Data Correlation System Using Inferred Geo-Location
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Solution Overview
Problem
Current data correlation systems face challenges in aligning and correlating disparate and unsynchronized data across multiple dimensions such as geo-space, time, entities, and events, especially when data lacks explicit location information, leading to difficulties in producing meaningful inferences and responses.
Innovation Solution
A system comprising a collection module, a geo-localization module, and a correlation module that collects data from various sources, identifies geographic locations, and correlates data based on geo-location, storing the correlations in a database and inferring locations if not explicitly provided, while also displaying the data on a user-interactable map.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional search engines and information retrieval systems are used to synthesize data from multiple sources, then data collection is simplified, but the ability to correlate and align data across multiple dimensions (geo-space, time, semantics) deteriorates
Solution Approach 1:
The patent introduces an inferencing module as an intermediary component that bridges the gap between simple data collection and complex correlation. This module infers missing attributes (geo-location, time, semantic tags) from available data, enabling correlation without requiring complete information from source systems.
Solution Approach 2:
The patent replaces manual semantic tagging and mechanical data alignment processes with automated inferencing algorithms. The system automatically infers geo-location from IP addresses or content analysis, deduces time from metadata or contextual clues, and generates semantic associations through machine learning, eliminating the need for manual intervention.
2Measurement precision
If data sources are required to provide explicit location metadata for correlation, then correlation accuracy improves, but data source compatibility and ease of integration deteriorates
Solution Approach 1:
The patent performs preliminary inferencing actions on data before correlation is needed. The system proactively infers geo-location, time, and semantic attributes from raw data, storing these inferred attributes alongside the original data. This preliminary preparation enables accurate correlation later without requiring data sources to provide complete information upfront.
Solution Approach 2:
The patent changes the parameter requirements for data correlation by accepting incomplete data with missing attributes and automatically inferring them. Instead of requiring precise location metadata as a prerequisite, the system transforms the problem by inferring location from other parameters (IP address, content analysis, contextual information), thereby accommodating diverse data sources with varying levels of completeness.
3Measurement precision
If manual semantic tagging is used to enable cross-modal association, then association accuracy improves, but processing time and operational complexity deteriorates
Solution Approach 1:
The patent replaces manual semantic tagging with automated inferencing mechanisms. The system uses machine learning algorithms and pattern recognition to automatically generate semantic associations between data elements, eliminating the need for human experts to manually tag content while maintaining high association accuracy through sophisticated inference rules.
Solution Approach 2:
The patent enables data to self-tag and self-describe through automated inferencing. The system analyzes data content, context, and relationships to automatically generate semantic metadata and associations without external intervention. This self-service approach allows the system to process and associate data autonomously, dramatically reducing processing time compared to manual methods.
4Loss of information
If comprehensive data correlation across multiple dimensions is implemented, then situational awareness and inference quality improve, but system complexity and computational requirements deteriorates
Solution Approach 1:
The patent segments the complex correlation task into distinct functional modules: collection module for data acquisition, geo-localization module for spatial attribution, temporal analysis module for time-based correlation, and inferencing module for semantic association. This segmentation allows each module to handle specific aspects of correlation independently, reducing overall system complexity while achieving comprehensive multi-dimensional analysis.
Data Source
AI summary
Methods and apparatuses of the present invention generally relate to generating actionable data based on multimodal data from unsynchronized data sources. In an exemplary embodiment, the method comprises receiving multimodal data from one or more unsynchronized data sources, extracting concepts from the multimodal data, the concepts comprising at least one of objects, actions, scenes and emotions, indexing the concepts for searchability; and generating actionable data based on the concepts.