18047153. INFERENCE OF RISK DISTRIBUTIONS simplified abstract (Dell Products L.P.)

INFERENCE OF RISK DISTRIBUTIONS

Organization Name

Dell Products L.P.

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 18047153 titled 'INFERENCE OF RISK DISTRIBUTIONS

Simplified Explanation

The abstract describes a system that uses labeled training data to fit an artificial intelligence risk model to predict maintenance costs for different products based on user features.

• The system fits an artificial intelligence risk model to data based on labeled training data.
• The labeled training data includes user features, product features, and corresponding maintenance cost labels.
• The fitted model is a tree model that can differentiate between groups of data with different maintenance cost distributions.
• The system can predict maintenance cost distributions for products based on user and product features.

Potential Applications

This technology can be applied in various industries such as manufacturing, retail, and service providers to predict maintenance costs for different products and optimize resource allocation.

Problems Solved

1. Predicting maintenance costs accurately based on user and product features. 2. Optimizing resource allocation and budget planning for maintenance activities.

Benefits

1. Cost savings by accurately predicting maintenance costs. 2. Improved decision-making based on predicted maintenance cost distributions. 3. Enhanced efficiency in resource allocation for maintenance activities.

Potential Commercial Applications

Predictive maintenance software for manufacturing companies Maintenance cost optimization tools for retail chains Service provider resource allocation platforms

Possible Prior Art

One possible prior art could be predictive maintenance software that uses machine learning algorithms to forecast maintenance costs based on historical data and equipment features.

What are the limitations of the system described in the abstract?

The abstract does not mention the scalability of the system to handle large datasets and real-time predictions.

How does the system handle outliers in the training data?

The system's approach to handling outliers in the training data is not specified in the abstract.

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.