Toronto-Dominion Bank had 40 patents in artificial intelligence during Q4 2023. The Toronto-Dominion Bank filed patents in Q4 2023 for optimized anomaly prediction using machine learning, real-time credit score prediction based on encrypted event data, preprocessing large inference files for downstream applications, and facilitating buy-now pay-later transactions using a digital wallet application with both fiat and cryptocurrency accounts. These patents focus on leveraging machine learning, encryption, and optimized processing techniques to enhance financial services and transactions for users. GlobalData’s report on Toronto-Dominion Bank gives a 360-degreee view of the company including its patenting strategy. Buy the report here.

Toronto-Dominion Bank grant share with artificial intelligence as a theme is 27% in Q4 2023. Grant share is based on the ratio of number of grants to total number of patents.

Recent Patents

Application: Systems and methods of optimizing machine learning models for automated anomaly detection (Patent ID: US20230419402A1)

The patent filed by The Toronto-Dominion Bank describes a method, system, and technique for optimized anomaly prediction using machine learning. The system includes an unsupervised clustering module to cluster account data sets, an anomaly detection module to label anomalies in the data sets, and a tree classification model to detect anomalies based on the labeled data. The system generates rules executable for anomaly spotting by defining combinations of feature characteristics resulting in outlier data, which can be applied to new data for classification.

The method involves clustering training data into clusters based on input features, determining distribution patterns for each cluster, identifying outlier data points within each cluster, and labeling them as anomalies. A tree classification model is then trained based on the labeled data to detect anomalies, and rules are extracted from the model for anomaly spotting. The method also includes applying the rules to new customer data to determine if they are outliers or normal. Overall, the system and method aim to efficiently detect anomalies in account data using machine learning models, providing a systematic approach for anomaly prediction and classification.

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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.