Cognitive Agents for Big Data Insight Extraction
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Solution Overview
Problem
Current technologies face challenges in efficiently processing and analyzing large volumes of big data, particularly 'dark data,' which is often neglected or underutilized, making it difficult to extract actionable insights in a timely manner.
Innovation Solution
A cognitive inference and learning system comprising multiple agents that process streams of data from various sources, performing cognitive operations to generate insights, utilizing techniques such as semantic analysis, goal optimization, collaborative filtering, common sense reasoning, natural language processing, and entity resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing approaches are used to handle big data, then data processing capacity is limited, but processing time and computational resources increase significantly
Solution Approach 1:
The patent segments the monolithic data processing system into multiple specialized cognitive agents (e.g., NLP agents, reasoning agents, learning agents) that operate in parallel. Each agent handles specific cognitive tasks independently, enabling concurrent processing of different data streams and reducing overall processing time while maintaining high productivity.
Solution Approach 2:
The patent introduces a new dimensional approach by adding temporal and contextual dimensions to data processing. Cognitive agents process not only the data itself but also its temporal evolution and contextual relationships, enabling more efficient extraction of actionable insights from dark data without linearly increasing processing time.
2Loss of information
If comprehensive data collection is performed to capture all potential insights, then data volume increases, but data management complexity and processing difficulty increase
Solution Approach 1:
The patent applies local quality by enabling different cognitive agents to process data with different levels of detail and complexity appropriate to their specific functions. For example, NLP agents perform detailed semantic analysis on text data while other agents handle structured data more efficiently, reducing overall system complexity while maintaining information completeness.
Solution Approach 2:
The patent introduces cognitive agents as intermediary components between raw data and final insights. These agents act as mediators that filter, interpret, and transform diverse data types into standardized cognitive representations, simplifying data management while preserving comprehensive information extraction.
3Measurement precision
If dark data is extensively analyzed to extract actionable insights, then insight quality improves, but computational resources and processing time increase
Solution Approach 1:
The patent implements partial action by having cognitive agents focus on analyzing only the most relevant portions of dark data based on contextual cues and priority indicators. Rather than exhaustively processing all dark data, agents selectively concentrate computational resources on high-value segments, maintaining insight quality while reducing overall computational resource consumption.
4Adaptability or versatility
If multiple data sources are integrated to provide comprehensive insights, then insight comprehensiveness improves, but system complexity and integration difficulty increase
Solution Approach 1:
The patent implements universality by designing cognitive agents with multi-functional capabilities that can process and integrate multiple data types and sources through common cognitive frameworks. Each agent is equipped with versatile cognitive operations (perception, reasoning, learning) that work across different data sources, reducing integration complexity while maintaining comprehensive insight generation.
Data Source
AI summary
An apparatus for providing cognitive insights comprising: a cognitive inference and learning system, the cognitive inference and learning system comprising a plurality of agents, the plurality of agents processing streams of data from a plurality of data sources, the processing the streams of data from the plurality of data sources via the plurality of agents performing a respective plurality of cognitive operations on the streams of data, at least one of the plurality of agents generating cognitive insights based upon the performing the respective plurality of cognitive operations on the streams of data from the plurality of data sources.