RFID API Data Normalization for Real-Time Retail Integration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing RFID systems face challenges with standardization, integration, security, and data utilization, leading to compatibility issues, data silos, and limited real-time data availability, which hinder optimal operations and user experience in retail environments.
Innovation Solution
A computer-implemented RFID API powered by AI, acting as a bridge between RFID systems, readers, backend systems, and cloud environments, facilitating seamless data exchange and real-time operations by leveraging AI for data processing and analysis, ensuring consistent updates and secure data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If RFID systems integrate multiple data formats and frequencies, then data compatibility and versatility improve, but system complexity increases
Solution Approach 1:
The patent implements an API layer that acts as an intermediary between RFID readers and backend systems. This API receives data from RFID readers using various frequencies and formats, standardizes it into a unified format, and then transmits it to backend systems. This mediator approach enables compatibility across multiple data formats and frequencies without requiring complex integration logic throughout the entire system, thus improving versatility while controlling complexity.
Solution Approach 2:
The system is divided into distinct functional layers: RFID reader layer, API layer, and backend system layer. Each layer handles specific tasks independently - the reader collects raw data, the API processes and standardizes it, and the backend system utilizes the processed data. This segmentation allows each component to be optimized independently, improving overall system adaptability without proportionally increasing total system complexity.
2Productivity
If real-time RFID data processing is implemented, then operational visibility and decision-making speed improve, but computational resource consumption increases
Solution Approach 1:
The API performs preliminary data standardization and validation before transmitting data to backend systems. By preprocessing and formatting data in real-time at the API layer, the system enables immediate operational visibility without requiring intensive computational resources at the backend. This preliminary action ensures data is ready for analysis as soon as it arrives, improving productivity while managing computational load efficiently.
3Loss of information
If comprehensive RFID data collection from multiple sources is performed, then data completeness and analytical insights improve, but data integration difficulty increases
Solution Approach 1:
The API is designed with universal functionality to handle multiple data formats, frequencies, and source types through a single unified interface. It can process data from various RFID readers, backend systems, and external sources using different protocols and formats, converting them all into a standardized structure. This multi-functional approach ensures comprehensive data collection from diverse sources while simplifying integration through a single universal processing layer.
Data Source
AI summary
The present disclosure relates to a method of real-time analysis of RFID data. The method includes hosting an application program interface (API) at a computer system located at a retail location. The computer system receives RFID tag data from RFID tag connected to objects at a first and a second frequency. The computer system also receives additional data describing the objects from a remote location. Thereafter, the received RFID tag data and the additional data is analyzed and stored in a first format. Based on the analyzed data, a first inquiry, in first format, is transmitted from the API to a backend system. A first response, in second format is received from the backend system by the API. The first response is then converted into to the first format, by the API. The method additionally includes analyzing data and updating the analyzed data with information from the received first response.