# 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. - **Patent:** US 12340293 - **Original title:** Machine learning model repository management and search engine - **Owner:** International Business Machines - **Granted:** 2025 - **Status:** Active - **Times cited:** 1 - **Field:** ai_ml, software, telecommunications, ecommerce, finance ## What it does 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. ## 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. ## 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. ## Real-world examples 1. IBM Watson Studio 2. Google Cloud AI Platform 3. Amazon SageMaker 4. Microsoft Azure Machine Learning 5. Enterprise AI/ML platforms ## Why it matters 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. ## 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. **Full plain-English explainer:** https://patentbrief.org/patent/us/12340293/machine-learning-model-repository-management-and-search-engine **Original patent:** https://patents.google.com/patent/US12340293 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [How Cloud Systems Automatically Create and Train AI Data Models](https://patentbrief.org/patent/us/11615208/dall-e-text-to-image-generation) — A cloud-based system that generates fake, privacy-safe data to train AI models, ensuring they remain accurate while protecting sensitive personal information. - [Training AI on Private Data Without Seeing It](https://patentbrief.org/patent/us/12518214/distributed-machine-learning-systems-including-generation-of-synthetic-data) — This patent describes a way to train artificial intelligence models using private data stored on many separate computers, by generating fake data that mimics the real data's patterns, so the private data itself never leaves its original location. - [How Amazon Created a Marketplace for Recommendation Algorithms](https://patentbrief.org/patent/us/8249948/facebook-like-button) — A system that lets website owners rent recommendation algorithms from third-party developers, with a built-in payment structure that rewards developers based on how well their algorithms perform. - [How an AI System Answers Financial Questions Using Specialized Bots](https://patentbrief.org/patent/us/12307349/systems-and-methods-of-large-language-model-driven-orchestration-of-task-specifi) — This patent describes an AI system where a large language model (LLM) directs specialized machine learning agents to answer natural language questions about financial data, then refines the answers using an adversarial AI. - [How Google Distributed Machine Learning Across Many Computers](https://patentbrief.org/patent/us/7222127/google-adsense) — A 2003 Google patent describing a way to build machine learning models by splitting the work across a large network of computers rather than a single machine.