GM Files Patent For In-Vehicle Context-Based Reminders

GM has filed a patent application for a context-based reminder system for use in vehicles. The application was assigned patent number US 2025/0069491 with the United States Patent and Trademark Office (USPTO) and was published on February 27^th, 2025. The GM patent application was originally submitted on August 25^th, 2023, and lists several Michigan-based engineers as the inventors, including Md Mhafuzul Islam, Arun Adiththan, and Richard Romano.

The patent describes a smart reminder system for vehicles designed to help occupants remember important tasks based on real-time contextual data. Unlike traditional reminder systems that provide generic notifications, such as maintenance alerts or seatbelt reminders, the system described in the GM patent application tailors the reminders by analyzing both vehicle and user-related factors.

The system incorporates a back-end server that communicates wirelessly with the vehicle’s onboard controllers, collecting data on vehicle-oriented conditions like fuel status, battery life, tire pressure, and location, as well as user-oriented factors such as time of day, trip length, and other personal details relevant to the journey. Once this data is collected, the system processes it through contextual analysis that combines vehicle and user data into meaningful patterns. A clustering algorithm organizes these data points into groups, and a scoring system determines whether a specific reminder should be triggered based on its relevance. If the reminder surpasses a set threshold, it is activated and sent to the occupant, such as through a visual prompt on a screen or a sound prompt via the audio system. The system also incorporates machine learning, refining its reminders based on user feedback. If a driver consistently ignores or acknowledges specific alerts, the system adapts to provide more useful and less intrusive notifications in the future.

What sets this technology apart from conventional reminder systems is its ability to generate custom-tailored reminders, as opposed to relying on pre-set alerts. Additionally, this system can learn from multiple vehicles, allowing it to improve accuracy over time through cross-vehicle data sharing.

As such this system can be beneficial for very specific tasks, such as prompting drivers to refuel before a long trip, reminding them to bring an ID badge for work, or alert them to check for items left in the back seat before exiting the vehicle.