PATENT CLAIM ANALYSIS

Application Number: 15910720
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2018-09
Patent Classification: ["704", "232000"]

Abstract:
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining, by a first sequence-training speech model, a first batch of training frames that represent speech features of first training utterances; obtaining, by the first sequence-training speech model, one or more first neural network parameters; determining, by the first sequence-training speech model, one or more optimized first neural network parameters based on (i) the first batch of training frames and (ii) the one or more first neural network parameters; obtaining, by a second sequence-training speech model, a second batch of training frames that represent speech features of second training utterances; obtaining one or more second neural network parameters; and determining, by the second sequence-training speech model, one or more optimized second neural network parameters based on (i) the second batch of training frames and (ii) the one or more second neural network parameters.

Claim (Index 15):
One or more non-transitory computer-readable media storing software that includes instructions, which, when executed by one or more computers, cause the one or more computers to perform operations comprising:\n obtaining multiple copies of a neural network of a speech model; asynchronously obtaining parameter values for the multiple copies of the neural network such that different copies of the neural network have different sets of parameter values; after obtaining the parameter values such that different copies of the neural network have different sets of parameter values, training the multiple copies of the neural network in parallel using different subsets of a set of training data, wherein training each copy of the neural network adjusts the parameter values for the copy of the neural network to generate adjusted parameter values; and updating the neural network of the speech model based on the adjusted parameter values generated for each of the multiple copies of the neural network.

Metadata:
- Claim Count in Document: 27.0
- Percentile: 90.0
- Lexical Diversity: 3.88889
- Patent Class: 704.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['14258139', '15013239', '15243838', '14983315', '15843047']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2465008321250439
- 35 USC 102 Novelty (BERT): 0.5702500575340028
- Combined Prediction Score: 0.2788757546659399
- Mean Citation Score: 303.553824
- Max Citation Score: 476.25327
- Similarity Product: 358.6333699461937

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