公开(公告)号：US12261872B2

公开(公告)日：2025-03-25

申请号：US17445172

申请日：2021-08-16

Applicant: Palantir Technologies Inc.

Inventor： Corentin Petit ,  Jacob Albertson ,  Marissa Kimball ,  Paul Baseotto ,  Pierre Cholet ,  Timur Iskhakov ,  Victoria Galano

IPC: H04L9/40 ,  G06F9/451 ,  G06N20/00

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.

公开(公告)号：US20220201030A1

公开(公告)日：2022-06-23

申请号：US17445172

申请日：2021-08-16

Applicant: Palantir Technologies Inc.

Inventor： Corentin Petit ,  Jacob Albertson ,  Marissa Kimball ,  Paul Baseotto ,  Pierre Cholet ,  Timur Iskhakov ,  Victoria Galano

IPC: H04L29/06 ,  G06F9/451 ,  G06N20/00

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.