Completeness Graph Debugging with UI Checklist Rules
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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 involving builder input to define completeness graphs through user interfaces, using fields, indicators, and modifiers to specify input conditions, allowing the knowledge engine to check for completeness efficiently, reducing processing and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional methods are used to define completeness graphs, then comprehensive coverage of all input combinations is achieved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent segments the completeness graph definition into modular components: fields, indicators, and modifiers. Each field can be independently configured with specific indicators (required, optional, conditional) and modifiers (default values, validation rules). This segmentation allows the system to process only relevant portions of the graph rather than analyzing every possible input combination, significantly reducing processing time while maintaining accuracy.
Solution Approach 2:
The patent introduces parameterized configuration where completeness graph definitions use variable parameters such as field names, indicator types, and modifier values that can be dynamically adjusted. By changing parameters at the configuration level rather than hardcoding all possibilities, the system achieves comprehensive coverage for relevant cases without processing irrelevant combinations, thus reducing overall processing time.
2Reliability
If conventional methods are used to define completeness graphs, then all possible input combinations are accounted for, but memory requirements and processing power increase
Solution Approach 1:
The patent extracts only the essential information needed for completeness verification from the full set of possible input combinations. By taking out and storing only the critical field-indicator-modifier relationships in the completeness graph data structure, the system reduces memory requirements while maintaining the ability to verify completeness accurately. Unrelevant combinations are discarded rather than stored and processed.
Solution Approach 2:
The patent applies partial action by processing only the necessary portions of the completeness graph during execution. Instead of evaluating all possible input combinations, the system processes only the fields and conditions actually present in the graph definition, achieving sufficient verification accuracy without the computational overhead of exhaustive analysis.
3Manufacturing precision
If conventional coding methods are used to define completeness graphs, then precise control over all input conditions is achieved, but ease of operation and user accessibility decrease
Solution Approach 1:
The patent introduces an intermediary layer between the user and the underlying code complexity. The user interface presents simplified configurations for defining completeness graphs, where users can specify fields, select from predefined indicators (required, optional, conditional), and set modifiers without writing code. This intermediary abstraction maintains precise control over input conditions while making the system accessible to non-programming users.
Solution Approach 2:
The system provides self-service functionality through automated generation of completeness graph code from user-friendly configurations. When users define their requirements through the simplified interface, the system automatically translates these into executable completeness graph definitions, eliminating the need for users to directly handle complex coding while maintaining precise control over the generated logic.
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.