PATENT CLAIM ANALYSIS

Application Number: 15967240
Application Type: Utility
Filing Date: 2018-04
Publication Date: 2018-08
Patent Classification: ["700", "257000"]

Abstract:
Apparatus and methods for training and controlling of, for instance, robotic devices. In one implementation, a robot may be trained by a user using supervised learning. The user may be unable to control all degrees of freedom of the robot simultaneously. The user may interface to the robot via a control apparatus configured to select and operate a subset of the robot's complement of actuators. The robot may comprise an adaptive controller comprising a neuron network. The adaptive controller may be configured to generate actuator control commands based on the user input and output of the learning process. Training of the adaptive controller may comprise partial set training. The user may train the adaptive controller to operate first actuator subset. Subsequent to learning to operate the first subset, the adaptive controller may be trained to operate another subset of degrees of freedom based on user input via the control apparatus.

Claim (Index 36):
The apparatus of  claim 31 , wherein:\n a performance measure associated with an execution of the target action absent the training input is smaller in magnitude than a performance measure determined based on the execution of the target action in accordance with the training input.

Metadata:
- Claim Count in Document: 5.0
- Percentile: 91.0
- Lexical Diversity: 2.48485
- Patent Class: 700.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['14070269', '14070114', '13918620', '14613237', '13918338']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.5193171641874743
- 35 USC 102 Novelty (BERT): 0.5398459437910846
- Combined Prediction Score: 0.5213700421478353
- Mean Citation Score: 367.526456
- Max Citation Score: 453.5272
- Similarity Product: 259.0506877595901

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