Granted Patent B2

US 12,615,178 · App. 18/795,138 · Granted Apr 28, 2026

Inventor: Newton Howard (Potomac, MD)

Assignee: Genesis Intelligence, LLC

H04L41/0654H04L41/16

Abstract

Claims (58)

1 . A method implemented in a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the computer system interconnected with a telecommunications system, the method comprising:

receiving, at the computer system, data relating to operation of the telecommunication system;

determining, at the computer system, one or more types of failures and/or faults that the computer system is operable to predict based on the data relating to the operation of the telecommunication system;

selecting, at the computer system, at least one machine learning model trained to detect and predict faults in the operation of the telecommunications system based on a combination of the data relating to the operation of the telecommunication system, processes history, and objectives;

obtaining, at the computer system, the at least one machine learning model;

selecting, at the computer system, computing infrastructure upon which to execute the at least one machine learning model, wherein the selected computing infrastructure comprises a mesh of interconnected micro-applications;

executing, at the computer system, the at least one machine learning model using the selected computing infrastructure to detect and predict faults in the operation of the telecommunication system; and

generating at least one alert for at least one of the detected and/or predicted faults.

2 . The method of claim 1 , further comprising automatically preventing at least some of the predicted faults.

3 . The method of claim 2 , wherein the data relating to operation of the telecommunications system comprises at least one of data from sensors, data from devices, data from servers, data from robots, and data from humans.

4 . The method of claim 3 , wherein the at least one machine learning model is obtained by

generating, at the computer system, a new model based on type, morphology, and parameter information.

5 . The method of claim 3 , wherein the at least one machine learning model is further obtained by:

determining, at the computer system, a combination of the selected and generated models that produces higher accuracy results than the selected and generated models; and

assembling, at the computer system, a combination of the selected and generated models based on the determination of the combination of the selected and generated models that produces higher accuracy results than the selected and generated models.

6 . The method of claim 5 , wherein the combination of the selected and generated models that produces higher accuracy results than the selected and generated models may be determined by selected and trained heuristics or by a machine learning model.

7 . A telecommunication system comprising a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor to perform:

receiving, at the computer system, data relating to operation of the telecommunication system;

determining, at the computer system, one or more types of failures and/or faults that the computer system is operable to predict based on the data relating to the operation of the telecommunication system;

selecting, at the computer system, at least one machine learning model trained to detect and predict faults in the operation of the telecommunications system based on a combination of the data relating to the operation of the telecommunication system, processes history, and objectives;

obtaining, at the computer system, the at least one machine learning model;

selecting, at the computer system, computing infrastructure upon which to execute the at least one machine learning model, wherein the selected computing infrastructure comprises a mesh of interconnected micro-applications;

executing, at the computer system, the at least one machine learning model using the selected computing infrastructure to detect and predict faults in the operation of the telecommunication system; and

generating at least one alert for at least one of the detected and/or predicted faults.

8 . The system of claim 7 , further comprising automatically preventing at least some of the predicted faults.

9 . The system of claim 8 , wherein the data relating to the operation of the telecommunication system comprises at least one of data from sensors, data from devices, data from servers, data from robots, and data from humans.

10 . The system of claim 9 , wherein the at least one machine learning model is obtained by at least one of:

selecting at least one model from among previously used processed models stored at the computer system;

selecting at least one model from among models obtained from public sources, proprietary sources, or both; and

generating a new model based on type, morphology, and parameter information.

11 . The system of claim 9 , wherein the at least one machine learning model is obtained by at least two of:

selecting at least one model from among previously used processed models stored at the computer system;

selecting at least one model from among models obtained from public sources, proprietary sources, or both;

generating a new model based on type, morphology, and parameter information;

determining a combination of the selected and generated models that produces higher accuracy results than the selected and generated models; and

assembling a combination of the selected and generated models based on the determination of the combination of the selected and generated models that produces higher accuracy results than the selected and generated models.

12 . The system of claim 11 , wherein the combination of the selected and generated models that produces higher accuracy results than the selected and generated models may be determined by selected and trained heuristics or by a machine learning model.

13 . A computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer system interconnected with a telecommunications system, to cause the computer system to perform a method comprising:

receiving, at the computer system, data relating to operation of the telecommunication system;

determining, at the computer system, one or more types of failures and/or faults that the computer system is operable to predict based on the data relating to the operation of the telecommunication system;

selecting, at the computer system, at least one machine learning model trained to detect and predict faults in the operation of the telecommunications system based on a combination of the data relating to the operation of the telecommunication system, processes history, and objectives;

obtaining, at the computer system, the at least one machine learning model;

selecting, at the computer system, computing infrastructure upon which to execute the at least one machine learning model, wherein the selected computing infrastructure comprises a mesh of interconnected micro-applications;

executing, at the computer system, the at least one machine learning model using the selected computing infrastructure to detect and predict faults in the operation of the telecommunication system; and

generating at least one alert for at least one of the detected and/or predicted faults.

14 . The computer program product of claim 13 , further comprising automatically preventing at least some of the predicted faults.

15 . The computer program product of claim 14 , wherein the data relating to the operation of the telecommunication system comprises at least one of data from sensors, data from devices, data from servers, data from robots, and data from humans.

16 . The computer program product of claim 15 , wherein the at least one machine learning model is obtained by at least one of:

selecting, at the computer system, at least one model from among previously used processed models stored at the computer system;

selecting, at the computer system, at least one model from among models obtained from public sources, proprietary sources, or both; and

generating, at the computer system, a new model based on type, morphology, and parameter information.

17 . The computer program product of claim 15 , wherein the at least one machine learning model is obtained by at least two of:

selecting, at the computer system, at least one model from among previously used processed models stored at the computer system;

selecting, at the computer system, at least one model from among models obtained from public sources, proprietary sources, or both;

generating, at the computer system, a new model based on type, morphology, and parameter information;

determining, at the computer system, a combination of the selected and generated models that produces higher accuracy results than the selected and generated models; and

assembling, at the computer system, a combination of the selected and generated models based on the determination of the combination of the selected and generated models that produces higher accuracy results than the selected and generated models.

18 . The computer program product of claim 17 , wherein the combination of the selected and generated models that produces higher accuracy results than the selected and generated models may be determined by selected and trained heuristics or by a machine learning model.

Assignments (1)

Continuity (8)

References Cited (10)