Tempus Labs had two patents in big data during Q1 2024. Tempus Labs Inc filed patents for a system and method to analyze de-identified patient data for predicting treatment responses, and for detecting gene fusions, splice variants, and evaluating RNA-seq sequencing complexity in cancer patients. The methods involve mRNA boundary analysis of next generation sequencing data to provide insights for clinicians in evaluating large datasets for potential therapeutic discoveries. GlobalData’s report on Tempus Labs gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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Tempus Labs had no grants in big data as a theme in Q1 2024.
Recent Patents
Application: Method and process for predicting and analyzing patient cohort response, progression, and survival (Patent ID: US20240087747A1)
Tempus Labs Inc. has developed a system and method for analyzing de-identified patient data to create dynamic user interfaces that can predict responses to specific treatments for patient populations. By automating the analysis of clinical, molecular, phenotypic, and response data, clinicians can efficiently evaluate large datasets to potentially uncover therapeutic insights. The method involves provisioning a cloud computing environment with access to databases and pre-defined notebooks, allowing users to select features defining datasets representing cohorts of cancer patients. These notebooks contain preconfigured elements for disease progression analysis, and user interface elements launch corresponding notebooks based on user selections. The system monitors user resource usage and adjusts the cloud computing environment accordingly.
The method also includes features such as restricting access to certain dataset portions, loading defined datasets into the cloud environment, prohibiting data egress, and providing different functional controls to dataset owners and other users. Users can select specific notebooks, model or analyze datasets, apply visualizations or reportings, and compare results from different cohorts simultaneously. Machine learning models, optimal parameter identification, and user-defined analyses are also supported. The system architecture involves logically partitioned workspaces with preconfigured services and compute resources, access rights for multiple users, and the ability to modify the environment based on resource usage. Overall, Tempus Labs' innovative approach aims to streamline data analysis for clinicians and researchers, potentially leading to valuable therapeutic discoveries.
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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.
