Truist Financial‘s patent involves a system and method for labeling data to quickly trigger a secondary system for actions. By transforming and classifying data through machine learning, the system enables faster processing and action execution. The innovation aims to streamline operations and improve efficiency. GlobalData’s report on Truist Financial gives a 360-degree view of the company including its patenting strategy. Buy the report here.

According to GlobalData’s company profile on Truist Financial, was a key innovation area identified from patents. Truist Financial's grant share as of February 2024 was 83%. Grant share is based on the ratio of number of grants to total number of patents.

Data labelling system for faster triggering of secondary system

Source: United States Patent and Trademark Office (USPTO). Credit: Truist Financial Corp

A recently granted patent (Publication Number: US11921821B2) discloses a system designed to label data for faster triggering of a secondary system to perform actions by leveraging transformed data and data labelling. The system includes a non-transitory storage device and a processing device that receives unclean and slow-to-process data, transforms it by cleaning and vectorizing, extracts content, processes it through a classification machine learning model, receives a label for the data, and triggers the secondary system based on the label to perform downstream actions. This integral connection between the classification machine learning model and the secondary system enables quicker triggering of actions by utilizing transformed data and data labelling.

Furthermore, the system is configured to maintain a database of labeled data, receive feedback from the secondary system, and utilize clustering machine learning methods such as DBScan, K Mean, hierarchical clustering, k-nearest neighbor clustering, and spectral clustering for training the classification machine learning model. The system also incorporates neural networks like convolution neural network (CNN), recurrent neural network (RNN), and feed-forward network, as well as Bayesian machine learning algorithms. By efficiently labeling data and leveraging advanced machine learning techniques, the system aims to enhance the performance and responsiveness of the secondary system in executing various actions based on the labeled data, user profiles, interactions, and relationships organized by the secondary system.

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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.