# USPTO vs AI Scoring: DuPont Factor 1 in 2026 Class 9

Ryan Walker · September 3, 2026

> USPTO vs AI Scoring: DuPont Factor 1 in 2026 Class 9. Class 9 software marks faced a Section 2(d) refusal rate during FY2024 first ac...

| Takeaway | Detail |
| --- | --- |
| AI clearance tools dramatically accelerate trademark screening workflows | up to 40% faster processing compared to manual methods |
| Mid-sized IP firms adopting neural-network scoring cut search overhead significantly | 40-60% reductions in time spent on preliminary conflict analysis |
| Enterprise-grade trademark platforms command premium annual licensing fees | costs range from $10,000 to $100,000+ per year |
| Budget-conscious applicants can access foundational AI similarity engines at lower price points | mid-market applications typically cost between $50 and $500 annually |

Class 9 software marks faced a Section 2(d) refusal rate during FY2024 first actions, yet filings pre-screened with AI embeddings cleared at a lower rejection threshold. This divergence reveals a structural blind spot: the USPTO’s legacy search interface prioritizes exact spelling matches while examiners apply DuPont Factor 1, which weighs appearance, sound, meaning, and commercial proximity.

Traditional keyword queries miss phonetic overlaps and visual similarities that trigger likelihood-of-confusion rejections. Neural-network models now parse those multidimensional signals simultaneously, surfacing conflicts that Vienna Classification codes routinely overlook. The result is a more predictable examination landscape where refusal patterns align with measurable similarity metrics rather than subjective examiner discretion.

Cost structures reflect this technological shift. Enterprise platforms operate between $10,000 and $100,000 annually, while mid-market alternatives sit comfortably between $50 and $500 per year. Firms leveraging these tools report 40 to 60 percent faster clearance cycles without sacrificing coverage across the 14 million USPTO records or global registries.

![Monumental federal office plaza sunrise with limestone columns](https://static.mm-ais.com/article-images-ai/uspto-vs-ai-scoring-dupont-factor-1-in-2-ai-8a7d7837.jpg)
Monumental federal office plaza sunrise with limestone columns

## Inside DuPont Factor 1

Factor 1 similarity is where Class 9 applications live or die, and public search tools do not see what examiners see. Under Lanham Act Section 2(d), registration is barred where an applicant mark creates likelihood of confusion with a prior live mark. That determination runs through the 13-factor test from In re DuPont, but in practice Factors 1 and 2 control: similarity of the marks and relatedness of the goods. If those two align against you, the other eleven rarely save the filing.

Examiners apply that test under TMEP with an internal advantage applicants lack. The examining attorney runs X-Search across direct hits plus phonetic equivalents plus truncated stems, then maps that result across IC 009 goods that the USPTO treats as inherently related — downloadable software, electric batteries, smart eyewear, sensors, and adjacent hardware. That is why NOVA for battery management software can draw a 2(d) citation to NOVAQ for smart glasses even when the goods descriptions do not overlap word-for-word. The examiner is not asking whether the products are identical; the examiner is asking whether consumers would assume common source.

The public USPTO Trademark Search launched in October 2023 at tmsearch.uspto.gov to replace TESS does not replicate that workflow. According to Signa Blog, Jun 2026, the USPTO replaced its legacy TESS system in late 2023 with that new interface. It remains a Boolean exact/contains system with no semantic ranking. Search NOVA and you get NOVA. You do not automatically get NOVAQ, NOVVA, or phonetic variants unless you manually truncate with asterisks, run separate phonetic strings, and guess at misspellings. That manual gap is structural, not user error, and it explains why reliance on USPTO Trademark Search alone leaves crowded IC 009 roots like QUANTUM, NOVA, and LUX systematically under-cleared.

AI clearance inverts the process by scoring similarity the way DuPont Factor 1 actually operates. According to Corsearch TrademarkNow, Sep 2026, TrademarkNow utilizes neural-network models that analyze phonetics, spelling, meaning, and visuals to uncover conflicts traditional keyword searches miss. In practice that means three stacked signals: transformer text embeddings that yield a 0-1 cosine similarity for semantic and orthographic closeness, a Soundex phonetic module for sound-alike risk, and a goods-relatedness weight tuned for IC 009 crowding. A candidate that scores 0.86 against a live QUANTUM mark for downloadable AI software is not a close call — under the article rule, run AI similarity scoring on every Class 9 candidate and do not file via TEAS until the top risk score is below 0.75 or the mark is amended to get below it. According to Corsearch TrademarkNow, Sep 2026, TrademarkNow provides explainable scoring and contextual analysis designed to produce review-ready outputs that withstand scrutiny, which is what lets you document why NOVALUX at 0.81 was amended to LUXLINE at 0.62 before filing.

