PATENT CLAIM ANALYSIS

Application Number: 16417522
Application Type: Utility
Filing Date: 2019-05
Publication Date: 2019-11
Patent Classification: ["706", "025000"]

Abstract:
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for reinforcement learning using agent curricula. One of the methods includes maintaining data specifying plurality of candidate agent policy neural networks; initializing mixing data that assigns a respective weight to each of the candidate agent policy neural networks; training the candidate agent policy neural networks using a reinforcement learning technique to generate combined action selection policies that result in improved performance on a reinforcement learning task; and during the training, repeatedly adjusting the weights in the mixing data to favor higher-performing candidate agent policy neural networks.

Claim (Index 19):
The system of  claim 18 , wherein a performance of a combination is based on a quality of the combined policy outputs generated during the training.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 100.0
- Lexical Diversity: 1.8
- Patent Class: 706.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['16380125', '15499832', '14097862', '15704969', '15977923']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3733956860914871
- 35 USC 102 Novelty (BERT): 0.5165451926421317
- Combined Prediction Score: 0.3877106367465516
- Mean Citation Score: 249.24804
- Max Citation Score: 270.2168
- Similarity Product: 172.74109313802714

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