PATENT CLAIM ANALYSIS

Application Number: 15926787
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2019-09
Patent Classification: ["345", "474000"]

Abstract:
This disclosure relates to methods, non-transitory computer readable media, and systems that use a motion synthesis neural network with a forward kinematics layer to generate a motion sequence for a target skeleton based on an initial motion sequence for an initial skeleton. In certain embodiments, the methods, non-transitory computer readable media, and systems use a motion synthesis neural network comprising an encoder recurrent neural network, a decoder recurrent neural network, and a forward kinematics layer to retarget motion sequences. To train the motion synthesis neural network to retarget such motion sequences, in some implementations, the disclosed methods, non-transitory computer readable media, and systems modify parameters of the motion synthesis neural network based on one or both of an adversarial loss and a cycle consistency loss.

Claim (Index 19):
The method of  claim 18 , wherein the target motion sequence comprises different predicted-joint-feature sets for a joint of the joints of the target skeleton at different times within the target motion sequence.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 90.0
- Lexical Diversity: 2.31667
- Patent Class: 345.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15807028', '15174863', '15224519', '15147222', '15872408']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.6299467627457407
- 35 USC 102 Novelty (BERT): 0.5084424002889514
- Combined Prediction Score: 0.6177963265000618
- Mean Citation Score: 221.958358
- Max Citation Score: 235.20923
- Similarity Product: 157.62798476625264

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