PATENT CLAIM ANALYSIS

Application Number: 15874075
Application Type: Utility
Filing Date: 2018-01
Publication Date: 2018-05
Patent Classification: ["704", "273000"]

Abstract:
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for modulating language model biasing. In some implementations, context data is received. A likely context associated with a user is determined based on at least a portion of the context data. One or more language model biasing parameters based at least on the likely context associated with the user is selected. A context confidence score associated with the likely context based on at least a portion of the context data is determined. One or more language model biasing parameters based at least on the context confidence score is adjusted. A baseline language model based at least on the one or more of the adjusted language model biasing parameters is biased. The baseline language model is provided for use by an automated speech recognizer (ASR).

Claim (Index 7):
The method of  claim 2 , wherein determining that the particular context that is associated with biasing the language model is no longer applicable comprises determining that a user that spoke the utterance is likely associated with another context that is inconsistent with the particular context.

Metadata:
- Claim Count in Document: 51.0
- Percentile: 86.0
- Lexical Diversity: 2.53333
- Patent Class: 704.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['14673731', '14739287', '15605475', '15432620', '14811190']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2754543553279599
- 35 USC 102 Novelty (BERT): 0.5389168195504608
- Combined Prediction Score: 0.30180060175021
- Mean Citation Score: 271.777962
- Max Citation Score: 368.43057000000016
- Similarity Product: 266.3442247755433

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

Dataset: test