18516673. SIMPLISTIC MACHINE LEARNING MODEL GENERATION TOOL FOR PREDICTIVE DATA ANALYTICS simplified abstract (STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY)
SIMPLISTIC MACHINE LEARNING MODEL GENERATION TOOL FOR PREDICTIVE DATA ANALYTICS
Organization Name
STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY
Inventor(s)
Suresh B. Gajendran of Richardson TX (US)
Mahesh Chandrappa of Bloomington IL (US)
Mark A. Dickneite of Bloomington IL (US)
Charles T. Fiala of Normal IL (US)
Rashid Zaheer of Bloomington IL (US)
SIMPLISTIC MACHINE LEARNING MODEL GENERATION TOOL FOR PREDICTIVE DATA ANALYTICS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18516673 titled 'SIMPLISTIC MACHINE LEARNING MODEL GENERATION TOOL FOR PREDICTIVE DATA ANALYTICS
Simplified Explanation
The patent application describes systems and methods for predictive data analytics, including generating a guided user interface, obtaining a dataset from a database, determining characteristics of data objects, identifying a subset of the dataset, selecting machine learning algorithms, training a machine learning model, and implementing the trained model in a cloud server for distribution to client devices.
- Explanation of the patent:
- Generating a guided user interface for user operations - Obtaining a dataset from a database - Determining characteristics of data objects - Identifying a subset of the dataset based on characteristics - Selecting machine learning algorithms - Training a machine learning model with the subset of data - Implementing the trained model in a cloud server for distribution to client devices
- Potential applications of this technology:
- Predictive analytics in various industries such as finance, healthcare, and marketing - Personalized recommendations for e-commerce platforms - Fraud detection in financial transactions
- Problems solved by this technology:
- Efficient data analysis and prediction - Automation of machine learning model training - Scalability for distributing trained models to multiple devices
- Benefits of this technology:
- Improved decision-making based on predictive analytics - Cost-effective data analysis solutions - Enhanced user experience with personalized recommendations
- Potential commercial applications of this technology:
- Data analytics software for businesses - Predictive maintenance solutions for industrial equipment - Customer behavior analysis tools for marketing companies
- Possible prior art:
- Existing predictive analytics software - Machine learning model training platforms
- Unanswered questions:
1. How does the guided user interface enhance the user experience in data analytics? 2. What specific machine learning algorithms are commonly used in training the models for predictive data analytics?
Original Abstract Submitted
Systems and methods for predictive data analytics are provided. A method comprises generating a guided user interface (GUI) that guides one or more user operations on the user interface including: obtaining, from a database, a dataset including a plurality of data objects; determining one or more characteristics associated with a first data object of the plurality of data objects; identifying a subset of the dataset based at least in part on the one or more characteristics; selecting at least one machine learning algorithm; and training a machine learning (ML) model with respect to the first data object using the subset of the dataset and the at least one machine learning algorithm to generate a trained ML model; implementing the trained ML model with respect to the first data object in a cloud server to enable distributing the trained ML model to a plurality of client device via a network.