# Equipment Anomaly Detection: MakinaRocks Patents on Autoencoders and Transfer Learning

Autoencoders are widely used to detect faults in manufacturing equipment. This article examines a MakinaRocks patent concerning autoencoder training.

Source: https://www.iplexlaw.co.kr/en/blog/870447

HOME / NEWS & INSIGHTS NEWS & INSIGHTS Equipment Anomaly Detection: MakinaRocks Patents on Autoencoders and Transfer Learning Autoencoders are widely used to detect faults in manufacturing equipment. This article examines a MakinaRocks patent concerning autoencoder training. AI & Software 2023.06.22 published IPLEX 1 min read Autoencoders are widely used to detect faults in manufacturing equipment. This article examines a MakinaRocks patent concerning autoencoder training. [Patent] Click the image to view the granted patent. According to this patent, the sub model is an autoencoder. Each of the multiple autoencoders is taught using training data obtained in different environments, such as time to create, manufacturing processes, and manufacturing recipes. The submodels are trained sequentially rather than independently in parallel. After the training for sub model 1 is completed, the training for sub model 2 is carried out. In this case, at least some of the weights of sub model 1 are shared (or reused) when learning of sub model 2. This learning leads to the next model without forgetting the knowledge learned from the previous model. Thus, the performance of the next model can be improved and the overall training time can be reduced. In other words, The key to this patent is to improve the performance of the model through transfer learning. Read the Korean source This article reflects the information available when it was published. Contact us to discuss your circumstances. Discuss this topic ↗ All articles TALK TO IPLEX Discuss your IP questions We consider your technology and business needs together. ↗ Contact us Newer Facebook AI Paper Analysis: DETR and End-to-End Object Detection with Transformers ↗ Older Post-Processing AI Prostate-Cancer Assessments: JLK Inspection Patent Analysis ↗ Related insights AI & Software 2026.10.01 FiX: fine-grained forgetting in softmax attention Yongduck Kim examines FiX’s feature-wise gates, numerical implementation and paged cache, distinguishing reported gains from unresolved limitations. ↗ Read article AI & Software 2026.09.30 MHAR: Reading earlier layers through different feature subspaces Yongduck Kim examines Multi-Head Attention Residuals: depth routing, reported training results, implementation costs and the relationship between technical features and effects. ↗ Read article AI & Software 2026.09.28 Column: Claude Computer Use and the Data That Trains AI Agents Writing for AI Times, IPLEX Managing Partner Yongduck Kim examines the training data behind computer-operating AI agents through U.S. Patent No. 12,585,862. ↗ Read article

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