Low precision training format patents: mapping block floating point, shared exponent and loss scaling claims

In this dataset it means claims combining a reduced-precision numerical representation — typically block floating point or shared exponent grouping — with the supporting numerical controls that keep training stable, such as loss scaling, accumulation precision, rounding mode selection or dynamic range management. A filing that only claims a generic quantization scheme without these specific mechanisms usually falls outside this scope. The search string behind this landscape requires both a format term and a numerical-control term to appear together, which is why the corpus centres on training-stability techniques rather than general model compression.

The assignee ranking behind this landscape covers 100 companies, with one clear leader holding 171 records — more than three times the fifth-place count of 47. The top five assignees combined account for 41.8% of all 1,095 records in scope, and the top ten account for 59.9%. Beyond that group, filing activity thins into a long tail of companies with single-digit record counts, so competitive attention should focus on the leading names and watch for new entrants rather than assuming an even spread.

Filing peaked in 2019 at 115 records and has declined since across complete-year data, with 2021's 69 filings dropping to 35 by 2024 — a 49% fall over that span. Because publication lags actual filing by roughly 18 months, the 2025 and 2026 figures in any dataset are still incomplete and will rise as more records publish, so it is too early to call the current trajectory a continued decline. The safest reading is that filing has cooled from its 2019 peak but the last one to two years remain an open question.