PATENT CLAIM ANALYSIS

Application Number: 15873070
Application Type: Utility
Filing Date: 2018-01
Publication Date: 2018-07
Patent Classification: ["382", "141000"]

Abstract:
An information processing apparatus and method are disclosed, each of which: using a set of normal data, learns a first model for determining the normal data; sets, out of a plurality of abnormality candidate areas, the abnormality candidate areas selected by a user as correct data and the abnormality candidate areas not selected by the user as incorrect data, to learn a second model for identifying the correct data and the incorrect data, each abnormality candidate area indicating a candidate area of an abnormality and detected based on the first model from each of a plurality of captured images; obtains the captured images; detects the abnormality candidate areas from the respective captured images, using the first model; determines whether the abnormality candidate areas detected belong to the correct data or the incorrect data, using the second model; and controls to output a determination.

Claim (Index 7):
The information processing apparatus according to  claim 6 ,\n wherein the circuitry learns the second model with any of linear determination, logistic regression, a SVM, a neural network, a K-nearest neighbor method, a decision tree, and deep learning.

Metadata:
- Claim Count in Document: 15.0
- Percentile: 86.0
- Lexical Diversity: 2.54839
- Patent Class: 382.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: True
- Related Applications: ['15872257', '12971500', '14533394', '14064536', '14519538']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3338761705184495
- 35 USC 102 Novelty (BERT): 0.5038746334940897
- Combined Prediction Score: 0.3508760168160135
- Mean Citation Score: 155.18229200000005
- Max Citation Score: 179.2563
- Similarity Product: 101.40699745686648

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