Automated Classification of Machine-Generated Textual Data

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Solution Overview

Problem

Existing solutions fail to effectively unify, classify, and ingest machine-generated textual data from diverse sources due to non-standardized structures, human-centric formatting, and the dynamic nature of data, leading to inefficient manual analysis and limited processing capacity for IT personnel.

Innovation Solution

A method and system for classifying machine-generated textual data into statistical metrics by receiving data from multiple sources, grouping it into events, processing elements, and determining metric types, enabling automated ingestion, processing, and analysis.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual analysis of machine-generated data is performed by IT personnel, then data can be understood and analyzed, but processing capacity is limited and productivity decreases

Engineering Contradiction:

Improvedata analysis capabilityVSAvoidprocessing capacity

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent replaces manual mechanical analysis by IT personnel with an automated computer-based system that uses natural language processing and machine learning algorithms to ingest, classify, and analyze machine-generated data, thereby eliminating human capacity limitations while maintaining analytical capability

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables machines to automatically process and analyze their own generated data through automated ingestion pipelines, classification algorithms, and root-cause analysis capabilities, freeing IT personnel from manual data processing tasks

Inventive Principle:

Principle #25Self-service

2Adaptability or versatility

If data from multiple vendors is aggregated, then comprehensive coverage is achieved, but data structure non-uniformity increases processing complexity

Engineering Contradiction:

Improvedata source coverageVSAvoidprocessing system complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements a universal data ingestion framework that can handle multiple data formats and structures from different vendors through a single standardized interface, using format detection and automatic adaptation to process diverse data sources without requiring separate processing pipelines for each vendor

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system introduces an intermediary layer consisting of format parsers, normalization routines, and schema mapping mechanisms that translate various vendor-specific data formats into a unified internal representation, simplifying the processing complexity while maintaining broad data source compatibility

Inventive Principle:

Principle #24Intermediary (Mediator)

3Ease of operation

If human-readable formatting is maintained, then data understandability is preserved, but automated processing efficiency decreases

Engineering Contradiction:

Improvedata understandabilityVSAvoidautomated processing efficiency

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The patent segments the data processing workflow into distinct stages: initial human-readable format preservation for ingestion, followed by automated classification and transformation into structured formats suitable for machine processing, allowing both understandability and efficiency to be optimized at different processing stages

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system dynamically adapts data formats based on processing stage requirements, maintaining human-readable formats during initial ingestion and analysis phases while automatically transforming data into optimized structured formats for automated processing, storage, and retrieval operations

Inventive Principle:

Principle #15Dynamics

4Extent of automation

If dedicated scripts are developed for parsing and categorizing data, then data processing automation is achieved, but computational resources and maintenance requirements increase

Engineering Contradiction:

Improvedata processing automationVSAvoidsystem maintenance requirements

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The patent transforms the automation approach from fixed dedicated scripts to adaptive machine learning models that automatically learn data patterns and processing rules, changing the system parameters from static code-based processing to dynamic model-based processing that adapts to new data formats without requiring script modifications

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system replaces the mechanical script-based processing approach with intelligent automated systems using natural language processing and machine learning algorithms that can automatically parse, classify, and process diverse data formats without requiring manual script development or maintenance for each data type

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS10963634B2Cross-platform classification of machine-generated textual data

Publication Date: 2021.03.30 SERVICENOW INC

AI summary

A system and method for classifying machine-generated textual data into statistical metrics are determined. The system comprises receiving machine-generated textual data from at least one data source; grouping the machine-generated textual data into a plurality of events; processing each event to determine a plurality of elements embedded therein; determining a type of each of the plurality of elements; and determining a statistical metric for each element based on at least on the type of the element.