PATENT CLAIM ANALYSIS

Application Number: 16056298
Application Type: Utility
Filing Date: 2018-08
Publication Date: 2020-02
Patent Classification: ["null", "null"]

Abstract:
One embodiment of the present invention sets forth a technique for performing ensemble modeling of ASR output. The technique includes generating input to a machine learning model from snippets of voice activity in the recording and transcriptions produced by multiple automatic speech recognition (ASR) engines from the recording. The technique also includes applying the machine learning model to the input to select, based on transcriptions of the snippet produced by at least one contributor ASR engine of the multiple ASR engines and at least one selector ASR engine of the multiple ASR engines, a best transcription of the snippet from possible transcriptions of the snippet produced by the multiple ASR engines. The technique further includes storing the best transcription in association with the snippet.

Claim (Index 7):
The method of  claim 6 , wherein applying the machine learning model to the input to select the best transcription of the snippet from the possible transcriptions of the snippet further comprises:\n inputting, into the machine learning model, a second transcription of the snippet from a second contributor ASR engine and the set of additional transcriptions of the snippet from the set of selector ASR engines; receiving, as output from the machine learning model, a second score representing a correctness of the second transcription of the snippet; and selecting the best transcription of the snippet based on a comparison of the first and second scores.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 96.0
- Lexical Diversity: 2.12903
- Patent Class: nan
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15474440', '12732788', '14685364', '10668141', '13190749']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.5247847785948725
- 35 USC 102 Novelty (BERT): 0.4741571199297626
- Combined Prediction Score: 0.5197220127283615
- Mean Citation Score: 158.481682
- Max Citation Score: 167.04045
- Similarity Product: 122.7310445147574

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 0
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test