Remote Data Enrichment via API Standardization
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Solution Overview
Problem
Structured data, such as event data and transactional data, often includes machine-friendly string entries that are not user-friendly and require significant processing resources and power for standardization, which can lead to increased memory overhead and latency when performed on user devices.
Innovation Solution
A remote data enrichment system that uses a secure API endpoint to extract partial strings from structured data, determine corresponding data structures in a database using fuzzy searches, and generate standardized names and location indicators, while conserving processing resources and power on user devices by leveraging larger databases and machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data standardization is performed on user devices, then data processing can be done locally, but memory overhead and processing resources are significantly increased
Solution Approach 1:
The patent extracts the data standardization function from user devices and relocates it to remote servers. The system maintains a centralized database of standardized data structures on remote servers, allowing user devices to send raw data for processing without storing large standardization databases locally. This extraction resolves the contradiction by maintaining standardization capability while eliminating local memory overhead.
Solution Approach 2:
The patent introduces an intermediary remote server system that mediates between user devices and data standardization requirements. The server acts as a middle layer, receiving raw data from user devices, performing standardization against centralized databases, and returning standardized results. This intermediary approach allows local devices to maintain simplicity while achieving comprehensive standardization through remote resources.
2Reliability
If data standardization is performed on user devices, then processing can be done locally, but processing time and latency increase
Solution Approach 1:
The patent implements preliminary action by pre-establishing comprehensive databases of standardized data structures, names, locations, and images on remote servers before processing requests. Fuzzy matching algorithms and comparison databases are pre-configured and optimized on servers with high-performance computing resources. This preliminary preparation enables rapid processing of user data without requiring complex local computation, thereby reducing latency while maintaining standardization accuracy.
3Measurement precision
If larger databases are used for data enrichment, then accuracy improves, but device complexity and resource requirements increase
Solution Approach 1:
The patent extracts large-scale databases from user devices and relocates them to remote servers. The system maintains comprehensive databases containing standardized names, locations, images, and fuzzy matching data structures on server infrastructure. User devices only need to send queries and receive enriched results, eliminating the need to store large databases locally. This extraction maintains high accuracy through access to extensive data while keeping user device complexity minimal.
4Measurement precision
If machine learning models are deployed on user devices, then data processing accuracy improves, but power consumption and processing demands increase
Solution Approach 1:
The patent extracts machine learning models from user devices and deploys them exclusively on remote servers. The system uses server-based ML models for fuzzy matching, data standardization, and enrichment tasks. User devices communicate with the server API to submit data and receive processed results, eliminating local ML inference requirements. This extraction maintains high processing accuracy through sophisticated server-based models while dramatically reducing power consumption on user devices.
Data Source
AI summary
In some implementations, a server may receive, from a user device and at a secure endpoint of an application programming interface, a set of structured data including a plurality of entries. The server may extract, from each entry, a corresponding partial string from a corresponding description string included in the entry, and may determine, for each partial string, a corresponding data structure in a database. The server may generate, for each entry, a standardized name and a location indicator based on the corresponding data structure, and may extract, for each data structure, an image corresponding to the data structure. Accordingly, the server may return, to the user device, a modified set of structured data including, for each entry, the standardized name, the location indicator, and the corresponding image.