

• Benchmarks and test protocols

  • Use recognized datasets, workloads, or protocols where possible. Specify versions, preprocessing steps, hardware, and software frameworks. State batch sizes, sequence lengths, and precision settings. This level of detail improves reproducibility and shows you considered the operational constraints that drive the effect.
  • Report central tendency (mean or median) and variation (confidence intervals, standard deviations), particularly if gains are modest. Examiners appreciate that variance is accounted for, which increases credibility.

• Ablation studies

  • Show what happens when the claimed feature is removed or replaced. If the effect diminishes accordingly, you have direct causal support. Structure the ablation so each tested variant corresponds to a clear claim element.
  • Keep ablations close to the claimed implementation; if you ablate a different module than what is claimed, the causal link weakens.

• Comparative examples

  • Compare against the closest prior art method under the same conditions. If the invention is a modification of a known architecture, demonstrate the effect relative to that baseline with matched hyperparameters and hardware.
  • Justify any differences in configuration to avoid accusations of unfair comparison. The closer the parity, the stronger the inference that the claimed features are responsible for the improvement.

• Resource profiling and system-level metrics

  • Provide profiling data: memory allocations, cache miss rates, kernel timings, power draw, bandwidth utilization. These system-level observations concretely demonstrate technical effects in real computing environments.
  • Distinguish training vs. inference phases when relevant. Many AI inventions affect one phase more than the other; clarity here prevents overbroad claims of benefit.

• Reproducibility and sufficiency support

  • Provide implementation details sufficient for a skilled person to practice the invention without undue burden. This supports Article 83 EPC (sufficiency). Document hyperparameter schedules, initialization schemes, and any control logic that is pivotal to the effect.
  • If the effect depends on a threshold or regimen (e.g., a quantization schedule that relaxes constraints after N epochs), disclose parameter ranges and heuristics. This both strengthens credibility and guards against enablement challenges.

To integrate these into prosecution:

• Map each claimed feature to at least one piece of supporting evidence. In your submissions, explicitly cross-reference: “Feature F1 is supported by ablation A1 and profiler results P1–P3.”
• Preempt typical objections by addressing scope: indicate the hardware classes for which the effect is expected, or specify tolerances (e.g., sequence lengths up to L) where the improvement holds.
• Keep a clean chain of custody from the specification to any later-filed data. While post-filing evidence can sometimes be considered to support plausibility, do not rely on it to introduce the effect for the first time. Seed the application with enough detail to make later data confirmatory rather than foundational.

This evidence strategy not only raises the plausibility of the effect but also aligns with the EPO’s problem–solution method: it helps you identify the closest prior art, define the objective technical problem in measurable terms, and show that the distinguishing features solve that problem with verifiable improvements.

4) Wording: Phrasing that avoids exclusions and foregrounds implementation-bound benefits

EPO practice excludes “presentation of information” and “programs for computers as such” unless there is a technical contribution. Your task is to use wording that focuses on the technical implementation and its consequences for system operation, rather than on cognitive or business outcomes.

Guiding principles for wording:

• Emphasize how data is processed at a technical level. Refer to memory operations, numerical precision, scheduling, cache behavior, bandwidth, and concurrency.
• Link measured improvements to concrete implementation choices. Avoid generic “AI improves X”; specify the mechanism (e.g., structured sparsity, quantization, kernel fusion) and the measurable effects (e.g., reduced DRAM accesses, fewer kernel launches, lower latency on defined hardware).
• Explicitly delimit the evaluation context (e.g., edge device class, GPU architecture) where helpful. This makes technical effects credible and avoids overbroad, speculative assertions.

Ready-to-use templates and phrase banks:

• Technical purpose framing

  • “The invention addresses the technical problem of reducing [inference latency/memory footprint/energy consumption] in [specified environment], by modifying [model architecture/dataflow/memory layout] to alter the operation of [processor/memory/subsystem].”
  • “A technical objective is to improve numerical stability under [low-precision arithmetic/quantized compute], thereby decreasing [overflow events/gradient divergence] during [training/inference].”

• Quantified outcome statements

  • “Under [hardware/software configuration], the proposed method reduces [metric] by [X% or absolute value] relative to [baseline], measured across [N runs/datasets], with [confidence interval/standard deviation].”
  • “The approach achieves [throughput/latency/power] of [value], at [batch size/sequence length/precision], maintaining [accuracy/error rate] within [tolerance] of the full-precision baseline.”

• Causality linkage to claimed features

  • “The reduction in [memory transfers/cache misses/kernel launches] is causally attributable to [claimed feature], which enforces [contiguous layout/structured sparsity/quantization schedule] that changes the execution pattern of [module] as evidenced by [profiler logs/ablation results].”
  • “The improvement in [robustness/energy efficiency] results from [specific control logic or parameterization], as the claimed [module] constrains [operation] to [range/sequence], thereby reducing [re-computation/synchronization overhead].”

• Avoiding presentation-of-information pitfalls

  • Instead of: “Presents more relevant content to users.” Prefer: “Allocates fewer bytes per token by applying [compression/quantization], reducing network transfer time by [value] and lowering end-to-end latency on [device] by [value].”
  • Instead of: “Improves decision quality of operators.” Prefer: “Decreases false positives in sensor anomaly flags by [value] on [benchmark], enabling fewer interrupts and reducing CPU wake-ups by [value].”

• Sufficiency-aligned disclosure phrases

  • “A skilled person can reproduce the effect by applying [schedule/algorithm] with parameters in [range], as demonstrated in [examples], which yield consistent improvements within [tolerance] across [hardware classes].”
  • “Sensitivity analysis indicates that the effect persists for [hyperparameter range], ensuring that the technical benefit is not contingent on a narrow, non-reproducible configuration.”

• Problem–solution alignment

  • “Relative to the closest prior art [citation], the distinguishing features [F1–F2] modify [computation/memory access/communication pattern], solving the objective technical problem of [stated problem], as demonstrated by [benchmark/ablation].”
  • “The claimed combination achieves a synergistic reduction in [resource] not derivable from the prior art’s independent teachings, as evidenced by [comparative results] under matched conditions.”

Finally, ensure consistency between the claims, description, and prosecution submissions. The claims should enumerate the specific technical features that drive the effect. The description should disclose enough implementation detail to render the effect plausible and repeatable. Submissions should tie metrics and evidence back to those claim features, using the three-part structure as a rhetorical spine. Avoid introducing new, unsupported effects later in prosecution. If additional data are filed, present them as confirmatory replication under the originally disclosed protocols.

By rigorously anchoring your statements in the EPO’s understanding of technical effect, constructing them with purpose–outcome–causality clarity, supporting them with aligned evidence, and wording them to emphasize technical implementation and measurable system improvements, you will produce technical effect statements that resonate with examiners. This approach not only increases the credibility of your case but also streamlines inventive-step analysis, strengthens sufficiency, and helps you steer clear of presentation-of-information and “as such” exclusions. The result is a persuasive, examiner-ready narrative that shows exactly how and why your AI invention improves the operation of a computer or technical system.