RWS secures US patent for AI that predicts translation effort at the point of authoring
Patent helps enterprises predict cost, effort and reuse potential at the point of content creation – before translation is even requested
Maidenhead, UK
2/19/2026 9:00:00 AM
2/19/2026 9:00:00 AM
RWS (RWS.L), a global AI solutions company, has been awarded US Patent No. 12,505,297 for an AI-powered system that enables organizations to understand the translation potential of their content as it is being authored – long before a translation project is scoped or commissioned.
Coming to Trados customers in 2026, the patented technology – Document Translation Feasibility Analysis Systems and Methods – analyzes a source document to identify how much content can be reused from previous translations, including cases where the wording has changed but the meaning remains the same. By operating at the authoring stage rather than within the translation workflow, the system gives content authors and project managers early visibility into expected effort, cost and reuse – shifting feasibility decisions upstream where they have the greatest impact.
“This patent addresses a critical gap in how enterprises manage multilingual content. By surfacing translation intelligence at the point of creation, teams can make informed decisions about cost, effort and reuse before a single translation request is raised - not after.”
Moving beyond sentence-level matching
Traditional translation tools rely heavily on exact or near-exact text matches. RWS’s new technology goes further by using AI to generate semantic signatures – meaning-based representations of text – and comparing them against large repositories of previously translated content.
In practice, this allows enterprises to see which parts of a document are already covered by existing linguistic assets and where genuinely new translation work is required – before projects are scoped or budgets are committed.
The patented approach is designed for enterprises managing complex content estates across multiple languages, markets and regulatory environments. By surfacing reuse potential and feasibility insights at an early stage, it enables organizations to make faster localization decisions and identify content that could benefit from translation – including content that would have been overlooked or deemed impractical to assess within traditional localization workflows. Combined with human oversight, this AI-driven analysis supports more predictable planning, better reuse of existing linguistic assets and consistent quality at scale.