Service Information Entry Pages Using Prompt-Guided ML Generation
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Solution Overview
Problem
Traditional service information entry pages are manually designed, requiring significant manpower and resources, and are not adaptable to different service types, leading to complex and costly maintenance and updates.
Innovation Solution
A method involving generating a first prompt input for machine learning models to guide them in creating service information entry requirements based on target service types, and using the model outputs to determine service information entry pages that indicate multiple information entry items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If service information entry pages are manually designed, then the pages can be customized to meet specific needs, but it requires significant manpower and resources and is not adaptable to different service types
Solution Approach 1:
The patent replaces the manual mechanical design process with an automated machine learning-based system. The ML model automatically generates service information entry pages based on service type descriptions, eliminating the need for manual page design while maintaining adaptability across different service types.
Solution Approach 2:
The system enables self-service by allowing the ML model to autonomously generate service information entry pages without human intervention. The model automatically processes service type descriptions and produces customized pages, making the system self-sufficient in the page generation task.
2Ease of manufacture
If service information entry pages are manually designed, then detailed customization is possible, but maintenance and updates become complex and costly
Solution Approach 1:
The patent replaces manual maintenance operations with automated ML-based updates. When service information changes, the system automatically regenerates affected pages through the ML model, eliminating manual maintenance tasks and reducing costs associated with updates and repairs.
Solution Approach 2:
The system handles maintenance by detecting parameter changes in service information and automatically regenerating pages based on updated parameters. This allows cost-effective updates by simply changing input parameters rather than manually redesigning pages.
3Adaptability or versatility
If machine learning models are used to generate service information entry pages, then adaptability to different service types is improved, but the system complexity increases
Solution Approach 1:
The patent applies a universal ML model that can handle multiple service types through a single unified architecture. The model takes service type descriptions as input and generates appropriate pages for any service type, eliminating the need for separate specialized models for each service type and reducing overall system complexity.
Solution Approach 2:
The system introduces a prompt template as an intermediary between the ML model and the service information entry generation process. The prompt template serves as a mediator that guides the model's output format and structure, simplifying the system by providing a standardized interface for handling different service types.
4Productivity
If manual design is used for service information entry pages, then control over page content is maintained, but time consumption is significant
Solution Approach 1:
The patent replaces time-consuming manual page design with automated ML-based generation. The model rapidly generates service information entry pages by processing service type descriptions, reducing page generation time from hours of manual work to seconds of automated computation.
Solution Approach 2:
The system performs preliminary action by pre-training the ML model on service information entry patterns and structures. This preliminary training enables the model to quickly generate pages for new service types without requiring extensive real-time guidance, significantly reducing production time.
Data Source
AI summary
According to embodiments of the disclosure, a method, a device, a medium and a program product for information interaction are provided. The method includes: generating a first prompt input for each of at least one machine learning model, the first prompt input being configured to guide a corresponding machine learning model to generate a service information entry requirement corresponding to a target service type; obtaining output of the at least one machine learning model by providing the first prompt input to the corresponding machine learning model; and determining a service information entry page corresponding to the target service type based on the output of the at least one machine learning model, the service information entry page at least indicating a plurality of information entry items.