System Improvement Data Generation Using Web Crawling and Machine Learning

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Solution Overview

Problem

Current methods for evaluating system performance are inadequate for developing tailored plans to improve metrics, lacking the ability to identify specific areas of low performance and calibrate steps effectively.

Innovation Solution

An apparatus and method utilizing a processor and memory to receive system and user data, generate a web index, classify data into performance categories, and create improvement plans through machine learning models, which are updated based on user feedback.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If current system performance evaluation methods are used, then evaluation can be performed, but customized improvement plans cannot be generated

Engineering Contradiction:

Improvecustomization capabilityVSAvoidperformance improvement guidance

Core Design Contradiction:

Adaptability or versatilityVSLoss of information

Solution Approach 1:

The system implements feedback loops where improvement plans are generated based on evaluated performance metrics, executed, and then re-evaluated. User feedback is collected and integrated into the machine learning model to continuously refine future improvement plan recommendations, creating a closed-loop system that adapts to actual performance outcomes.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The machine learning model enables the system to automatically analyze performance data, identify improvement areas, and generate customized plans without requiring manual intervention. The system self-adjusts by learning from historical data and feedback to autonomously create relevant improvement recommendations.

Inventive Principle:

Principle #25Self-service

2Adaptability or versatility

If comprehensive data collection and machine learning models are implemented, then customized improvement plans can be generated, but system complexity increases

Engineering Contradiction:

Improvecustomization capabilityVSAvoidsystem structure

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system divides the complex task of performance evaluation and improvement planning into separate modular components: data collection modules, classification modules, machine learning model modules, and plan generation modules. This segmentation allows each component to be developed, tested, and maintained independently while working together to provide customized improvement plans.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The machine learning model acts as an intermediary between raw performance data and customized improvement plans. It processes and interprets complex data patterns, translating them into actionable recommendations. This intermediary layer simplifies the overall system architecture by providing a clear transformation pipeline from data to decisions.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Measurement precision

If web crawling and data classification are used, then performance metrics can be collected and analyzed, but processing time increases

Engineering Contradiction:

Improveperformance metric accuracyVSAvoiddata processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary data collection and classification in advance, maintaining updated indices and categorized performance metrics. This pre-processing allows the machine learning model to quickly query and analyze relevant data without performing comprehensive web crawling and classification during each evaluation cycle, significantly reducing processing time while maintaining accuracy.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250021914A1Apparatus and method for generating system improvement data

Publication Date: 2025.01.16 THE BLUE COLLAR SUCCESS GRP LLC

AI summary

An apparatus for generating system improvement data, wherein the apparatus comprises a processor; and a memory containing instructions configuring the processor to: receive system data relating to an organizational identifier, wherein receiving the system data comprises training and utilizing a web crawler to generate a web index; generating a query as a function of the organizational identifier; and retrieving the system data as a function of the web index and the organizational identifier; receive user data related to a plurality of users; classify the system data and user data to a performance range category; generate, as a function of the performance range category, improvement data; create an improvement plan as a function of the improvement data wherein generating the improvement plan further comprises generating a machine learning model; and update the improvement plan as a function of user feedback.