Multi-Modal Data Correlation via Geo-Spatial Inference
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Solution Overview
Problem
Conventional search engines and information retrieval systems are ineffective in correlating and synthesizing data from multiple sources across various modalities like geo-space, time, entities, and events, especially when location metadata is absent, leading to difficulties in making meaningful inferences from cross-modal data.
Innovation Solution
A system comprising a collection module, geo-localization module, correlation module, and inferencing module that collects data from multiple sources, identifies geographic locations, correlates data based on these locations, and infers location information when explicit metadata is lacking, using techniques like keyword analysis, voice recognition, and image feature matching against geo-referenced databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional search engines and information retrieval systems are used to retrieve data from multiple sources, then data collection is simple, but the ability to correlate and synthesize data from multiple modalities (geo-space, time, entities, events) is weak
Solution Approach 1:
The patent introduces an intermediary correlation system that acts as a mediator between raw multi-modal data sources and the final synthesized information. This intermediary layer performs geo-localization, temporal alignment, and semantic correlation across different data modalities (text, images, video, audio) without requiring the end user to directly manage the complexity of multi-source integration. The system mediates the correlation process by introducing intermediate representation layers that standardize diverse data formats into a unified multi-dimensional framework.
Solution Approach 2:
The patent segments the complex correlation task into distinct functional modules: geo-localization module for spatial coordination, temporal alignment module for time synchronization, entity recognition module for identifying objects and subjects, and event correlation module for linking related occurrences. Each module handles a specific aspect of multi-modal correlation independently, then integrates results through a unified correlation framework, making the overall system more manageable and adaptable.
2Adaptability or versatility
If data is collected from multiple information streams without geo-location, then data collection is comprehensive, but the ability to align and correlate data spatially is lost
Solution Approach 1:
The patent applies preliminary geo-localization action by automatically determining geographic coordinates for data sources before correlation processing. The system performs geo-localization in advance by analyzing metadata, recognizing landmarks in images, parsing address information from text, or using GPS coordinates from mobile devices. This preliminary spatial tagging enables subsequent correlation operations to efficiently align data from multiple sources without requiring complex real-time spatial analysis during the correlation phase.
Solution Approach 2:
The patent replaces manual or mechanical geo-localization methods with automated computational approaches. Instead of requiring manual tagging of geographic information or simple keyword matching, the system employs image recognition algorithms to identify landmarks, natural language processing to extract location information from text, and database matching against geo-referenced datasets. This substitution of mechanical/manual processes with automated computational mechanisms enables comprehensive spatial alignment across diverse data modalities.
3Loss of information
If semantic tagging is manually applied to web content as in the Semantic Web approach, then meaningful associations can be derived, but the process is time-consuming and much Internet data remains untagged
Solution Approach 1:
The patent implements self-service semantic extraction where the system automatically derives meaning and associations from untagged web content without requiring manual intervention. The correlation system performs self-directed semantic analysis by recognizing patterns in the data, inferring relationships between entities and events, and generating semantic tags autonomously. This self-service approach enables the system to process large volumes of untagged Internet data efficiently, extracting meaningful information through automated correlation algorithms rather than manual tagging processes.
Solution Approach 2:
The patent replaces manual semantic tagging with automated computational semantics. Instead of relying on human experts to manually annotate web content with meaningful tags and relationships, the system employs natural language processing, machine learning algorithms, and pattern recognition to automatically derive semantic information. The correlation engine substitutes mechanical human labeling processes with automated information extraction techniques that can process vast amounts of untagged data rapidly, inferring semantic relationships through contextual analysis and cross-modal correlation.
4Adaptability or versatility
If image and video content without location metadata is used, then data availability is high, but the ability to correlate content with geographical location is difficult
Solution Approach 1:
The patent introduces intermediary geo-localization techniques that serve as mediators between untagged media content and geographic location information. The system employs intermediate representation layers including landmark recognition databases, image feature matching against geo-referenced datasets, and contextual analysis of surrounding information from correlated data streams. These intermediary processes translate visual and audio features of untagged media into geographic coordinates by comparing against known location databases and inferring position from contextual clues in correlated information streams.
Solution Approach 2:
The patent replaces manual location annotation of media content with automated computational geo-localization. Instead of requiring manual tagging of geographic information in images and videos, the system employs computer vision algorithms to recognize landmarks and geographic features, machine learning models to infer location from visual patterns, and database matching techniques to correlate media content with geo-referenced datasets. This substitution enables automatic geographic correlation of vast amounts of untagged media content that would be impractical to annotate manually.
Data Source
AI summary
Embodiments of the present invention are directed towards methods and apparatus for generating a common operating picture of an event based on the event-specific information extracted from data collected from a plurality of electronic information sources. In some embodiments, a method for generating a common operating picture of an event includes collecting data, comprising image data and textual data, from a plurality of electronic information sources, extracting information related to an event from the data, said extracted information comprising image descriptors, visual features, and categorization tags, by applying statistical analysis and semantic analysis, aligning the extracted information to generate aligned information, recognizing event-specific information for the event based on the aligned information, and generating a common operating picture of the event based on the event-specific information.