Query Evaluation System for Natural Language Concept Testing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face challenges in quantitatively assessing the impact of high-level concepts expressed in natural language, particularly in identifying metrics and performing operations to evaluate these concepts without causing downtime or reconfiguration of systems.
Innovation Solution
A query evaluation system that leverages natural language processing and machine learning to decode user queries, identify quantitative evaluation criteria, and perform simulations to test the performance of concepts without actual deployment or reconfiguration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to assess the impact of high-level concepts, then quantitative evaluation can be achieved, but system downtime and reconfiguration are required
Solution Approach 1:
The system performs preliminary analysis by parsing natural language concepts into structured representations and identifying relevant metrics before actual system changes are made. This allows evaluation to be prepared in advance, so when changes are deployed, the assessment is already underway or can be performed without interruption.
Solution Approach 2:
The system creates virtual copies or simulated representations of the system state to perform assessments on these copies rather than on the actual running system. This allows quantitative evaluation to be performed on duplicate data structures without affecting the primary system's operation, eliminating downtime.
2Measurement precision
If actual deployment and reconfiguration are performed to test concepts, then performance assessment is possible, but computational costs and complexity increase
Solution Approach 1:
The system introduces an intermediary layer consisting of parsing modules, concept representation structures, and metric identification components that mediate between natural language input and system configuration. This intermediary layer translates high-level concepts into actionable parameters without requiring direct manual reconfiguration, reducing complexity.
Solution Approach 2:
The system replaces manual mechanical reconfiguration processes with automated software-based concept parsing and parameter extraction. Instead of physically or manually reconfiguring systems to test concepts, the system uses computational methods to interpret natural language and automatically derive the necessary configuration changes and performance metrics.
3Measurement precision
If users manually identify metrics and perform operations to evaluate concepts, then assessment can be conducted, but user expertise and time requirements increase
Solution Approach 1:
The system enables self-service by automatically performing metric identification and evaluation operations that would otherwise require user expertise. The concept parsing module autonomously extracts relevant parameters from natural language input, and the system automatically queries or calculates the necessary metrics without requiring users to manually identify or compute them.
Solution Approach 2:
The system implements feedback mechanisms where the results of concept evaluation are automatically fed back to users in an interpretable format. This closed-loop feedback allows users to see the impact of their high-level concepts without needing to understand the complex intermediate steps of metric identification and calculation, reducing the perceived effort and expertise required.
Data Source
AI summary
Methods and systems are described herein for a system that enables individual users or entities to assess high-level concepts expressed in natural language by identifying quantitative evaluation criteria for evaluating the concept. For example, a query evaluation system is provided herein that receives a user's query including natural language, e.g., indicative of a higher-level concept or idea to be deployed. The system may identify quantitative evaluation criteria for evaluating the concept, perform back-testing (e.g., to see how a particular strategy would have performed in the past) and allow the user to create a specific investment portfolio that tracks the original intent of the user. The concept may be tested, and its performance evaluated before deploying it to a user portfolio.