Crowdsourced Database Update for Detergent Dosing and Water Hardness
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Solution Overview
Problem
Existing database systems for detergents and water hardness are prone to becoming outdated, especially with new product launches and varying regional water conditions, leading to inaccurate dosing and operational inefficiencies in automatic dosing devices.
Innovation Solution
A method that utilizes sensors to detect detergent product identification, transmits information requests to databases, and leverages crowdsourcing services to systematically update and complete database information, ensuring precision in detergent dosing and water hardness adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional database systems are used to store detergent information, then the system has a structured storage framework, but the database quickly becomes outdated when new products are marketed
Solution Approach 1:
The system implements feedback loops where usage data from automatic dosing devices is continuously collected and fed back to trigger database updates. When a detergent product is detected via sensors, the system checks the database for information, and if the product is new or information is missing, it initiates a crowdsourcing request to gather dosage instructions, thereby continuously updating the database with current product data.
Solution Approach 2:
The system enables self-service by allowing the database to automatically request and acquire new detergent information through crowdsourcing services without manual intervention. The automated dosing devices themselves contribute to database completion by providing usage data that triggers information gathering for new products.
2Loss of information
If OCR scan methods are used to read dosage instructions, then direct product information can be extracted, but the method is technically complex and fails with damaged or soiled packages
Solution Approach 1:
The system introduces an intermediary approach by using sensor-based product identification (such as barcode scanning or RFID) followed by database lookup and crowdsourcing verification, rather than directly OCR-ing the packaging. This intermediary database layer simplifies the system by separating product identification from information extraction, making it more robust to packaging conditions.
Solution Approach 2:
The patent replaces the mechanical/visual OCR system with an electronic sensor-based identification system combined with digital database retrieval. Instead of using optical character recognition that requires clear visual access to text, the system uses sensors to detect product identifiers and electronically retrieves information, substituting a fragile visual system with a more robust electronic one.
3Reliability
If databases are manually updated, then data quality can be controlled, but the process is time-consuming and cannot keep pace with new product launches
Solution Approach 1:
The system enables self-service by allowing the database to automatically request and acquire new detergent information through crowdsourcing services without manual intervention. The automated dosing devices themselves contribute to database completion by providing usage data that triggers information gathering for new products.
Solution Approach 2:
The system implements feedback loops where usage data from automatic dosing devices is continuously collected and fed back to trigger database updates. When a detergent product is detected via sensors, the system checks the database for information, and if the product is new or information is missing, it initiates a crowdsourcing request to gather dosage instructions, thereby continuously updating the database with current product data.
4Stability of the object's composition
If regional water hardness variations are not updated, then the system maintains stability, but dosing accuracy deteriorates in areas with changed water conditions
Solution Approach 1:
The system transitions from static water hardness values to dynamic, location-specific data that can be updated as conditions change. By using GPS coordinates and postal codes to identify service areas and linking them to water hardness information from crowdsourcing, the system adapts to regional variations and updates automatically when new water hardness data is collected.
Solution Approach 2:
The system applies local quality by maintaining different water hardness values for different geographical locations rather than using a single global value. Each location receives customized water hardness data based on its specific regional conditions, allowing the dosing system to be optimized for local water characteristics while maintaining overall system stability.
Data Source
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AI summary
The invention relates to a method for completing and/or updating at least one database for detergents, wherein at least one product identification of a detergent is sensorially detected and, based on the product identification, an information request is transmitted to a database, wherein information about the detergent associated with the product identification is missing, incomplete, or outdated in the database, and wherein the database generates a request to at least one crowdsourcing service to determine information about the detergent associated with the product identification. Furthermore, the invention relates to a method for completing and/or updating at least one database for water hardness and a system.