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 20):
A system, comprising:\n a memory that stores instructions, and a processor that is coupled to the memory and, when executing the instructions, is configured to:\n generate 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; and \n for each snippet in the snippets:\n apply 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, wherein the machine learning model uses one or more transcriptions generated by the at least one selector ASR engine to measure an accuracy of one or more transcriptions generated by the at least one contributor ASR engine, and wherein the machine learning model is trained based on a first historical performance of the at least one selector ASR engine, wherein a second historical performance of the at least one contributor ASR engine is better than the first historical performance of the at least one selector ASR engine; and \n store the best transcription in association with the snippet.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 96.0
- Lexical Diversity: 2.12903
- Patent Class: nan
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15474440', '12732788', '14685364', '10668141', '13190749']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.496826953817526
- 35 USC 102 Novelty (BERT): 0.4652620513061301
- Combined Prediction Score: 0.4936704635663865
- Mean Citation Score: 158.481682
- Max Citation Score: 167.04045
- Similarity Product: 129.689324215883

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