US Patent Application 18324321. AGILE ITERATION FOR DATA MINING USING ARTIFICIAL INTELLIGENCE simplified abstract
Contents
AGILE ITERATION FOR DATA MINING USING ARTIFICIAL INTELLIGENCE
Organization Name
Mastercard International Incorporated
Inventor(s)
Siddhesh Dongare of Kalyan (IN)
AGILE ITERATION FOR DATA MINING USING ARTIFICIAL INTELLIGENCE - A simplified explanation of the abstract
This abstract first appeared for US patent application 18324321 titled 'AGILE ITERATION FOR DATA MINING USING ARTIFICIAL INTELLIGENCE
Simplified Explanation
- The patent application describes techniques for training an entity resolution model. - The techniques involve receiving input from a user, including a minimum viable data product (MVDP) scope, a product scope, and a data mining scope. - Based on this input, a data mining goal is determined. - One or more proof of concept (PoC) models are defined based on the data mining goal. - One of the PoC models is selected for training. - The selected PoC model is iteratively trained to generate a trained deep learning model. - The trained deep learning model is then tested and validated against a predefined achievable loss metric. - A sample labelled dataset is used for testing the trained model. - The techniques aim to improve the accuracy and effectiveness of entity resolution models.
Original Abstract Submitted
Techniques for training an entity resolution model are presented. The techniques include receiving a minimum viable data product (MVDP) scope, a product scope, and a data mining scope from a user. A data mining goal is determined based on the MVDP scope, product scope, and data mining scope. One or more proof of concept (PoC) models are defined based on the data mining goal, and one of the PoC models is selected for training. A trained deep learning model is generated by iteratively training the selected PoC model. The trained deep learning model is then tested and validated against a predefined achievable loss metric using a sample labelled dataset for testing.
