The Concept
In standard Neural Machine Translation, the primary objective is semantic integrity — preserving the meaning of the source text. In high-stakes Patent Law, meaning is insufficient. The translation must also adhere to a rigid structural architecture that dictates how that meaning is presented.
Structural compliance refers to the enforcement of non-semantic, morpho-syntactic frameworks required by statute or local patent practice. These are not stylistic preferences — they are binary constraints.
"A translation that is semantically accurate but structurally non-compliant — e.g., using an active verb where a noun is mandated — can render a patent claim invalid on its face."
The Challenge: When AI Resists the Law
Generic LLMs are trained on the "average internet," where linguistic naturalness and conversational flow are rewarded. Consequently, they aggressively resist the artificial, repetitive, and disjointed syntax of "Patentese."
The Conflict
| What the Model Wants |
What the Law Demands |
| Conjugate verbs to create flow |
Nominalize verbs to define static scope |
| Use natural articles ("a," "the") fluidly |
Enforce rigid definite/indefinite distinction |
| Use de generically or du for flow |
Distinguish Class (de) vs. Instance (du) |
| Translate tokens left-to-right (Head-Final) |
Invert to Head-Initial French noun phrase structure |
The Alignment Goal
We must retrain the model to suppress its fluency bias and recognize specific trigger words as commands to switch into a rigid, non-standard grammatical mode. This requires encoding binary constraints that override the model's statistical preferences.
The Four Critical Constraints
Constraint 1
Verb Nominalization
Method Claims with "comprising"
The Rule: In French patent practice, when a method claim uses the transition comprising (comprenant), subsequent steps must be nominalized (turned into nouns), not left as infinitive verbs.
Why It Matters: Patent claims define a static scope of protection. Infinitive verbs suggest ongoing actions; nominalized forms define discrete structural elements.
❌ INCORRECT (Fluent but Invalid):
"Procédé comprenant : fixer un élément..."
(Method comprising: to fix an element...)
✓ CORRECT (Structurally Compliant):
"Procédé comprenant : la fixation d'un élément..."
(Method comprising: the fixing of an element...)
The NMT Problem: Generic models default to infinitive constructions because they are statistically more common in non-patent French. Alignment requires fine-tuning to recognize comprenant as a nominalization trigger.
→ View Constraint 1 Case Catalog
Constraint 2
Definite / Indefinite Article Distinction
Antecedent Basis & Referential Integrity
In general linguistics, the choice between "A" (Indefinite) and "The" (Definite) is often a matter of style. In Intellectual Property, it is a matter of strict legal logic known as Antecedent Basis.
The "Closed Loop" Logic
A patent claim functions as a rigorous closed loop. The rules are binary:
- Introduction ("A" / "An"): Must introduce a new element for the first time.
- Reference ("The" / "Said"): Must reference an element already defined.
The AI Blind Spot
Generic models treat articles as low-value tokens. When faced with complex quantifiers like "of the at least some," they prioritize linguistic fluidity and default to the smoother — but legally fatal — indefinite structure "d'au moins certaines," effectively deleting the definitive pointer and introducing New Matter.
→ View Constraint 2 Case Catalog
Constraint 3
Genitive Form Differentiation
Class vs. Instance — Prepositional Scope
The Challenge: Unlike the binary rules of verbs, genitive forms exist in a "Gray Zone." The choice of preposition (de, du, or Zero-Marker) defines the engineering relationship between components. The AI typically fails in two opposite directions.
Failure Mode A — Over-Specification (Definiteness Bias)
Source: "Device handle" (Generic type)
❌ AI Output: "Poignée du dispositif"
Verdict: Implies a specific device exists when it doesn't (False Antecedent).
Failure Mode B — Under-Grammaticization (Anglicism Trap)
Source: "Target chamber" (Functional housing)
❌ AI Output: "Chambre cible" (Juxtaposition)
✓ Correct: "Chambre de cible" (Functional marker)
The Solution: Because this constraint depends on intent (Is it a label or a function?), it cannot be fully automated. The system applies a "Prepositional Lock" on known functional classes, but novel compounds trigger Expert Review.
→ View Constraint 3 Case Catalog
Constraint 4
Syntactic Linearity Bias
Complex Noun Phrases & Modification Scope
The Rule: English technical nomenclature is Head-Final (Modifiers + Noun), whereas French is Head-Initial (Noun + Modifiers). Generic NMT models process tokens sequentially without performing the necessary syntactic inversion, disconnecting the noun from its modifiers and creating legally indefinite claims.
Source: "Electrocardiogram, ECG, Lead" (single component)
❌ Generic NMT (Linear):
"Électrocardiogramme, ECG, Dérivation"
Verdict: Three separate items — a machine, an acronym, a wire.
✓ Aligned Model (Head-Initial Inversion):
"Dérivation d'électrocardiogramme (ECG)"
Verdict: Single legal object correctly defined.
The NMT Problem: "Linearity Bias" causes the model to output tokens in the exact English order, producing noun phrase lists rather than syntactically valid French compounds. Alignment requires explicit Head-Initial restructuring rules.
→ View Constraint 4 Case Catalog