# US 12,764,036 B2 · Network resource models and technologies for actions executed according to machine learning inference reports

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- Number: US 12,764,036 B2
- Document ID: US12764036B2
- Status: Granted
- Kind: B2
- Application number: 18/935,367
- Filed: 2024-11-01
- Published: 2025-02-20
- Granted: 2026-10-13
- Earliest claimed: 2023-11-02
- Assignees: Intel Corporation
- Applicants: Intel Corporation
- Inventors: Yizhi Yao
- Web page: https://patentgrep.com/patent/US12764036B2

## CPC

- H04L 41/16: using machine learning or artificial intelligence
- H04L 41/5067: Customer-centric QoS measurements
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)

## Abstract

This disclosure describes systems, methods, and devices related to optimized resource technologies. A device may create a Managed Object Instance (MOI) representing actions executed based on artificial intelligence or machine learning (AI/ML) inference function. The device may notify a management and network service (MnS) consumer about the creation of the MOI. The device may execute actions by a network or management function acting as the consumer of the inference output. The device may manage performance of the AI/ML inference function.

## Claims

### Claim 1

An apparatus, the apparatus comprising processing circuitry coupled to storage, the processing circuitry configured to:
create a Managed Object Instance (MOI) representing actions executed based on artificial intelligence or machine learning (AI/ML) inference function;
notify a management and network service (MnS) consumer about the creation of the MOI;
execute actions by a network or management function acting as the consumer of the inference output; and
manage performance of the AI/ML inference function.

### Claim 2 (depends on 1)

The apparatus of claim 1, wherein the AI/ML inference function includes an Analytics Logical Function (AnLF), management data analytics function (MDAF), AI/ML enabled Energy Saving function, AI/ML enabled MRO function, or AI/ML enabled MLB function.

### Claim 3 (depends on 2)

The apparatus of claim 2, wherein the AI/ML inference function is represented by an MOI.

### Claim 4 (depends on 3)

The apparatus of claim 3, wherein the MOI representing the AnLF contains attributes indicating supported analytics ID(s).

### Claim 5 (depends on 1)

The apparatus of claim 1, wherein the MOI representing the actions executed based on the AI/ML inference output contains at least one of the following attributes: identifier of the inference output, actions executed, or timestamp of action completion.

### Claim 6 (depends on 1)

The apparatus of claim 1, wherein the MnS consumer is configured to take actions based on the executed actions and resulting performance.

### Claim 7 (depends on 6)

The apparatus of claim 6, wherein the actions taken by the MnS consumer include deactivating the AI/ML inference function.

### Claim 8 (depends on 6)

The apparatus of claim 6, wherein the MnS consumer requests re-training of the ML model for the AI/ML inference function.

### Claim 9 (depends on 6)

The apparatus of claim 6, wherein the MnS consumer uses a different ML model to support the AI/ML inference function.

### Claim 10 (depends on 1)

The apparatus of claim 1, wherein an MnS producer and the MnS consumer are configured for performance management of the AI/ML inference function.

### Claim 11

A non-transitory computer-readable medium storing computer-executable instructions which when executed by one or more processors result in performing operations comprising:
creating a Managed Object Instance (MOI) representing actions executed based on artificial intelligence or machine learning (AI/ML) inference function;
notifying a management and network service (MnS) consumer about the creation of the MOI;
executing actions by a network or management function acting as the consumer of the inference output; and
managing performance of the AI/ML inference function.

### Claim 12 (depends on 11)

The non-transitory computer-readable medium of claim 11, wherein the AI/ML inference function includes an Analytics Logical Function (AnLF), management data analytics function (MDAF), AI/ML enabled Energy Saving function, AI/ML enabled MRO function, or AI/ML enabled MLB function.

### Claim 13 (depends on 12)

The non-transitory computer-readable medium of claim 12, wherein the AI/ML inference function is represented by an MOI.

### Claim 14 (depends on 13)

The non-transitory computer-readable medium of claim 13, wherein the MOI representing the AnLF contains attributes indicating supported analytics ID(s).

### Claim 15 (depends on 11)

The non-transitory computer-readable medium of claim 11, wherein the MOI representing the actions executed based on the AI/ML inference output contains at least one of the following attributes: identifier of the inference output, actions executed, or timestamp of action completion.

### Claim 16 (depends on 11)

The non-transitory computer-readable medium of claim 11, wherein the MnS consumer is configured to take actions based on the executed actions and resulting performance.

### Claim 17 (depends on 16)

The non-transitory computer-readable medium of claim 16, wherein the actions taken by the MnS consumer include deactivating the AI/ML inference function.

### Claim 18 (depends on 16)

The non-transitory computer-readable medium of claim 16, wherein the MnS consumer requests re-training of the ML model for the AI/ML inference function.

### Claim 19 (depends on 16)

The non-transitory computer-readable medium of claim 16, wherein the MnS consumer uses a different ML model to support the AI/ML inference function.

### Claim 20

A method comprising:
creating a Managed Object Instance (MOI) representing actions executed based on artificial intelligence or machine learning (AI/ML) inference function;
notifying a management and network service (MnS) consumer about the creation of the MOI;
executing actions by a network or management function acting as the consumer of the inference output; and
managing performance of the AI/ML inference function.