Cognitive Localization for Smart Appliance Item Translation
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Solution Overview
Problem
Existing localization services for smart appliances are inadequate in handling new or unfamiliar circumstances, failing to provide effective real-time assistance and language translation for users and appliances when encountering items with RFID tags containing information in different languages or conventions.
Innovation Solution
A method that identifies users and scans items using RFID tags, integrates with a cognitive localization server to translate and provide localization information, storing this data in an item repository for future reference, ensuring smart appliances operate correctly across different languages and conventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing localization services are used for smart appliances, then basic language translation is provided, but they cannot handle new or unfamiliar circumstances effectively
Solution Approach 1:
The system performs preliminary actions by scanning RFID tags on items before processing, pre-fetching and storing localization information in an item repository during off-peak times, and pre-translating common item information. This prepares the system in advance for subsequent localization requests, enabling it to handle new circumstances effectively while maintaining reliability through pre-established translation capabilities and item databases.
2Ease of operation
If cognitive localization services are integrated to translate item information, then user comprehension is improved, but system complexity increases
Solution Approach 1:
The patent introduces an appliance cognitive localization server as an intermediary component that handles translation and localization tasks. This server acts as a mediator between the smart appliance and the cognitive services, encapsulating the complexity of language translation, cultural adaptation, and item information processing within a dedicated service layer. The appliance itself remains relatively simple while delegating complex localization tasks to the intermediary server, thus improving user comprehension without significantly increasing appliance complexity.
Solution Approach 2:
The system implements self-service mechanisms where the cognitive localization server automatically detects language requirements, selects appropriate translations, and retrieves or creates item information without manual intervention. The item repository automatically stores and retrieves localization data, and the system self-adapts to new items by scanning RFID tags and autonomously processing new localization requests. This automation reduces the need for complex manual configuration while maintaining high ease of operation for users.
3Measurement precision
If RFID scanning and cognitive services are implemented for all items, then localization accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary scanning and storage of RFID tag information from items during off-peak periods or in advance of actual use. Localization information is pre-fetched and cached in the item repository before it is actually needed. This preliminary action allows the system to have translation and item data ready when users need them, thereby maintaining high localization accuracy while minimizing processing time during actual appliance operation.
Solution Approach 2:
The system implements partial processing by scanning and translating only the most critical item information needed for immediate appliance operation, rather than processing all possible data fields. For common items with existing RFID tags, the system uses cached localization data without re-scanning or re-translating. This selective approach provides sufficient localization accuracy for effective appliance operation while significantly reducing processing time by avoiding unnecessary full scans and translations of all item attributes.
Data Source
AI summary
A system for automated localization of information for smart appliances identifies a user of the smart appliance via a user interface. The system receives scanned input associated with an item from a scanning component. The system requests cognitive services from an appliance cognitive localization server, where the cognitive services integrates localization information with the item information. The system provides the localization information to the smart appliance, and operates the smart appliance using the localization information and the item information. The system retrieves the localization information and the item information from an item repository during a subsequent scan of the item using the scanning device.