Self-Learning Log Entry Classification System
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Solution Overview
Problem
Existing electronic data archival systems face challenges with inaccurate data classification, inefficient storage, and unorganized record keeping, making it difficult to meaningfully identify and classify large quantities of electronic data in a timely manner.
Innovation Solution
A self-learning analytical attribute and clustering segmentation system that uses machine-learning categorization methods, including text classifiers, multiclass classifiers, and combination classifiers to accurately categorize log entries by identifying datafields, metafields, and contexts, and continuously updates classifiers for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data archival systems are used to store large quantities of electronic data, then storage capacity is provided, but data classification accuracy deteriorates and organization efficiency worsens
Solution Approach 1:
The patent segments data classification into multiple hierarchical levels (e.g., primary classification, secondary classification, tertiary classification). Each level handles specific aspects of data organization, allowing the system to manage large volumes of data systematically while maintaining high classification accuracy and organization efficiency simultaneously
Solution Approach 2:
The patent implements preliminary data processing steps including data validation, normalization, and preliminary tagging before main classification. This preliminary action prepares data in advance, reducing the complexity of subsequent classification tasks and improving both accuracy and efficiency
2Productivity
If manual data classification methods are used, then flexibility in handling diverse data types is maintained, but classification speed and accuracy deteriorate
Solution Approach 1:
The patent incorporates feedback mechanisms where classification results are continuously evaluated and used to refine classification rules and algorithms. This feedback loop enables the system to learn from past classifications, improving accuracy over time while maintaining high processing speeds through optimized algorithms
Solution Approach 2:
The patent replaces manual mechanical classification processes with automated electronic classification systems using algorithms and machine learning models. This substitution dramatically increases classification speed while maintaining or improving accuracy through consistent, error-free automated decision-making
3Measurement precision
If comprehensive data analysis is performed to improve classification accuracy, then classification precision improves, but processing time increases
Solution Approach 1:
The patent applies partial analysis by selecting and applying only the most relevant analysis methods and data features for each specific classification task. Rather than performing exhaustive comprehensive analysis on all data, the system uses intelligent feature selection to achieve high accuracy with reduced processing time
Solution Approach 2:
The patent divides comprehensive data analysis into multiple parallel processing stages, each handling specific aspects of data evaluation. This segmentation allows the system to process different data dimensions simultaneously, maintaining thorough analysis while reducing overall processing time through parallel execution
Data Source
AI summary
A self-learning system for analytical attribute and clustering segmentation may be provided. A text classifier may identify a log description of a log entry in response to text of the log description being associated with indicators of a word model. A datafield classifier may generate a datafield metrics including an accuracy value of the attribute identifiers representing the datafield. A metafield classifier may generate a context metrics for the context of the log entry, the context metrics including an accuracy value of the attribute identifiers representing the metafields. A combination classifier may form a weighted classification set and select an attribute identifier as being representative of the datafield based on the weighted classification set. The combination classifier may further evaluate an attribute importance value of each attribute identifier, and select an attribute identifier having a top attribute importance value.