Compiled AI Model Generation via Data Cleansing
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Solution Overview
Problem
Existing technologies face challenges in accurately and efficiently classifying diverse data sets to generate a compiled artificial intelligence (AI) model.
Innovation Solution
An apparatus and method that utilize a processor to receive data sets from user devices, convert them using a machine-learning model into a cleansed data format, and generate an accumulated model as training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data sets from multiple sources are processed to generate a compiled AI model, then the model robustness and accuracy are improved, but the data processing complexity and time consumption increase
Solution Approach 1:
The patent segments the data processing workflow into distinct modules: data reception from multiple user devices, data conversion using machine-learning models, data cleansing, and accumulated model generation. This segmentation allows each component to handle specific tasks independently, reducing overall processing complexity while maintaining model robustness through comprehensive multi-source data integration
Solution Approach 2:
The patent applies preliminary action by converting data sets to a standardized format and cleansing them before generating the compiled AI model. The machine-learning model performs preliminary data conversion and cleansing operations, preparing the data in advance for more efficient model generation and reducing the computational burden during the final compilation stage
2Measurement precision
If data sets are converted and cleansed using machine-learning models, then data accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent implements self-service by using the machine-learning model to automatically perform data conversion and cleansing operations without manual intervention. The system autonomously processes raw data from multiple sources, converting it to standardized formats and cleansing it, which maintains high data accuracy while reducing the time cost associated with manual data preparation
Solution Approach 2:
The patent applies parameter changes by transforming raw data sets into standardized cleansed data formats through the machine-learning model. This parameter transformation optimizes the data structure and quality, improving measurement precision while the automated nature of the transformation reduces processing time compared to manual methods
Data Source
AI summary
An apparatus and method for generating a compiled artificial intelligence (AI) model. The apparatus incudes a processor that is configured to receive data sets from user devices. The processor is further configured to convert the data sets using a machine-learning model into a cleansed data format and generate an accumulated model using the converted data sets as training data.