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 14):
An image segmentation system for segmenting an image, the system comprising:\n a processor device; and a computer-readable non-transitory storage medium, coupled with the processor device, having instructions stored thereon, which, when executed by the processor device, perform actions comprising:\n receiving an image feature data structure that encodes images features corresponding to an image; \n employing a first recurrent neural network (RNN) to generate an n-gram feature data structure that encodes n-gram features corresponding to an ordered set of tokens included in a natural language phrase that references a portion of the image; \n employing a second RNN to iteratively update a current state of a segmentation map based on the image feature data structure and the n-gram feature data structure, wherein the second RNN propagates a current state of the segmentation map; \n generating a segmented image based on the iteratively updated current state of the segmentation map, wherein the segmented image indicates the portion of the image referenced by the natural language phrase.

Metadata:
- Claim Count in Document: 13.0
- Percentile: 96.0
- Lexical Diversity: 2.25676
- Patent Class: 382.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15458887', '15166177', '15715400', '15976647', '15817161']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2707028676283534
- 35 USC 102 Novelty (BERT): 0.644687305314565
- Combined Prediction Score: 0.3081013113969745
- Mean Citation Score: 300.7816860000001
- Max Citation Score: 663.8055
- Similarity Product: 550.8314682795703

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