Context Augmentation for Multi-Source Transaction Data Processing
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Solution Overview
Problem
The management and processing of electronic transaction data across multiple platforms and formats is challenging due to differences in data organization, classification, and storage protocols, leading to difficulties in parsing, processing, and categorizing large volumes of transaction data intelligently.
Innovation Solution
A networked computing system that contextualizes and augments transaction data from multiple sources by retrieving and processing data from various platforms, including bank statements, emails, geo-location, and social media, to provide a centralized and intuitive management system for users, enabling efficient data categorization and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored across multiple platforms with different organization techniques, then data coverage and source diversity are improved, but data management complexity and processing difficulty increase
Solution Approach 1:
The patent introduces a centralized data processing system that acts as an intermediary between multiple data sources and users. This system retrieves data from various platforms (bank statements, emails, geo-location, social media), standardizes the data formats, and presents unified access to users, thereby managing the complexity of multi-source data integration.
Solution Approach 2:
The data processing system performs multiple functions including data retrieval from diverse sources, data standardization, contextualization, and presentation. This multi-functional approach consolidates what would otherwise require separate systems for each data source, reducing overall system complexity.
2Ease of operation
If data from multiple sources is centralized for unified management, then data accessibility and user control are improved, but system complexity and data integration challenges increase
Solution Approach 1:
The system segments data processing into distinct modules: data retrieval from different sources, data standardization, contextualization, and presentation. Each module handles specific tasks independently, making the overall integration manageable despite the diversity of data sources.
Solution Approach 2:
The system transforms data from various sources by changing its parameters - converting different formats, schemas, and structures into a unified standardized format. This parameter transformation enables seamless integration while maintaining ease of access for users.
3Measurement precision
If traditional data processing methods are used across different platforms, then platform independence is maintained, but intelligent categorization and analysis capabilities are limited
Solution Approach 1:
The system automatically retrieves, standardizes, and contextualizes data without requiring manual intervention. It self-adapts to different data sources by implementing their specific protocols and formats, enabling intelligent categorization and analysis while maintaining platform independence.
Data Source
AI summary
Various embodiments are directed to the centralized processing of data from multiple sources and/or augmenting the data with contextual data from multiple sources across different accounts and systems. Embodiments disclose a context augmentation module to supplement and aggregate data from multiple sources, extrapolate contextual data, and augment the data with the contextual data on transactions conducted by a user. By gathering data from multiple sources associated with the user, contextual data may be extrapolated and can be applied to incoming transaction data about a transaction conducted by the user. The contextual data may also be applied to classify the transaction data, increasing the total number of transactions as well as contextualizing the ones with additional corresponding data. Proper contextualization and classification of the transaction data of users across multiple accounts, platforms, sources, and systems is beneficial and advantageous in resolving problems associated with data processing across multiple sources and platforms.