Interactive Query Results With Role-Based Action Components
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Solution Overview
Problem
Businesses face challenges in accessing and interpreting interrelated data stored across various databases and services, as users often prefer intuitive interactions over manual queries, and existing virtual assistants provide limited, non-customized outputs.
Innovation Solution
A system utilizing configurable interactive components that process natural language prompts, leverage Large Language Models (LLMs), and a unified graph schema to provide role-based, secure, and contextually relevant data outputs, including interactive components for user interaction and action execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If virtual assistants provide static text or audio outputs, then the system complexity is low, but the user satisfaction and result meaningfulness deteriorate due to lack of customization
Solution Approach 1:
The system dynamically selects and configures interactive components based on user attributes, query type, and data characteristics. The output format transitions from static text/audio to dynamic interactive elements such as sortable tables, drill-down charts, and action-triggering buttons, adapting to user needs in real-time
Solution Approach 2:
The virtual assistant integrates multiple output mechanisms into a unified system that can provide text, audio, and various interactive components (tables, charts, buttons) through a single interface. The system universally handles different user roles and preferences through role-based configuration
2Productivity
If users manually write queries and perform research using multiple sources, then data access precision is high, but the time consumption increases significantly
Solution Approach 1:
The system introduces a natural language processing intermediary that translates user-friendly queries into precise structured queries. The NLP layer acts as a mediator between simple user input and complex data retrieval operations, maintaining query accuracy while eliminating manual query writing
Solution Approach 2:
The system pre-processes and structures data from multiple sources into a unified schema before user queries are submitted. Data normalization, entity resolution, and relationship mapping are performed in advance, enabling fast retrieval without sacrificing precision
3Loss of information
If virtual assistants provide generic answers, then the ease of operation is high, but the loss of information increases due to lack of user-specific context
Solution Approach 1:
The system tailors output characteristics to specific user roles and attributes. Different user types (e.g., executives vs. analysts) receive differently configured interactive components with varying levels of detail, interactivity, and action capabilities, preserving relevant context for each user segment
Data Source
AI summary
Described are systems and processes providing meaningful responses to natural language (NL) prompts issued by a user and providing the responses to the user with configurable interactive components selected based in part on a role of the user that enables the user to engage with the responses in meaningful ways. The configurable interactive components may enable a user to perform certain tasks with output data in a response, such as play content, edit content, and/or perform actions by implementation of software code added to the configurable interactive components. The actions may include implementing approvals, sending electronic messages, and/or scheduling meetings, among many other actions.