Consumer Consent Request Timing Using Availability Prediction
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Solution Overview
Problem
Existing systems face inefficiencies in requesting consumer consent for actions due to consumers often being unavailable, leading to multiple requests and increased resource consumption, causing inconvenience for both consumers and merchants.
Innovation Solution
A computer-implemented method determines a time interval where the probability of consumer consent exceeds non-consent, using AI and Boolean decision trees based on historical data and attributes like financial solvability, personal attributes, and device status to optimize consent requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple consent requests are sent to consumers, then the merchant can ensure obtaining consent, but hardware, computational, and network resources are consumed unnecessarily
Solution Approach 1:
The system performs preliminary analysis of consumer behavior patterns and financial solvability attributes before sending consent requests. By predicting the likelihood of consumer availability and consent probability in advance, the system determines optimal timing for requests, avoiding unnecessary multiple attempts and reducing computational and network resource consumption while maintaining reliable consent acquisition.
2Productivity
If consent requests are sent at arbitrary times, then the merchant can initiate transactions promptly, but consumers may be unavailable or unable to provide consent due to timing
Solution Approach 1:
The system dynamically adjusts the timing of consent requests based on real-time analysis of consumer behavior patterns, financial solvability attributes, and historical data. By making the request timing adaptive rather than static, the system optimizes both transaction completion speed and consumer availability, ensuring requests are sent when consumers are most likely to be available and able to provide consent.
3Ease of operation
If the system analyzes multiple attributes and historical data to determine optimal timing, then consumer availability is optimized, but system complexity increases
Solution Approach 1:
The system automatically analyzes consumer behavior patterns, financial solvability attributes, and historical data to determine optimal consent request timing without requiring manual intervention or complex user configuration. The self-service approach simplifies operation for users while managing system complexity internally through automated pattern recognition and prediction algorithms.
Data Source
AI summary
The present invention relates to a method (10) of requesting consent from a consumer to complete an action. The method (10) comprises: determining (1) a time interval during which a probability of a consumer providing consent to complete the action exceeds a probability of the consumer not providing consent to complete the action; and requesting (2) consent from the consumer to complete the action during the time interval. Also disclosed is a system (30) for requesting consent from a consumer to complete an action. The system (30) comprises a probability processor (31) and a request processor (32). The probability processor (31) is configured to determine a time interval during which a probability of a consumer providing consent to complete the action exceeds a probability of the consumer not providing consent to complete the action. The request processor (32) is configured to request consent from the consumer to complete the action during the time interval. The action may comprise: a payment transaction, an exchange of data, and/or an update to terms and conditions of use or a service agreement. The method (10) and/or system (30) may be used in the completion of a merchant-initiated transaction.