PATENT CLAIM ANALYSIS

Application Number: 16108110
Application Type: Utility
Filing Date: 2018-08
Publication Date: 2020-01
Patent Classification: ["704", "232000"]

Abstract:
Systems and methods are disclosed for end-to-end neural networks for speech recognition and classification and additional machine learning techniques that may be used in conjunction or separately. Some embodiments comprise multiple neural networks, directly connected to each other to form an end-to-end neural network. One embodiment comprises a convolutional network, a first fully-connected network, a recurrent network, a second fully-connected network, and an output network. Some embodiments are related to generating speech transcriptions, and some embodiments relate to classifying speech into a number of classifications.

Claim (Index 1):
A speech recognition system, comprising:\n a processor; a memory, the memory comprising instructions for an end-to-end speech recognition neural network comprising: a convolutional neural network configured to receive acoustic features of an utterance and output a first representation of the utterance; a first fully-connected neural network configured to receive a first portion of the first representation of the utterance and a plurality of copies of the first fully-connected neural network for receiving additional portions of the first representation of the utterance, the first fully-connected neural network and the copies configured to collectively output a second representation of the utterance; a recurrent neural network configured to receive the second representation of the utterance from the fully-connected neural network and output a third representation of the utterance; a second fully-connected neural network configured to receive the third representation of the utterance and output a fourth representation of the utterance, wherein the fourth representation of the utterance comprises a word embedding; an output neural network configured to receive the fourth representation of the utterance from the second fully-connected neural network and output an indication of one or more words corresponding to the utterance; wherein the output neural network comprises an output node for each word of a vocabulary, each output node configured to output a probability that the corresponding word is a correct transcription of the utterance, and the output neural network produces a probability distribution over the output nodes for the correct transcription of the utterance; wherein only the output nodes of the output neural network are associated with words of the vocabulary; wherein the end-to-end speech recognition neural network directly transcribes the utterance to a word without transcribing the utterance to characters and without transcribing the utterance to phonemes; wherein the end-to-end speech recognition neural network directly transcribes the utterance to a word using only neural network components and without using any non-neural network components; wherein the end-to-end speech recognition neural network comprises only neural network layers trained in an end-to-end backpropagation performed through all neural network layers and does not include separately trained components.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 96.0
- Lexical Diversity: 1.63793
- Patent Class: 704.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['16108107', '16108109', '15243838', '14877673', '14811939']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3060428070730077
- 35 USC 102 Novelty (BERT): 0.5510012205061864
- Combined Prediction Score: 0.3305386484163256
- Mean Citation Score: 337.846998
- Max Citation Score: 403.5461
- Similarity Product: 295.35990320534705

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