Video Object Recognition for Seamless Shopping Integration
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Solution Overview
Problem
Existing technologies lack the ability to identify and provide purchase options for items shown in video content, limiting user engagement and convenience.
Innovation Solution
A system that uses computer vision techniques to analyze video frames, identify objects of interest, and search online shopping systems for matching or similar products, allowing users to purchase these items directly from the video content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If computer vision techniques are used to identify objects in video content, then user ability to purchase items is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary object detection system that acts as a mediator between video content and the shopping system. This separate module processes video frames to identify objects, then communicates findings to the shopping interface, resolving the complexity issue by isolating the computer vision functionality from the core shopping platform.
Solution Approach 2:
The system integrates multiple functions into a unified platform: video playback, object detection, product database searching, and e-commerce transactions. By making the system universal and multi-functional, the complexity of adding object identification is amortized across existing infrastructure, reducing the net increase in system complexity.
2Ease of operation
If object recognition is implemented in video content, then shopping convenience is improved, but processing time increases
Solution Approach 1:
The system performs preliminary object detection and product matching in advance, storing results for quick retrieval during video playback. By pre-processing video content to identify objects and match them with product database entries before the user watches, the system minimizes real-time processing delays and maintains shopping convenience.
3Measurement precision
If comprehensive product matching is performed, then purchase accuracy is improved, but computational load increases
Solution Approach 1:
The system implements partial matching by focusing on key distinguishing features of objects rather than performing exhaustive comprehensive analysis. It identifies the most salient characteristics needed for accurate product matching and processes only those aspects, achieving sufficient accuracy while reducing computational energy consumption through selective rather than complete analysis.
Data Source
AI summary
Devices, systems, and methods are provided for smart shopping based on recognition of objects presented in video. A method may include identifying, by a first device, using a machine learning model using a computer vision technique, objects represented in video content; determining that an object of the objects is available for purchase using an online retail system; causing concurrent presentation of the video content and a first indication that the object is available for purchase using the online retail system; receiving, from a second device, a second indication of a user selection of the first indication, wherein the user selection is indicative of a request to present additional information associated with the object; generating, based on the request, presentation data including the additional information and an option to purchase the object using the online retail system; and causing, based on the request, presentation of the presentation data.