ATM Content Delivery Using Transaction Context Personalization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
ATMs do not provide a user-specific experience during transactions, failing to leverage user data for personalized content delivery.
Innovation Solution
Implementing a system that uses machine learning models to analyze user profiles and transaction histories to generate context-specific content for display on ATMs, incorporating data from sensors and external sources to enhance user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If ATM displays generic content during transactions, then device complexity is low, but user experience and personalization are poor
Solution Approach 1:
The patent introduces servers as an intermediary component between the ATM and the user. These servers host machine learning models and manage user profile data, effectively decoupling the personalization intelligence from the ATM hardware itself. This allows the ATM to deliver user-specific content without bearing the full complexity of the AI processing infrastructure.
Solution Approach 2:
The system enables self-service personalization by automatically analyzing user profiles, transaction histories, and contextual data through machine learning models. The content delivery is autonomously customized based on user characteristics and current transaction context, eliminating the need for manual configuration while achieving high adaptability.
2Adaptability or versatility
If ATM collects and processes user data for personalization, then user experience improves, but data privacy and security risks increase
Solution Approach 1:
Servers act as a secure intermediary that handles sensitive user data processing. The ATM communicates with these servers to retrieve personalized content without directly accessing or storing raw user profile information. This architectural separation reduces the attack surface at the ATM level while enabling sophisticated personalization through secure server-side data handling.
3Productivity
If ATM displays content during transaction duration, then user engagement increases, but transaction time may be extended
Solution Approach 1:
The system applies partial personalization by selecting and displaying only the most relevant content items during the transaction, rather than presenting all available personalized content. This selective approach ensures that engagement-enhancing content is delivered without significantly extending the core transaction duration, balancing user engagement with transaction efficiency.
Data Source
AI summary
Systems and methods for customer-specific content delivery via an automated teller machine (ATM) may include one or more server(s) which receive, from an ATM, data indicative of a user profile corresponding to a user of the ATM, the user performing a transaction via the ATM; identify a transaction history associated with the user profile; determine, via one or more first machine learning models hosted on the one or more servers, a context corresponding to at least one transaction of the transaction history; generate, via one or more second machine learning models, a content item according to the context determined by the one or more first machine learning models; and transmit the content item for display by the ATM to the user, the ATM displaying the content item for at least a portion of a duration in which the transaction is performed via the ATM.