Cognitive Model Trust Activation for Large-Scale Anomaly Analysis

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Solution Overview

Problem

Current computing systems face challenges in dynamically adapting to changing circumstances and processing large, seemingly infinite amounts of data, particularly in identifying qualitative behavior criteria and scaling performance across a large actor population.

Innovation Solution

A cognitive modeling system that receives actors and assets, creates data dictionary entries for a taxonomy, computes trust as a fuzzy number, and activates a cognitive model for anomaly analysis, allowing for dynamic parameter adjustment and efficient data processing.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional numerical analysis methods are used, then processing speed is improved, but the ability to handle qualitative behavior criteria deteriorates

Engineering Contradiction:

Improveprocessing speedVSAvoidhandling qualitative behavior criteria

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The patent transforms qualitative behavior criteria into quantitative parameters by establishing mapping relationships between qualitative descriptions (e.g., 'unusual amount of email') and numerical thresholds. This allows the system to process qualitative concepts through numerical computation while maintaining the semantic meaning of the original criteria.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent introduces an intermediary layer (the analysis system with predefined criteria mappings) that translates between qualitative user requirements and quantitative data processing. This intermediary enables analysts to work with intuitive qualitative concepts while the system performs efficient numerical analysis.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Stability of the object's composition

If fixed parameter systems are used, then system stability is improved, but the ability to dynamically adapt to changing circumstances deteriorates

Engineering Contradiction:

Improvesystem stabilityVSAvoiddynamic parameter adjustment

Core Design Contradiction:

Stability of the object's compositionVSAdaptability or versatility

Solution Approach 1:

The patent implements dynamic parameter adjustment by allowing the analysis system to modify thresholds and criteria based on changing contextual conditions (e.g., peak-usage time vs. low-usage time). The system can adapt numerical values for qualitative criteria according to temporal, environmental, or operational context while maintaining overall system stability through structured adaptation mechanisms.

Inventive Principle:

Principle #15Dynamics

3Measurement precision

If detailed context-specific numerical values are used, then measurement precision is improved, but the complexity of defining and maintaining criteria deteriorates

Engineering Contradiction:

Improvecontext-specific numerical precisionVSAvoidcriteria definition and maintenance complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent creates a universal framework for defining qualitative behavior criteria that can be applied across multiple contexts and data types. By establishing reusable mapping templates and standardized criterion structures, the system achieves context-specific precision without requiring separate custom definitions for each scenario, thereby reducing overall complexity.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Measurement precision

If manual translation of qualitative criteria to numerical values is performed, then accuracy is improved, but time consumption deteriorates

Engineering Contradiction:

Improvetranslation accuracyVSAvoidtranslation time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent performs preliminary action by pre-defining mapping relationships between qualitative criteria and numerical thresholds during system setup. This allows the translation from qualitative to quantitative to occur automatically during data analysis without requiring manual intervention at the time of analysis, significantly reducing time consumption while maintaining accuracy through pre-validated mappings.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11238350B2Cognitive modeling system

Publication Date: 2022.02.01 SCIANTA ANALYTICS LLC

AI summary

The present design is directed to a cognitive system including a receiver configured to receive a set of actors and associated actor information and receive assets and their associated asset information, a creation apparatus configured to create data dictionary entries for a taxonomy based on the set of actors and the assets and create a cognitive model using the data dictionary entries for a time period, and a computing apparatus configured to compute trust of the cognitive model as a fuzzy number and activate the cognitive model if trust of the cognitive model is above a cognitive model trust threshold. When the cognitive model is activated, the cognitive modeling system is configured to schedule a collection of tasks to run that perform regular extraction of actions from an original data source and perform at least one anomaly analysis associated with the cognitive model.