Intent-Driven Adaptive Recommendation System Using Predicted User Intent
Summary
The USPTO published patent application US20260099871A1 on April 9, 2026, disclosing a system for predicting user intent during online sessions and generating semantically customized service recommendations. The invention uses machine learning to analyze user interaction data, predict intent sequences, and rank recommendations based on predicted behavior patterns. Application No. 18907624 was filed on October 7, 2024.
This patent application publication represents a notable disclosure in the AI-driven personalization space, specifically covering intent prediction techniques applied to online recommendation systems. The broad claims covering predicted intent sequences and semantic re-ranking could potentially affect how technology companies approach personalization features. Companies developing similar recommendation technologies should conduct freedom-to-operate analyses once the application progresses toward grant.
What changed
The USPTO published a patent application disclosing an intent-driven adaptive recommendation system that predicts user intent during online sessions and generates semantically customized ranked recommendations. The system analyzes initial user interaction data, predicts intent sequences, and re-ranks recommendations based on predicted user behavior patterns. The technology applies machine learning techniques to personalize service offering recommendations in real-time.
Technology companies developing recommendation engines, e-commerce platforms, or online service systems should review this application to assess potential overlap with their existing or planned recommendation technologies. Patent applications represent early-stage intellectual property disclosures and do not yet grant enforceable rights. Companies should monitor the prosecution status of this application to determine whether and when any resulting patent issues.
Archived snapshot
Apr 20, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
INTENT-DRIVEN ADAPTIVE RECOMMENDATION FOR AN ENHANCED USER ENGAGEMENT
Application US20260099871A1 Kind: A1 Apr 09, 2026
Inventors
Omar Odibat, Glincy Mary Jacob, Manish Mitruka, Reagan Mitchell, Geetha R Subramanyam, Umar Mohamed Iyoob Umar
Abstract
A technique for predicting and recommending service offerings includes obtaining an initial dataset related to a user interaction with an online recommendation system during a user online session, generating a predicted intent of the user online session, and generating an initial set of recommendations based on the predicted intent of the user online session. The technique includes ranking the initial set of recommendations to generate a set of ranked recommendations, generating a predicted sequence of actions of the user, and determining that the ranked set of recommendations are to be re-ranked into a re-ranked set of recommendations. The technique includes generating a semantically customized ranked set of recommendations using at least one of the ranked set of recommendations or the re-ranked set of recommendations and providing the semantically customized ranked set of recommendations to the user.
CPC Classifications
G06Q 30/0631
Filing Date
2024-10-07
Application No.
18907624
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