Communication Data Aggregation Across Channels for Unified Analysis
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Solution Overview
Problem
Existing systems struggle with efficiently capturing, organizing, and analyzing data from disparate communication channels, particularly in industries like lending, where data is often exchanged through multiple channels with different protocols, making it difficult to compile and use for decision-making.
Innovation Solution
A data aggregation platform that intercepts communications, classifies and translates data, and stores it in structured and unstructured buckets within a database, using machine learning algorithms to ensure completeness and security, enabling real-time analysis and centralized data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is collected from multiple communication channels with different protocols, then data completeness improves, but data organization complexity increases
Solution Approach 1:
The patent introduces a data aggregation platform as an intermediary system that sits between multiple communication channels and the final data storage. This platform receives data from various channels (email, text, video conference, etc.), normalizes it into a common format, and then stores it in a centralized database. The intermediary handles the complexity of protocol translation and data normalization, preventing this complexity from propagating to the overall system architecture.
Solution Approach 2:
The system segments data collection and processing into distinct modular components: communication channel interfaces, data normalization layer, and storage layer. Each communication channel has its own interface module that handles protocol-specific details, while the central normalization layer applies统一的 data standards. This segmentation allows the system to maintain data completeness from multiple sources while managing organization complexity through modular design.
2Measurement precision
If manual data collection and organization is performed, then data accuracy improves, but time consumption increases
Solution Approach 1:
The data aggregation platform implements automated self-service mechanisms that perform data collection, normalization, and validation without manual intervention. The system automatically intercepts communications, extracts relevant data, applies normalization rules, and stores results in the centralized database. This automation maintains data accuracy through consistent application of validation rules while eliminating the time consumption associated with manual data collection and organization.
Solution Approach 2:
The system performs preliminary data normalization and validation automatically as data is being collected from communication channels, before the data needs to be analyzed or used for decision-making. By preprocessing and organizing data in advance through automated pipelines, the system ensures data accuracy is established early in the process while preventing time loss that would occur if manual organization were required later.
3Ease of operation
If data is stored in disparate formats across different channels, then data accessibility improves, but analysis efficiency decreases
Solution Approach 1:
The patent implements a data normalization layer that converts data from various communication channels into a unified format and structure. All incoming data regardless of source (email, text message, video conference transcripts) is transformed into a standardized schema with consistent field names, data types, and organizational structure. This homogenization enables both easy accessibility through a single interface and high analysis efficiency by eliminating format conversion requirements.
Data Source
AI summary
Aggregating data retrieved from communications between parties. A method includes receiving a requesting electronic communication sent from a data recipient to a data submitter, wherein the requesting electronic communication comprises a request for data to be assigned to an identified data bucket within an aggregated data package. The method includes receiving a responsive electronic communication sent from the data submitter to the data recipient. The method includes stripping data from the responsive electronic communication and storing the stripped data in the identified data bucket within the aggregated data package.