# Improving AI Predictions by Factoring in How Much You Trust Each Model > This patent describes a method for making an AI system more accurate by combining several individual AI models and giving more weight to the ones considered more trustworthy. - **Patent:** US 20230044102 - **Original title:** Ensemble machine learning models incorporating a model trust factor - **Owner:** Noblis - **Status:** Active - **Times cited:** 31 - **Field:** ai_ml, software, telecommunications, finance, biotech, automotive ## What it does This patent describes a method for training an ensemble machine learning model, which is like a team of AI models working together. First, it receives data about how much to trust each individual AI model in the team (Claim 1a). This 'trust score' can be based on things like how sensitive the model is to bad input data or how confident its past predictions were (Claim 5). Next, it calculates an estimated prediction error for each model, but this error is adjusted by its trust score (Claim 1b). A less trusted model might have its errors penalized more. Then, it uses these adjusted error estimates to figure out a 'normalized weight' for each model (Claim 1c). Finally, it uses these normalized weights to determine the overall prediction equation for the entire ensemble, giving more influence to the more trusted models (Claim 1d). For example, if you have three AI models predicting stock prices, and one is known to be very reliable with certain types of data, this method would give that reliable model's predictions more say in the final combined forecast. ## What it does NOT cover - Does not cover single machine learning models that do not combine predictions from multiple individual models. - Does not cover ensemble models that combine predictions without explicitly calculating and using a 'trust score' for each individual model. - Does not cover methods where the trust score does not influence the prediction error estimate or the normalized weights of the individual models. - Does not cover trust scores that are not real numbers, specifically those outside the 0.0 to 1.0 range as described in Claim 3. - Does not cover ensemble methods where the output prediction equation is not determined, at least in part, by these normalized weights. ## The clever bit The clever part is explicitly integrating a 'trust factor' into the mathematical calculation of how much each individual model's prediction contributes to the final combined output. Instead of just relying on past performance metrics, this method allows for a more nuanced understanding of a model's reliability to directly influence its weighting. ## Real-world examples 1. Fraud detection systems combining multiple AI models 2. Medical diagnostic tools using various AI algorithms 3. Autonomous vehicle perception systems blending sensor data interpretations 4. Financial forecasting platforms integrating diverse predictive models 5. Cybersecurity threat detection systems ## Why it matters As AI models become more complex and are used in critical applications like healthcare or finance, ensuring their reliability and accuracy is crucial. This patent offers a way to build more robust AI systems by explicitly incorporating a measure of trust into how multiple models collaborate. This can lead to more dependable predictions, especially when individual models might have varying levels of quality or applicability to different data. ## Frequently asked questions ### What does Improving AI Predictions by Factoring in How Much You Trust Each Model cover? This patent describes a method for making an AI system more accurate by combining several individual AI models and giving more weight to the ones considered more trustworthy. ### Who owns patent US 20230044102? This patent is owned by Noblis. ### When does this patent expire? This patent is expected to expire on December 21, 2041, when the invention enters the public domain. ### What is patent US 20230044102 cited by? This patent has been cited by 31 later patents that build on its ideas. ### What problem does this patent solve? As AI models become more complex and are used in critical applications like healthcare or finance, ensuring their reliability and accuracy is crucial. This patent offers a way to build more robust AI systems by explicitly incorporating a measure of trust into how multiple models collaborate. This can lead to more dependable predictions, especially when individual models might have varying levels of quality or applicability to different data. ### What does this patent NOT cover? Does not cover single machine learning models that do not combine predictions from multiple individual models. **Full plain-English explainer:** https://patentbrief.org/patent/us/20230044102/ensemble-machine-learning-models-incorporating-a-model-trust-factor **Original patent:** https://patents.google.com/patent/US20230044102 --- _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 Multiple AI Models Detect Unusual Behavior on Computer Networks](https://patentbrief.org/patent/us/12438891/anomaly-detection-based-on-ensemble-machine-learning-model) — This patent describes a computer system that uses several artificial intelligence models working together to spot unusual and potentially dangerous activity from users or devices on a computer network. - [Training AI Models Together with Unlabeled Data Using a Teacher](https://patentbrief.org/patent/us/20220012637/federated-teacher-student-machine-learning) — This patent describes a way for multiple AI systems to learn together from data that hasn't been manually labeled, using a 'teacher' AI to create temporary labels for a 'student' AI. - [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 Training Systems Share AI Knowledge Without Sharing Private Student Data](https://patentbrief.org/patent/us/11915111/federated-machine-learning-in-adaptive-training-systems) — This patent describes a system where different student training centers can improve their AI models by sharing general learning patterns, not private student data, to make training better for everyone. - [How to Check if an AI Model Has Been Tampered With](https://patentbrief.org/patent/us/11544411/machine-learning-model-validation-and-authentication) — This patent describes a method to verify the integrity and authorized use of an artificial intelligence model by feeding it a special digital key and comparing its response to a known good one.