PATENT CLAIM ANALYSIS

Application Number: 15908594
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2019-03
Patent Classification: ["706", "030000"]

Abstract:
Provided is a method for detecting an abnormal session including a request message received by a server from a client and a response message generated by the server, the method including transforming at least a part of messages included in the session into data in the form of a matrix, transforming the data in the form of the matrix into a representation vector a dimension of which is lower than a dimension of the matrix of the data using a convolutional neural network, and determining whether the session is abnormal by arranging the representation vectors obtained from the messages in an order in which the messages are generated to compose a first representation vector sequence, and analyzing the first to representation vector sequence using an long short term memory (LSTM) neural network.

Claim (Index 1):
A method for detecting an abnormal session including a request message received by a server from a client and a response message generated by the server, the method comprising:\n transforming at least a part of messages included in the session into data in the form of a matrix; transforming the data in the form of the matrix into a representation vector, a dimension of which is lower than a dimension of the matrix of the data, using a convolutional neural network; and determining whether the session is abnormal by arranging the representation vectors obtained from the messages in an order in which the messages are generated to compose a first representation vector sequence, and analyzing the first representation vector sequence using a long short-term memory (LSTM) neural network, wherein the determining of whether the session is abnormal includes determining whether the session is abnormal on the basis of a difference between the first representation vector sequence and the second representation vector sequence.

Metadata:
- Claim Count in Document: 21.0
- Percentile: 88.0
- Lexical Diversity: 2.17188
- Patent Class: 706.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: True
- Related Applications: ['15817161', '15817153', '15817165', '15424711', '15408526']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3936881189171537
- 35 USC 102 Novelty (BERT): 0.5156056444340134
- Combined Prediction Score: 0.4058798714688397
- Mean Citation Score: 271.129328
- Max Citation Score: 314.98544
- Similarity Product: 210.95033852018355

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