Ribbon Communications has filed a patent for a method performed by a computing system to optimize a communication network. The method involves receiving a request defining a set of demands for the network, selecting optimization functions based on network characteristics and a decision tree, determining an initial solution, and iteratively applying updated optimization functions to reduce costs and produce an optimal solution within a time constraint. GlobalData’s report on Ribbon Communications gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Ribbon Communications, Network traffic analysis was a key innovation area identified from patents. Ribbon Communications's grant share as of September 2023 was 59%. Grant share is based on the ratio of number of grants to total number of patents.
Optimizing communication network using selected optimization functions
A recently filed patent (Publication Number: US20230261942A1) describes a method performed by a computing system for optimizing a communication network. The method involves receiving a request that defines a set of demands for the network, each demand having multiple endpoints, a diversity policy, a number of routes, and a set of constraints. The system then selects a subset of optimization functions based on the characteristics of the network and a decision tree. Using this subset of functions, an initial solution for the network is determined by assigning routes to each demand. Within a time constraint, the system iteratively applies an updated subset of optimization functions with modified parameters to reduce the cost of the initial solution and produce an optimal solution.
The decision tree used in the method can be hardcoded, meaning it is pre-defined and fixed. Alternatively, it can be a rule-based decision tree, which determines the optimization functions based on a set of rules. The decision tree can also be produced and updated by a machine learning function, allowing for adaptive optimization based on data and patterns.
The method further includes probing the execution times of solving for demands to determine the initial solution and using these execution times to configure subsequent iterations. Solutions generated by different executions of the optimization functions and parameters are ranked to identify the most effective ones.
In the iterative process of modifying the initial solution, demands are sorted based on a ranking parameter, and the characteristics of the demands are adjusted to arrive at an updated solution. The subset of optimization functions used in the method includes various functions such as minimal-cost flow, integer linear programming, path finding with excluded edges, in-out graph, Djikstra's algorithm, and Yen's algorithm.
Another aspect of the patent describes a computing system that implements the optimization method. The system includes a processor and machine-readable instructions that receive a request for optimizing a communication network, select a subset of optimization functions based on network characteristics and a decision tree, determine an initial solution for the network using these functions, and iteratively apply a subset of optimization functions with modified parameters to reduce the cost of the initial solution within a time constraint.
The characteristics of the communication network considered in the optimization process include a diversity policy, which can involve Shared Risk Link Groups (SRLG) or Shared Risk Node Groups (SRNG). The decision tree used in the computing system can be hardcoded, rule-based, or machine-learning based.
Overall, this patent presents a method and computing system for optimizing communication networks by selecting and applying appropriate optimization functions based on network characteristics and a decision tree. The iterative process aims to reduce the cost of the initial solution and produce an optimal solution within a given time constraint.
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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
