PATENT CLAIM ANALYSIS

Application Number: 15878113
Application Type: Utility
Filing Date: 2018-01
Publication Date: 2019-05
Patent Classification: ["704", "232000"]

Abstract:
The disclosed technology teaches a deep end-to-end speech recognition model, including using multi-objective learning criteria to train a deep end-to-end speech recognition model on training data comprising speech samples temporally labeled with ground truth transcriptions. The multi-objective learning criteria updates model parameters of the model over one thousand to millions of backpropagation iterations by combining, at each iteration, a maximum likelihood objective function that modifies the model parameters to maximize a probability of outputting a correct transcription and a policy gradient function that modifies the model parameters to maximize a positive reward defined based on a non-differentiable performance metric which penalizes incorrect transcriptions in accordance with their conformity to corresponding ground truth transcriptions; and upon convergence after a final backpropagation iteration, persisting the modified model parameters learned by using the multi-objective learning criteria with the model to be applied to further end-to-end speech recognition.

Claim (Index 11):
A deep end-to-end speech recognition system, comprising:\n an input port that receives digital audio samples of a signal comprising speech; and a deep end-to-end speech recognition processor comprising hardware and a stack of layers running on the hardware including convolution layers and recurrent layers, coupled to the input port and configurable to process the digital audio samples, recognize speech from the audio samples, and output transcriptions corresponding to recognized speech; wherein the deep end-to-end speech recognition processor includes parameters trained using a multi-objective learning criteria on training data comprising speech samples temporally labeled with ground truth transcriptions; and wherein the multi-objective learning criteria update the processor parameters over one thousand to millions of backpropagation iterations by combining, at each iteration, a maximum likelihood objective function that modified the processor parameters to maximize a probability of outputting a correct transcription, and a policy gradient function that modified the processor parameters to maximize a positive reward defined based on a non-differentiable performance metric which penalizes incorrect transcriptions in accordance with their conformity to corresponding ground truth transcriptions.

Metadata:
- Claim Count in Document: 15.0
- Percentile: 86.0
- Lexical Diversity: 1.77647
- Patent Class: 704.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15358102', '15258799', '15397327', '15843047', '15258836']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2690716658062367
- 35 USC 102 Novelty (BERT): 0.499102533659531
- Combined Prediction Score: 0.2920747525915661
- Mean Citation Score: 244.84407
- Max Citation Score: 276.69717
- Similarity Product: 217.7060994494677

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