Digital Detective Security Architecture for Scoped Crime Detection

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Conventional digital enforcement and investigative systems rely on probabilistic techniques that lead to false positives, lack transparency, and fail to enforce mission scope or jurisdictional authority, resulting in diminished accuracy, institutional trust, and increased legal exposure.

Innovation Solution

A unified digital enforcement platform with a Digital Detective System and Security Enforcement Engine, utilizing rule-based hybrid KRR AI agents and Network Sequencing Chains (NSCs) that traverse structured DAGs, ensuring deterministic, explainable, and jurisdictionally aligned investigative processes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If machine learning models and probabilistic techniques are used to evaluate potential violations, then the system can process large volumes of data quickly, but the accuracy and reliability of enforcement decisions deteriorate due to false positives and lack of legal criteria

Engineering Contradiction:

Improvedata processing speedVSAvoidenforcement decision accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent replaces machine learning models with a rule-based system that uses explicitly defined legal criteria and policy rules to evaluate potential violations. This substitution eliminates the probabilistic nature of ML while maintaining high processing capability through automated rule evaluation, thereby improving reliability without sacrificing productivity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system changes the evaluation parameter from probabilistic risk scores to deterministic rule matching. By transforming the decision-making mechanism from statistical inference to explicit rule application, the system achieves both speed and accuracy through structured query processing against predefined legal frameworks.

Inventive Principle:

Principle #35Parameter changes

2Productivity

If machine learning models operate as black boxes, then the system achieves high processing efficiency, but transparency and explainability deteriorate, limiting oversight and legal verification

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidreasoning path traceability

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The patent replaces opaque machine learning models with transparent rule-based reasoning that explicitly tracks decision paths through structured queries. Each enforcement decision can be traced back to specific legal criteria and policy rules, providing full explainability while maintaining automated processing efficiency through rule engine optimization.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system incorporates feedback mechanisms that log and track reasoning paths through structured query execution. This feedback loop enables complete traceability of enforcement decisions to their legal basis, allowing for oversight and audit while preserving processing efficiency through automated rule evaluation.

Inventive Principle:

Principle #23Feedback

3Adaptability or versatility

If dynamic logic execution paths are allowed to evolve, then the system adapts to new patterns, but jurisdictional authority and mission scope enforcement deteriorate, producing bulk-flagged outputs

Engineering Contradiction:

Improvepattern detection flexibilityVSAvoidmission scope enforcement

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent segments the enforcement logic into distinct, pre-defined modules corresponding to specific jurisdictions and missions. Each module operates independently with its own rule set, preventing runaway logic while maintaining adaptability through modular updates. This segmentation ensures that logic evolution remains bounded by legal authority while detecting new patterns within permitted scopes.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system implements controlled dynamics where logic paths can evolve through structured updates to rule definitions, while execution paths remain constrained by predefined jurisdictional boundaries. This allows adaptive pattern detection within legal frameworks while preventing uncontrolled logic expansion that would undermine mission scope enforcement.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20260058825A1Secure digital detective system with self destruction capability

Publication Date: 2026.02.26 WESTGATE DATA SCIENCE LLC

AI summary

The present disclosure provides techniques for identification of potential illicit activities (e.g., crimes) and/or abnormalities in large datasets. The techniques fuse data from various sources to purge normal records, analyze records using digital detective models, identify and utilize network-sequencing-chains to collect and process records, and generate reports (e.g., civic profile(s)) from the output of the digital detective models. The techniques comprise receiving data from data sources (e.g., government entities), pre-processing the data to determine records indicating illicit or abnormal behavior, determining crime types, inputting profiles into machine learning models trained to flag potential crimes, and generating encrypted data objects based on the output for review by authorized personnel. Robust security measures such as mission lock enforcement, quorum-governed privilege systems, and self-destruct capabilities may provide a digital security architecture to protect sensitive data and ensure system security.