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DIFF Subgroup
G10L 15/197

Probabilistic grammars, e.g. word n-grams

Introduced: January 2013

Full Title

Full titles differ between systems:

IPC:

Speech recognition > Speech classification or search > using natural language modelling > using context dependencies, e.g. language models > Grammatical context, e.g. disambiguation of recognition hypotheses based on word sequence rules > Probabilistic grammars, e.g. word n-grams

CPC:

Speech recognition (G10L17/00 takes precedence) > Speech classification or search > using natural language modelling > using context dependencies, e.g. language models > Grammatical context, e.g. disambiguation of the recognition hypotheses based on word sequence rules > Probabilistic grammars, e.g. word n-grams

No child classifications to compare. This is a leaf node in both IPC and CPC.

Top Applicants

Top Applicants (IPC)

Class G10,2013–2023, worldwide · Source: EPO PATSTAT

  1. SAMSUNG ELECTRONICS COMPANY KR 5,218
  2. GOOGLE US 4,644
  3. FRAUNHOFER DE 4,601
  4. SONY CORPORATION JP 2,380
  5. HUAWEI TECHNOLOGIES COMPANY CN 2,179
  6. YAMAHA CORPORATION 2,120
  7. IBM (INTERNATIONAL BUSINESS MACHINES CORPORATION) US 1,974
  8. MICROSOFT TECHNOLOGY LICENSING US 1,916
  9. AMAZON TECHNOLOGIES US 1,866
  10. DOLBY LABORATORIES LICENSING CORPORATION US 1,843

Top Applicants (CPC)

Class G10,2013–2023, worldwide · Source: EPO PATSTAT

  1. SAMSUNG ELECTRONICS COMPANY KR 6,277
  2. GOOGLE US 5,429
  3. FRAUNHOFER DE 4,984
  4. SONY CORPORATION JP 2,808
  5. HUAWEI TECHNOLOGIES COMPANY CN 2,577
  6. MICROSOFT TECHNOLOGY LICENSING US 2,382
  7. DOLBY LABORATORIES LICENSING CORPORATION US 2,123
  8. QUALCOMM US 2,117
  9. IBM (INTERNATIONAL BUSINESS MACHINES CORPORATION) US 2,049
  10. AMAZON TECHNOLOGIES US 1,924