Distributed Signal Processing for Personalized Kiosk Output
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Solution Overview
Problem
Current technologies face challenges in providing real-time, customized user experiences due to limitations in data exchange efficiency and user data analysis, especially with the increasing demand for enhanced connectivity and personalized interactions in environments like self-service kiosks.
Innovation Solution
A system utilizing 5G technologies to continuously scan for user devices, analyze location and user data through machine learning, and generate customized user experiences, including interactive interfaces and authentication processes, to provide tailored outputs on devices such as ATMs or kiosks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data transmission methods are used, then network congestion increases and data throughput decreases, but real-time customized output generation is required
Solution Approach 1:
The system segments data processing across multiple computers in a distributed network architecture. Each computer processes specific user data locally before transmission, dividing the overall data throughput task into manageable segments that reduce network congestion and improve efficiency.
Solution Approach 2:
User data is pre-processed and analyzed before real-time output generation. The system performs preliminary machine learning analysis on user data to generate predictions in advance, reducing the computational burden during real-time customized output generation and improving data throughput.
2Adaptability or versatility
If comprehensive user data is collected and analyzed, then personalized user experiences improve, but system complexity and processing requirements increase
Solution Approach 1:
The patent introduces a specialized machine learning processing layer that acts as an intermediary between raw user data and customized output generation. This intermediary layer handles the complex analysis of user data, isolating the complexity from the main system architecture while enabling comprehensive personalization.
Solution Approach 2:
The system employs self-learning machine learning models that automatically adapt and improve their analysis capabilities without requiring manual intervention. The models process user data autonomously, generating personalized predictions while the system self-optimizes, reducing operational complexity despite comprehensive data analysis.
3Ease of operation
If real-time signal processing is implemented, then customized user experience generation is enabled, but data transmission time and processing delays increase
Solution Approach 1:
The patent merges data collection, analysis, and output generation into an integrated real-time processing pipeline. By combining these functions into a unified system that operates continuously, the patent eliminates sequential processing delays while maintaining real-time customized user experience generation.
Solution Approach 2:
The system implements continuous signal processing and data analysis operations that never interrupt. User data is constantly analyzed and updated through continuous machine learning processes, enabling real-time customized output generation without periodic processing delays or system pauses.
Data Source
AI summary
Arrangements for dynamic customized experience generate and control are provided. In some examples, a signal emitted from a computing device may be detected. A location of the computing device may be determined or received. A user associated with the computing device may be identified and user data may be requested from one or more computing systems. The received location data and user data may be analyzed using machine learning to generate a user prediction. The user prediction may include a particular function, preferred method or requirements for authentication to another computing device, such as a self-service kiosk, a preferred layout or arrangement for data provided, and the like. Based on the generated user prediction, a customized user experience output may be generated and transmitted to a computing device (e.g., self-service kiosk, user computing device, or the like) for display to the user.