Chubb. has filed a patent for a recommendation engine system that utilizes multiple propensity models to generate accurate recommendations. The system receives input data from various sources, trains the models, evaluates their accuracy, selects the best model, and provides it to the system for recommendation generation. GlobalData’s report on Chubb 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 Chubb, Extrusion-based additive manufacturing was a key innovation area identified from patents. Chubb's grant share as of January 2024 was 65%. Grant share is based on the ratio of number of grants to total number of patents.
Recommendation engine generation system
The patent application (Publication Number: US20230401624A1) describes a system and method for selecting a propensity model from multiple models to generate recommendations based on input data. The system involves receiving input data from various sources, training propensity models with the input data, evaluating the accuracy of the models using training and testing data, and selecting the most suitable model based on predefined thresholds. The selected model is then used to provide recommendations to a system for decision-making.
Furthermore, the method includes determining the characteristics of parameters in the input data to meet propensity modeling thresholds, transforming parameters that do not meet the thresholds, and selecting a collaborative filtering model if necessary. The system also involves receiving second input data, evaluating the characteristics of parameters, and selecting a collaborative filtering model if the thresholds are not met. The system aims to improve the accuracy and effectiveness of generating recommendations by selecting the most appropriate propensity model based on accuracy and threshold criteria. The patent application also covers a computer storage medium encoded with instructions to perform the described operations, emphasizing the practical implementation of the system and method for recommendation generation.
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