# Smart System Learns Industrial Machine Behavior > This patent describes a smart system that uses sensors and machine learning to predict outcomes like production volume or maintenance needs for industrial machines, based on industry-specific data. - **Patent:** US 12099911 - **Original title:** Systems and methods for learning data patterns predictive of an outcome - **Owner:** Strong Force IoT Portfolio 2016 - **Granted:** 2024 - **Status:** Active - **Times cited:** 3 - **Field:** industrial_automation, ai_ml, software, semiconductors, telecommunications ## What it does This patent covers a system designed for industrial settings. It uses multiple sensors to gather data about a machine's operation. A central controller then uses a machine learning component to analyze this data. This analysis helps the system learn patterns that predict specific outcomes, such as the machine's reaction rate, how much it produces, or if it needs maintenance. Crucially, the machine learning starts with a pre-built model, informed by industry feedback, and can also learn to select the most useful sensors for its predictions. For example, it could predict when a specific manufacturing machine will require a part replacement based on its vibration and temperature readings. ## What it does NOT cover - Systems that do not use machine learning to analyze data patterns. - Systems that do not learn predictive patterns for industrial machine outcomes. - Systems that do not use industry-specific feedback to seed their learning models. - Systems that do not learn a preferred subset of input sensors. - Systems predicting outcomes unrelated to industrial machines, like weather. ## The clever bit The core innovation is seeding the machine learning model with industry-specific feedback and learning a preferred subset of sensors. This means the system doesn't start from scratch; it's guided by prior knowledge relevant to the specific industrial context, making its predictions more accurate and efficient from the outset. ## Real-world examples 1. Predictive maintenance systems in factories 2. Process optimization in chemical plants 3. Production volume forecasting in manufacturing 4. Smart energy grid management ## Why it matters This patent is relevant to the growing field of Industrial Internet of Things (IIoT). It provides a framework for making industrial processes more efficient and predictable by leveraging data analytics and machine learning. Such systems can help reduce downtime and optimize resource allocation in factories and other industrial environments. ## Frequently asked questions ### What does Smart System Learns Industrial Machine Behavior cover? This patent describes a smart system that uses sensors and machine learning to predict outcomes like production volume or maintenance needs for industrial machines, based on industry-specific data. ### Who owns patent US 12099911? Strong Force IoT Portfolio 2016 owns this patent, granted in 2024. ### When does this patent expire? This patent is expected to expire on December 7, 2042, when the invention enters the public domain. ### What is patent US 12099911 cited by? This patent has been cited by 3 later patents that build on its ideas. ### What problem does this patent solve? This patent is relevant to the growing field of Industrial Internet of Things (IIoT). It provides a framework for making industrial processes more efficient and predictable by leveraging data analytics and machine learning. Such systems can help reduce downtime and optimize resource allocation in factories and other industrial environments. ### What does this patent NOT cover? Systems that do not use machine learning to analyze data patterns. **Full plain-English explainer:** https://patentbrief.org/patent/us/12099911/systems-and-methods-for-learning-data-patterns-predictive-of-an-outcome **Original patent:** https://patents.google.com/patent/US12099911 --- _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: - [AI System for Diagnosing Machines and Managing Data in Factories](https://patentbrief.org/patent/us/11126171/methods-and-systems-of-diagnosing-machine-components-using-neural-networks-and-h) — This patent describes a system that uses artificial intelligence to detect problems in industrial machines while smartly adjusting how much data it collects and sends to avoid overwhelming the network. - [Industrial Monitoring System with Haptic Alerts and Smart Data Storage](https://patentbrief.org/patent/us/11392109/methods-and-systems-for-data-collection-in-an-industrial-refining-environment-wi) — This patent describes a system for collecting data in industrial settings like oil refineries, detecting unusual conditions, smartly managing how much data is saved, and alerting workers with touch-based feedback on a wearable device. - [How AI Models Learn from Both Local and Central AI Systems](https://patentbrief.org/patent/us/20260065072/knowledge-acquisition-for-distributed-learning-models) — This patent describes a system for training specialized AI models at local sites, like factories, by combining insights from small local AI systems and large central AI systems, especially when data patterns change. - [Industrial Equipment Data Organized into Smart Knowledge Graphs](https://patentbrief.org/patent/us/20230195095/industrial-knowledge-graph-and-contextualization) — Honeywell's patent describes a system that collects data from industrial equipment, makes sense of it using rules, and organizes it into a smart graph for better control and actions. - [Efficiently Combining Data from Different Sensors](https://patentbrief.org/patent/us/12737436/systems-and-methods-of-sensor-data-fusion) — This patent describes a system for combining information from many different types of sensors more efficiently by carefully selecting and processing the data, reducing the computer power needed.