# US 12,224,915 B2 · AI-supported network techniques

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- Number: US 12,224,915 B2
- Document ID: US12224915B2
- Status: Granted
- Kind: B2
- Application number: 17/477,667
- Filed: 2021-09-17
- Published: 2022-03-24
- Granted: 2025-02-11
- Earliest claimed: 2020-09-24
- Assignees: Level 3 Communications, LLC
- Applicants: Level 3 Communications, LLC
- Inventors: Steven M. Casey; William R. Henning; Stephen Opferman; Todd A Borchert; Rahul Chowdhury
- Web page: https://patentgrep.com/patent/US12224915B2

## CPC

- H04L 41/16: using machine learning or artificial intelligence
- H04L 41/0631: using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
- H04L 41/069: using logs of notifications; Post-processing of notifications
- H04L 41/145: {involving simulating, designing, planning or modelling of a network}
- H04L 43/04: Processing captured monitoring data, e.g. for logfile generation
- H04L 41/0213: Standardised network management protocols, e.g. simple network management protocol [SNMP]
- H04L 41/142: using statistical or mathematical methods
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)

## Abstract

Examples of the present disclosure relate to an AI-supported CDN. In examples, a data processing engine processes log data of a CDN node according to a model to identify an issue. An issue indication is provided to a solution generation engine, which generates a set of solutions to automatically resolve the issue. The set of solutions is provided to a solution implementation engine, which iteratively implements solutions to resolve the issue using solution implementation information associated with a given solution. Thus, the data processing engine need not have knowledge regarding the specific hardware and/or software used within the CDN. Similarly, the solution generation engine need not have knowledge of the structure of the CDN and/or configuration of devices associated with the identified issue, such that the solution implementation engine provides a layer of abstraction between a solution and the implementation-specific details used to implement the solution within the CDN.

## Claims

### Claim 1

A system comprising:
at least one processor; and
memory, operatively connected to the at least one processor and storing instructions that, when executed by the at least one processor, cause the system to perform a set of operations, the set of operations comprising:
receiving, from a node of a content distribution network (CDN), log data comprising one or more events associated with a computing device of the node;
processing the log data using a model to determine an issue;
selecting a solution generation engine from a plurality of solution generation engines based at least in part on the determined issue;
generating, by the selected solution generating engine and based on the determined issue, a set of solutions to resolve the issue, wherein generating the set of solutions comprises
evaluating a knowledge graph based at least in part on the determined issue to identify a cause node;
evaluating a rule of a relationship to determine whether the determined issue satisfies the rule, wherein the relationship associates a solution node with the cause node; and
based on determining the determined issue satisfies the rule, adding a solution associated with the solution node to the set of solutions;
selecting a first solution of the set of solutions; and
implementing the first solution to resolve the issue.

### Claim 2 (depends on 1)

The system of claim 1, wherein implementing the first solution to resolve the issue comprises:
identifying a set of instructions associated with the first solution; and
performing the identified set of instructions to implement the first solution.

### Claim 3 (depends on 1)

The system of claim 1, wherein the solution generation engine is selected based at least in part on the computing device.

### Claim 4 (depends on 1)

The system of claim 1, wherein the set of operations further comprises:
storing at least a part of the log data associated with the determined issue as training data; and
training an updated model using the training data.

### Claim 5 (depends on 4)

The system of claim 4, wherein the training data further comprises at least a part of the log data associated with a routine operation of the computing device.

### Claim 6 (depends on 1)

The system of claim 1, wherein the set of operations further comprises:
determining the first solution did not resolve the issue;
selecting a second solution of the set of solutions; and
implementing the second solution to resolve the issue.

### Claim 7 (depends on 1)

The system of claim 1, wherein processing the log data comprises:
identifying at least a part of the log data associated with routine operation of the computing device to generate filtered log data; and
processing the filtered log data using the model to determine the issue.

### Claim 8 (depends on 1)

The system of claim 1, wherein the model is a first model and the issue is a first issue, and wherein processing the log data to determine the issue further comprises:
processing the log data using a second model to identify a second issue; and
selecting the first issue as the determined issue based at least in part on determining a performance metric for with the first model is higher than the performance metric for the second model.

### Claim 9 (depends on 8)

The system of claim 8, wherein the performance metric is one of:
a confidence score associated with processing the log data;
an average confidence score based on historical model performance; or
a prediction accuracy.

### Claim 10 (depends on 1)

The system of claim 1, wherein the knowledge graph is generated from at least one of:
a manual;
a knowledge base article;
a trouble ticket;
current event data;
an electronic message;
a planned maintenance data base; or
a network inventory system.

### Claim 11 (depends on 1)

The system of claim 1, wherein the set of operations further comprises:
determining that a performance metric of the model is below a predetermined threshold;
based on determining that the performance metric is below the predetermined threshold, retraining the model using historical log data, wherein the historical log data comprises at least a part of the received log data; and
processing additional log data using the retrained model to determine a second issue.

### Claim 12 (depends on 1)

The system of claim 1, wherein the model is a first model and the set of operations further comprises:
determining that a performance metric of the first model is below a predetermined threshold;
based on determining that the performance metric is below the predetermined threshold, selecting a second model to use in place of the first model; and
processing additional log data using the second model to determine a second issue.

### Claim 13 (depends on 1)

The system of claim 1, wherein selecting the solution generation engine further comprises identifying a hardware device or software package associated with the issue and selecting the solution generation based on the identified hardware device or software package.

### Claim 14 (depends on 1)

The system of claim 1, wherein selecting the solution generation engine further comprises evaluating a computing functionality associated with the issue and selecting the solution generation engine based on the evaluated computing functionality.

### Claim 15

A method comprising:
receiving, from a node of a content distribution network (CDN), log data comprising one or more events associated with a computing device of the node;
processing the log data using a model to determine an issue;
selecting a solution generation engine from a plurality of solution generation engines based at least in part on the determined issue;
generating, by the selected solution generation engine and based on the determined issue, a set of solutions to resolve the issue, wherein generating the set of solutions comprises evaluating a knowledge graph based at least in part on the determined issue to identify a cause node;
evaluating a rule of a relationship to determine whether the determined issue satisfies the rule, wherein the relationship associates a solution node with the cause node; and
based on determining the determined issue satisfies the rule, adding a solution associated with the solution node to the set of solutions;
selecting a first solution of the set of solutions; and
implementing the first solution to resolve the issue.

### Claim 16 (depends on 15)

The method of claim 15, wherein implementing the first solution to resolve the issue comprises:
identifying a set of instructions associated with the first solution; and
performing the identified set of instructions to implement the first solution.

### Claim 17 (depends on 15)

The method of claim 15, further comprising:
storing at least a part of the log data associated with the determined issue as training data; and
training an updated model using the training data.

### Claim 18 (depends on 17)

The method of claim 17, wherein the training data further comprises at least a part of the log data associated with a routine operation of the computing device.

### Claim 19 (depends on 15)

The method of claim 15, wherein processing the log data comprises:
identifying at least a part of the log data associated with routine operation of the computing device to generate filtered log data; and
processing the filtered log data using the model to determine the issue.