Selective Data Capture Containers for Issue Resolution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large entities face challenges in efficiently identifying and addressing system, network, or application issues due to the overwhelming volume of data from multiple sources, often containing irrelevant information that complicates troubleshooting and data interpretation.
Innovation Solution
A system that generates data containers specific to issues, using machine learning to evaluate and store relevant data, and executes translation layers to convert data into usable formats, enabling quick access and accurate interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is collected from multiple sources to evaluate system issues, then the quantity of available data increases, but the complexity of data management and interpretation increases
Solution Approach 1:
The patent segments data from multiple sources into distinct data containers, each associated with specific issues, systems, or applications. This segmentation organizes the overwhelming volume of data into manageable units that can be independently evaluated and processed, reducing the complexity of data management while preserving the quantity of available data.
Solution Approach 2:
The patent introduces data containers as intermediary structures between raw data sources and analysis processes. These containers act as mediators that receive, organize, and prepare data for evaluation, simplifying the interface between diverse data sources and the issue evaluation system.
2Loss of information
If all received data is stored for issue evaluation, then the completeness of information increases, but the time required to identify relevant data increases
Solution Approach 1:
The patent performs preliminary actions by pre-generating data containers based on machine learning data, historical data, and user experience before issues occur. This preliminary organization of data structures enables rapid identification and retrieval of relevant data when issues arise, reducing data identification time while maintaining information completeness.
Solution Approach 2:
The system uses machine learning to evaluate received data and provide feedback on whether data should be added to existing containers or preserved for further evaluation. This feedback mechanism enables intelligent filtering and routing of data, reducing the time to identify relevant information while preserving complete data sets for comprehensive issue evaluation.
3Loss of information
If data from unrelated applications and systems is included, then the comprehensiveness of data collection increases, but the accuracy of issue interpretation decreases
Solution Approach 1:
The patent segments data into issue-specific containers that are selectively associated with particular systems, applications, or issue types. This segmentation allows the system to maintain comprehensive data collection across multiple sources while ensuring that only relevant data is evaluated for each specific issue, thereby preserving interpretation accuracy.
Solution Approach 2:
The patent applies local quality by assigning different characteristics and associations to different data containers based on their specific issue contexts. Each container is tailored to hold data relevant to particular systems or issue types, ensuring that data quality and relevance are optimized locally for each evaluation context rather than treating all data uniformly.
4Adaptability or versatility
If data is collected in various formats from multiple sources, then the versatility of data collection increases, but the difficulty of data interpretation increases
Solution Approach 1:
The patent creates a universal data container structure that can accommodate data from multiple sources in various formats. These containers serve multiple functions: receiving diverse data formats, organizing data by issue type, and preparing data for analysis. This universal structure enables versatile data collection while simplifying interpretation through consistent organization and optional translation functions.
Data Source
AI summary
Systems for selective data capture and translation are provided. In some examples, a system, may receive data from one or more systems, networks, applications, devices, or the like. The data may include data associated with one or more issues occurring at the system, network, application, device, or the like. In some examples, a plurality of data containers may be generated. In some arrangements, each data container may be associated with a different issue, type of issue, system, application, or the like. The data containers may be generated in response to receiving data associated with an issue or may be pre-generated. In some arrangements, the received data may be evaluated (e.g., using machine learning) to determine whether it should be added to one or more data containers of the plurality of data containers. If so, the data may be added and, if not the data may be preserved and/or further evaluated to determine whether it should be added to a different data container.