The market is divided.
In conversations with clients, we encounter two contrasting expectations: some explicitly want AI to be used, while others prefer to avoid AI tools. Both expectations are understandable – and SMD can accommodate both.
The expectation: faster processes, lower costs, and more efficient screenings.
Key challenges in using AI
According to 300 executives at large enterprises:
name compliance with AI regulation as one of their biggest challenges.
name explainability & transparency as one of their biggest challenges.
Source: “Great Expectations” study by Gowling WLG / FT Longitude, 2026. Survey of 300 senior executives from companies with annual revenues of at least GBP 500 million, conducted from late January to mid-February 2026.
SMD’s answer addresses both expectations: use AI purposefully, keep data under control, safeguard quality.
At SMD, word similarity is calculated by an algorithm.
Not by generative AI.
Assessing the similarity of word marks – both in searching and watching – is our core competence. We determine the degree of similarity using our proprietary SMD algorithm – developed over decades and continously refined.
Similarity is determined using fixed, traceable criteria – not by a generative model whose output can vary even when the input is identical.
The degree of similarity between two terms is calculated algorithmically — not estimated by AI.
For ordered searches and watches, our legally trained staff select the most relevant hits and compile the report.
Where SMD already uses AI today.
Concrete and hands-on – in client-facing services as well as internal processes.
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No AI · proven
Word mark similarity
reproducible algorithms
Deliberately NO generative AI: established algorithms always return the same result for the same input and same data set.
AI can provide supplementary information to help customers prioritize search results. The search logic itself remains under human control and is reproducible.
Image recognition
Logo AI Knockout
Pre-Search
Analyses visual structures within seconds – ideal for screenings, preliminary searches, and large numbers of logo variants or countries.
For in-depth searches, conventional device mark searches remain the quality benchmark. Final hit selection and reporting: qualified staff.
Decision support
AI Goods & Services Score
Faster Comparison of goods & services
The tool returns the proximity of the goods and services of two trademarks as a percentage, plus a brief reasoning of the analysis.
The output supports the review process, while the interpretation of the result and any conclusions remain with the client.
Opposition management
AI Conflict Potential
optional add-on in TMZOOM
An AI trained by trademark attorneys automatically assesses alert marks found by our algorithm – and delivers a recommended action based on a transparent and structured analysis on similarity, overlap of goods and services, distinctiveness, and likelihood of confusion.
The SMD client weighs out the arguments and decides – the final evaluation remains his professional responsibility.
Data quality
Better data basis
Vienna Classification
A model trained on SMD data adds missing Vienna classes – device marks are classified more completely and become easier to find.
Curated trademark data and professional review in the background safeguard data quality.
Client service
Watch order review
e.g. after a new Nice edition (NCL 13-2026)
Compares large lists, surfaces changes faster, and prepares possible adjustments in a structured way.
Whether and how coverage needs to be adjusted is up to the client – in consultation with SMD.
Development
Agentic Coding
Software development
Supports implementing, reviewing, and evolving code – more speed and efficiency in the development process.
Professional oversight, security review, and responsibility stay with experienced developers.
Internal & creative
Internal processes
Reports, drafts, concepts
Provides groundwork, structure, or ideas – for reports, first drafts, copy editing, or brainstorming.
Assessment, selection, professional review, and final execution are done by people.
Same input. Same result?
Run the same search several times. A generative AI tool may answer differently each time. SMD’s algorithms are reproducible.
Result per run
Every run – identical
In a specific test, an AI search tool assessed the same trademark differently across repeated runs. In one run, the mark was not taken into account at all and was therefore not available to the attorney responsible for the matter. For decisions that need to be reliable, this demonstrates a real risk. SMD relies on algorithms and human expertise for the best quality results.
The more commonplace AI becomes, the greater the risk that healthy skepticism toward automatically generated results will fade.
If erroneous AI output goes uncorrected, the incentive to apply due care may also diminish over time.
Greater scope requires more control
Drag the slider: the more that depends on a search, the more depth and control it needs.
A lean approach combining automated methods with targeted AI support may be sufficient here. Fast, cost-efficient, and suitable as a pre-selection.
SMD aligns its choice of method with purpose and risk: efficient when a first orientation is enough – in-depth and quality-controlled where decisions depend on the result.
Use AI purposefully. Keep data under control. Safeguard quality.
It supports – it is not automatically a promise of quality.
Word similarity searching rests on proven, reproducible methods.
Only the data required for the task – no trademark names, owners, or context.
AI supports. Assessment, control, and responsibility stay with people.
Reliable results require reproducible methods, controlled data, and professional expertise.