Device Health Score Optimization via Cognitive Analysis
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Solution Overview
Problem
Conventional asset management systems rely on time-based lifecycle replacements for computer devices, which are inefficient as devices degrade at different rates and perform varying roles, leading to unnecessary replacements and financial decisions made without considering actual operational performance.
Innovation Solution
A system that utilizes cognitive analysis and machine learning to determine a device health score based on structured and unstructured data, recommending optimal refresh cycles by analyzing device performance, user persona, and support tickets, allowing for customizable weighting and prioritization of replacement decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If time-based lifecycle replacement is used for computer devices, then device replacement is simplified and standardized, but unnecessary replacements occur and costs increase
Solution Approach 1:
The patent changes the replacement decision parameter from fixed time-based criteria to dynamic health score-based criteria. The system continuously monitors multiple device parameters (performance metrics, error rates, user feedback) and calculates a composite health score that dynamically determines when replacement is actually needed, rather than following a predetermined time schedule.
Solution Approach 2:
The device effectively monitors its own health status through integrated sensors and software that track performance degradation, errors, and usage patterns. This self-diagnosis capability allows the system to automatically identify when a device should be replaced without requiring external assessment, enabling more precise and cost-effective replacement timing.
2Device complexity
If devices are replaced at fixed time intervals, then inventory management is simplified, but device performance and user productivity are not optimized
Solution Approach 1:
The system implements continuous feedback loops where device health data, performance metrics, and user productivity information are constantly collected and analyzed. This feedback mechanism allows the inventory management system to adapt replacement schedules based on actual device condition and impact on productivity, rather than following rigid time-based protocols.
Solution Approach 2:
The patent transforms the static, fixed-interval replacement schedule into a dynamic system that continuously adjusts replacement timing based on real-time device health assessment. The health score calculation incorporates multiple changing variables including performance degradation rates, error frequencies, and user feedback, allowing the system to optimize replacement timing for each individual device based on its actual condition.
3Measurement precision
If cognitive analysis of unstructured data is implemented, then device health assessment accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent employs a multi-functional data processing platform that handles diverse data types (structured logs, unstructured user feedback, sensor data) through a unified cognitive analysis engine. This universal system uses natural language processing, machine learning, and pattern recognition capabilities to analyze all data sources simultaneously, extracting health indicators from multiple sources through a single integrated process rather than separate specialized systems.
Solution Approach 2:
The system creates a composite health score by integrating multiple data sources and analysis methods. The health assessment combines structured performance metrics with unstructured user feedback, error logs, and sensor data, weighting each component based on its reliability and relevance. This composite approach synthesizes information from heterogeneous sources into a single actionable health indicator, managing complexity through integration rather than separation.
Data Source
AI summary
Approaches for device refresh determinations utilizing cognitive, machine learning, and predictive techniques are provided. A computer-implemented method includes: obtaining, by a computer device, structured data associated with at least one user device; obtaining, by the computer device, unstructured data associated with the at least one user device; classifying, by the computer device, the unstructured data into categories; determining, by the computer device, a device health score for the at least one user device based on the structured data and the classified unstructured data; and generating, by the computer device, a user interface that displays the device health score.