RFID Product Data Repository for Real-Time Cloud Migration
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Solution Overview
Problem
Current data collection systems struggle to efficiently gather and migrate digital identity data from multiple sources, leading to inefficiencies and delays in accessing meaningful data for retailers, service providers, and consumers, as they often lack the ability to aggregate and analyze data in real-time and conform to future use cases.
Innovation Solution
A system and method for collecting, packaging, and delivering data from multiple sources to various destinations, utilizing a repository and destination cloud applications that manage digital identities, combine data from edge devices, and publish it in a searchable format, enabling trusted data exchange and manipulation based on product-specific information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is collected and managed by multiple separate data processing points internally, then each organization can maintain its own data standards and security, but data accessibility and real-time analysis are delayed and inefficient
Solution Approach 1:
The patent introduces a centralized data repository as an intermediary layer between multiple data processing points and various destinations. This repository receives data from multiple sources in different formats, standardizes it, and makes it accessible to various destinations in real-time, eliminating the need for multiple separate processing points while maintaining data security and standards.
Solution Approach 2:
The centralized data repository serves multiple functions simultaneously: it acts as a data collection point, a standardization engine, a security layer, and a real-time distribution hub. This multi-functional approach replaces multiple specialized data processing points with a single universal system that handles all data accessibility needs.
2Quantity of substance
If data is collected from multiple sources in various formats, then comprehensive data coverage is achieved, but data aggregation and analysis become cumbersome and inefficient
Solution Approach 1:
The patent applies parameter changes by transforming data from various formats and sources into a standardized structure within the centralized repository. The system automatically adjusts data parameters (formats, schemas, encoding) to a common standard, enabling efficient aggregation and analysis without manual intervention for each data source.
Solution Approach 2:
The system segments the complex data aggregation process into distinct functional layers: data collection from multiple sources, standardization transformation, validation, and distribution. This segmentation allows each layer to handle specific aspects of data complexity independently, making the overall system more manageable and efficient.
3Reliability
If data processing points manage data internally with multiple intermediaries, then data security and control are maintained, but meaningful data access and analysis are hindered
Solution Approach 1:
The centralized data repository acts as a trusted intermediary that maintains security and control while enabling easy data access. Organizations can configure the repository to receive and standardize data according to their security requirements, while the repository itself provides standardized, secure access to authorized destinations without requiring internal processing points to expose their internal systems.
4Reliability
If data is not standardized before delivery to destinations, then data can be sent in original formats preserving source integrity, but destinations cannot efficiently utilize the data for various use cases
Solution Approach 1:
The system performs parameter changes by transforming data into standardized formats within the centralized repository while maintaining the ability to trace back to original sources. This standardization enables destinations to efficiently utilize data for various use cases including real-time analysis, historical reporting, and future applications without compromising source integrity.
Data Source
AI summary
A data collection, data packaging, and data delivery system that includes methods that provide for accurate digital identity data, inventory data, and associated information from multiple sources to be repackaged and delivered to various destinations are disclosed. A source, such as an edge device, is used to monitor an RFID tagged product and is configured to send data about the RFID tagged product to a designated cloud application. The received data is combined with other product specific data and is sent either directly, or via an intermediate software, to a destination cloud application. The destination cloud application is configured to manipulate the data, adjust pricing for the products, and publish the information in a searchable format for consumers in a local area to determine, for example, if the products are locally available.