It refers to systems that combine classical sparse, lexical retrieval (inverted indices, term weighting) with learned dense-vector retrieval, joined by a fusion step such as reciprocal rank fusion. In this dataset, a filing only qualifies if it addresses both a core retrieval mechanism and a practical adaptation problem like domain shift, query expansion or index synchronization. That combination is what separates hybrid retrieval patents from patents that claim a dense encoder or an inverted index in isolation.

The ranking is led by a single assignee with 72 of the 595 records in scope, well ahead of the fifth-place holder at 20 records. The top 5 assignees combined hold 45.9% of all records, and the top 10 hold 59.0%, so the field has a clearly concentrated head. Behind that, the ranking extends across 100 companies, with most holding only a handful of records each.

Filings grew sharply from 9 in 2021 to 38 in 2024, a +322% increase over that span and the highest year recorded in the dataset. Figures for 2025 and 2026 appear lower, but that reflects the roughly 18-month lag between filing and publication, not an actual slowdown. Any conclusion about the field cooling off should wait until those later years have had time to fully publish.