Toronto-Dominion Bank had seven patents in regtech during Q2 2024. The Toronto-Dominion Bank has filed patents related to optimizing decision tree models by assigning criteria, identifying nodes with high purity, generating rules, and embedding them within the model. Another patent involves using machine learning models to auto-adjudicate loan applications based on personal attributes of users. Additionally, there is a patent for automatically assessing and optimizing machine learning models by identifying suboptimal pockets and generating interpretable rules for improvement. GlobalData’s report on Toronto-Dominion Bank gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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Toronto-Dominion Bank had no grants in regtech as a theme in Q2 2024.
Recent Patents
Application: Decision tree model training process (Patent ID: US20240161185A1)
The patent filed by The Toronto-Dominion Bank describes a method and apparatus for decision tree model training and rule generation. The operation involves assigning criteria to nodes, executing the model on training data, identifying nodes with high purity, generating rules based on these nodes, and embedding the rules back into the model for storage. The apparatus includes a processor and storage for executing these steps, while the method involves assigning criteria, executing the model, identifying interconnected nodes with high purity, generating rules, and embedding them back into the model for storage.
The claims detail the specific functionalities of the apparatus and method, such as determining node purity based on historical data, identifying subsets of nodes based on purity thresholds, dynamically adjusting purity thresholds, recording outputs via blockchain ledger, and encoding training data for model execution. The non-transitory computer-readable medium contains instructions for performing these steps, including assigning criteria, executing the model, identifying interconnected nodes with high purity, generating rules, and embedding them back into the model for storage. The medium also includes functionalities for determining node purity, identifying subsets of nodes based on purity thresholds, dynamically adjusting thresholds, and encoding training data for model execution.
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