OBJECT AFFINITY DETERMINATION AND SCORING SYSTEM

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OBJECT AFFINITY DETERMINATION AND SCORING SYSTEM - A simplified explanation of the abstract

This abstract first appeared for US patent application 18187864 titled 'OBJECT AFFINITY DETERMINATION AND SCORING SYSTEM

The abstract describes an object affinity determination and scoring system that helps object providers locate related objects through various methods such as rule generation, machine-learning model training, and user interface interaction.

The system supports the generation of affinity rules through a rule generation user interface.
It allows for the training and retraining of a machine-learning model to generate affinity scores.
The affinity scoring module provides a user interface with an input portion for users to determine the affinity of selected objects to each other.

Potential Applications: - Content recommendation systems - E-commerce product suggestions - Social media friend recommendations

Problems Solved: - Difficulty in locating related objects - Inefficient matching of objects - Lack of personalized recommendations

Benefits: - Improved object matching accuracy - Enhanced user experience - Increased user engagement

Commercial Applications: Title: Object Affinity Determination System for Enhanced User Experience This technology can be used in various industries such as e-commerce, social media, and content platforms to provide personalized recommendations and enhance user engagement.

Questions about Object Affinity Determination System: 1. How does the system generate affinity rules? The system generates affinity rules through interaction with a rule generation user interface, allowing object providers to define relationships between objects.

2. What is the role of the machine-learning model in determining affinity scores? The machine-learning model is used to train and retrain to generate affinity scores based on the relationships between objects, improving the accuracy of object matching.

Original Abstract Submitted

An object affinity determination and scoring system is described that is configured to support control by object providers in locating related objects. In a first example, an affinity system supports generation of affinity rules through interaction with a rule generation user interface. In a second example, the affinity system supports training and retraining of a machine-learning model to generate the affinity score. In a third example, the affinity scoring module supports output of a user interface having an input portion that supports user interaction to determine an affinity of selected objects to each other.