# How to Automatically Tune Machine Learning Settings Using Smart Math > A system that uses advanced statistical modeling to automatically find the best settings for complex machine learning models, saving engineers from manual trial and error. - **Patent:** US 11501192 - **Original title:** Systems and methods for Bayesian optimization using non-linear mapping of input - **Owner:** University of Toronto - **Granted:** 2022 - **Status:** Active - **Times cited:** 0 - **Field:** ai_ml, software, consumer_electronics ## What it does This patent describes a way to automate the tuning of machine learning systems, specifically by finding the best hyper-parameters. Hyper-parameters are the configuration settings, like learning rates or layer sizes, that define how a model learns. The system uses a probabilistic model—a mathematical way of representing uncertainty—to predict which settings will yield the best performance. The core innovation is applying a non-linear mapping to these settings before the model processes them, which helps the system better understand complex, non-obvious relationships between settings and outcomes. By iteratively evaluating the system and updating the model, it converges on the optimal configuration. ## What it does NOT cover - Does not cover manual tuning of machine learning models by human engineers. - Does not cover optimization methods that rely solely on linear transformations. - Does not cover the actual architecture of the neural network being tuned, only the process of selecting its hyper-parameters. - Does not cover non-probabilistic search methods like simple grid search or random search. ## The clever bit By using a non-linear one-to-one mapping to transform the input domain, the system can model complex, warped landscapes of hyper-parameters that traditional Gaussian processes might struggle to represent accurately. ## Real-world examples 1. AutoML platforms for neural network architecture search 2. Hyper-parameter optimization frameworks for deep learning 3. Automated model training pipelines in cloud AI services ## Why it matters Tuning machine learning models is notoriously time-consuming and expensive, often requiring massive computing resources. By automating this process, this technology enables faster development cycles and better-performing AI models. It represents a shift toward 'AutoML,' where the infrastructure itself learns how to optimize its own configuration. ## Frequently asked questions ### What does How to Automatically Tune Machine Learning Settings Using Smart Math cover? A system that uses advanced statistical modeling to automatically find the best settings for complex machine learning models, saving engineers from manual trial and error. ### Who owns patent US 11501192? University of Toronto owns this patent, granted in 2022. ### When does this patent expire? This patent is expected to expire on November 15, 2042, when the invention enters the public domain. ### What problem does this patent solve? Tuning machine learning models is notoriously time-consuming and expensive, often requiring massive computing resources. By automating this process, this technology enables faster development cycles and better-performing AI models. It represents a shift toward 'AutoML,' where the infrastructure itself learns how to optimize its own configuration. ### What does this patent NOT cover? Does not cover manual tuning of machine learning models by human engineers. **Full plain-English explainer:** https://patentbrief.org/patent/us/11501192/alphafold-protein-structure-prediction **Original patent:** https://patents.google.com/patent/US11501192 --- _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: - [Automated AI for Adapting to New Data Without Retraining](https://patentbrief.org/patent/us/20230162023/system-and-method-for-automated-transfer-learning-with-domain-disentanglement) — This patent describes an automated system that builds artificial intelligence models capable of adapting to new, different data without needing full retraining, by learning to ignore irrelevant changes. - [How to Automatically Detect and Fix Changes in AI Model Data](https://patentbrief.org/patent/us/10599957/systems-and-methods-for-detecting-data-drift-for-data-used-in-machine-learning-m) — This patent describes a system that automatically notices when the real-world data an AI model sees changes, causing its predictions to become less accurate, and then fixes the model. - [How AI Automatically Labels Images and Checks Its Own Work](https://patentbrief.org/patent/us/11023780/siri-on-device-speech-recognition) — A method for training AI to label images by calculating how uncertain it is about its own predictions, allowing it to verify its accuracy without human help. - [How Projection Neural Networks Speed Up AI Predictions](https://patentbrief.org/patent/us/11544573/llama-large-language-model-architecture) — A method for making artificial intelligence models faster and more efficient by using fixed, non-trainable projections to simplify complex data before processing. - [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.