Patent Application EP3682346A1 - Predicting Non-Observable Parameters for Digital Components Using Neural Networks
Summary
The European Patent Office published patent application EP3682346A1 for Google LLC, disclosing an AI-based system and method for predicting non-observable parameters of digital components using neural networks and machine learning techniques. The invention relates to IPC classifications G06N 3/09 and G06F 16/958, covering computational methods for estimating hidden or unmeasurable digital asset characteristics.
What changed
The EPO published European patent application EP3682346A1 for Google LLC (filed by inventors Young, Conley, and Holt), disclosing a system and method for predicting non-observable parameters of digital components using neural network architectures. The patent covers techniques in machine learning (G06N 3/09) and digital content processing (G06F 16/958) to estimate parameters that cannot be directly measured or observed in digital systems.
This is a patent publication notice with no compliance requirements. Technology companies and AI developers researching parameter prediction systems should review the published claims to assess potential patent landscape implications or opportunities for licensing. No action is required for regulatory compliance purposes.
Source document (simplified)
PREDICTING NON-OBSERVABLE PARAMETERS FOR DIGITAL COMPONENTS
Publication EP3682346A1 Kind: A1 Mar 25, 2026
Applicants
Google LLC
Inventors
YOUNG, Andrew Richard, CONLEY, Jonathan Joseph, HOLT, Gary R.
IPC Classifications
G06F 16/958 20190101AFI20251107BHEP G06N 3/0499 20230101ALI20251107BHEP G06N 3/09 20230101ALI20251107BHEP
Designated States
AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LI, LT, LU, LV, MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TR
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