Enhancing User Data From Digital Communications
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Solution Overview
Problem
Organizations face challenges in effectively targeting specific audiences with their outreach efforts, as existing digital communication data is not adequately utilized to identify and engage individuals most likely to respond to advertising and political campaigns, leading to inefficient resource allocation and advertising spending.
Innovation Solution
A computer-implemented method and system that detect transaction requests related to issue-specific invitations, generate datasets with issue-specific data elements, associate these datasets with users, and append additional user data to create enhanced datasets for targeted outreach and research purposes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If organizations spend more on advertising to broaden reach, then the number of potential customers increases, but the efficiency of resource allocation decreases
Solution Approach 1:
The patent segments the broad population into specific target groups based on transaction data, device information, and user behavior patterns. By dividing the advertising audience into segmented groups with shared characteristics, organizations can direct advertising efforts to specific segments rather than broadcasting to everyone, thereby increasing efficiency while maintaining reach.
Solution Approach 2:
The system performs preliminary analysis of transaction requests and device data before advertising campaigns begin. By pre-identifying target users through data collection and pattern recognition, the system prepares targeted advertising lists in advance, allowing organizations to allocate resources more efficiently when campaigns are executed.
2Measurement precision
If organizations collect more user data to improve targeting accuracy, then the precision of audience identification increases, but the complexity of data processing increases
Solution Approach 1:
The patent creates a universal data processing framework that handles multiple types of data (transaction requests, device information, user behavior) through a single integrated system. The server performs multiple functions including data collection, analysis, pattern recognition, and target user identification within one platform, reducing the complexity that would arise from separate processing systems.
Solution Approach 2:
The system automatically processes and analyzes collected data without requiring manual intervention. The server autonomously identifies patterns in transaction requests, cross-references device information, and generates target user lists, allowing the data processing complexity to be managed automatically rather than requiring complex manual procedures.
3Quantity of substance
If organizations contact more individuals to maximize outreach, then the coverage of marketing campaigns increases, but the time and resources required increase
Solution Approach 1:
The patent replaces manual data analysis and target identification processes with automated computational systems. The server automatically processes transaction data, analyzes patterns, and generates target user lists, substituting mechanical human analysis with efficient algorithmic processing that dramatically reduces the time required to prepare and execute outreach campaigns.
Solution Approach 2:
The system introduces an intermediary processing layer between raw data collection and final outreach execution. The server acts as a mediator that transforms raw transaction requests and device information into refined target user lists, enabling efficient coordination between data collection and marketing execution without requiring direct, time-consuming manual intervention at each step.
Data Source
AI summary
A computer-implemented method for enhancing and utilizing user data derived from digital interactions includes receiving user submission data comprising records generated by input into a client side application interface by a first user on a first computing device and transmitted from the first computing device to the aggregation point via at least one of a data transmission service and a data transmission provider. The method includes generating a first dataset associated with the first user, generating a data model based on data in the first dataset and one or more correlations based on attributes in the first dataset, and generating a modeled dataset based on the data model.


