PATENT CLAIM ANALYSIS

Application Number: 15976361
Application Type: Utility
Filing Date: 2018-05
Publication Date: 2019-11
Patent Classification: ["370", "351000"]

Abstract:
An optimal path suggestion tool in a Software-Defined Networking (SDN) architecture to predict a router's future usage based on an analysis of the router's historical usage over a given period of time in the past and to recommend an optimal routing path within the network in view of the predicted future usages of the routers/switches in the network. The optimal path suggestion tool is an analytical, plug-and-play model usable as part of an SDN controller to provide more insights into different routing paths based on the future usage of each router. A Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model in the suggestion tool analyzes the historical usage data of a router to predict its future usage. A Deep Boltzmann Machine (DBM) model in the suggestion tool recommends an optimal routing path within the SDN-based network upon analysis of the LSTM-RNN based predicted future usages of routers/switches in the network.

Claim (Index 7):
The method of  claim 1 , wherein the LSTM-RNN model includes a plurality of sequential stages having temporal dependence, and wherein training the LSTM-RNN model comprises:\n for each pair of stages in the plurality of sequential stages, using, by the computing device, a Teacher's Force method to provide the following as an input from a time-wise preceding stage in the pair to a time-wise succeeding stage in the pair:\n an actual average usage of the routing element over a one-day period immediately prior to a day associated with a usage pattern over which the LSTM-RNN model is currently being trained.

Metadata:
- Claim Count in Document: 29.0
- Percentile: 93.0
- Lexical Diversity: 2.14474
- Patent Class: 370.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15379010', '15253659', '15718861', '15817165', '15817161']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4973785686477958
- 35 USC 102 Novelty (BERT): 0.5020359659200476
- Combined Prediction Score: 0.497844308375021
- Mean Citation Score: 177.233034
- Max Citation Score: 188.01337
- Similarity Product: 117.84248836345196

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