Palantir Technologies had 19 patents in artificial intelligence during Q2 2024. Palantir Technologies Inc filed patents in Q2 2024 for methods of determining geolocations of composite entities from heterogeneous data sources, database configuration management, comparative modeling system for machine-learning algorithms, obfuscation of image portions with access permissions, and image search using text recognition and vector generation for candidate images. GlobalData’s report on Palantir Technologies gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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Palantir Technologies had no grants in artificial intelligence as a theme in Q2 2024.
Recent Patents
Application: Determining geolocations of composite entities based on heterogeneous data sources (Patent ID: US20240211505A1)
The patent filed by Palantir Technologies Inc. describes a method for determining geolocations of composite entities by associating elementary objects with composite objects and retrieving information from heterogeneous data sources. The method involves receiving datasets with time identifiers and geolocations from different data sources, applying a rule set based on an ontology to determine the geolocation of the composite object, and storing this information in an object database. Additionally, the method includes generating geofencing alerts, interpolating geolocations between time frames, and inferring relationships between composite objects based on matching geolocations of associated elementary objects.
The system described in the patent includes processors and memories configured to perform the method operations, such as determining associations between elementary and composite objects, receiving and processing data from various sources, and generating geolocation data responses. The system also includes functionalities for determining confidence levels in geolocations, generating geofencing alerts, and storing geolocation information in an object database. Furthermore, the system can infer relationships between composite objects based on matching geolocations of associated elementary objects over time, providing a comprehensive solution for determining geolocations of composite entities based on heterogeneous data sources.
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