Dell products l.p. (20240135283). MULTI-LAYER MICRO MODEL ANALYTICS FRAMEWORK IN INFORMATION PROCESSING SYSTEM simplified abstract
Contents
- 1 MULTI-LAYER MICRO MODEL ANALYTICS FRAMEWORK IN INFORMATION PROCESSING SYSTEM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MULTI-LAYER MICRO MODEL ANALYTICS FRAMEWORK IN INFORMATION PROCESSING SYSTEM - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
MULTI-LAYER MICRO MODEL ANALYTICS FRAMEWORK IN INFORMATION PROCESSING SYSTEM
Organization Name
Inventor(s)
Sujit Kumar Sahoo of Bangalore (IN)
Dhilip S. Kumar of Bangalore (IN)
Ajay Maikhuri of Bangalore (IN)
Devaraj Marappa of Krishnagiri District (IN)
MULTI-LAYER MICRO MODEL ANALYTICS FRAMEWORK IN INFORMATION PROCESSING SYSTEM - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135283 titled 'MULTI-LAYER MICRO MODEL ANALYTICS FRAMEWORK IN INFORMATION PROCESSING SYSTEM
Simplified Explanation
The patent application describes a multi-layer micro model analytics framework for analyzing data, where micro models are built for different stages of a process and then assembled for analysis.
- The method involves building two or more micro models, each consisting of a user interaction layer and a predictive learning layer.
- The micro models are trained based on input to the user interaction layer and data accessible by the predictive learning layer.
- The assembled micro models are used to perform analysis for the given process, such as a new product introduction process.
Potential Applications
This technology can be applied in various industries for analyzing different processes, such as supply chain management, customer relationship management, and financial forecasting.
Problems Solved
This technology helps in improving decision-making by providing insights and predictions at different stages of a process, leading to more efficient and effective outcomes.
Benefits
The benefits of this technology include enhanced data analysis capabilities, improved process optimization, and better understanding of complex systems.
Potential Commercial Applications
One potential commercial application of this technology could be in the field of healthcare for analyzing patient treatment processes and optimizing healthcare delivery.
Possible Prior Art
One possible prior art could be traditional data analytics methods that do not involve the use of multi-layer micro models for analyzing processes.
=== What are the specific industries where this technology can be most beneficial? The article does not specify the specific industries where this technology can be most beneficial.
=== How does this technology compare to existing data analytics frameworks? The article does not provide a comparison of this technology to existing data analytics frameworks.
Original Abstract Submitted
techniques are disclosed for a multi-layer micro model analytics framework for analyzing or otherwise processing data. for example, a method comprises building two or more micro models respectively for two or more stages of a given process, wherein each micro model of the two or more micro models comprises a user interaction layer and a predictive learning layer that coordinate to train the micro model based on input to the user interaction layer and data accessible by the predictive learning layer for the corresponding stage of the two or more stages of the given process. the method then assembles the two or more micro models to perform analysis for the given process. in one non-limiting example, the given process is a new product introduction process and each micro model is built and trained to perform analytics for a specific lifecycle stage of the process.
