Excitation Element Modeling from Augmented Evaluation Metrics
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Solution Overview
Problem
Data used to improve systems is often incomplete and unreliable, especially when user input is involved, leading to inefficiencies in system improvement processes.
Innovation Solution
An apparatus and method that utilizes a processor and memory to generate a representation data structure, determine evaluation metrics, augment them, and train an excitation element machine learning model to provide an excitation element through a user interface, enhancing data reliability and completeness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If data is collected from a single source (the system to be improved), then the data collection process is simple, but the data completeness and reliability deteriorate
Solution Approach 1:
The patent introduces an intermediary data collection mechanism that gathers additional data from external sources (social media, search engines, public databases) to supplement the primary system's data. This intermediary layer enriches the evaluation metrics with information from multiple independent sources, thereby improving data reliability without significantly complicating the core collection process
Solution Approach 2:
The system implements multi-functional data collection capabilities by integrating multiple data sources (system logs, user feedback, social media, search engine data, public databases) into a unified evaluation framework. This universal approach allows the same evaluation metric to be assessed across diverse data sources, enhancing both completeness and reliability
2Quantity of substance
If users provide input data, then the system can gather additional information, but the data completeness deteriorates because users answer only a subset of required questions
Solution Approach 1:
The patent employs intermediary data sources (social media platforms, search engines, public databases) to fill in the information gaps left by user-submitted data. These intermediaries automatically gather and structure additional information about the system and its context, compensating for the incomplete user responses while maintaining data relevance
Solution Approach 2:
The system implements a feedback mechanism where initial user inputs trigger automated data collection from external sources, which then feeds back into the evaluation process. This iterative feedback loop continuously enriches the dataset, transforming partial user inputs into comprehensive evaluation metrics through systematic supplementation
Data Source
AI summary
Described herein is an apparatus and method for determining an excitation element. In some embodiments, an apparatus may include a computing device configured to, using a representation generator, generate a representation data structure; determine a plurality of evaluation metrics as a function of the representation data structure; generate an augmented plurality of evaluation metrics by interpolating into the plurality of evaluation metrics an additional evaluation metric; determine an excitation element by training an excitation element machine learning model on a training dataset including a plurality of example evaluation metrics as inputs correlated to a plurality of example excitation elements as outputs; and generating an excitation element as a function of the augmented plurality of evaluation metrics using the trained excitation element machine learning model; and display the excitation element to a user through a user interface at a display device.