Palantir Technologies Inc. patent applications on 2025-06-19
Patent Applications by Palantir Technologies Inc. on June 19th, 2025
Palantir Technologies Inc.: 7 patent applications
Palantir Technologies Inc. has applied for patents in the areas of G06F9/5027 (Allocation of resources, e.g. of the central processing unit [CPU], 1), G06F11/3409 ({for performance assessment}, 1), G06F16/24542 ({Plan optimisation}, 1), G06F16/248 (Presentation of query results, 1), G06F16/258 ({Data format conversion from or to a database}, 1), H04L63/10 ({for controlling access to devices or network resources}, 1), H04L63/1433 ({Vulnerability analysis}, 1)
With keywords such as: instance, system, method, terminating, example, computing, instances, autoscaling, groups, platforms in patent application abstracts.
Top Inventors:
- Mahmoud Abdelsalam of Kirkland WA US (1 patents)
- Ryan McNamara of Seattle WA US (1 patents)
- Ashray Jain of London GB (1 patents)
- Greg DeArment of Seattle WA US (2 patents)
- Jason Ma of Seattle WA US (1 patents)
Patent Applications by Palantir Technologies Inc.
Abstract: system and method for terminating instances and autoscaling instance groups of computing platforms. for example, a method includes determining whether an instance of an instance group is identified as eligible for termination. the method further includes, in response to determining that the instance of the instance group is identified as eligible for termination, terminating the eligible instance. the terminating the eligible instance includes, in response to a runtime of the eligible instance being equal to or larger than a predetermined maximum lifetime, terminating the eligible instance. the terminating the eligible instance further includes, in response to the runtime being smaller than the predetermined maximum lifetime, detaching the eligible instance from the instance group to allow a new instance to be associated with the instance group, and in response to the eligible instance being detached from the instance group: waiting for the new instance to be associated with the instance group, and evicting each pod associated with the detached instance. the method is performed using one or more processors.
20250199932. AGENT EVALUATION FRAMEWORK (Palantir Technologies .)
Abstract: a system may receive a first user input requesting to provide an evaluator agent configuration for an evaluator agent. a system may receive a second user input specifying information associated with an agent to be evaluated. a system may receive a third user input specifying an evaluation tool, wherein the evaluation tool is configurable to evaluate the information associated with the agent. a system may receive a fourth user input specifying an evaluation tool configuration associated with the evaluation tool. a system may create the evaluator agent based on the evaluator agent configuration, wherein the evaluator agent configuration comprises an indication of the information associated with the agent to be evaluated, an indication of the evaluation tool, and an indication of the evaluation tool configuration. a system may include evaluating, using the evaluator agent, the information associated with the agent.
Abstract: system and method for generating and displaying data pipelines according to certain embodiments. for example, a method includes: receiving a natural language (nl) query; receiving a model result generated based on the nl query, the model result including a query in a standard query language, the model result being generated using one or more computing models; and generating the data pipeline based at least in part on the query in the standard query language, the data pipeline comprising one or more data pipeline elements, at least one data pipeline element of the one or more pipeline elements being corresponding to a query component of the query in the standard query language.
20250200060. VISUAL ANALYSIS DATA USING SEQUENCED DATASET REDUCTION (Palantir Technologies .)
Abstract: systems and methods for implementing sequenced filter templates to intelligently reduce a dataset to find useful patterns and source data are disclosed. an expert investigative user may configure a filter template comprising a series of filters organized in a sequence desired by the expert user. the filter template can be customized by an end user to reduce a dataset and perform guide investigation of the reduced dataset.
Abstract: example embodiments described herein pertain to a geographic information system (gis), configured to obtain geospatial data representing a geographic area, assign a projection and coordinate system to the geospatial data, apply a transformation to the geospatial data, and generate a tile cache based on the transformed geospatial data, the tile cache including the determined projection and coordinate system.
Abstract: a method of providing ingress control comprises managing one or more replicas of an application on a software platform; creating an annotation resource that includes one or more annotations for the software platform; creating an ingress resource for a specific annotation of the one or more annotations, the specific annotation being in a specification for the application; receiving a request to access the application from a device external to the software platform, the request matching the specific annotation; and routing the request to a replica of the one or more replicas based on the ingress resource.
20250202927. ENHANCED MACHINE LEARNING REFINEMENT ALERT GENERATION SYSTEM (Palantir Technologies .)
Abstract: systems and methods are provided for enhanced machine learning refinement and alert generation. an example method includes accessing datasets storing customer information reflecting transactions of customers. individual risk scores are generated for the customers based on the customer information. generating the risk score includes providing identified occurrences of scenario definitions and customer information as input to one or more machine learning models, the scenario definitions identifying occurrences of specific information reflected in the datasets, with the machine learning models assign respective risk scores to the customers. an interactive user interface is presented. the interactive user presents summary information associated with the risk scores, with the interactive user interfaces enabling an investigation into whether a particular customer is exhibiting risky behavior, responds to user input indicating feedback usable to update the one or more machine learning models or scenario definitions, with the feedback triggering updating of the machine learning models.