Automated Parameterized Modeling and Scoring for Early Analysis
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Solution Overview
Problem
Conventional project design planning systems require detailed, specific parameter information that is often unavailable until later stages, lack intelligence and automation, and fail to leverage historical project data for efficient design analysis.
Innovation Solution
An automated parameterized modeling and scoring intelligence system that includes a server device with a processor, memory, and network interface, utilizing a database to store historical project data and enabling intelligent, automated parameter modeling and scoring, capable of extending parameters during processing and integrating with client/server environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional project design planning methods are used, then detailed design analysis can be performed, but significant time and manual user effort are required
Solution Approach 1:
The system performs preliminary actions by automatically generating multiple design alternatives and pre-analyzing them against historical project data before the user makes final decisions. The automated parameterized modeling creates initial designs with varying parameters, and the scoring system pre-evaluates them using historical comparisons, so that when users review designs, the most promising options are already identified and analyzed.
Solution Approach 2:
The system uses historical project data as copies of past successful designs to inform current design decisions. By storing and comparing against historical projects with known outcomes, the system leverages past experiences without requiring manual re-analysis of historical cases, thus maintaining measurement precision while reducing time investment.
2Measurement precision
If conventional solutions are used, then design review can be performed, but detailed specific parameter information is required which may not be available until later stages
Solution Approach 1:
The system dynamically adapts its parameter requirements based on the project stage and available information. It uses parameterized modeling where parameters can be defined, modified, and refined as more information becomes available. The scoring system adjusts its analysis depth based on the completeness of parameter data, allowing meaningful design review even with limited initial parameters while maintaining the ability to incorporate detailed parameters later for more precise analysis.
Solution Approach 2:
The system employs parameter changes by allowing design parameters to be defined at different levels of detail and modified as the project progresses. The automated modeling generates designs with placeholder parameters that can be refined later, and the scoring system evaluates designs based on available parameters while flagging areas where additional parameter detail would improve analysis accuracy.
3Loss of information
If conventional solutions are used, then design analysis can be performed, but historical project data is not leveraged for recognizing similarities
Solution Approach 1:
The system implements feedback by continuously comparing current design parameters against historical project data and using the results to refine design recommendations. The scoring system provides feedback on how current designs compare to historical successes and failures, allowing the system to learn from past outcomes and improve future design suggestions without requiring complex manual analysis of historical patterns.
Solution Approach 2:
The system performs self-service by automatically selecting and comparing relevant historical projects based on parameter similarity, eliminating the need for manual historical data analysis. The automated parameterized modeling and scoring system independently identifies patterns in historical data and applies them to current designs, leveraging historical information through self-directed analysis rather than requiring external expertise to interpret historical patterns.
4Measurement precision
If conventional solutions are used, then parameter modeling can be performed, but manual user intervention is required at each step
Solution Approach 1:
The system performs self-service by automatically executing the complete parameter modeling and scoring process without requiring manual user intervention at each step. Users define high-level design parameters and objectives, then the automated system handles parameter refinement, generates design alternatives, performs historical comparisons, and produces scored recommendations, maintaining measurement precision through automated consistent application of scoring criteria while dramatically improving ease of operation.
Solution Approach 2:
The system replaces manual mechanical processes of parameter modeling and analysis with automated computational processes. Instead of manual parameter adjustment and historical comparison, the system uses automated parameterized modeling algorithms and computational scoring methods that consistently apply mathematical models to evaluate designs, maintaining precision while eliminating repetitive manual operations.
5Reliability
If conventional solutions are used, then design planning can be performed, but late-stage design changes occur due to previously unconsidered options
Solution Approach 1:
The system performs preliminary action by generating and evaluating multiple design alternatives early in the process, including options that might be overlooked in conventional sequential design. The automated scoring system pre-identifies potential issues and compares designs against historical data before commitments are made, reducing the likelihood of late-stage changes while maintaining productivity through parallel evaluation of multiple possibilities rather than sequential single-path design.
Data Source
AI summary
An automated parameterized modeling and scoring intelligence system includes a parameterized score estimation software tool and a parameterized score optimization software tool. The parameterized score estimation software tool processes design metrics associated with a current project according to historical project data selected based on a similarity with at least some of the design metrics to determine a score estimation for the current project. The parameterized score optimization software tool processes the score estimation based on external application data retrieved from an external application to determine an expected yield for the current project. A user of the system may iterate against the score estimation or the expected yield by changing one or more of the parameters used to determine same. The iteration may result in a score estimation or expected yield different from the initial versions thereof, such as to identify an optimal design for the current project.