How AI Automatically Builds Efficient Neural Networks for Limited Devices
Google's patent describes a method for automatically designing efficient neural networks by optimizing their internal structure, called 'computational cells,' to meet specific limits on resources like memory or processing speed.
Original patent title: “Resource constrained neural network architecture search”
Google's patent describes a method for automatically designing efficient neural networks by optimizing their internal structure, called 'computational cells,' to meet specific limits on resources like memory or processing speed. Owned by Google with 22 claims and 2 forward citations, and it is expected to expire in 2042.
Coverage
What does this patent actually cover?
This patent details a computer-implemented method for automatically designing neural networks that are efficient for specific tasks and hardware. It starts by defining 'computational cells' (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1), which are like small, reusable blueprints for parts of a neural network, composed of nodes (representing data) and edges (representing operations). For each operation, the system replaces it with a 'linear combination of candidate operations' (Claim 2), where each candidate operation has a 'mixing weight' controlled by 'computational cell hyper parameters.' The system then repeatedly adjusts these hyperparameters and weights to optimize how well the network performs (minimize 'validation loss function') while strictly adhering to 'computational resource constraints' (Claim 3, 9), such as limits on memory, processing power (float point operations), or how fast it can make predictions (inference speed). Finally, it uses these optimized settings to generate the complete neural network (Claim 1). For example, this could be used to create a highly efficient AI model for a smartwatch that needs to recognize speech without draining its small battery or exceeding its limited memory.
The gap
What does this patent NOT cover?
- Does not cover manual design of neural network architectures by human engineers.
- Does not cover architecture search methods that do not use 'computational cells' defined as directed graphs of nodes and edges (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
- Does not cover optimization methods that do not involve replacing operations with a 'linear combination of candidate operations' (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 2).
- Does not cover optimization without specific 'computational resource constraints' (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1, 9) like memory, FLOPS, or inference speed.
- Does not cover methods that do not iteratively adjust 'computational cell hyper parameters and computational cell weights' during the optimization process (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 3).
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The noveltynoveltyThe requirement that an invention be different from anything publicly known before its priority date.Read more → lies in automatically searching for optimal neural network architectures by representing network operations as flexible 'linear combinations' of candidate options. This allows for simultaneous optimization of these combinations and the network's overall structure (computational cells) under explicit, user-defined resource limits, systematically balancing performance with real-world hardware constraints.
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
AI models for smartwatches
On-device speech recognition on smartphones
Edge AI for security cameras and drones
Machine learning in embedded systems for industrial control
Efficient AI for Internet of Things (IoT) devices
Why it matters
The bigger picture
This technology is crucial for deploying powerful AI models on devices with limited processing power, such as smartphones, smart home devices, or embedded systems. By automatically finding efficient network designs, it reduces the need for human experts to manually fine-tune models, saving time and resources. This allows AI to be integrated into more everyday products without requiring expensive, high-end hardware, making AI more pervasive and accessible.
Filed
August 19, 2022
Market context
Who's building on this
Companies in this space
Google, as the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is actively developing and using these techniques for its AI products, especially for on-device AI and cloud-based machine learning services. Other major tech companies like Meta, Microsoft, and Amazon, along with specialized AI optimization startups, are also working on neural architecture search (NAS) and efficient AI model deployment to make their AI offerings more competitive and widely applicable.
Market impact
This type of technology has significantly enabled the widespread deployment of sophisticated AI models on resource-limited devices, expanding the reach of AI beyond cloud servers. It helps reduce the cost of AI inference and makes AI more accessible, driving innovation in edge computing and embedded AI. This approach also fosters competition in developing more efficient AI hardware and software solutions, pushing the boundaries of what's possible with constrained computing.
Claim 1 — Plain English
What this patent covers
This patent details a computer-implemented method for automatically designing neural networks that are efficient for specific tasks and hardware. It starts by defining 'computational cells' (Claim 1), which are like small, reusable blueprints for parts of a neural network, composed of nodes (representing data) and edges (representing operations). For each operation, the system replaces it with a 'linear combination of candidate operations' (Claim 2), where each candidate operation has a 'mixing weight' controlled by 'computational cell hyper parameters.' The system then repeatedly adjusts these hyperparameters and weights to optimize how well the network performs (minimize 'validation loss function') while strictly adhering to 'computational resource constraints' (Claim 3, 9), such as limits on memory, processing power (float point operations), or how fast it can make predictions (inference speed). Finally, it uses these optimized settings to generate the complete neural network (Claim 1). For example, this could be used to create a highly efficient AI model for a smartwatch that needs to recognize speech without draining its small battery or exceeding its limited memory.
The clever bit
The novelty lies in automatically searching for optimal neural network architectures by representing network operations as flexible 'linear combinations' of candidate options. This allows for simultaneous optimization of these combinations and the network's overall structure (computational cells) under explicit, user-defined resource limits, systematically balancing performance with real-world hardware constraints.
What it does not cover
- Does not cover manual design of neural network architectures by human engineers.
