Paige.AI had 25 patents in artificial intelligence during Q1 2024. Paige.AI Inc’s patents focus on utilizing machine learning systems to process digital medical images of tissue specimens. These systems can predict deficiencies in specimens, continuous values in salient regions, quality designations, disease designations, biomarker presence, and the presence of external contaminants. The technology aims to improve the accuracy and efficiency of pathology analysis by automating processes such as ordering additional slides, predicting biomarker expression levels, and detecting contaminants in images. GlobalData’s report on Paige.AI gives a 360-degree view of the company including its patenting strategy. Buy the report here.

Paige.AI grant share with artificial intelligence as a theme is 32% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.

Recent Patents

Application: Systems and methods for processing images to prepare slides for processed images for digital pathology (Patent ID: US20240095920A1)

The patent filed by Paige.AI Inc. describes systems and methods for processing electronic images of specimens, particularly slides containing tissue samples from patients. The method involves using a machine learning system to analyze the images and predict deficiencies in the specimen, such as the need for a new stain or recut. Based on these predictions, the system can automatically order an additional slide to be prepared. The technology also allows for the consideration of factors like tissue type, diagnostic data, and the generation of visual indicators for users. Additionally, the system can detect the need for image enhancements or improved slides, triggering the automatic ordering of additional slides. The training of the machine learning system involves processing a variety of training images and associated data to improve the accuracy of predictions regarding stain deficiencies and other factors related to specimen analysis.

Overall, the patent outlines a computer-implemented method and system for efficiently analyzing electronic images of tissue specimens using machine learning technology. By automating the process of identifying deficiencies in specimens and determining the need for additional slides, the system aims to streamline and enhance the accuracy of specimen analysis in medical settings. The technology also allows for the storage of relevant data and information about the specimens, contributing to a comprehensive and data-driven approach to specimen processing and analysis.

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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.