Carlyle Group has been granted a patent for a system that utilizes machine learning models to generate content items based on reliability criteria. The system categorizes requests into two groups and generates content from models that meet specified reliability standards, enhancing targeted content delivery. GlobalData’s report on Carlyle Group 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 Carlyle Group, was a key innovation area identified from patents. Carlyle Group's grant share as of June 2024 was 78%. Grant share is based on the ratio of number of grants to total number of patents.
Content generation using reliable machine learning models
The patent US12008598B2 describes a sophisticated system and method for generating content items based on a series of requests, utilizing trained machine learning models. The system comprises a memory and a processing device that receives multiple requests, each linked to a target with specific characteristics. It identifies two subsets of machine learning models: one that meets a defined reliability criterion and another that does not. The requests are categorized into two groups based on the ratio of models in each subset. For requests in the first group, the system generates content items using templates from the reliable models, while for the second group, it employs templates from the less reliable models.
Further, the system enhances its machine learning models by incorporating feedback on the performance of generated content items. For each request in the second group, the system collects data on whether the target performed the desired action after receiving the content. This information is used to retrain the models in the second subset, potentially moving those that meet the reliability criterion into the first subset. The process includes determining content template components and selecting elements based on the outputs of the trained models, ensuring that the generated content is tailored to maximize the likelihood of the target performing the desired action. The patent outlines a methodical approach to content generation that leverages machine learning to improve reliability and effectiveness over time.
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