Spatiotemporal Content Sequences via Knowledge Graph
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Solution Overview
Problem
Current data-driven and video analytics tools are inefficient in identifying semantic connections between original and edited digital media content, and the extracted information is often not structured in a queryable format, limiting it to low-level features.
Innovation Solution
A computer-implemented method that extracts information from digital media content, structures it into a knowledge graph, and composes new content based on spatial and temporal criteria from search queries, using machine learning and natural language processing to classify labels and associate concepts with fragments, allowing for the retrieval and composition of digital media content that meets user-defined queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually search through entire digital media content using playback controls, then they can find specific events, but the process is time-consuming and inefficient
Solution Approach 1:
The system performs preliminary actions by automatically analyzing digital media content beforehand and structuring it into a knowledge graph with temporal and spatial relationships. This preprocessing enables rapid querying without manual playback, resolving the contradiction between search accuracy and time consumption.
Solution Approach 2:
A knowledge graph serves as an intermediary data structure between raw digital media content and user queries. It enables efficient semantic search by representing events, objects, and their spatiotemporal relationships in a queryable format, eliminating the need for manual content scanning.
2Loss of information
If existing video analytics tools extract information from digital media content, then information can be obtained, but the extracted information is not structured in a queryable format and is limited to low-level features
Solution Approach 1:
The system segments digital media content into discrete events and objects, representing them as separate nodes in a knowledge graph. This segmentation preserves detailed information while organizing it into a structured format that balances completeness with queryability.
Solution Approach 2:
The system transforms flat video data into a multi-dimensional knowledge graph structure incorporating temporal and spatial dimensions. This dimensional transformation enables complex semantic queries while maintaining information completeness through structured relationships.
Data Source
AI summary
One or more processor can automatically identify, structure and retrieve spatial and/or temporal sequences of digital media content according to semantic specification. Digital media content can be received and information from digital media content can be extracted. Based on the information, a knowledge graph can be constructed or structured to include at least one of spatial and temporal representation of the digital media content. A search query can be received associated with the digital media content. Based on traversing the knowledge graph structure according to at least one of spatial and temporal criterion mapped from the search query, new digital media content can be composed which meets the search query.