Camera Platform for Schedule-Based Clothing Recommendations
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Solution Overview
Problem
Mobile devices and wearable devices face challenges in supporting efficient user interactions, such as limited text entry and navigation, leading to computational inefficiencies.
Innovation Solution
A camera platform and object inventory control system that utilizes machine learning to recognize objects in digital images, generate user profiles, and provide recommendations based on user schedules, enabling efficient interaction and metadata collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If mobile devices and wearable devices are used for user interactions, then portability and accessibility are improved, but user interaction efficiency and computational efficiency deteriorate due to limited text entry and navigation capabilities
Solution Approach 1:
The patent introduces a camera as an intermediary device that captures images of physical objects (such as clothing items) to enable object recognition and metadata collection. This intermediary approach allows users to interact with the system through image capture rather than limited text entry, bridging the gap between mobile device portability and efficient interaction capabilities.
2Measurement precision
If object recognition and machine learning processing are implemented, then user profile accuracy and recommendation quality are improved, but computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by capturing images of physical objects and collecting metadata in advance. The camera captures images and the system collects object metadata before user profile generation and recommendation processing, reducing the computational burden during actual user interactions by pre-processing data when computational resources are more readily available.
Data Source
AI summary
Camera platform techniques are described. In an implementation, a plurality of digital images and data describing times, at which, the plurality of digital images are captured is received by a computing device. Objects of clothing are recognized from the digital images by the computing device using object recognition as part of machine learning. A user schedule is also received by the computing device that describes user appointments and times, at which, the appointments are scheduled. A user profile is generated by the computing device by training a model using machine learning based on the recognized objects of clothing, times at which corresponding digital images are captured, and the user schedule. From the user profile, a recommendation is generated by processing a subsequent user schedule using the model as part of machine learning by the computing device.