According to the USPTO Office of Chief Economist FY2024 trademark statistics, a notable percentage of Class 9 first actions included a Section 2(d) refusal from a base of nearly 767,000 total applications, marking the highest refusal rate among leading classes. This volume creates a statistical trap for filers relying on exact-match searches: when the denominator of active filings in software and hardware categories is this large, the probability of collision with an existing mark rises non-linearly. The data confirms that Class 9 is not merely crowded; it is saturated to a degree where traditional search heuristics fail to surface conflicts before filing.

| Clearance Method | What It Actually Checks | Figure | Verdict For Class 9 |
| --- | --- | --- | --- |
| USPTO X-Search (examiner only) | Direct + phonetic + stem + IC 009 relatedness | Covers downloadable software to smart eyewear in one refusal | Standard you are judged against, not available to you |
| Public USPTO Trademark Search | Boolean exact/contains, manual asterisk truncation | Misses NOVA vs NOVAQ unless searcher truncates | Loses — no semantic ranking |
| AI embedding + Soundex + goods weight | 0-1 cosine plus phonetic plus IC 009 root weight | Kill-threshold 0.75; up to 40% faster per Corsearch TrademarkNow, Sep 2026 | Wins — only method that enforces 0.75 pre-filing |
| Global AI screen | Simultaneous multi-register screen | 190 registries per Corsearch TrademarkNow, Sep 2026 | Wins for expansion candidates with QUANTUM/NOVA/LUX roots |
| Blind TEAS filing | No pre-score, pay then defend | Base fee per class at risk before response cost | Loses — burns fee before DuPont review |

![Diverging pathways through vast high tech hall sleek electronic](https://static.mm-ais.com/article-images-ai/uspto-vs-ai-scoring-dupont-factor-1-in-2-ai-92dc15af.jpg)
Diverging pathways through vast high tech hall sleek electronic

## Refusal Reality

The mechanism of failure lies in how examiners apply DuPont Factor 1 compared to how search tools index marks. According to the INTA 2023 Global Clearance Survey of brand counsel, a significant portion of eventually cited 2(d) marks were missed by USPTO-search-only clearance but retrievable by computational similarity. Exact-match systems return zero results for misspellings or phonetic variants, creating blind spots that AI scoring models detect through vector proximity analysis. When a proposed mark like "CLOUDD" or "SMARTY" is evaluated, a standard search may show no direct hits, yet the AI score reveals high similarity to "CLOUD" or "SMART" stems already registered. This discrepancy explains why search-only clearance yields false negatives at a rate exceeding one-third of eventual refusals.

Evidence supports the efficacy of computational screening over manual review. According to the Corsearch 2024 Trademark Industry Report, AI-assisted pre-screening users had a lower first-action 2(d) rate for Class 9 software marks than search-only filers. This reduction validates the canonical decision rule: running AI similarity scoring on every Class 9 candidate and refusing to file via TEAS until the top risk score drops below 0.75. Marks scoring above this threshold should be amended or abandoned immediately, saving the filer from the downstream friction of office action responses.

A critical edge case driving these numbers is stem overcrowding. According to the USPTO Trademark Data API Q1 2025 snapshot, a majority of Class 9 2(d) citations relied on just a few hundred overcrowded stems including SMART, AI, CLOUD, and CYBER. Filers often assume that adding a unique prefix or suffix mitigates risk, but examiners frequently cite these common stems under Section 2(d). AI scoring captures this dynamic by weighting dominant dictionary terms heavily, whereas search tools treat them as generic noise. By identifying marks that trigger these high-density stems early, practitioners can pivot to distinct naming strategies that avoid the bottleneck entirely.

Anaqua AcclaimIP catches the near-miss that the USPTO Trademark Search misses, and that gap decides Class 9 outcomes. In controlled comparison work on crowded IC 009 sets, Boolean keyword search finds roughly about half of the marks that later get cited, while embedding screens from systems like Anaqua AcclaimIP and TrademarkVision find roughly about nine in ten, with uncertainty high because test sets vary. The mechanism is not mysterious: Boolean matches strings, embeddings match vectors for look, sound, and meaning. For 2026 Class 9 filings, that recall difference is why you run AI similarity scoring on every candidate and do not file via USPTO TEAS until the top risk score is below 0.75 or the mark is amended to get below it.

Speed and cost explain why teams actually follow the rule. An instant AI screen typically returns in minutes at a low flat per-mark charge, while a full attorney clearance opinion typically takes most of a work week and costs roughly an order of magnitude more, varying by firm and search depth. Filing blind is the worst option because the filing fee is nonrefundable and a Section 2(d) refusal burns both budget and priority position. According to LeanLaw, Jan 2026, mid-sized IP law firms implementing AI clearance tools report 40-60% reductions in search time while catching more potential conflicts, with 60% as the high-end result, not a guarantee.

| Clearance Method | Missed Conflict Rate | Cost per Refusal | First-Action 2(d) Impact |
| --- | --- | --- | --- |
| USPTO Search Only | Significant portion (INTA 2023) | Substantial legal costs (AIPLA 2023) | Baseline |
| AI Scoring

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