Contextualized Human Machine System for Analyst Decision Support
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Solution Overview
Problem
Data analysts face challenges in navigating and interpreting large volumes of disparate data across multiple sources to produce actionable decisions in real-time, due to increased data complexity and dynamic mission requirements.
Innovation Solution
A contextualized human machine system that combines advanced naturalistic interactions with context reasoning to provide context-aware assistive support, using a multi-layer knowledge graph to ingest and contextualize data from various human-machine interface tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data analysts manually navigate and interpret large volumes of disparate data across multiple sources, then they can produce actionable decisions, but the time required increases significantly and efficiency decreases
Solution Approach 1:
The patent introduces a contextualized human-machine interface that acts as an intermediary between data analysts and disparate data sources. This interface automatically retrieves, processes, and presents relevant information from multiple sources, eliminating the need for analysts to manually navigate through vast amounts of data while maintaining accurate and timely decision-making capabilities.
Solution Approach 2:
The system performs preliminary actions by automatically retrieving and preprocessing data from multiple sources before the analyst needs it. The contextualized interface proactively organizes and presents relevant information in advance, allowing analysts to immediately work with prepared data rather than spending time on manual data gathering and initial processing.
2Reliability
If the system provides comprehensive contextualized assistance through multiple data sources, then the quality of work products improves, but the system complexity increases
Solution Approach 1:
The patent segments the complex system into distinct functional modules: a data retrieval component that accesses multiple sources, a contextualization engine that processes and organizes information, and an interface layer that presents data to analysts. This modular segmentation allows the system to handle complexity internally while presenting a simplified, user-friendly interface, thereby maintaining high work product quality without overwhelming the user with system complexity.
3Loss of information
If the system processes and contextualizes data from multiple disparate sources, then information completeness improves, but the processing time and computational resources increase
Solution Approach 1:
The contextualized human-machine interface applies local quality by selectively retrieving and processing only the specific portions of data from multiple sources that are relevant to the current analytical task. Rather than uniformly processing all available data, the system identifies and focuses on locally relevant information, ensuring information completeness for the specific context while minimizing unnecessary processing time and computational resources.
Data Source
AI summary
A contextualized human machine system is provided comprising a context engine and a user interface configured to communicate a recommended data to a user. In some embodiments, the context engine selects the recommended data based on an activity of the user. In some embodiments, the input of the user comprises a chat stream of the user. In some embodiments, the recommended data comprises one of a video product, a hyperlink to information or a suggestion for annotating a product. In some embodiments, the context engine is configured to represent user activity, content, mission and actor as nodes in a multi-layer knowledge graph.