AI Assistant for Enterprise System Navigation
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Solution Overview
Problem
Complexity of enterprise systems, such as insurance systems, makes it difficult for users to learn and navigate, leading to resource wastage, delays, and mistakes due to the need for extensive training.
Innovation Solution
A management system utilizing machine learning models to identify query subjects, contexts, and workflows, processing queries and product/service data to determine key performance indicators and decisions, and providing these insights via virtual assistants or interfaces, thereby automating user navigation and resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If enterprise systems provide comprehensive functionality and features, then system capability and productivity are improved, but system complexity increases making it difficult for users to learn and navigate
Solution Approach 1:
The patent introduces an AI assistant as an intermediary between users and the complex enterprise system. The assistant receives natural language queries from users, processes them through multiple machine learning models (context model, intelligent automation model, insights model), and translates them into system-appropriate actions. This mediator layer shields users from system complexity while maintaining full system functionality.
Solution Approach 2:
The system employs self-service mechanisms where the AI assistant autonomously navigates the complex enterprise system on behalf of users. The intelligent automation model determines workflows, and the insights model provides recommendations without requiring users to understand or manually configure the underlying complex system architecture.
2Productivity
If enterprise systems provide comprehensive functionality, then system capability is improved, but user training time and resources increase
Solution Approach 1:
The AI assistant serves as a training-free intermediary that handles complex system interactions. Users learn to interact with the assistant through simple natural language rather than investing time in learning the complex enterprise system interface and workflows.
Solution Approach 2:
The patent replaces the mechanical learning process (users manually learning system operations) with an intelligent system that automatically understands and executes tasks. The machine learning models substitute for human learning and adaptation, eliminating the need for extensive user training.
3Productivity
If users navigate complex enterprise systems without extensive training, then resource efficiency is improved, but user mistakes and system errors increase
Solution Approach 1:
The system implements multiple feedback loops through its machine learning models. The context model validates query understanding, the intelligent automation model verifies workflow appropriateness, and the insights model checks recommendation validity. This layered feedback mechanism reduces errors while maintaining resource efficiency.
Solution Approach 2:
The AI assistant acts as a reliable intermediary that enforces proper system usage patterns. It validates user intentions, determines appropriate workflows, and provides guidance based on business rules, thereby reducing errors even when users lack extensive training.
Data Source
AI summary
In some implementations, a device may receive, from a user device, a query for information from a user. The device may receive, from an enterprise system, data identifying products and services to be offered by the user. The device may process the query and the data, with a model, to identify a subject of the query and a context for the subject. The device may process the subject and the context, with a model, to determine a workflow for the user. The device may process the data, the subject of the query, and the context for the subject, with a model, to determine a key performance indicator or a decision associated with one of the products or the services. The device may provide information identifying the workflow, the key performance indicator, and/or the decision to the user device via a virtual assistant or a user interface.