Completeness Graph Definition via Interactive Interface Checklists
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for defining and modifying completeness graphs in knowledge engines are resource-intensive, requiring extensive coding knowledge and time, and result in high processing requirements due to the need to account for millions of possible input combinations, making them inefficient for large and complex systems.
Innovation Solution
A method that uses a user interface to define completeness graphs through fields, indicators, and modifiers, allowing users to specify which input is required based on conditions, reducing the need to check every possible input combination, thereby decreasing processing requirements and memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to define completeness graphs by accounting for every possible input combination, then measurement precision and reliability are improved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent segments the completeness verification process into two distinct phases: (1) a comprehensive static analysis phase that builds a completeness graph modeling all possible input combinations, and (2) an efficient dynamic execution phase that uses the pre-built graph to quickly determine completeness. This segmentation allows the system to perform thorough verification once during graph construction, then reuse that structure for rapid subsequent verifications without reprocessing all input combinations each time.
Solution Approach 2:
The patent performs preliminary action by pre-computing the completeness graph that encompasses all possible input combinations before actual completeness checks are needed. The graph is constructed in advance with all nodes representing possible states and edges representing transitions, so that during runtime, the system only needs to traverse the pre-built graph rather than generating and analyzing all input combinations from scratch each time.
2Reliability
If conventional methods are used to define completeness graphs, then reliability is improved, but device complexity and processing power requirements increase
Solution Approach 1:
The patent divides the computational workload into two segments: the complex graph construction phase performed once during system initialization or module definition, and the simple graph traversal phase performed during runtime. The complexity of modeling all input combinations is confined to the initial segmentation, while operational complexity is minimized by leveraging the pre-structured graph for efficient completeness determination.
Solution Approach 2:
The patent performs the computationally intensive action of modeling all possible input combinations in advance during graph construction. By pre-computing the completeness graph with all possible states and transitions, the system eliminates the need for complex real-time analysis during execution, thereby reducing operational device complexity while maintaining comprehensive verification capability.
3Measurement precision
If conventional methods are used to account for millions of input combinations, then measurement precision is improved, but energy consumption increases
Solution Approach 1:
The patent segments energy-intensive operations from energy-light operations by performing comprehensive analysis once during graph construction and simple lookups during execution. The high-energy activity of modeling all input combinations is isolated to the initial graph building phase, while subsequent completeness checks consume minimal energy by traversing the pre-built structure, thereby decoupling measurement precision from continuous high energy consumption.
Solution Approach 2:
The patent performs the energy-intensive preliminary action of comprehensively modeling all input combinations before actual use. By pre-computing the completeness graph with exhaustive coverage during initialization, the system pays the energy cost once upfront, then achieves accurate completeness verification with minimal additional energy expenditure during runtime operations.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for defining a completeness of input, including receiving a first input, comprising: a set of fields associated with an operation; a set of indicators, wherein at least one indicator in the set of indicators is associated with a field in the set of fields; and a set of modifiers, wherein each modifier is associated with at least one indicator in the set of indicators, wherein a combination of the set of indicators and the set of modifiers indicates fields of the set of fields that require input to execute the operation; receiving a second input associated with the set of indicators and the set of modifiers; receiving, from the knowledge engine, a result based on the second input; and determining whether the second input contains required input to the fields that require input based on the result.