Hash-Based Error Pattern Recognition for Automated Recovery

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Solution Overview

Problem

Self-service systems face inefficiencies in error management due to manual identification and resolution processes, inability to learn from historical errors, and lack of preemptive mitigation strategies, leading to user frustration and system downtime.

Innovation Solution

A cryptographic hash signature architecture with an automated recovery framework that uses unique checksum values, a rules engine, and machine learning algorithms to recognize error patterns and implement predefined corrective actions, adapting over time to enhance efficiency and reliability.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual identification and resolution processes are used for errors, then system complexity is reduced, but error resolution time increases and productivity decreases

Engineering Contradiction:

Improveerror resolution speedVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system implements self-service error resolution by automatically detecting errors, generating cryptographic hash signatures, matching them against a database of known errors, and executing predefined remediation scripts without human intervention. The error management system serves itself by maintaining an autonomous loop of error detection, analysis, and correction

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary action by pre-defining remediation scripts and corrective actions for known error patterns before they occur. When an error is detected and matched against the database, the corresponding pre-prepared script is immediately executed, eliminating the need for manual analysis and resolution planning

Inventive Principle:

Principle #10Preliminary action

2Reliability

If traditional error management methods are used, then ease of operation is maintained, but the system cannot learn from historical errors and reliability decreases

Engineering Contradiction:

Improvesystem reliabilityVSAvoidoperational simplicity

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system implements feedback by continuously monitoring error occurrences, storing cryptographic hash signatures and resolution outcomes in a database, and using this historical data to improve future error detection and resolution. The system learns from each error incident by updating its database, enabling it to recognize patterns and respond more effectively to recurring errors

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system replaces mechanical manual error management processes with an automated computational system that uses cryptographic hash functions, database querying, and automated script execution. This substitution transforms the error management approach from human-operated to machine-executed, enabling continuous operation without manual intervention

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If automated recovery frameworks are implemented, then productivity increases, but device complexity and computational overhead increase

Engineering Contradiction:

Improveerror resolution efficiencyVSAvoidcomputational overhead

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system extracts only the essential identifying characteristics of errors into cryptographic hash signatures, which are compact fixed-length values. This extraction process converts complex error data into concise representations that can be rapidly compared against the database, reducing computational overhead while maintaining error identification accuracy

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system changes parameters by transforming error data into cryptographic hash values with fixed length and specific properties. This parameter transformation enables efficient storage and comparison operations, as the hash signatures have consistent size and can be rapidly processed regardless of the original error data complexity

Inventive Principle:

Principle #35Parameter changes

4Loss of time

If manual error resolution processes are used, then device complexity is low, but loss of time occurs due to inability to provide preemptive mitigation

Engineering Contradiction:

Improveerror resolution timeVSAvoidsystem architecture complexity

Core Design Contradiction:

Loss of timeVSDevice complexity

Solution Approach 1:

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12561193B2Cryptographic hash signature for error pattern recognition with an automated recovery framework

Publication Date: 2026.02.24 BANK OF AMERICA CORP

AI summary

Systems, computer program products, and methods are described for a cryptographic hash signature architecture aimed at error pattern recognition and automated recovery. This framework receives live error data, generates unique cryptographic hash signatures (checksums), and matches these against a stored checksum database to identify known error patterns. If a match exists, predefined scripts execute corrective actions. Otherwise, machine learning algorithms assess the error to suggest solutions. The system employs a rules engine applying predefined algorithms to ensure solutions match system protocols. It updates a historical database with error and resolution data, refining the knowledge base. Successful resolutions are verified, with outcomes enhancing the machine learning model's future accuracy. This innovative approach boosts system reliability and user satisfaction by reducing manual interventions and downtime.