PATENT CLAIM ANALYSIS

Application Number: 16116609
Application Type: Utility
Filing Date: 2018-08
Publication Date: 2019-01
Patent Classification: ["382", "156000"]

Abstract:
The invention is directed towards segmenting images based on natural language phrases. An image and an n-gram, including a sequence of tokens, are received. An encoding of image features and a sequence of token vectors are generated. A fully convolutional neural network identifies and encodes the image features. A word embedding model generates the token vectors. A recurrent neural network (RNN) iteratively updates a segmentation map based on combinations of the image feature encoding and the token vectors. The segmentation map identifies which pixels are included in an image region referenced by the n-gram. A segmented image is generated based on the segmentation map. The RNN may be a convolutional multimodal RNN. A separate RNN, such as a long short-term memory network, may iteratively update an encoding of semantic features based on the order of tokens. The first RNN may update the segmentation map based on the semantic feature encoding.

Claim (Index 11):
The method of  claim 8 , wherein the plurality of image features are identified within the image based on an image feature identification model.

Metadata:
- Claim Count in Document: 13.0
- Percentile: 96.0
- Lexical Diversity: 2.25676
- Patent Class: 382.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15458887', '15166177', '15715400', '15976647', '15817161']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3061297222653188
- 35 USC 102 Novelty (BERT): 0.6126446828196151
- Combined Prediction Score: 0.3367812183207485
- Mean Citation Score: 300.7816860000001
- Max Citation Score: 663.8055
- Similarity Product: 443.6115040912629

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

Dataset: test