PATENT CLAIM ANALYSIS

Application Number: 15859971
Application Type: Utility
Filing Date: 2018-01
Publication Date: 2018-07
Patent Classification: ["382", "156000"]

Abstract:
An electronic apparatus includes a processor configured to obtain a plurality of images, extract deep features with respect to the plurality of images using a feature extraction model, classify the plurality of images into certain groups using the extracted deep features and a classification model, display a result of the classification on the display, determine whether the feature extraction model and/or the classification model need to be updated using the result of the classification, and train and update at least one of the feature extraction model and the classification model based on a result of the determination. The electronic apparatus may estimate a deep feature of an image using a rule-based or artificial intelligence (AI) algorithm. When the deep feature of the image is estimated using the AI algorithm, the electronic apparatus may use a machine learning, neural network, or deep learning algorithm, or the like.

Claim (Index 3):
The electronic apparatus of  claim 2 , wherein the processor is configured to execute the at least one instruction to cause the electronic apparatus to store the deep features in an exchangeable image file format (EXIF) with respect to the plurality of images.

Metadata:
- Claim Count in Document: 45.0
- Percentile: 86.0
- Lexical Diversity: 2.31884
- Patent Class: 382.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: True
- Related Applications: ['13574878', '15850393', '14956326', '15439896', '14024418']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3276622621408084
- 35 USC 102 Novelty (BERT): 0.5045177855890067
- Combined Prediction Score: 0.3453478144856282
- Mean Citation Score: 231.547486
- Max Citation Score: 244.75534
- Similarity Product: 178.88836385974884

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