PATENT CLAIM ANALYSIS

Application Number: 15977923
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 computer storage media, for asynchronous deep reinforcement learning. One of the systems includes a plurality of workers, wherein each worker is configured to operate independently of each other worker, and wherein each worker is associated with a respective actor that interacts with a respective replica of the environment during the training of the deep neural network.

Claim (Index 15):
The computer-storage media of  claim 14 , wherein the reinforcement learning technique is a Q-learning technique, and wherein performing the iteration comprises:\n determining a maximum output of a target network for any of the actions in the predetermined set of actions when the environment replica is in the next state, wherein the target network is the same neural network as the Q network but with possibly different parameter values; determining an error from the maximum output of the target network, the actual reward, and the Q value for the current action; and determining the current gradient using the error.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 93.0
- Lexical Diversity: 1.58696
- Patent Class: 706.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15016173', '15367094', '15349900', '15977913', '15349894']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4019421733142061
- 35 USC 102 Novelty (BERT): 0.5086744723634212
- Combined Prediction Score: 0.4126154032191276
- Mean Citation Score: 225.962432
- Max Citation Score: 296.19855
- Similarity Product: 225.79443642905656

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

Dataset: test