PATENT CLAIM ANALYSIS

Application Number: 16220499
Application Type: Utility
Filing Date: 2018-12
Publication Date: 2019-05
Patent Classification: ["726", "023000"]

Abstract:
According to some embodiments, a method for training a malware detector comprising a deep learning algorithm is described, which comprises converting a set of malware files and non malware files into vectors by using a feature based dictionary, and/or by using a conversion into an image, and providing prospects that the files constitute malware. Various features and combinations of features are described to build a feature based dictionary and adapt its size. According to some embodiments, a method for detecting a malware by using a malware detector comprising a deep learning algorithm is described, which comprises converting a file into a vector by using a feature based dictionary, and/or by using a conversion into an image, and providing prospects that the file constitutes malware. Methods for providing a plurality of prospects and aggregating these prospects are provided. Additional methods and systems in the field of malware detection are also described.

Claim (Index 19):
The data structure of  claim 17 , wherein the list of features of each dictionary comprises features obtained from at least a selection of a subset of features of another dictionary of larger size, said selection involving performing at least one of:\n using at least a statistical algorithm to select said subset of features from features of said another dictionary; using at least a linear reduction algorithm to select said subset of features from features of said another dictionary; and using at least a non-linear reduction algorithm to select said subset of features from features of said another dictionary.

Metadata:
- Claim Count in Document: 4.0
- Percentile: 98.0
- Lexical Diversity: 2.4697
- Patent Class: 726.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['14929902', '13163010', '14038682', '14985944', '15639805']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2955379511158752
- 35 USC 102 Novelty (BERT): 0.5780418231332765
- Combined Prediction Score: 0.3237883383176153
- Mean Citation Score: 261.399308
- Max Citation Score: 537.14636
- Similarity Product: 375.07918815142153

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

Dataset: test