Agilent Technologies had three patents in artificial intelligence during Q1 2024. Agilent Technologies Inc has filed patents for methods and systems using autofluorescence in samples, training a ground truth generator machine learning model for biological object identification, and a gas chromatography system with automated troubleshooting capabilities. GlobalData’s report on Agilent Technologies gives a 360-degree view of the company including its patenting strategy. Buy the report here.

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Agilent Technologies had no grants in artificial intelligence as a theme in Q1 2024.

Recent Patents

Application: Analysis of embedded tissue samples using fluorescence-based detection (Patent ID: US20240102932A1)

The patent filed by Agilent Technologies Inc. describes methods and systems for utilizing autofluorescence of naturally-occurring components in a sample embedded in an embedding medium to determine the amount of tissue or cell preparation exposed at the surface of the sample. The disclosed methods involve irradiating the sample to induce autofluorescence, obtaining an image of the emitted autofluorescence, and determining the percentage of tissue or cell preparation at the surface of the embedding medium. Additionally, the patent covers techniques for preparing tissue specimens with regions of interest, imaging biological tissue samples, and identifying different cell types within embedded tissue samples based on autofluorescence characteristics.

Furthermore, the patent details a method for training an artificial intelligence system to identify regions of interest in embedded tissue samples by irradiating the sample to induce autofluorescence, obtaining an image of the emitted autofluorescence, annotating the image to indicate regions of interest, and inputting the annotated image into the AI system for learning. The AI system can be based on various technologies such as machine learning, deep learning, neural networks, convolutional neural networks, statistical model-based systems, or deterministic algorithm-based analysis systems. The training process involves annotating stained images of the tissue sample and mapping them to the autofluorescence image to train the AI system to identify regions of interest in unannotated images, even on unstained tissue samples.

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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.