Context-Aware Object Storage and Retrieval Indexing
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Solution Overview
Problem
Conventional systems fail to effectively store and retrieve images and context details of objects of interest, leading to difficulties in accessing and comparing features of objects, especially when the user relies on memory, which can be unreliable and time-consuming.
Innovation Solution
A method and system that capture images and environmental context of objects of interest, index them using identified features, and allow for later retrieval based on contextual clues, enabling intuitive access and comparison of objects in an augmented reality environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional storage systems are used to store images and context details, then storage capacity is maintained, but retrieval efficiency and accuracy deteriorate
Solution Approach 1:
The system performs preliminary actions by capturing and storing environmental context information (images, audio, metadata) at the moment an object of interest is identified. This context is stored alongside the object image, creating a rich dataset before retrieval is needed. When a user later provides partial context clues, the system can quickly search through pre-stored context information to retrieve the correct object, significantly reducing both retrieval time and improving accuracy compared to conventional systems that only store basic object data.
2Reliability
If detailed context information is captured and stored for every object, then retrieval accuracy improves, but data storage requirements and system complexity increase
Solution Approach 1:
The system applies partial action by selectively capturing context information based on what is necessary for effective retrieval. Rather than storing every possible detail about every object, the system captures key environmental context elements (surrounding images, audio snippets, metadata) that are most useful for identification. This selective approach maintains high retrieval accuracy while avoiding the excessive complexity and storage requirements of capturing all possible data.
3Reliability
If users rely on memory to recall object features, then no additional storage is needed, but retrieval accuracy and user experience deteriorate
Solution Approach 1:
The system introduces an intermediary layer between the user's memory and the stored objects. Instead of relying directly on user memory to recall specific object features, the user provides contextual clues (such as describing the environment or situation), and the system acts as a mediator by searching through stored context information to retrieve the correct object. This intermediary process significantly improves both accuracy and speed compared to direct memory recall.
Data Source
AI summary
One embodiment provides a method, including: capturing at least one image of an object that is of interest to a user; identifying and capturing an environmental context of the object, wherein the environmental context (i) identifies a plurality of features of the environment surrounding the object, and (ii) comprises context captured from different modalities; storing the at least one image and the environmental context of the object, wherein the storing comprises indexing the object within the remote storage location using the identified features of the environment; receiving a request for the at least one image of the object; accessing the remote storage location and retrieving the at least one image of the object, wherein the retrieving comprises (i) searching for the at least one of the plurality of features and (ii) retrieving the at least one image of an object; and displaying the at least one image.