Delta Electronics had five patents in artificial intelligence during Q1 2024. Delta Electronics Inc has filed patents for a high-throughput automated nucleic acid preprocessing system, an image information transformation method for image analysis devices, and an image analysis method for camera apparatus to analyze landforms in detection images. The preprocessing system allows simultaneous processing of different sample batches, the image information transformation method involves generating false object data based on user license identification, and the image analysis method analyzes landforms based on predefined installation angles and height comparisons. GlobalData’s report on Delta Electronics gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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Delta Electronics grant share with artificial intelligence as a theme is 20% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: High-throughput automated preprocessing method and system for nucleic acid detection (Patent ID: US20240093267A1)
The patent filed by Delta Electronics Inc. describes a high-throughput automated preprocessing method and system for a nucleic acid preprocessing apparatus. The system includes a control system, sample transfer area, nucleic acid extraction area, and reagent setup area. The method involves user interaction through a user interface to select sampling tube type, test protocol, and extraction protocol. The control system then guides the user to set up consumables and performs tasks such as sample transfer, nucleic acid extraction, reagent deployment, and nucleic acid transfer. The system allows for simultaneous preprocessing of different sample batches in the sample transfer, extraction, and reagent setup areas.
The claims associated with the patent detail the specific steps involved in the high-throughput automated preprocessing method, including recommendations based on sample type, automatic setup of tube tray layout, confirmation of consumable configurations through image recognition, and task monitoring through the user interface. The system also includes features such as scanning tags on sampling tubes, transferring nucleic acids between plates, sealing test plates, and providing abnormality notifications for troubleshooting. Overall, the method and system aim to streamline and enhance the efficiency of nucleic acid preprocessing by automating various tasks and ensuring accurate sample handling throughout the process.
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