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 9):
A system for training motion synthesis neural networks to generate retargeted skeleton motion sequences that reflect motion sequences of initial skeletons comprising:\n at least one processor; at least one non-transitory computer memory comprising a motion synthesis neural network that includes an encoder recurrent neural network, a decoder recurrent neural network, and a forward kinematics layer; and instructions that, when executed by at least one processor, cause the system to:\n provide training input joint features for joints of a training initial skeleton to the motion synthesis neural network, wherein the training input joint features correspond to an initial time of a training motion sequence; \n utilize the encoder recurrent neural network and the decoder recurrent neural network to generate predicted joint rotations for joints of a training target skeleton for an initial time of a training target motion sequence based on the training input joint features; \n utilize the forward kinematics layer to generate predicted joint features for joints of the training target skeleton for the initial time of the training target motion sequence based on the predicted joint rotations; and \n train the motion synthesis neural network to generate target skeleton motion sequences from initial skeleton motion sequences based on the predicted joint features for the joints of the training target skeleton.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 90.0
- Lexical Diversity: 2.31667
- Patent Class: 345.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15807028', '15174863', '15224519', '15147222', '15872408']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.6026443825065448
- 35 USC 102 Novelty (BERT): 0.5024924495292601
- Combined Prediction Score: 0.5926291892088165
- Mean Citation Score: 221.958358
- Max Citation Score: 235.20923
- Similarity Product: 165.717819150573

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