Intent Orchestration Platform for Unified ML Customer Engagement
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Solution Overview
Problem
Existing machine learning models are often built for specific purposes, lacking flexibility and integration, making it difficult for enterprise organizations to leverage the full capabilities of artificial intelligence for automated services.
Innovation Solution
A computing platform that trains intent orchestration models using historical data from multiple sources, identifies individual intent, selects engagement output generation models, and determines communication channels to deliver customer engagement outputs efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are built for specific purposes, then model accuracy for specific tasks is improved, but flexibility and integration between models deteriorates
Solution Approach 1:
The patent implements a universal machine learning platform that can execute multiple different machine learning models through a common interface and infrastructure. The system allows organizations to deploy, manage, and integrate various specialized models (for fraud detection, customer service, marketing, etc.) within a single unified environment, enabling one system to perform multiple functions while maintaining the specific accuracy benefits of each specialized model
2Reliability
If multiple specialized machine learning models are deployed, then task-specific performance is improved, but system complexity increases
Solution Approach 1:
The patent combines multiple specialized machine learning models into a unified platform with centralized management capabilities. The system merges model deployment, training, monitoring, and execution functions into a single integrated infrastructure, reducing the operational complexity that would arise from managing separate systems for each specialized model while maintaining the high performance benefits of task-specific models
3Productivity
If enterprise organizations use multiple separate machine learning models, then specific business functions are improved, but ability to leverage full AI capabilities for automated services deteriorates
Solution Approach 1:
The patent creates a universal AI platform that enables enterprises to leverage the full spectrum of machine learning capabilities across all business functions. The system provides a comprehensive environment that supports various AI techniques (supervised learning, unsupervised learning, reinforcement learning, deep learning) and allows organizations to apply these capabilities across marketing, sales, customer service, fraud detection, and other functions through a unified interface, maximizing the overall AI potential rather than limiting it to isolated use cases
Data Source
AI summary
Aspects of the disclosure relate to computing platforms that utilize machine learning to perform output generation based on intent identification. The computing platform may train intent orchestration models (e.g., intent identification, output generation, or communication channel) using historical data. The computing platform may data corresponding to an individual. Based on the data, the computing platform may select intent identification models, and may use them to identify an intent. Based on the intent of the individual, the computing platform may select engagement output generation models, and may use them to generate a customer engagement output. The computing platform may use a communication channel model to identify a communication channel. The computing platform may send commands directing display of the customer engagement output, which may cause a user device to display the customer engagement output using the communication channel.