Contextual Media Query Indexing for Missing Identifiers
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Solution Overview
Problem
Current search engines struggle to provide accurate and relevant search results for queries lacking media asset identifiers, particularly for past media consumption or events, as they lack a mechanism to track previously encountered content and contexts.
Innovation Solution
A system that automatically monitors and stores media samples with contextual information on a computing device, such as a smartphone, allowing for the generation of relevant search results even when identifiers are missing, by linking media samples to their capture contexts like time, location, and associated devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If present systems automatically identify media samples captured by a smartphone, then identification accuracy for current media is improved, but the system cannot handle queries about past media consumption or events
Solution Approach 1:
The system performs preliminary actions by automatically capturing media samples and storing them with contextual information (time, location, device identifiers) in advance. This preliminary indexing enables the system to respond to queries about past media consumption without requiring real-time capture, thus extending versatility to historical queries while maintaining identification accuracy.
2Measurement precision
If the system stores and processes media samples with contextual information, then query accuracy for past events is improved, but system complexity increases
Solution Approach 1:
The system segments the query processing function into two independent components: (1) a media sample capture and indexing subsystem that automatically stores media with contextual metadata, and (2) a query processing subsystem that retrieves and matches samples based on user queries. This segmentation reduces overall system complexity by allowing each component to operate independently with well-defined interfaces.
Solution Approach 2:
The system introduces an intermediary database layer that stores media samples with contextual information. This intermediary structure acts as a buffer between the media capture process and the query processing, simplifying the architecture by providing a standardized interface for both input and output operations while enabling accurate query results.
3Productivity
If the system automatically monitors and captures media samples periodically, then coverage of past media events is improved, but energy consumption increases
Solution Approach 1:
The system implements periodic action by capturing media samples at scheduled intervals rather than continuously. This periodic sampling maintains adequate coverage of media events over time while significantly reducing energy consumption compared to continuous monitoring, as the device can enter low-power states between capture intervals.
Data Source
AI summary
Systems and methods for facilitating contextual queries based on media samples automatically captured by a computing device are disclosed herein. A server receives from a computing device over a communication network, a media sample of a media asset automatically captured by the computing device. The server obtains contextual information corresponding to the captured media sample. The server stores the media sample in a memory indexed by the contextual information. The server receives, from the computing device over the communication network, a query that includes a criterion but lacks an identifier of the media asset. The server identifies the media sample in the memory by matching the query criterion to the contextual information. The server generates a reply to the query based on the identifying of the media sample and communicates the reply to the computing device over the communication network.