Royal Bank of Canada had 15 patents in artificial intelligence during Q3 2023. The Royal Bank of Canada has filed patents for various technologies in Q3 2023. One patent describes a computing system that uses machine learning to generate empathy-based outputs, such as notifications and automatic service delivery, by analyzing historical behavior and environmental factors. Another patent involves a system and method for managing a time-varying resource pool, which includes identifying recurring and irregular resource allocations to forecast the resource pool value. Additionally, there is a patent for training a selective network using a differentiable function and noise from a base distribution. Lastly, there is a patent for an artificial neural network and training method that addresses data imbalanced regression by considering similarities in the feature and label spaces. GlobalData’s report on Royal Bank of Canada gives a 360-degreee view of the company including its patenting strategy. Buy the report here.

Royal Bank of Canada grant share with artificial intelligence as a theme is 60% in Q3 2023. Grant share is based on the ratio of number of grants to total number of patents.

Recent Patents

Application: Systems and methods for empathy-based machine learning (Patent ID: US20230274155A1)

The patent filed by the Royal Bank of Canada describes a computing system that generates empathy-based machine learning outputs. The system takes into account historical behavior, circumstantial knowledge, and empathy model weights to generate insights such as notifications, automatic service delivery, and payments. The system includes a processor, computer memory, and data storage. It maintains a trained empathy-based representation of the user using a set of machine learning models that track different empathy-based aspects of the user. It also maintains a trained circumstance-based representation of the user using another machine learning model. The system receives candidate insight data objects and applies biasing weights from the circumstance-based representation to the empathy-based representation to generate real-time prediction scores for each candidate insight. The candidate insight with the highest score is then transmitted to a user interface associated with the user.

The system can be further configured to receive outcome data associated with the presentation of the candidate insight to the user. Based on this outcome data, the system can retrain the machine learning models to improve their accuracy. The empathy-based aspects tracked by the machine learning models include curiosity, preconceptions, inspirations, direct experience, listening, and imagination. The user interface includes interactive control elements that allow users to modify the model weightings, thereby changing the tuning matrix applied to the machine learning models.

The system also includes a machine learning model that tracks environmental features associated with the user's contextual environment, such as time, weather, and location. It can determine whether the user is currently in transit and obtain additional features associated with the vehicle the user is using for transit. The outcome data representative of the user's interactions with the notification can include payment interactions.

In summary, the patent describes a system and method for generating empathy-based machine learning outputs. The system uses historical behavior, circumstantial knowledge, and empathy model weights to generate insights for users. It includes multiple machine learning models that track different empathy-based aspects of the user and a separate model for tracking environmental features. The system can be trained and retrained based on outcome data to improve its accuracy.

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