Methods of modelling systems or performing predictive maintenance of lithographic systems

This invention describes a method for diagnosing problems in complex systems, such as lithography machines. It works by analyzing all the different events occurring within the system to determine which events cause others. A computer system then identifies specific patterns of cause-and-effect, called "directed cycles," and uses these patterns to classify the root cause of a fault condition. The claims focus on this specific approach to fault classification.

Why it matters: Filed in 2016, this invention predates the significant advancements in causal AI and explainable AI (XAI) that have emerged in the last decade. Modern machine learning frameworks and increased computational power make it much more feasible to implement sophisticated causal inference algorithms and identify complex "directed cycles" in real-time system data, which was a more specialized and computationally intensive task in 2016.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedSoftware logic, data processing, causal inference algorithms

3 AI reinvention directions for this patent

See all three

AI gives you a few directions you could take this. Pick one, and we check whether your version is different enough to patent, then write the filing.

Reinvent this with AI

Where this one came from

Every morning we go through millions of abandoned patents and pick out 15 worth your time. This was one of today’s.

This one

+10