beta
/User Search Category Predictor
Abstract

Described herein are embodiments for improving search engine results of listings of For Sale Objects (FSOs). A search engine may be improved by implementing rules that resolve ambiguity between listings for different (FSOs) that match the same search inputs. An unsupervised machine learning module may evaluate candidate rules and identify improvements that may not be obvious to a human evaluator. An ecommerce site that combines the improved search engine with the unsupervised machine learning module may dynamically evaluate search results using different candidate rules and iteratively improve search results.

Full Text

What is claimed is:

Described herein are embodiments for improving search engine results of listings of For Sale Objects (FSOs). A search engine may be improved by implementing rules that resolve ambiguity between listings for different (FSOs) that match the same search inputs. An unsupervised machine learning module may evaluate candidate rules and identify improvements that may not be obvious to a human evaluator. An ecommerce site that combines the improved search engine with the unsupervised machine learning module may dynamically evaluate search results using different candidate rules and iteratively improve search results.
Timeline
Filed
02/25/2026
Published
07/02/2026
Granted
Not Available
IPC Codes(3)
G06Q 30/0201:Market modelling; Market analysis; Collecting market data
G06F 16/957:Browsing optimisation, e.g. caching or content distillation
G06F 17/18:for evaluating statistical data