PATENT CLAIM ANALYSIS

Application Number: 15871945
Application Type: Utility
Filing Date: 2018-01
Publication Date: 2019-07
Patent Classification: ["382", "157000"]

Abstract:
Methods of encoding image data for loading into an artificial intelligence (AI) integrated circuit are provided. The AI integrated circuit may have an embedded cellular neural network for implementing AI tasks based on the loaded image data. An encoding method may apply image splitting, principal component analysis (PCA) or a combination thereof to an input image to generate a plurality of output images. Each output image has a size smaller than the size of the input image. The method may load the output images into the AI chip, execute programming instructions contained in the AI chip to generate an image recognition result based on the at least one of the plurality of output images, and output the image recognition result. The encoding method also trains a convolution neural network (CNN) and loads the weights of the CNN into the AI integrated circuit for implementing the AI tasks.

Claim (Index 18):
The system of  claim 17 , wherein programming instructions for applying PCA to the additional input image to generate the additional output image comprise programming instructions configured to:\n determine a volumetric data based on a plurality of sub-areas and the plurality of channels in the additional input image; perform volumetric PCA over the volumetric data to determine one or more PCA components, each corresponding to one of the one or more channels in the additional output image; and for each sub-area in the plurality of sub-areas of the additional input image:\n determine a voxel based on that sub-area and the plurality of channels in the additional input image, \n project the voxel over the one or more PCA components, and \n map the projected voxel into a corresponding pixel in the additional output image, wherein each channel in the additional output image corresponds to one of the one or more PCA components.

Metadata:
- Claim Count in Document: 24.0
- Percentile: 86.0
- Lexical Diversity: 2.16
- Patent Class: 382.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15871933', '15871918', '10983194', '16075540', '09536820']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3490513443354901
- 35 USC 102 Novelty (BERT): 0.5157439284526674
- Combined Prediction Score: 0.3657206027472078
- Mean Citation Score: 208.123184
- Max Citation Score: 281.87912
- Similarity Product: 179.0873930239868

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

Dataset: test