F. Hoffmann-La Roche had 78 patents in future of work during Q1 2024. The patents filed by F. Hoffmann-La Roche Ltd in Q1 2024 focus on innovative methods and systems for predicting cell viability and glycan distribution in bioreactors during biomolecule manufacturing processes, as well as automated specimen processing systems for biological samples on slides. Additionally, a slide carrier design is disclosed to optimize slide processing operations. Another patent describes a method for separating and sorting cellular particles from tissue samples into different populations. 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 future of work as a theme is 43% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Prediction of viability of cell culture during a biomolecule manufacturing process (Patent ID: US20240084240A1)
The patent filed by F. Hoffmann-La Roche Ltd. discloses a method, system, and computer-readable medium for predicting cell viability in a bioreactor during a biomolecule manufacturing process. The method involves inputting at least three manufacturing process parameters into a machine learning model trained to predict cell viabilities. These parameters can be related to factors such as time elapsed, base added, volume of the cell culture, air sparged, dissolved oxygen, pH, and temperature. The trained model then generates an indicator of cell viability based on the analysis of these parameters.
The system described in the patent includes a processor and memory storing instructions for receiving and analyzing the manufacturing process parameters to predict cell viability. The method prioritizes the first set of process parameters over the second set in terms of their impact on cell viability prediction. The trained machine learning model can be a neural network or decision-tree based model, and it is trained with manufacturing process training records to accurately predict cell viability based on the input parameters. Overall, the patent outlines a comprehensive approach to predicting cell viability in a bioreactor during biomolecule manufacturing, utilizing machine learning models and various manufacturing process parameters to optimize the process.
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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.
