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 5):
The method according to  claim 1 , comprising using a combination of at least a statistical algorithm, a linear reduction algorithm and a non-linear reduction algorithm to select said subset of features from the features of the first size dictionary.

Metadata:
- Claim Count in Document: 4.0
- Percentile: 98.0
- Lexical Diversity: 2.4697
- Patent Class: 726.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['14929902', '13163010', '14038682', '14985944', '15639805']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2718148238511968
- 35 USC 102 Novelty (BERT): 0.5745274943387246
- Combined Prediction Score: 0.3020860908999496
- Mean Citation Score: 261.399308
- Max Citation Score: 537.14636
- Similarity Product: 369.6386382440471

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