Digital Assistant UI Generation Using Intermediate Representations
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Solution Overview
Problem
Existing digital assistants face challenges in efficiently and flexibly presenting backend responses to users due to cumbersome manual mapping processes, leading to static configurations, high computational costs, and increased risk of timeouts and incorrect outputs when using direct template filling by generative machine learning models.
Innovation Solution
Employing a generative AI model to generate an intermediate representation, which is then resolved outside the AI context, using scripting or condensed metalanguage representations to reduce token output and improve efficiency, scalability, and accuracy in rendering user interface elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual mapping processes are used to present backend responses, then configuration flexibility is achieved, but device complexity and time consumption increase
Solution Approach 1:
The system enables self-service by automatically generating UI element configurations from backend response data without requiring manual mapping processes. The digital assistant autonomously transforms API responses into appropriate user interface elements, eliminating the need for developers to create and maintain complex mapping rules.
Solution Approach 2:
The patent replaces the mechanical manual mapping process with an automated generative AI system. Instead of manually configuring UI elements through complex mapping rules, the system uses a generative model to automatically create appropriate UI configurations from backend responses, substituting human effort with intelligent automation.
2Extent of automation
If direct template filling is used by generative machine learning models, then automation is improved, but reliability decreases due to timeouts and incorrect outputs
Solution Approach 1:
The system introduces an intermediary processing layer between the generative model and final UI output. The model first generates candidate UI configurations, which then undergo validation and refinement steps to ensure correctness. This intermediary process reduces timeouts and incorrect outputs while maintaining high automation levels.
Solution Approach 2:
The system implements feedback mechanisms where the generated UI configurations are validated against expected outcomes and backend response structures. Incorrect or incomplete generations are detected and corrected through iterative refinement, improving reliability while maintaining automation efficiency.
3Loss of information
If comprehensive backend responses are processed, then information completeness is improved, but computational load and time consumption increase
Solution Approach 1:
The system extracts only the essential and relevant information from comprehensive backend responses for UI generation. Instead of processing all available data, the generative model identifies and utilizes key fields needed for creating appropriate UI elements, reducing computational load while maintaining information completeness.
Solution Approach 2:
The system applies partial processing by focusing computational resources on generating UI elements for the most important backend response fields. Rather than uniformly processing all response data, the model prioritizes critical information, reducing overall computational load while preserving essential information completeness.
Data Source
AI summary
Systems and methods described herein relate to the use of generative artificial intelligence to facilitate rendering of user interface elements in a user interface associated with a digital assistant. A backend response is automatically generated in response to user input provided via the user interface associated with the digital assistant. Prompt data is generated. The prompt data includes an instruction to generate an intermediate representation of an output data structure supported by the digital assistant. The prompt data is provided to a generative machine learning model to obtain the intermediate representation. The intermediate representation is processed to obtain the output data structure. One or more user interface elements are rendered based on the output data structure. The one or more user interface elements present the response data via the user interface associated with the digital assistant.