How a System Finds and Creates Machine Learning Models
This patent describes a system that helps users find existing machine learning models or algorithms for a specific task and, if needed, automatically trains a new model using a selected algorithm.
Original patent title: “Machine learning model repository management and search engine”
This patent describes a system that helps users find existing machine learning models or algorithms for a specific task and, if needed, automatically trains a new model using a selected algorithm. Granted to International Business Machines in 2025 with 20 claims and 1 forward citation, and it is expected to expire in 2039.
Coverage
What does this patent actually cover?
This system acts like a smart librarian for machine learning. First, it registers many machine learning algorithms and their details (metadata) in a special index (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1). When a user needs a machine learning model for an analytics task, they tell the system what they need through a user interface (Claim 1). The system then converts this request into search terms. It first looks for already trained machine learning models that fit the criteria (Claim 1). If it finds none, it then searches its index of *algorithms* to find suitable ones (Claim 1). It shows the user a list of matching algorithms, potentially ranked by how well they fit the request (Claim 3). If the user picks an algorithm, the system then trains a brand new machine learning model using that chosen algorithm, employing standardized tools called universal APIs (Claim 1, Claim 4). For example, if a user needs a model to predict house prices, the system might first check for existing house price prediction models. If none are found, it would then suggest algorithms known for regression tasks, and if the user selects one, it would train a new house price prediction model.
The gap
What does this patent NOT cover?
- Does not cover systems that only store and search for already trained machine learning models without also indexing and suggesting algorithms for new model training.
- Does not cover systems that only provide a repository for machine learning algorithms without also offering a search engine based on user-specified analytics tasks.
- Does not cover training machine learning models without the use of a plurality of universal application programming interfaces (APIs) as specified in 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 systems that do not first attempt to find existing trained models before searching for algorithms to train new ones, as described in 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 systems that do not generate and store metadata models for each registered machine learning algorithm or trained model.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The clever part is the two-tiered search approach: first checking for existing *trained models* and only then, if none are found, searching for *algorithms* to train a new one, all while using universal APIs for consistent training.
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
IBM Watson Studio
Google Cloud AI Platform
Amazon SageMaker
Microsoft Azure Machine Learning
Enterprise AI/ML platforms
Why it matters
The bigger picture
This patent addresses a significant challenge in machine learning development: finding or creating the right model for a specific task efficiently. By providing a structured way to search for both existing models and the algorithms to build new ones, it helps streamline the development process. This approach can reduce redundant work and accelerate the deployment of AI solutions across various industries.
Filed
July 18, 2019
Granted
June 24, 2025
Market context
Who's building on this
Companies in this space
International Business Machines Corp (IBM) continues to build on technologies related to machine learning platforms and MLOps, which includes managing and deploying models and algorithms. Other major cloud providers like Google, Amazon, and Microsoft are also heavily invested in creating comprehensive platforms that offer similar capabilities for managing and deploying machine learning assets.
Market impact
This type of system helps standardize and accelerate the development and deployment of machine learning solutions. It reduces the barrier for developers to find and utilize AI, potentially leading to faster innovation and broader adoption of AI across industries. Such frameworks are becoming essential components of modern MLOps (Machine Learning Operations) platforms, which aim to streamline the entire lifecycle of machine learning models.
Claim 1 — Plain English
What this patent covers
This system acts like a smart librarian for machine learning. First, it registers many machine learning algorithms and their details (metadata) in a special index (Claim 1). When a user needs a machine learning model for an analytics task, they tell the system what they need through a user interface (Claim 1). The system then converts this request into search terms. It first looks for already trained machine learning models that fit the criteria (Claim 1). If it finds none, it then searches its index of *algorithms* to find suitable ones (Claim 1). It shows the user a list of matching algorithms, potentially ranked by how well they fit the request (Claim 3). If the user picks an algorithm, the system then trains a brand new machine learning model using that chosen algorithm, employing standardized tools called universal APIs (Claim 1, Claim 4). For example, if a user needs a model to predict house prices, the system might first check for existing house price prediction models. If none are found, it would then suggest algorithms known for regression tasks, and if the user selects one, it would train a new house price prediction model.
The clever bit
The clever part is the two-tiered search approach: first checking for existing *trained models* and only then, if none are found, searching for *algorithms* to train a new one, all while using universal APIs for consistent training.
What it does not cover
- Does not cover systems that only store and search for already trained machine learning models without also indexing and suggesting algorithms for new model training.
- Does not cover systems that only provide a repository for machine learning algorithms without also offering a search engine based on user-specified analytics tasks.
- Does not cover training machine learning models without the use of a plurality of universal application programming interfaces (APIs) as specified in Claim 1.
- Does not cover systems that do not first attempt to find existing trained models before searching for algorithms to train new ones, as described in Claim 1.
- Does not cover systems that do not generate and store metadata models for each registered machine learning algorithm or trained model.
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
Early stage
Citation count
6/40
Early citations
Claim breadth
13/20
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
0/20
Independent or smaller assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →
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
$75K – $240K
Midpoint $150K · 12.9 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
20 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Syeda-Mahmood, T. F., & Gur, Y. (2025). How a System Finds and Creates Machine Learning Models (U.S. Patent No. 12,340,293). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/12340293/machine-learning-model-repository-management-and-search-engine
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="US12340293"></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 11615208 · 2023 · Capital One Services LLC
How Cloud Systems Automatically Create and Train AI Data Models
US 12518214 · 2026 · Nant Holdings IP
Training AI on Private Data Without Seeing It
US 8249948 · 2012 · Amazon Technologies Inc
How Amazon Created a Marketplace for Recommendation Algorithms
US 12307349 · 2025 · Broadridge Financial Solutions
How an AI System Answers Financial Questions Using Specialized Bots
More to explore
More in AI & Machine Learning
US 10452978 · 2019 · Google LLC
How AI Models Understand Language Using 'Attention'
US 6523026 · 2003 · Huntsman International LLC
How Computers Find Hidden Connections Between Different Fields of Knowledge
US 11615208 · 2023 · Capital One Services LLC
How Cloud Systems Automatically Create and Train AI Data Models
US 10402750 · 2019 · Facebook Inc
How Facebook Uses Deep Learning to Predict What You Might Like
New to patents?
Common Questions
Frequently Asked Questions
What does How a System Finds and Creates Machine Learning Models cover?
This patent describes a system that helps users find existing machine learning models or algorithms for a specific task and, if needed, automatically trains a new model using a selected algorithm.
Who owns patent US 12340293?
International Business Machines owns this patent, granted in 2025.
When does this patent expire?
This patent is expected to expire on July 18, 2039, when the invention enters the public domain.
What is patent US 12340293 cited by?
This patent has been cited by 1 later patents that build on its ideas.
What problem does this patent solve?
This patent addresses a significant challenge in machine learning development: finding or creating the right model for a specific task efficiently. By providing a structured way to search for both existing models and the algorithms to build new ones, it helps streamline the development process. This approach can reduce redundant work and accelerate the deployment of AI solutions across various industries.
What does this patent NOT cover?
Does not cover systems that only store and search for already trained machine learning models without also indexing and suggesting algorithms for new model training.
Same assignee
More from International Business Machines
Patent monitoring