PATENT CLAIM ANALYSIS

Application Number: 15977891
Application Type: Utility
Filing Date: 2018-05
Publication Date: 2018-09
Patent Classification: ["706", "025000"]

Abstract:
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a neural network used to select actions performed by a reinforcement learning agent interacting with an environment. In one aspect, a method includes maintaining a replay memory, where the replay memory stores pieces of experience data generated as a result of the reinforcement learning agent interacting with the environment. Each piece of experience data is associated with a respective expected learning progress measure that is a measure of an expected amount of progress made in the training of the neural network if the neural network is trained on the piece of experience data. The method further includes selecting a piece of experience data from the replay memory by prioritizing for selection pieces of experience data having relatively higher expected learning progress measures and training the neural network on the selected piece of experience data.

Claim (Index 6):
The method of  claim 3 , wherein the priority is set to a maximum value for a piece of experience data that has not yet been used in training.

Metadata:
- Claim Count in Document: 9.0
- Percentile: 93.0
- Lexical Diversity: 2.14865
- Patent Class: 706.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15349894', '15016173', '15643266', '14097862', '15704969']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3912487109397936
- 35 USC 102 Novelty (BERT): 0.5740826250479814
- Combined Prediction Score: 0.4095321023506124
- Mean Citation Score: 266.29192
- Max Citation Score: 455.5543
- Similarity Product: 452.94018457561134

Labels:
- Claim Label 101: 0
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 0
- Label 101 Adjusted: 0

Dataset: test