AI Identity Profiling for Dynamic Audience Segmentation
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Solution Overview
Problem
Traditional telecommunication networks provide a sparse picture of identity, as demographic and group affiliations are not explicitly identified, and existing data analysis approaches become outdated due to dynamic content in remote databases.
Innovation Solution
The use of artificial intelligence to analyze social media data, sensor data, and networked sources, incorporating features like slang terms, group affiliations, and beliefs, to enrich identity profiles and adapt to changing terminology and trends in real-time, enabling more accurate demographic and interest group analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional database approaches are used to store identity information, then data storage is simple and straightforward, but the picture of identity is sparse and lacks demographic and group affiliation details
Solution Approach 1:
The patent implements nested profiling where multiple levels of identity information are organized hierarchically. Core demographic data is nested within broader profile structures, while detailed group affiliations and behavioral characteristics are nested within demographic categories. This allows comprehensive identity representation while maintaining organized, manageable data structures that don't overwhelm storage systems.
Solution Approach 2:
The patent adds dimensional depth to identity profiles by incorporating temporal dimensions (historical behavior changes), spatial dimensions (geographic location data), and categorical dimensions (multiple overlapping group affiliations). This multi-dimensional approach enriches sparse traditional profiles without requiring proportional increases in storage complexity, as the additional dimensions are integrated through structured metadata.
2Reliability
If static data analysis approaches are used, then processing is simple and fast, but the analysis becomes outdated due to dynamic content in remote databases
Solution Approach 1:
The patent implements dynamic profiling systems that continuously adapt to changing data in remote databases. Profile structures are designed to be updated in real-time as new information becomes available, with automatic detection of changes in demographic data, group affiliations, and behavioral patterns. This dynamic approach maintains analysis reliability without requiring complete reprocessing of all data, as only changed elements are updated.
Solution Approach 2:
The system incorporates feedback mechanisms where analysis results are continuously monitored and used to refine future queries and data collection strategies. When dynamic content changes are detected in remote databases, the system automatically adjusts its analysis parameters and re-queries relevant data sources, creating a closed-loop system that maintains accuracy while optimizing processing efficiency through selective updates rather than continuous full-reprocessing.
3Loss of information
If comprehensive identity profiling is implemented, then demographic and group affiliation analysis is enriched, but data processing and analysis complexity increases
Solution Approach 1:
The patent segments comprehensive identity profiles into distinct modular components: demographic segments (age, gender, location), affiliation segments (group memberships, organizational ties), and behavioral segments (preferences, activities). Each segment can be independently processed, analyzed, and updated. This segmentation reduces overall system complexity by allowing parallel processing of separate data types and enabling selective analysis of only relevant segments for specific queries.
4Reliability
If real-time data analysis is implemented, then audience segmentation is current and accurate, but processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary indexing and pre-processing of demographic and behavioral data as it is collected or updated in remote databases. Data is organized into pre-computed aggregates and indexed structures before actual analysis queries are executed. This preliminary action allows real-time segmentation queries to retrieve results rapidly without performing complete data processing at query time, significantly reducing processing time while maintaining segmentation accuracy.
Data Source
AI summary
A distributed telecommunication network including a VPN, a cellular network, a LAN, an Ethernet network, a server, a database, all communicatively coupled to the backbone of the network, a Wi-Fi network, a firewall, and a mobile device, communicatively coupled to the Wi-Fi network and the cellular network, wherein the database is configured to store a profile of a user of the distributed telecommunication network, the profile including a biography of the user, user relationships, a plurality of posts, an activity level of the user, and wherein the server is configured to determine whether the profile belongs to a category using a weighted average of a biography indication related to the biography of the profile and a post indication related to posts and relationship indication related to user relationships.