F. Hoffmann-La Roche had 17 patents in big data during Q1 2024. The patents filed by F. Hoffmann-La Roche Ltd in Q1 2024 include machine-learning models for predicting tumor phenotypes based on gene expression levels, a system for selecting optimal treatment based on patient characteristics and treatment metrics, a healthcare data management system for processing capacity management, a model-assisted system for predicting patient survivability without structured ECOG scores, and a method for analyzing tissue section images to detect co-localization of different phenotypes. GlobalData’s report on F. Hoffmann-La Roche gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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F. Hoffmann-La Roche grant share with big data as a theme is 29% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Tumor phenotype prediction using genomic analyses indicative of digital-pathology metrics (Patent ID: US20240105283A1)
The patent filed by F. Hoffmann-La Roche Ltd. describes a computer-implemented method that utilizes a machine-learning model to predict tumor phenotypes based on gene expression data. The method involves identifying a set of genes informative of CD8+ cell quantity or spatial distribution, generating cluster assignments, determining the corresponding phenotype (immune-excluded, immune-desert, or inflamed/infiltrated), and outputting treatment recommendations based on the predicted phenotype. The method also includes selecting treatment candidates based on the phenotype, with specific genes like GZMA, GZMB, and CD40LG being part of the predefined set used for prediction.
Furthermore, the patent details the process of identifying the set of genes using digital pathology images, CD8+ cell categorization, and regression models. The method involves cluster analysis to determine spatial representations of gene expression data and assign phenotypes to clusters. The patent also covers the system and computer-program product for implementing the method, emphasizing the use of machine learning, cluster analysis, and gene expression data to predict tumor phenotypes and recommend treatment strategies. Overall, the patent focuses on leveraging computational techniques to enhance personalized treatment approaches based on tumor gene expression profiles and CD8+ cell characteristics.
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