Predictive Gift Subscription Profiles for Real-Time Gift Relevance

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Solution Overview

Problem

Existing gift subscription systems often fail to accurately predict and provide gifts that align with the recipient's preferences and needs when the purchaser is not the recipient, leading to unwanted or irrelevant gifts and a negative customer experience.

Innovation Solution

A predictive gift subscription framework that uses machine learning to generate and update a gift recipient's profile based on interactions, demographics, and events, minimizing active information solicitation from both the giver and recipient, to dynamically tailor gift recommendations in real-time.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If machine learning models continuously update recipient profiles in real-time, then gift prediction accuracy improves, but computational resources and processing time increase

Engineering Contradiction:
Improvegift prediction accuracyVSAvoidcomputational resource consumption
Core Design Contradiction:
Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary actions by updating the recipient profile and computational model in advance before gift selection is needed. Profile updates occur based on triggered events (demographic changes, life events) rather than continuous real-time processing, allowing the system to prepare predictions proactively while reducing peak computational demands during gift selection moments.

Inventive Principle:
Principle #10Preliminary action

Solution Approach 2:

The system dynamically adjusts the frequency and timing of profile updates based on the recipient's life stage and event triggers. Rather than continuous updates, the system adapts its processing intensity to match actual needs - updating profiles when significant life changes occur (marriage, birth, career changes) while maintaining lighter processing during stable periods, thus balancing accuracy with resource consumption.

Inventive Principle:
Principle #15Dynamics

2Measurement precision

If the system actively solicits information from gift giver and recipient, then profile accuracy improves, but user burden and interaction friction increase

Engineering Contradiction:
Improveprofile accuracyVSAvoiduser interaction burden
Core Design Contradiction:
Measurement precisionVSEase of operation

Solution Approach 1:

The system implements self-service by automatically updating recipient profiles based on triggered events without requiring active user input. When life events occur (marriage, birth, career changes), the system automatically detects and processes these events to update profiles, allowing the system to serve itself rather than requiring continuous manual information provision from users.

Inventive Principle:
Principle #25Self-service

Solution Approach 2:

The system uses feedback from triggered events and gift recipient actions to automatically refine profiles. Instead of actively soliciting information, the system observes and processes feedback signals from life events and gift interactions to iteratively improve profile accuracy, reducing the need for direct user input while maintaining high measurement precision.

Inventive Principle:
Principle #23Feedback

3Reliability

If gift subscriptions are personalized based on detailed recipient analysis, then gift relevance improves, but system complexity increases

Engineering Contradiction:
Improvegift relevanceVSAvoidsystem complexity
Core Design Contradiction:
ReliabilityVSDevice complexity

Solution Approach 1:

The system segments the complex profile into distinct components: demographic information, life event history, and triggered event data. This segmentation allows the computational model to process and analyze different aspects of recipient data independently, making the overall system more manageable while maintaining the ability to generate highly relevant personalized gift recommendations through integrated analysis.

Inventive Principle:
Principle #1Segmentation

Data Source

PatentUS12511681B2Systems and methods for predictive gift subscriptions
Publication Date: 2025.12.30 SYNCHRONY BANK
  • US12511681B2 patent drawing
  • US12511681B2 patent drawing
  • US12511681B2 patent drawing

AI summary

Systems and methods for predictive gift subscriptions are provided. A gift subscription service may store a gift subscription and a profile associated with a gift recipient. The gift subscription service may then identify a time to send a gift to the gift recipient and associate a computational model with the gift recipient and the profile. When the gift subscription service determines a change in the profile, the gift subscription service may then update the computational model associated with the gift recipient in real-time by applying a machine learning algorithm to the change in the profile and use the updated computation model to formulate a prediction for the gift recipient that may then be used to identify a set of gifts to present to the gift recipient. The gift subscription service may then present the set of gifts to the gift recipient.