Gift preference prediction using social media data

ChangeBridge: Patent Apps - Business Methods (G06Q)

Published April 2nd, 2026

Detected April 2nd, 2026

Summary

The USPTO published patent application US20260094195A1 by inventor Robert Hoffer for a system and method that uses natural language processing, image recognition, and machine learning models to analyze social media data and generate personalized gift recommendations. The recommender system extracts behavioral and interest profiles from social media activity and includes privacy selection features for users. This publication makes the invention publicly available for examination.

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What changed

This patent application discloses a gift recommendation system that processes social media data using NLP, image recognition, and machine learning to create recipient profiles and predict gift preferences. The system includes a privacy module that respects user data selections. CPC classifications include G06Q30/0631 (purchasing recommendations), G06Q10/40 (AI), and G06Q30/0641 (personalized recommendations). Application number 18903859 was filed October 1, 2024 and published April 2, 2026.

This is an informational patent publication with no compliance requirements or regulatory obligations. Companies developing AI-driven recommendation systems or gift e-commerce platforms should review this application to assess potential patent landscape implications for their own technologies. The inclusion of privacy selection features indicates awareness of data protection considerations in personalized recommendation systems.

Source document (simplified)

← USPTO Patent Applications

SYSTEM AND METHOD FOR PREDICTING GIFT PREFERENCES USING SOCIAL MEDIA DATA

Application US20260094195A1 Kind: A1 Apr 02, 2026

Inventors

Robert Hoffer

Abstract

A system and method are provided for predicting gift preferences using a person's social media data. For example, natural language processing, image recognition, and machine learning models can be used by a recommender system to generate personalized gift recommendations based on a person's behaviors and interests extracted from their social media activity. The recommender system can use a machine learning (ML) model for text-and image-based analysis to distill semantic content from social media data of the gift recipient, generating a profile from the semantic content. A prediction model predicts gift preferences based on the profile, and gift recommendations are made based on the gift preferences. The recommender system includes features to adhere to the user's data-privacy selections.