AI Forecasting System for Technology Failures
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Solution Overview
Problem
Current technologies lack the ability to forecast technology-related incidents and failures in infrastructure environments, making it difficult to anticipate and mitigate potential issues before they occur, leading to significant impacts on businesses and clients.
Innovation Solution
A method using artificial intelligence models and machine learning techniques, specifically employing a random-forest regressor algorithm, to analyze historical data and generate forecasts for potential failures within specific systems, including predictions of failures within seven-day or thirty-day intervals, along with recommendations for mitigating actions, displayed through a user interface dashboard and interactive chatbot.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI algorithms are applied to analyze historical data for forecasting failures, then the ability to predict future failures is improved, but the system complexity increases
Solution Approach 1:
The system performs preliminary actions by training AI algorithms on historical failure data in advance, building predictive models before actual failures occur. This allows the system to forecast future failures proactively rather than reactively, improving reliability through pre-computed insights while managing complexity through structured model development
Solution Approach 2:
The patent introduces an intermediary layer of AI algorithms and machine learning models that mediate between historical data and future failure predictions. This intermediary processing layer transforms raw historical data into actionable forecasts, enabling complex predictive analytics without directly exposing system complexity to end users through the dashboard interface
2Measurement precision
If detailed analysis of historical data is performed to identify potential problems, then the accuracy of failure forecasts is improved, but the time required for analysis increases
Solution Approach 1:
The system performs preliminary data processing and pattern recognition on historical data in advance, pre-computing features and relationships that will be useful for future predictions. This preliminary analysis reduces the computational burden during actual forecasting operations, maintaining high accuracy while reducing real-time analysis time
Solution Approach 2:
The patent applies partial analysis by focusing computational resources on the most critical failure patterns and high-risk systems rather than analyzing all historical data uniformly. The AI algorithms identify and prioritize significant patterns, providing sufficiently accurate forecasts for critical failures without expending excessive computational time on less critical data points
3Adaptability or versatility
If comprehensive forecasts with multiple time intervals are provided, then the usefulness of the forecasting system is improved, but the amount of information to process increases
Solution Approach 1:
The forecasting system segments predictions into multiple distinct time intervals (e.g., 7-day, 30-day, 90-day forecasts) rather than providing a single aggregated prediction. This segmentation allows users to access specific time-frame forecasts based on their needs, reducing information processing load by enabling selective viewing of relevant time intervals while maintaining comprehensive forecasting capability
Solution Approach 2:
The system applies local quality by providing different levels of forecast detail and accuracy for different time intervals and system components. Critical short-term forecasts receive higher analytical priority and more detailed analysis, while longer-term forecasts provide strategic overview information. This differentiated approach optimizes information quality where needed while reducing overall processing requirements
Data Source
AI summary
A method and a system for forecasting technology-related incidents and/or failures using artificial intelligence models and machine learning techniques are provided. The method includes: receiving a data set that relates to a system; analyzing the data set to identify a potential problem with respect to the system; and generating a report that includes a forecast that relates to the identified potential problem. The analysis is performed by applying an artificial intelligence (AI) algorithm that is trained by using historical data that relates to incidents and failures associated with the system.