# How AI Cameras Check Factory Parts for Flaws > This patent describes an automated system using a camera and artificial intelligence to inspect manufactured parts, identifying defect types, locations, and confidence levels, then communicating this data to a factory controller. - **Patent:** US 12243216 - **Original title:** System and method for AI visual inspection - **Owner:** Musashi Auto Parts Canada - **Granted:** 2025 - **Status:** Active - **Times cited:** 0 - **Field:** manufacturing, ai_ml, robotics, industrial_automation, quality_control, automotive, consumer_electronics ## What it does The system uses a camera to capture images of a manufactured item (Claim 1). A 'node computing device' then analyzes these images using a specialized AI model, specifically a machine-learning based object detection model (Claim 1). This model is trained to spot various types of flaws, like a scratch or a dent, and can classify them into three or more categories (Claim 1). It outputs 'defect data' which includes the type of defect, its exact location, and how confident the AI is about its finding (Claim 1). This defect data is then sent to a 'programmable logic controller' (PLC), which is a computer that controls factory machinery (Claim 1). For example, if inspecting a car part, the system could take pictures, identify a specific crack, and then tell the PLC to reject that part. The system can also use a robotic arm to move the camera (Claim 3) or rotate the part being inspected (Claim 4) to get a full view, and can even stitch multiple images together for a complete picture (Claim 5). It can also confirm a defect by tracking it across multiple image frames before reporting it (Claim 7). ## What it does NOT cover - Does not cover visual inspection systems that use traditional rule-based computer vision instead of a machine-learning based object detection model. - Does not cover systems that only classify defects into fewer than three categories or detect only a single type of defect (Claim 1). - Does not cover systems where the AI output lacks specific defect class, location, and a confidence level (Claim 1). - Does not cover systems where the AI output is not sent to a programmable logic controller (PLC) for factory automation (Claim 1). - Does not cover manual human inspection processes, as it specifies an automated AI system. ## The clever bit The clever part is combining a machine-learning object detection model that performs multiclass classification with a PLC, ensuring the AI not only spots flaws but also provides detailed defect data (class, location, confidence) directly to factory automation systems. This allows for precise, automated responses like rejecting a part or moving a camera for further inspection. ## Real-world examples 1. Automotive parts manufacturing quality control 2. Electronics assembly line inspection 3. Pharmaceutical packaging defect detection 4. Food processing quality checks 5. Metal fabrication flaw detection ## Why it matters This patent is significant for improving quality control in manufacturing. By automating the inspection process with AI, factories can detect defects more consistently and quickly than human inspectors. This leads to higher product quality, reduced waste from faulty parts, and lower labor costs. The ability to precisely locate and classify defects also helps manufacturers understand and fix issues in their production lines. ## Frequently asked questions ### What does How AI Cameras Check Factory Parts for Flaws cover? This patent describes an automated system using a camera and artificial intelligence to inspect manufactured parts, identifying defect types, locations, and confidence levels, then communicating this data to a factory controller. ### Who owns patent US 12243216? Musashi Auto Parts Canada owns this patent, granted in 2025. ### When does this patent expire? This patent is expected to expire on September 30, 2040, when the invention enters the public domain. ### What problem does this patent solve? This patent is significant for improving quality control in manufacturing. By automating the inspection process with AI, factories can detect defects more consistently and quickly than human inspectors. This leads to higher product quality, reduced waste from faulty parts, and lower labor costs. The ability to precisely locate and classify defects also helps manufacturers understand and fix issues in their production lines. ### What does this patent NOT cover? Does not cover visual inspection systems that use traditional rule-based computer vision instead of a machine-learning based object detection model. **Full plain-English explainer:** https://patentbrief.org/patent/us/12243216/system-and-method-for-ai-visual-inspection **Original patent:** https://patents.google.com/patent/US12243216 --- _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 to Inspect Moving Objects on a Production Line with a Moving Camera](https://patentbrief.org/patent/us/20250377309/method-for-material-testing-of-an-object-in-a-production-andor-conveyor-line-and) — This patent describes a method and apparatus for inspecting specific defect locations on objects moving along a production line by having an optical camera move alongside the object to take images without stopping the line. - [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. - [How Computers Track the Position and Angle of Multiple Objects](https://patentbrief.org/patent/us/12211234/rapid-iterative-development) — A method for using multiple cameras to track the exact location and orientation of many similar objects in a 3D space, even when they overlap. - [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 to Update AI on Small Devices with Slow Internet](https://patentbrief.org/patent/us/20250363357/systems-and-methods-for-deploying-and-updating-neural-networks-at-the-edge-of-a-) — This patent describes a method for efficiently updating artificial intelligence models on small, internet-connected devices, like smart cameras, by sending only the changes, or 'patches,' instead of the entire updated model, which saves bandwidth.