Dynamic Offers API for Real-Time Interaction Personalization

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Solution Overview

Problem

Existing systems fail to dynamically tailor offers to user interactions in real-time across different websites or application landing pages associated with payment instrument services, leading to inefficient and irrelevant offer presentation.

Innovation Solution

A dynamic offers API that processes user interaction data through machine learning algorithms to select and present offers based on user interactions, utilizing unique identifiers and external data connectivity platforms to enhance offer personalization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If traditional static offer systems are used, then system complexity is low, but offer relevance to user interactions deteriorates

Engineering Contradiction:

Improveoffer relevanceVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system transitions from static offer presentation to dynamic offer selection that adapts in real-time based on user interactions. The machine learning model continuously processes user interaction data from multiple websites and dynamically selects offers tailored to each user's current behavior patterns, making the system responsive and adaptive rather than fixed.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

A machine learning model is introduced as an intermediary between user interaction data and offer selection. This intermediary processes raw interaction data, identifies patterns and user intent, and translates them into personalized offer recommendations, bridging the gap between user behavior and relevant offers without requiring direct complex rule-based systems.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If real-time processing of user interaction data is implemented, then offer personalization improves, but processing time increases

Engineering Contradiction:

Improveoffer personalizationVSAvoidprocessing time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The machine learning model is pre-trained on historical user interaction data before deployment. This preliminary training establishes the model's ability to quickly process and interpret interaction patterns during real-time operation. The model learns from past data offline, enabling fast real-time inferences without requiring complex processing during actual offer selection moments.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces traditional mechanical rule-based offer selection systems with a machine learning-based intelligent system. Instead of using rigid if-then rules that require extensive processing of multiple conditions, the neural network model directly maps interaction patterns to offer recommendations through learned representations, significantly reducing processing time while improving personalization accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If machine learning algorithms are used to select offers, then offer selection accuracy improves, but computational resources consumed increases

Engineering Contradiction:

Improveoffer selection accuracyVSAvoidcomputational resources

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system processes only the most relevant user interaction features and data points necessary for accurate offer selection, rather than analyzing every possible attribute. The machine learning model is designed to focus on key interaction patterns and behavioral signals that have the highest predictive value for offer relevance, reducing unnecessary computational overhead while maintaining high accuracy.

Inventive Principle:

Principle #16Partial or excessive action

4Quantity of substance

If user interaction data from multiple external sources is integrated, then data completeness improves, but system complexity increases

Engineering Contradiction:

Improvedata completenessVSAvoiddata integration complexity

Core Design Contradiction:

Quantity of substanceVSDevice complexity

Solution Approach 1:

The machine learning model serves multiple functions simultaneously: it processes user interaction data, identifies patterns across different data sources, selects relevant offers, and adapts to changing user behaviors. This multi-functional approach consolidates what would otherwise require separate specialized systems for each task, reducing overall system complexity while handling diverse data sources effectively.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250272714A1Dynamic offers application programming interfaces

Publication Date: 2025.08.28 SYNCHRONY BANK

AI summary

Systems and methods are provided through which user online interactions are dynamically processed through an application programming interface (API) to identify and present a set of offers according to the user online interactions. In response to an API call to identify offers presentable to a user, the API obtains user interaction data associated with the user and processes this data, a set of available offers, and a set of parameters corresponding to an interface being accessed by the user through a machine learning algorithm to select a set of offers to be presented through the interface. The API continuously monitors user interactions with the set of offers to update the machine learning algorithm in real-time.