Dell products l.p. (20240127300). INFERENCE OF RISK DISTRIBUTIONS simplified abstract
Contents
- 1 INFERENCE OF RISK DISTRIBUTIONS
INFERENCE OF RISK DISTRIBUTIONS
Organization Name
Inventor(s)
Ofir Ezrielev of Be'er Sheva (IL)
Amihai Savir of Newton MA (US)
Noga Gershon of Be'er Sheva (IL)
INFERENCE OF RISK DISTRIBUTIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240127300 titled 'INFERENCE OF RISK DISTRIBUTIONS
Simplified Explanation
The patent application describes a system that can fit an artificial intelligence risk model to data based on labeled training data to predict maintenance costs for products.
- The system uses labeled training data with features of users and products, along with corresponding maintenance cost labels, to create a tree model that can differentiate between groups of data with different maintenance cost distributions.
- When a feature of a user and a product are inputted into the fitted model, the system can produce an output indicating a predicted maintenance cost distribution.
Potential Applications
This technology could be applied in various industries such as manufacturing, retail, and service providers to predict maintenance costs for products and optimize budgeting and resource allocation.
Problems Solved
This technology helps in accurately predicting maintenance costs for products, allowing companies to plan and budget effectively, reduce unexpected expenses, and improve overall operational efficiency.
Benefits
The system provides a data-driven approach to predicting maintenance costs, leading to better decision-making, cost savings, and improved maintenance strategies for businesses.
Potential Commercial Applications
- Predictive maintenance software for manufacturing companies
- Budget optimization tools for retail chains
- Service management platforms for maintenance service providers
Possible Prior Art
One possible prior art could be predictive maintenance software that uses machine learning algorithms to forecast equipment failures and maintenance needs based on historical data and patterns.
What are the limitations of the system described in the patent application?
The patent application does not mention any potential limitations or challenges that may arise when implementing the system in real-world scenarios.
How does the system handle data privacy and security concerns?
The patent application does not provide information on how the system addresses data privacy and security concerns related to the collection and use of user and product data for predicting maintenance costs.
Original Abstract Submitted
a system can fit an artificial intelligence risk model to data based on labeled training data to produce a fitted model, wherein the labeled training data comprises respective features of users and products, and corresponding labels of respective maintenance costs applicable to the products, and wherein the fitted model comprises a tree model that is configured to differentiate between groups of the data with differing maintenance cost distributions. the system can, in response to applying a first input to the fitted model, produce an output that indicates a predicted maintenance cost distribution, wherein the first input comprises a feature of a user of the users and a product of the products.
