Social Linking Graph Analytics for Multimedia Entities
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing solutions for tagging multimedia content, such as images and videos, are often inaccurate or incomplete, and fail to account for context and relationships between individuals, leading to difficulties in identifying and visualizing social patterns and relationships.
Innovation Solution
A method and system for generating social linking scores based on contextual analysis of multimedia content elements, using signatures and metadata to identify social patterns and relationships between entities, with a social linking graph being generated to represent these connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tagging is used to identify persons in multimedia content, then users can provide contextual information, but the tagging becomes inaccurate and incomplete due to human error and inconsistency
Solution Approach 1:
The system enables automatic tagging by having the multimedia content itself provide the information needed for identification. The person identification module automatically extracts and tags persons depicted in images and videos without requiring manual intervention, while still capturing contextual information through social linking analysis.
Solution Approach 2:
The patent replaces manual mechanical tagging processes with automated computational systems. The person identification module uses image recognition and video analysis algorithms to automatically identify and tag persons, substituting human manual effort with automated technological processes that improve both accuracy and efficiency.
2Productivity
If existing automatic tagging solutions are used to identify subject matter, then tagging efficiency improves, but the solutions fail to account for context and relationships between individuals
Solution Approach 1:
The system nests multiple levels of analysis within the tagging process. The person identification module first identifies individual persons, then the social linking module nests additional analysis to identify relationships between those persons, creating layered contextual information that preserves both individual and relational data.
Solution Approach 2:
The patent adds a new dimension to automatic tagging by incorporating social relationship analysis. Beyond basic person identification, the system analyzes spatial relationships, interaction patterns, and contextual cues to infer social links, transforming one-dimensional tagging into multi-dimensional contextual understanding.
3Adaptability or versatility
If social media platforms store images showing individuals, then users can share content, but the platforms cannot identify or visualize relationships between the individuals in the content
Solution Approach 1:
The system uses feedback from multiple sources to improve relationship detection. The person identification module provides feedback on detected persons, the social linking module uses this feedback to analyze relationships, and the generated analytics feed back into improving the identification and linking processes, creating a continuous improvement cycle.
Solution Approach 2:
The patent performs preliminary person identification and relationship analysis before content is fully processed or displayed. By pre-identifying persons and pre-analyzing social links, the system prepares relationship data in advance, making it readily available for visualization and reducing the computational difficulty of real-time relationship detection.
Data Source
AI summary
A system and method for generating analytics for entities depicted in multimedia content, including: identifying at least one social pattern based on social linking scores of a plurality of entities indicated in a social linking graph, wherein each social pattern is identified at least by comparing one of the social linking scores to a predetermined social pattern threshold, wherein each social linking score is generated based on contexts of at least one multimedia content element (MMCE) in which at least two of the plurality of entities are depicted, wherein each context is determined based on a plurality of concepts of one of the at least one MMCE, wherein each concept matches at least one signature generated for the at least one MMCE above a predetermined threshold; and generating, based on the identified at least one social pattern, analytics for the plurality of entities depicted in the social linking graph.