eBay had eight patents in metaverse during Q1 2024. eBay Inc filed patents in Q1 2024 for aspect pre-selection using machine learning in chat bots, physical object boundary detection in augmented reality, location-based augmented reality digital ticketing systems, and storing data in a distributed application architecture using blockchain technology. These patents showcase innovative techniques for enhancing user experiences and data management. GlobalData’s report on eBay gives a 360-degree view of the company including its patenting strategy. Buy the report here.
eBay grant share with metaverse as a theme is 37% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Aspect pre-selection using machine learning (Patent ID: US20240095490A1)
The patent filed by eBay Inc. describes aspect pre-selection techniques using machine learning to implement a chat bot that prompts users to specify aspects of a category during natural-language conversations. The data from these conversations is used to train a model, which is then utilized in subsequent conversations to automatically include aspects of the category in search queries without user intervention. This technology aims to enhance user experience by streamlining the search process based on previous interactions.
The method outlined in the patent involves receiving inputs selecting objects in digital images, identifying objects using object recognition, extracting categories, generating data on aspects associated with categories using machine learning, and performing searches for digital content related to the objects based on these aspects. The augmented reality digital content is then outputted, providing users with relevant information in real-time. The patent also covers the selection of models based on object categories, user-specific models, and the use of previous user data to train models. Additionally, the patent details the use of live camera feeds, rendering digital content relative to objects in the feed, and displaying indications of included aspects in searches, further enhancing user engagement and search efficiency.
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