F. Hoffmann-La Roche had nine patents in digitalization during Q4 2023. The patents filed by F. Hoffmann-La Roche Ltd in Q4 2023 involve a computer-implemented method for extracting medical data from text fields and generating replacement text strings, as well as systems and methods for assessing stain titer levels in images by analyzing stain color and intensity features to calculate a weighted average titer score. GlobalData’s report on F. Hoffmann-La Roche gives a 360-degreee view of the company including its patenting strategy. Buy the report here.
F. Hoffmann-La Roche grant share with digitalization as a theme is 55% in Q4 2023. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Text entry assistance and conversion to structured medical data (Patent ID: US20230359677A1)
The patent filed by F. Hoffmann-La Roche Ltd. describes a computer-implemented method that involves receiving input text strings containing medical data, identifying keywords, querying a medical data category database, retrieving templates, inserting data values into templates, and displaying replacement text strings in a user interface. The method aims to streamline the process of entering medical data by automating the generation of text strings based on identified keywords and medical data categories.
Furthermore, the patent includes claims detailing the steps involved in the method, such as displaying candidate replacement text strings, generating text strings in different languages, ranking candidate replacement text strings based on similarity, predicting user input based on historical data, storing selected text strings as structured or unstructured medical data, and utilizing natural language processing models. The apparatus and non-transitory computer-readable medium described in the patent provide a technical implementation of the method, allowing for efficient processing and display of medical data in a user interface. Overall, the patent focuses on improving the accuracy and efficiency of entering medical data through automated text generation based on identified keywords and medical data categories.
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