ATM Content Personalization Through Transaction-Context Machine Learning
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 artificial intelligence systems that analyze user profiles and transaction histories to generate context-based content for display on ATMs, utilizing machine learning models to provide personalized recommendations and advertisements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional ATM systems are used without AI analysis, then device complexity is low, but user experience and content relevance are poor
Solution Approach 1:
The patent introduces AI models and processing systems as intermediaries between the ATM hardware and the user. These intermediaries analyze user profiles, transaction histories, and contextual data to generate personalized content, thereby resolving the contradiction by adding complexity only where needed to enable adaptability without redesigning the entire ATM system
Solution Approach 2:
The system segments the content delivery process into distinct components: user data collection, profile analysis, context determination, content generation, and content delivery. This segmentation allows each component to be optimized independently, enabling user-specific content delivery while managing overall system complexity through modular architecture
2Loss of information
If AI models analyze user data in real-time, then content relevance improves, but processing time increases
Solution Approach 1:
The system performs preliminary analysis of user profiles and transaction histories before the actual transaction occurs. By pre-processing and storing analyzed user data and preferences, the system minimizes real-time processing requirements during the transaction, thus maintaining both context understanding and transaction speed
Solution Approach 2:
The AI analysis focuses on determining the specific context relevant to the current transaction rather than re-analyzing all user data. The system applies analysis locally to the immediate transaction context, using pre-processed user profiles to quickly generate relevant content without excessive processing time
3Adaptability or versatility
If personalized content is generated for each user, then user satisfaction increases, but system resource consumption increases
Solution Approach 1:
The system creates simplified copies or representations of user profiles and preferences that can be quickly referenced during transactions. Instead of performing full AI analysis for each interaction, the system uses pre-generated user models and content templates, reducing computational resource consumption while maintaining personalization capability
Solution Approach 2:
The system adjusts the level of personalization and analysis depth based on transaction parameters and user preferences. By dynamically changing parameters such as analysis granularity, content length, and recommendation depth, the system optimizes resource consumption while delivering appropriate levels of personalization for different scenarios
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.