ForeScout Technologies. has been granted a patent for a method that utilizes an artificial intelligence model to classify entities based on a defined taxonomy framework. The process involves generating queries, receiving classification options, and determining entity classifications based on selected options from a classification model. GlobalData’s report on ForeScout Technologies gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on ForeScout Technologies, was a key innovation area identified from patents. ForeScout Technologies's grant share as of July 2024 was 47%. Grant share is based on the ratio of number of grants to total number of patents.
Entity classification using ai and taxonomy framework
The granted patent US12074895B1 outlines a method and system for classifying entities using a taxonomy framework that incorporates multiple levels of classification granularity. The process begins with identifying information related to an entity and generating a first query that includes this information along with a set of classification options at a specified granularity level. This query is then submitted to a classification model, which selects an option from the provided set. The classification of the entity is determined based on this selection. The method allows for iterative querying, enabling the system to refine classifications by generating additional queries that explore deeper levels of the taxonomy until a leaf node is reached or sufficient information is unavailable.
The patent further details that the taxonomy framework is structured as a classification tree, where each level represents a different granularity, with broader classifications at higher levels. The classification model may utilize a large language model to enhance its decision-making capabilities. Subsequent queries can be generated in response to selections made at previous levels, allowing for a hierarchical approach to classification. The system is designed to operate on a processing device with memory, and it can also be implemented through a non-transitory computer-readable medium containing instructions for executing the classification process. This structured approach aims to improve the accuracy and efficiency of entity classification across various applications.
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