Toronto-Dominion Bank had 20 patents in artificial intelligence during Q1 2024. The Toronto-Dominion Bank has filed patents for a system and method to examine data from a source, automatically manage storage resources of a distributed file system, and improve a non-autoregressive transformer model by training a student model based on a teacher model. The methods involve generating statistical models, adjusting space quota limits based on projected storage usage data, and reducing the number of iterative applications of the model while maintaining output quality. GlobalData’s report on Toronto-Dominion Bank gives a 360-degree view of the company including its patenting strategy. Buy the report here.

Toronto-Dominion Bank grant share with artificial intelligence as a theme is 50% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.

Recent Patents

Application: System and method for examining data from a source (Patent ID: US20240062117A1)

The patent filed by The Toronto-Dominion Bank describes a system and method for examining data from a source by generating statistical models based on historical data and selecting the best model to generate a new forecast. This new forecast is then compared against current data to identify any unexpected values, which are then outputted for further analysis. The device includes a processor, data interface, and memory storing executable instructions to carry out these steps, with the option to interrupt downstream processes upon detecting unexpected data points and upload results to a graphical user interface.

The method involves accessing statistical models based on historical data, selecting the best model for forecasting current data, generating a new forecast, comparing it with current data to identify unexpected values, and outputting the results. The process includes training models with historical data subsets, forecasting future data, and repeating for each model. The device can automatically execute upon receiving new data, apply data integrity operations, and flag unexpected data points. Additionally, the method allows for interrupting downstream processes upon detecting unexpected values and includes various checks and operations to ensure data accuracy and validity. The patent also covers the use of different statistical models like constant, linear, and quadratic models, as well as criteria for determining unexpected values based on prediction intervals.

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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.