Object affinity scoring for digital service search
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Solution Overview
Problem
Conventional search techniques for digital services face inefficiencies in locating related objects due to the vast number of objects available, leading to computational inefficiencies and lack of control for object providers in defining object relationships.
Innovation Solution
An object affinity determination and scoring system that allows object providers to generate affinity rules and train machine-learning models to quantify the compatibility of objects, enabling more precise search results and user interaction to determine object affinities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional search techniques are used to locate objects in digital services, then the system can handle a vast number of objects, but user efficiency in locating objects of interest deteriorates and computational inefficiencies increase
Solution Approach 1:
The patent replaces conventional mechanical search techniques with an affinity-based scoring system that uses machine learning models to automatically determine object relationships. This substitution enables the system to efficiently handle vast numbers of objects by computing affinity scores rather than relying on manual or conventional search methods, thereby improving user efficiency while maintaining the ability to manage large object quantities
Solution Approach 2:
The patent introduces an affinity scoring module as an intermediary between objects and search results. This module computes affinity scores that quantify the compatibility between objects, serving as a mediator that improves search efficiency. The affinity scores act as an intermediate representation that enables faster and more accurate object location compared to conventional search techniques
2Device complexity
If conventional search techniques are used, then the system can operate with simple search logic, but computational inefficiencies and power consumption increase
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing affinity scores for object pairs in advance. This allows the search system to quickly retrieve pre-computed affinity scores rather than performing complex computations during actual search operations, thereby reducing computational inefficiencies and power consumption while maintaining simple search logic at query time
3Adaptability or versatility
If object providers want control over search results, then they can define affinity rules, but the system complexity increases
Solution Approach 1:
The patent implements feedback mechanisms that allow object providers to define affinity rules and receive feedback on how these rules affect search results. The system processes provider-defined affinity rules through the affinity scoring module, enabling control over search results while managing system complexity through structured feedback loops between providers and the search system
Data Source
AI summary
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.