AI-Driven ATM Interface Flow for Faster Transaction Sessions
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Solution Overview
Problem
Conventional automated teller machines (ATMs) present user interfaces in a static, linear sequence, which can be inefficient and require users to navigate through multiple screens for each banking operation, leading to increased computing resource usage and unnecessary network communication.
Innovation Solution
Implement an artificial intelligence engine trained with machine learning data to predict and dynamically present user interfaces based on real-time and historical data, allowing for non-linear interaction and optimized resource use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional ATMs present user interfaces in a static, linear sequence, then the system structure is simple and reliable, but the productivity is low and computing resources are wasted
Solution Approach 1:
The patent applies dynamics by transforming the static, linear user interface sequence into a dynamic, adaptive interface presentation system. The ATM uses machine learning models to predict user intentions and dynamically generates customized interface sequences, allowing the system to adapt its behavior based on real-time analysis rather than following a fixed predetermined path.
Solution Approach 2:
The patent implements preliminary action by using machine learning models to predict user intentions before the user actually performs actions. The system pre-generates customized interface sequences based on predicted user needs, so that when users arrive at the ATM, they are immediately presented with relevant interfaces rather than navigating through unnecessary screens.
2Loss of time
If conventional ATMs require navigation through multiple screens for each banking operation, then the device complexity is low, but the loss of time increases
Solution Approach 1:
The patent introduces an intermediary element - the machine learning model - that acts as a mediator between the user and the ATM interface system. This intermediary analyzes user behavior patterns, predicts intentions, and generates optimized interface sequences, thereby reducing the time users spend navigating through multiple screens without requiring complex manual programming of interface logic.
Solution Approach 2:
The system implements feedback mechanisms by continuously analyzing user interactions, transaction data, and behavioral patterns to refine its predictions and interface generation. The machine learning model learns from past user behavior and adjusts its interface recommendations accordingly, creating a feedback loop that improves efficiency over time while maintaining manageable system complexity.
3Adaptability or versatility
If conventional ATMs present all possible user interfaces in sequence, then the adaptability is low, but the ease of operation is maintained through standardization
Solution Approach 1:
The patent applies local quality by customizing different parts of the user interface experience based on specific user characteristics and predicted intentions. Rather than applying a uniform interface sequence to all users, the system tailors the interface presentation to match individual user needs, behaviors, and preferences while maintaining core functional consistency.
Solution Approach 2:
The system changes parameters dynamically by adjusting interface sequences, displayed options, and navigation paths based on analyzed user data. The machine learning model modifies interface parameters such as the order of screens, the prominence of certain options, and the level of detail provided, thereby achieving adaptability while preserving ease of operation through data-driven optimization.
Data Source
AI summary
A computing device comprises a communications module; a processor coupled with the communications module; and a memory coupled to the processor and storing processor-executable instructions which, when executed by the processor, configure the processor to initiate an automated teller machine session; engage an artificial intelligence engine to determine at least one dynamic user interface to be presented during the automated teller machine session; and present the at least one dynamic user interface on a display of an automated teller machine.