AI User Snapshot Analytics for Faster Decisioning
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Solution Overview
Problem
There is a need for an intelligent way to improve decisioning processes based on user interactions within a network environment.
Innovation Solution
A system utilizing an AI engine that aggregates user data from various communication channels, parses and tokenizes it to generate user snapshots, computes probability scores for predicted user actions, and generates notifications or recommendations based on these predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data analysis methods are used to process user interactions, then implementation simplicity is maintained, but decisioning accuracy and predictive capability deteriorate
Solution Approach 1:
The patent introduces an AI engine as an intermediary component between raw user data and decisioning processes. This AI engine aggregates user data from multiple communication channels, parses and tokenizes the data, and generates probability scores for predicted user actions. By inserting this intelligent intermediary layer, the system achieves high decisioning accuracy without requiring complex custom analysis solutions throughout the entire system.
Solution Approach 2:
The patent replaces traditional mechanical data analysis methods with AI-based predictive analytics. Instead of using conventional rule-based or statistical analysis systems, the invention employs machine learning models that continuously learn from user interaction patterns across various channels. This substitution enables the system to automatically adapt and improve decisioning accuracy over time without manual intervention.
2Measurement precision
If comprehensive user data from multiple channels is aggregated and analyzed, then predictive accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by continuously aggregating and preprocessing user data from multiple communication channels in the background, before predictive analysis is needed. The AI engine maintains updated user profiles and interaction histories, so when a prediction is required, the system can quickly query pre-processed data rather than collecting and analyzing raw data from scratch. This approach enables accurate predictions while minimizing real-time processing delays.
3Speed
If real-time monitoring and continuous data aggregation is implemented, then decisioning timeliness improves, but system resource consumption increases
Solution Approach 1:
The patent implements periodic action by continuously monitoring user interactions at optimized intervals rather than processing every single data point in real-time. The AI engine aggregates data from communication channels at scheduled intervals, processes batches of user interactions, and updates predictions periodically. This periodic approach maintains decisioning timeliness while significantly reducing computational resource consumption compared to true real-time continuous processing.
Data Source
AI summary
A system is provided for artificial intelligence based predictive analytics of electronic user data. In particular, the system may comprise an artificial intelligence (“AI”) engine that continuously aggregates user data associated with a user and/or a group of users based on internal and external data sources across various different communication channels. The AI engine may then parse and tokenize the user data to generate a complete user snapshot associated with the user and/or the group of users. Based on analyzing the user data, the AI engine may generate a probability score associated with a predicted user action within the network environment. Based on the predicted user action and the probability score associated with the predicted user action, the system may drive decisioning processes within the network environment and/or generate one or more outputs to be presented on one or more user computing devices in and out of the network environment.