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.
Original patent title: “System and method for AI visual inspection”
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. Granted to Musashi Auto Parts Canada in 2025 with 22 claims, and it is expected to expire in 2040.
Coverage
What does this patent actually cover?
The system uses a camera to capture images of a manufactured item (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 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).
The gap
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.
- Does not cover systems that only classify defects into fewer than three categories or detect only a single type of defect (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
- Does not cover systems where the AI output lacks specific defect class, location, and a confidence level (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
- Does not cover systems where the AI output is not sent to a programmable logic controller (PLC) for factory automation (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
- Does not cover manual human inspection processes, as it specifies an automated AI system.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
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.
The Patent Drawing
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
Automotive parts manufacturing quality control
Electronics assembly line inspection
Pharmaceutical packaging defect detection
Food processing quality checks
Metal fabrication flaw detection
Why it matters
The bigger picture
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.
Filed
September 30, 2020
Granted
March 4, 2025
Market context
Who's building on this
Companies in this space
Companies like Cognex, Keyence, and Basler are major players in industrial vision systems, increasingly integrating AI for defect detection. Startups focused on AI for manufacturing quality control, such as Inspekto and Landing AI, are also active. Musashi Auto Parts, the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, likely uses this internally to enhance its own manufacturing processes.
Market impact
This patent contributes to the ongoing shift in manufacturing towards 'Industry 4.0' and smart factories. It enables higher levels of automation and precision in quality control, reducing reliance on manual inspection and improving overall production efficiency. The detailed defect data allows for better process optimization, directly impacting product quality and cost structures across various manufacturing sectors.
Claim 1 — Plain English
What this patent covers
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).
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.
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.
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
Patent enters public domain
PatentBrief Score
Impact Score
Early stage
Citation count
0/40
No citations yet
Claim breadth
15/20
Broad claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
20/20
Granted within 5 years
Assignee scale
0/20
Independent or smaller assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$35K – $112K
Midpoint $70K · 14.1 yr remaining · industry ×1.5
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
Claim text not yet imported for this patent
The original legal language
Original claims
22 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Shehata, R., & Bufi, M. (2025). How AI Cameras Check Factory Parts for Flaws (U.S. Patent No. 12,243,216). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/12243216/system-and-method-for-ai-visual-inspection
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
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.
Patent monitoring