Coupang has been granted a patent for a method of database reconciliation. The method involves receiving attribute data from multiple sources, generating attribute categories, determining data scores, generating reconciled data, storing the data in a database, and providing the reconciled data for display. The method also includes training machine learning models to process data and making predictions based on image characteristics. The patent aims to improve the accuracy and quality of attribute data for products. GlobalData’s report on Coupang gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Coupang, AI for workflow management was a key innovation area identified from patents. Coupang's grant share as of September 2023 was 41%. Grant share is based on the ratio of number of grants to total number of patents.
Database reconciliation method for attribute data of products
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A recently granted patent (Publication Number: US11775565B2) describes a method and system for database reconciliation. The method involves receiving attribute data from multiple sources, each with a source score based on source status and active time period. The attribute data is categorized, and machine learning models are trained to analyze images and determine data scores based on image characteristics and product specifications. The highest-scoring attribute data is then reconciled and stored in a database.
The system includes a processor and memory with instructions to perform the method steps. It receives attribute data from multiple sources, generates attribute categories, and trains machine learning models to analyze images and assign data scores. The highest-scoring attribute data is reconciled and stored in a database. The reconciled data can be displayed on a user interface with interactive elements, and user interactions can trigger purchase requests to an external system.
The method also includes determining data scores for images based on factors like image size, pixel, and resolution. For attribute data types related to product names, data scores are determined based on source scores. Similarly, for attribute data types related to product specifications, data scores are determined based on the number of fields associated with each source and source scores. The fields can include size, color, model, production year, or serial number.
The source status is determined based on system retail status, brand vendor status, or third party status. The length of active time period for a source is determined based on the earliest registration time. System retail status has a higher source score than brand vendor status, and brand vendor status has a higher source score than third party status. In cases where sources have the same status, the source with a longer active time period has a higher source score.
Overall, this patent describes a method and system for reconciling attribute data from multiple sources using machine learning models and image analysis. The approach aims to provide higher quality data for products and facilitate user interactions and purchases through a user interface.
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