Rule-Based Data Categorization Lookup Flow
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Solution Overview
Problem
Large databases often contain inconsistencies and require extensive processing time to categorize data, making it difficult to access and update information efficiently, which can impact user satisfaction.
Innovation Solution
Implementing a rule-based data categorization system with a lookup flow that prioritizes and validates rules, allowing for real-time categorization by traversing only relevant rules, thereby reducing processing time and increasing workflow efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rule-based data categorization is applied to large databases, then data categorization accuracy is improved, but processing time increases
Solution Approach 1:
The patent segments the rule evaluation process into a hierarchical structure where rules are organized in levels. The system evaluates rules at higher levels first and skips to lower levels only when necessary, dividing the rule evaluation workload into manageable segments that reduce overall processing time while maintaining categorization accuracy.
Solution Approach 2:
The patent implements preliminary validation of rules before they are applied to data categorization. The validation process checks rule consistency, applicability, and potential conflicts in advance, so that when rule-based categorization is executed, the system only needs to evaluate validated rules, reducing processing time while ensuring accuracy.
2Reliability
If all rules are applied to categorize data, then categorization completeness is improved, but system complexity increases
Solution Approach 1:
The patent extracts and separates the validation logic from the categorization logic. The validation module independently validates rules and returns validated rules to the categorization module, which then processes only the validated rules. This extraction reduces system complexity by isolating complex validation operations from the main categorization flow while maintaining completeness through systematic validation.
Solution Approach 2:
The patent introduces a validation module as an intermediary between rule definition and rule application. This intermediary component validates rules and filters out invalid or redundant rules before they reach the categorization process, reducing the complexity of the main system while ensuring that all necessary rules are included for complete categorization.
3Measurement precision
If manual data categorization is used, then data accuracy is improved, but productivity decreases
Solution Approach 1:
The patent implements self-service through automated rule-based categorization that validates and applies rules independently without human intervention. The system automatically validates rules for consistency and applicability, then applies them to categorize data at high speed, eliminating the need for manual categorization while maintaining accuracy through systematic validation of categorization rules.
Data Source
AI summary
Categorization of data may be performed using rule-based data categorization. Rules may be used to categorize the data into a limited number of categories for a temporary, ad hoc, or updatable process. The rules may be validated to make sure the rules are not ambiguous, contradictory, unattainable, or otherwise ineffective. The rules may include a priority value which may be used as a tie breaker when more than one rule applies to a record. To implement the rules, a lookup flow may be generated to enable rapid or quick traversal of applicable rules for a particular lookup while bypassing at least a portion of the rules. In some embodiments, a record (e.g., an item, etc.) may be categorized during a transaction process to determine a tax classification, a promotion, or another type of classification, which may then be used to complete the transaction.