US 12,293,414 B1
Intelligent asset evaluation systems using multi-modal data analysis with neural network architectures and personalization
Sidney VanNess, Granite Bay, CA (US)
Assigned to Recursive Capital, Inc., Roseville, CA (US)
Filed by Recursive Capital, Inc., Roseville, CA (US)
Filed on Dec. 5, 2024, as Appl. No. 18/969,267.
Int. Cl. G06Q 40/06 (2012.01)
CPC G06Q 40/06 (2013.01) 20 Claims
OG exemplary drawing
 
1. An asset evaluation system comprising one or more processing devices and one or more non-transitory storage devices for storing instructions, wherein execution of the instructions by the one or more processing devices causes the one or more processing devices to execute functions comprising:
collecting, by a data collection module stored on the one or more non-transitory storage devices, multi-modal asset data corresponding to a plurality of assets;
extracting, by an artificial intelligence (AI) analysis engine executed by the one or more processing devices, asset features from the multi-modal asset data, at least in part, by analyzing image content and textual content included in the multi-modal asset data, wherein extracting the asset features includes:
executing one or more computer vision functions to extract visual features from the image content included in the multi-modal asset data, the one or more computer vision functions at least including one or more object detection tasks and/or one or more classification tasks performed on the image content; and
executing one or more natural language processing (NLP) functions to extract textual features from the textual content included in the multi-modal asset data;
wherein the asset features comprise both the visual features extracted by the one or more computer vision functions and the textual features extracted by the one or more NLP functions;
creating a user profile that stores personalization data corresponding to an end-user;
generating, by a value scoring module executed by the one or more processing devices, value scores for each of the plurality of assets, at least in part, by correlating the asset features extracted by the AI analysis engine with the personalization data stored in the user profile; and
generating, based on execution of the instructions by the one or more processing devices, asset analysis results for the end-user based, at least in part, on the value scores associated with the plurality of assets.