Cognitive Fabric Nodes Automating Data Analysis

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Solution Overview

Problem

Intelligence analysts face challenges in efficiently collecting and correlating high volumes of disparate data, leading to a shortened 'data to decision' cycle, limited trust in AI for automation, and loss of institutional knowledge due to complex operational environments and asymmetric threats.

Innovation Solution

A cognitive fabric system comprising a network of intelligent nodes with communication interfaces and on-board processors that share and analyze data using analytic processing software, generating objects and adapting AI models based on human analyst feedback to automate analytic tradecraft.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If analysts manually collect and correlate data from disparate sources, then data accuracy and trust are maintained, but the data to decision cycle time increases and productivity decreases

Engineering Contradiction:

Improvedata analysis throughputVSAvoiddata to decision cycle time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces manual analyst mechanics with automated AI agents that perform data collection, correlation, and analysis. Multiple AI agents work in parallel to process data from disparate sources, eliminating the sequential manual process while maintaining accuracy through collaborative verification among agents.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent divides the data analysis process into independent AI agent components, each specializing in specific tasks such as data collection, correlation, validation, and analysis. This segmentation allows parallel processing of different data streams and functions, dramatically increasing throughput while reducing overall cycle time.

Inventive Principle:

Principle #1Segmentation

2Productivity

If AI automates analytic processes, then productivity increases and cycle time decreases, but analyst trust in AI accuracy decreases

Engineering Contradiction:

Improveautomated data analysis capacityVSAvoidanalyst trust in AI accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent implements feedback loops where AI agents continuously validate their findings against multiple data sources and cross-check correlations. Analysts can provide feedback on AI-generated insights, which is then used to refine and improve the automated analysis processes, building trust through demonstrated accuracy and continuous improvement.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent merges human analyst expertise with AI automation capabilities in a collaborative framework. Analysts oversee and validate AI-generated insights while maintaining the ability to intervene and provide contextual judgment, creating a hybrid system that combines the speed of AI with the trustworthiness of human analysis.

Inventive Principle:

Principle #5Merging (Combining)

3Adaptability or versatility

If analysts operate in globally distributed environments, then operational coverage expands, but institutional knowledge and best practices are lost when analysts transition

Engineering Contradiction:

Improveglobal operational coverageVSAvoidinstitutional knowledge retention

Core Design Contradiction:

Adaptability or versatilityVSLoss of information

Solution Approach 1:

The patent creates digital twins or knowledge repositories that capture and store the expertise, methodologies, and insights of analysts. When analysts transition, their institutional knowledge is preserved in the system through documented procedures, analyzed case studies, and trained AI models, ensuring continuity across global operations.

Inventive Principle:

Principle #26Copying

4Adaptability or versatility

If multiple baselines are managed across operational environments, then adaptability to different environments improves, but algorithm drift increases and accuracy decreases

Engineering Contradiction:

Improveoperational environment adaptabilityVSAvoidalgorithm accuracy

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The patent dynamically adjusts algorithm parameters and thresholds based on the specific operational environment and data characteristics. Rather than using fixed baselines, the system adapts parameters in real-time to maintain optimal accuracy across diverse environments, preventing algorithm drift through continuous calibration.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS10902333B2Methods and systems for collecting and analyzing data using a cognitive fabric to provide cognitive agility for the analytic tradecraft

Publication Date: 2021.01.26 BOOZ ALLEN HAMILTON INC

AI summary

A system and method are disclosed for collecting and analyzing data in a cognitive fabric. The system can include a network of intelligent nodes, each node being configured for sharing or receiving data as a function of analytic processing to be performed at the node. Each node having an on-board processor to generate an object from shared data and the analytic processing software