Using AI to Manage Wireless Network Resources Smarter
Intel's 2025 patent on using a hierarchy of AI models to manage wireless network resources, making them more efficient by learning from network measurements and rewards.
Original patent title: “Reinforcement learning (RL) and graph neural network (GNN)-based resource management for wireless access networks”
Intel's 2025 patent on using a hierarchy of AI models to manage wireless network resources, making them more efficient by learning from network measurements and rewards. Granted to Intel in 2025 with 23 claims and 3 forward citations, and it is expected to expire in 2041.
Coverage
What does this patent actually cover?
This patent describes a computer system, called a 'computing node,' designed to help manage wireless networks like the 5G and beyond (Next Generation or NG). It uses a smart approach with multiple Artificial Intelligence (AI) models, specifically machine learning models, organized in layers like a pyramid. The system takes measurements from the network and uses these AI models to make decisions. A key part is how these models communicate: a model at a lower level gets instructions from a model at a higher level, and it also uses its own measurements to send signals. This allows the system to train the AI models using 'reward functions,' which are like scores that tell the AI if it's doing a good job. For example, a model might decide how to best allocate bandwidth based on traffic data and instructions from a higher-level model overseeing network stability.
The gap
What does this patent NOT cover?
- Does not cover AI models that are not organized in a multi-level hierarchy.
- Does not cover systems that do not generate reward functions for training AI models.
- Does not cover wireless networks that are not 'Next Generation' (NG) networks.
- Does not cover systems where control signaling only comes from a single AI model.
- Does not cover network management that doesn't use network measurements.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The innovation lies in structuring the AI models in a hierarchy, allowing for more sophisticated and layered decision-making. This enables models at different levels to specialize in different aspects of network management, from real-time adjustments to longer-term strategic planning, all trained through a system of rewards.
The Patent Drawing
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
5G network resource management
Future wireless network optimization
Intelligent network controllers (RICs)
Why it matters
The bigger picture
As wireless networks become more complex with 5G and future technologies, managing their resources efficiently is crucial. This patent offers a method for using AI to dynamically optimize network performance, potentially leading to better speeds, lower latency, and more reliable connections for users.
Filed
September 23, 2021
Granted
March 4, 2025
Market context
Who's building on this
Companies in this space
Intel, as the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is likely developing and implementing this technology in its own networking hardware and solutions. Major telecommunications equipment manufacturers and mobile network operators are also active in developing AI-driven network management systems.
Market impact
This patent contributes to the ongoing effort to make wireless networks smarter and more autonomous. By providing a framework for hierarchical AI-based resource management, it can enable operators to handle increasing data demands and new services with greater efficiency and adaptability.
Claim 1 — Plain English
What this patent covers
This patent describes a computer system, called a 'computing node,' designed to help manage wireless networks like the 5G and beyond (Next Generation or NG). It uses a smart approach with multiple Artificial Intelligence (AI) models, specifically machine learning models, organized in layers like a pyramid. The system takes measurements from the network and uses these AI models to make decisions. A key part is how these models communicate: a model at a lower level gets instructions from a model at a higher level, and it also uses its own measurements to send signals. This allows the system to train the AI models using 'reward functions,' which are like scores that tell the AI if it's doing a good job. For example, a model might decide how to best allocate bandwidth based on traffic data and instructions from a higher-level model overseeing network stability.
The clever bit
The innovation lies in structuring the AI models in a hierarchy, allowing for more sophisticated and layered decision-making. This enables models at different levels to specialize in different aspects of network management, from real-time adjustments to longer-term strategic planning, all trained through a system of rewards.
What it does not cover
- Does not cover AI models that are not organized in a multi-level hierarchy.
- Does not cover systems that do not generate reward functions for training AI models.
- Does not cover wireless networks that are not 'Next Generation' (NG) networks.
- Does not cover systems where control signaling only comes from a single AI model.
- Does not cover network management that doesn't use network measurements.
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
Patent enters public domain
PatentBrief Score
Impact Score
Strong
Citation count
12/40
Early citations
Claim breadth
15/20
Broad claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
20/20
Granted within 5 years
Assignee scale
20/20
Major company or institution
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$94K – $300K
Midpoint $187K · 15.1 yr remaining · industry ×1.6
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
Claim text not yet imported for this patent
The original legal language
Original claims
23 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Talwar, S., Orhan, O., Swamy, V. N., & Nikopour, H. (2025). Using AI to Manage Wireless Network Resources Smarter (U.S. Patent No. 12,245,052). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/12245052/reinforcement-learning-rl-and-graph-neural-network-gnn-based-resource-management
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
Frequently Asked Questions
What does Using AI to Manage Wireless Network Resources Smarter cover?
Intel's 2025 patent on using a hierarchy of AI models to manage wireless network resources, making them more efficient by learning from network measurements and rewards.
Who owns patent US 12245052?
Intel owns this patent, granted in 2025.
When does this patent expire?
This patent is expected to expire on September 23, 2041, when the invention enters the public domain.
What is patent US 12245052 cited by?
This patent has been cited by 3 later patents that build on its ideas.
What problem does this patent solve?
As wireless networks become more complex with 5G and future technologies, managing their resources efficiently is crucial. This patent offers a method for using AI to dynamically optimize network performance, potentially leading to better speeds, lower latency, and more reliable connections for users.
What does this patent NOT cover?
Does not cover AI models that are not organized in a multi-level hierarchy.
Patent monitoring