IoT Auto Reward Allocation Token Selection

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Solution Overview

Problem

Existing systems fail to efficiently manage transactions by identifying the optimal combination of transaction tokens that provide the greatest benefit, especially when multiple incentives and rewards programs are involved.

Innovation Solution

A computer-implemented method and system that utilizes Internet of Things (IoT) data from sensors to identify current incentives for products and determine the set of transaction tokens that offer the greatest benefit, incorporating both product-specific and token-specific incentives.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If users manually manage multiple transaction tokens and incentives, then they can track their rewards, but the process becomes time-consuming and complex

Engineering Contradiction:

Improveaccuracy in identifying optimal token combinationVSAvoidtime required to analyze and select transaction tokens

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system automatically identifies the optimal combination of transaction tokens by analyzing current incentives and token-specific benefits without requiring user intervention. The processor executes algorithms that evaluate multiple factors including product-specific incentives, category bonuses, and token redemption values to autonomously select the best payment strategy

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The manual process of comparing and selecting transaction tokens is replaced by an automated computational system. The processor uses software algorithms to perform the analysis that would otherwise require manual user effort, substituting mechanical human decision-making with automated digital processing

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If users analyze all available incentives and token combinations, then they can maximize rewards, but the complexity of the process increases

Engineering Contradiction:

Improvereward maximization efficiencyVSAvoidcomplexity of incentive analysis system

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The incentive analysis process is divided into distinct functional modules: one component retrieves current product incentives, another evaluates token-specific benefits, and a third synthesizes this information to determine the optimal combination. This segmentation allows each module to handle a specific aspect of the analysis independently

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system is designed to handle multiple types of incentives and transaction tokens through a unified framework. The same processing logic applies whether analyzing cash back offers, points programs, or promotional discounts, making the system adaptable to various reward structures without requiring separate specialized processes

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Speed

If the system automatically selects transaction tokens, then transaction speed increases, but the requirement for real-time data processing increases

Engineering Contradiction:

Improvetransaction completion speedVSAvoidcomputational resources required for real-time analysis

Core Design Contradiction:

SpeedVSUse of energy by moving object

Solution Approach 1:

The system pre-loads and caches incentive data and token benefit information before transactions occur. By having this data readily available in memory rather than retrieving it in real-time during the transaction, the system reduces computational overhead and accelerates the token selection process

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250045792A1Smart IoT Auto Reward Allocation

Publication Date: 2025.02.06 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

A computer implemented method manages a transaction. A number of processor units receives Internet of things data from a set of Internet of things sensors. The Internet of things data comprises current incentives for a group of products. The number of processor units identifies a set of transaction tokens for purchasing the group of products with a greatest benefit using the current incentives applicable to the group of products and token incentives for the transaction tokens. The number of processor units completes the transaction for the group of products using the set of transaction tokens.