Palantir Technologies had 85 patents in digitalization during Q4 2023. Palantir Technologies Inc filed patents in Q4 2023 for systems and methods related to predictive maintenance, dataset synchronization, data integration workspace management, attributing data to entities using disparate data sets, and generating event and entity descriptor objects for networked systems. These inventions aim to improve data analysis, prediction, and management processes for various applications. GlobalData’s report on Palantir Technologies gives a 360-degreee view of the company including its patenting strategy. Buy the report here.
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Palantir Technologies grant share with digitalization as a theme is 56% in Q4 2023. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Predictive modelling (Patent ID: US20230418282A1)
The patent filed by Palantir Technologies Inc. describes systems, methods, and computer-readable media for predicting maintenance events associated with resources. The method involves retrieving historic maintenance data preceding the maintenance event, identifying relevant data indicative of the event, storing this data as precursor signals, and monitoring future maintenance data from related resources to predict future occurrences of the event. The system utilizes an ontology to determine and update data associated with maintenance events, incorporating sensor data and allowing for modifications to the ontology to enhance predictive capabilities, potentially using machine learning models like decision trees or neural networks.
The computer-implemented method and system outlined in the patent focus on leveraging historic maintenance data to predict future maintenance events across various resources. By utilizing an ontology-based approach, the system can efficiently identify precursor signals of maintenance events, update these signals based on new data, and monitor subsequent maintenance data to forecast future occurrences. The incorporation of sensor data, ability to modify the ontology for improved data analysis, and potential use of machine learning models enhance the system's predictive capabilities, offering a comprehensive solution for anticipating maintenance needs and optimizing resource management.
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