Module Completeness Graphs with Interactive Checklist Validation
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Solution Overview
Problem
Conventional methods for defining and modifying completeness graphs in knowledge engines are resource-intensive, requiring extensive time, computing power, and deep programming knowledge, leading to inefficient processing and memory usage.
Innovation Solution
A method that allows users to define completeness graphs through builder input referencing fields and conditions, using indicators and modifiers, which reduces processing requirements by only checking for specified conditions rather than all possible input combinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional methods are used to define completeness graphs by accounting for every possible input combination, then completeness verification accuracy is improved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent segments the completeness verification process by dividing input fields into groups and using hierarchical checking. Instead of evaluating all possible input combinations simultaneously, the system processes fields in organized segments through a tree structure, where parent nodes represent field groups and child nodes represent individual fields. This segmentation reduces the computational complexity from exponential to linear time complexity.
Solution Approach 2:
The patent implements partial action by using heuristics and rules of thumb to check only the most critical or likely incomplete fields first. The system performs completeness verification on a subset of fields based on priority rules, rather than exhaustively checking every single field in every possible combination. This approach achieves sufficient verification accuracy with significantly reduced processing time.
2Reliability
If conventional methods are used to define completeness graphs, then thorough input validation is achieved, but computing power and memory requirements increase
Solution Approach 1:
The patent segments the input validation process into hierarchical levels where field groups are validated at parent nodes and individual fields at child nodes. This segmentation allows the system to validate inputs in an organized manner, processing only necessary portions of the data structure and avoiding redundant computations across all possible input combinations, thereby reducing memory and computational requirements.
Solution Approach 2:
The patent performs preliminary actions by pre-defining rules, heuristics, and validation criteria for field groups before execution. These preliminary configurations enable the system to quickly determine which fields require validation and in what order, avoiding the need to compute validation requirements dynamically for every possible input scenario, thus reducing computational overhead.
3Reliability
If users manually account for every possible input combination in completeness graphs, then verification completeness is improved, but ease of operation deteriorates due to requiring deep programming knowledge
Solution Approach 1:
The patent implements self-service by enabling the system to automatically generate and execute completeness verification rules based on pre-configured heuristics and field metadata. Users simply define high-level field groups and basic validation criteria, and the system automatically derives the verification logic, eliminating the need for users to manually program complex validation rules for every possible input combination. This makes the system accessible to non-programmers while maintaining verification thoroughness.
Solution Approach 2:
The patent creates a universal framework for completeness verification that works across different field types, data structures, and validation scenarios through a single hierarchical tree approach. The same basic mechanism handles diverse validation requirements by applying generic rules and heuristics adapted to specific contexts, eliminating the need for specialized programming knowledge for each case while maintaining comprehensive verification capabilities.
4Measurement precision
If more code is written to account for all input combinations, then completeness checking accuracy is improved, but processing requirements and power consumption increase
Solution Approach 1:
The patent segments the completeness checking logic into hierarchical tree structures where validation rules are distributed across parent and child nodes. This segmentation eliminates the need for monolithic code that processes all input combinations simultaneously, replacing it with modular, incremental processing that checks fields in an organized sequence, reducing the computational power required while maintaining checking accuracy.
Solution Approach 2:
The patent changes the parameter of code execution from exhaustive enumeration of all input combinations to selective processing based on field priority, completeness rules, and heuristic evaluation. By dynamically adjusting which fields are checked and in what order based on predefined parameters, the system achieves high checking accuracy with significantly reduced processing power requirements compared to uniform exhaustive checking.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for encoding rules defining a completeness of input, including receiving a first input comprising one or more tuples, wherein a tuple of the one or more tuples comprises one or more fields associated with an operation, one or more indicators, and one or more modifiers; receiving a second input associated with the one or more tuples; providing, to a knowledge engine, the first input and the second input; receiving, from the knowledge engine, a result based on the first input and the second input; determining, based on the result, a first symbol associated with a first tuple of the one or more tuples; and displaying the first symbol, wherein the first symbol indicates whether the first tuple is complete.


