Lyft had five patents in robotics during Q1 2024. Lyft Inc’s patents filed in Q1 2024 focus on utilizing sensor data from vehicles to create structured representations of the environment, determining optimal pick-up and drop-off locations based on physical characteristics of the area, and displaying vehicle environment awareness through abstract representations on mobile devices or embedded vehicle devices. These innovations aim to enhance the overall user experience and efficiency of Lyft‘s services. GlobalData’s report on Lyft gives a 360-degree view of the company including its patenting strategy. Buy the report here.

Lyft grant share with robotics as a theme is 40% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.

Recent Patents

Application: Approaches for encoding environmental information (Patent ID: US20240104932A1)

The patent filed by Lyft Inc. describes systems, methods, and computer-readable media for accessing parameter-based encodings that represent an environment captured by sensors in vehicles. These encodings identify agents and their locations within the environment, which are then clustered to determine scenarios associated with the environment. The method involves labeling the encodings based on similarity, determining new scenarios or scenario families, and identifying maneuvers associated with these scenarios. Additionally, the patent discusses using machine learning models to determine scenarios and generate labels for the encodings, as well as clustering subsets of encodings to identify sub-scenarios or scenario sub-families based on features like location, motion, geometry, distance, and time information.

The patent claims detail a computer-implemented method, system, and non-transitory computer-readable storage medium for clustering parameter-based encodings, determining labels for these encodings, and applying labels based on similarity and features associated with the encodings. The method includes identifying new scenarios, determining maneuvers, and utilizing machine learning models to classify scenarios and generate labels. The system and storage medium also perform similar operations, such as clustering subsets of encodings, determining scenarios based on interactions between vehicles and agents, and generating labels for scenarios and scenario families. Overall, the patent focuses on efficiently analyzing and categorizing data from vehicle sensors to understand and respond to various scenarios in an environment.

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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.