View source for 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==

[[:Category:STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY|STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY]]


[[Category:STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY]]

==Inventor(s)==

[[:Category:Suresh B. Gajendran of Richardson TX (US)|Suresh B. Gajendran of Richardson TX (US)]][[Category:Suresh B. Gajendran of Richardson TX (US)]]

[[:Category:Mahesh Chandrappa of Bloomington IL (US)|Mahesh Chandrappa of Bloomington IL (US)]][[Category:Mahesh Chandrappa of Bloomington IL (US)]]

[[:Category:Mark A. Dickneite of Bloomington IL (US)|Mark A. Dickneite of Bloomington IL (US)]][[Category:Mark A. Dickneite of Bloomington IL (US)]]

[[:Category:Charles T. Fiala of Normal IL (US)|Charles T. Fiala of Normal IL (US)]][[Category:Charles T. Fiala of Normal IL (US)]]

[[:Category:Rashid Zaheer of Bloomington IL (US)|Rashid Zaheer of Bloomington IL (US)]][[Category: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.



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