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 10):
The method of  claim 8 , further comprising:\n feeding at least one of the plurality of output images into a first layer of an embedded cellular neural network (CeNN) architecture in the AI chip; feeding at least one of the plurality of output image channels of the additional output image into the first layer of the embedded CeNN; running the CeNN in the AI chip by executing instructions in the CeNN to determine an image recognition result based on the at least one of the plurality of output images and at least one of the plurality of output image channels of the additional output image; and outputting the image recognition result.

Metadata:
- Claim Count in Document: 24.0
- Percentile: 86.0
- Lexical Diversity: 2.16
- Patent Class: 382.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15871933', '15871918', '10983194', '16075540', '09536820']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3232503266265631
- 35 USC 102 Novelty (BERT): 0.509076726697487
- Combined Prediction Score: 0.3418329666336556
- Mean Citation Score: 208.123184
- Max Citation Score: 281.87912
- Similarity Product: 196.71246581637385

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