- Does not cover architecture search methods that do not use 'computational cells' defined as directed graphs of nodes and edges (Claim 1).
- Does not cover optimization methods that do not involve replacing operations with a 'linear combination of candidate operations' (Claim 2).
- Does not cover optimization without specific 'computational resource constraints' (Claim 1, 9) like memory, FLOPS, or inference speed.
- Does not cover methods that do not iteratively adjust 'computational cell hyper parameters and computational cell weights' during the optimization process (Claim 3).
Patent timeline
Application submitted to the patent office
Patent enters public domain
PatentBrief Score
Impact Score
Moderate
Citation count
10/40
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
0/20
Older than 20 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
$62K – $200K
Midpoint $125K · 15.8 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
22 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Dai, S., Slocum, J. F., Yang, M., Jin, X., & Wang, J. How AI Automatically Builds Efficient Neural Networks for Limited Devices (U.S. Patent No. 20,220,414,425). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/20220414425/resource-constrained-neural-network-architecture-search
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
Embed
Add this patent to your site
Drop this plain-English patent card into any blog post or article — free, no signup. It always links back to the full breakdown here.
<div data-patentlens-widget data-patent-number="US20220414425"></div> <script src="https://patentbrief.org/embed.js" async></script>
Stay in the loop
Get a weekly digest of new patents.
One email per week. No spam. Unsubscribe anytime.
Keep exploring
Related patents you should know
US 4683195 · 1987
How to Make Billions of Copies of a DNA Segment
This patent describes the Polymerase Chain Reaction (PCR), a method to rapidly create many copies of a specific piece of DNA or RNA, enabling its detection and analysis.
Cetus Corp
US 8697359 · 2014
How to Edit Genes in Human Cells Using an Engineered CRISPR System
This patent describes an engineered CRISPR-Cas9 system for precisely cutting DNA in eukaryotic cells to change how genes work, opening the door for gene editing in complex organisms.
Massachusetts Institute of Technology
US 7657849 · 2010
How the iPhone's Slide-to-Unlock Gesture Works
Apple's 2010 patent describes unlocking a device by dragging a specific graphical image across the touchscreen along a predefined path, a gesture that became iconic with the original iPhone.
Apple Inc
US 4733665 · 1988
How Doctors Implant a Permanent Stent Using a Balloon
This patent describes the method for placing a permanent, expandable wire mesh tube inside a blood vessel or other body tube using a balloon-tipped catheter to widen it and keep it open.
Expandable Grafts Partnership
US 4965188 · 1990
How to Make Many Copies of a DNA Piece with Heat
This patent describes the Polymerase Chain Reaction (PCR) method, a technique to make millions of copies of a specific DNA segment using a heat-resistant enzyme and repeated temperature changes.
Cetus Corp
US 4235871 · 1980
How to Encapsulate Active Materials in Lipid Bubbles Efficiently
This patent describes a method for trapping biologically active substances inside tiny, multi-layered fat bubbles called liposomes, using a specific water-in-oil emulsion and gel-forming process to improve how much material gets captured.
Individual
Semantically similar
You might also find these interesting
US 12632737 · 2026 · GDM Holding
How AI Designs Its Own Brains: Hierarchical Neural Architecture Search
US 20250371349 · Intel
How AI Learns to Run Faster on Specific Computer Chips
US 20220383078 · Huawei Technologies Co
Adapting AI Models to Fit Device Resources
US 20250363357 · Ubotica Technologies
How to Update AI on Small Devices with Slow Internet
More to explore
More in Software & Internet
US 4405829 · 1983 · Massachusetts Institute of Technology
How RSA Public-Key Encryption Keeps Digital Messages Secret
US 6285999 · 2001 · Leland Stanford Junior University
How Websites Get Ranked by Importance
US 5960411 · 1999 · Amazon com Inc
How Amazon's One-Click Ordering Works for Online Purchases
US 7669123 · 2010 · Facebook Inc
Displaying Friends' Activities in a Social Network Feed
New to patents?
Common Questions
Frequently Asked Questions
What does How AI Automatically Builds Efficient Neural Networks for Limited Devices cover?
Google's patent describes a method for automatically designing efficient neural networks by optimizing their internal structure, called 'computational cells,' to meet specific limits on resources like memory or processing speed.
Who owns patent US 20220414425?
This patent is owned by Google.
When does this patent expire?
This patent is expected to expire on August 19, 2042, when the invention enters the public domain.
What is patent US 20220414425 cited by?
This patent has been cited by 2 later patents that build on its ideas.
What problem does this patent solve?
This technology is crucial for deploying powerful AI models on devices with limited processing power, such as smartphones, smart home devices, or embedded systems. By automatically finding efficient network designs, it reduces the need for human experts to manually fine-tune models, saving time and resources. This allows AI to be integrated into more everyday products without requiring expensive, high-end hardware, making AI more pervasive and accessible.
What does this patent NOT cover?
Does not cover manual design of neural network architectures by human engineers.
Same assignee
More from Google
Patent monitoring