Conversational Bot Generation from API Schemas
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional conversational bot creation is time-consuming and cumbersome, requiring intensive human computation and resulting in limited scope, with deep learning-based models behaving unpredictably and lacking user annotation capabilities for natural language understanding and generation.
Innovation Solution
A method for automatically generating conversational bots by parsing API schemas, generating sentences, constructing deep learning models, and utilizing conversational feedback to improve natural language understanding and generation, allowing users to annotate and retrain the model for improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If deep learning-based language models are used to automate conversational bot creation, then productivity is improved, but reliability deteriorates due to unpredictable behavior
Solution Approach 1:
The system implements feedback loops where user interactions with the conversational bot are captured and used to retrain the deep learning model. This continuous feedback mechanism allows the system to maintain high productivity through automation while improving reliability by learning from actual user behavior patterns, making the bot's responses more predictable and aligned with user expectations over time.
Solution Approach 2:
The system enables self-service through automated model retraining using user feedback without requiring manual intervention from conversation designers. The bot automatically ingests new data from user interactions, retrains its language model, and deploys improvements, maintaining high productivity while enhancing reliability through continuous self-optimization based on real-world performance data.
2Reliability
If manual design of conversational artifacts is performed, then reliability is improved through human control, but productivity deteriorates due to time-consuming processes
Solution Approach 1:
The system performs preliminary actions by automatically generating initial conversational bot designs from API schemas before user interaction begins. This automated preliminary design phase maintains reliability through structured API-based constraints while dramatically improving productivity by eliminating manual design steps. The generated bot can then be refined through user feedback, combining automated efficiency with human-oriented improvement.
Solution Approach 2:
The system replaces the mechanical process of manual conversation design with an automated deep learning-based generation process. Instead of human designers manually creating conversational artifacts, the system uses AI models to generate bot designs from API schemas, substituting human labor with automated intelligence to achieve both high productivity and maintained reliability through programmatic consistency.
3Device complexity
If deep learning models are trained without user annotation capability, then device complexity is reduced, but measurement precision deteriorates in natural language understanding
Solution Approach 1:
The system implements feedback mechanisms where user annotations and corrections are captured and used to retrain the deep learning model. This feedback loop maintains relatively simple device architecture while continuously improving measurement precision in natural language understanding by learning from actual user correction data, allowing the model to adapt to nuanced language patterns without requiring complex pre-training.
Solution Approach 2:
The system performs preliminary automated training of the deep learning model using available data before deployment, establishing a baseline level of natural language understanding with manageable complexity. This preliminary training phase prepares the model for production use while maintaining simplicity, and subsequent user feedback further refines precision without requiring fundamental changes to the system architecture.
Data Source
AI summary
A method, computer system, and a computer program product for generating a conversational bot for an application programming interface (API) is provided. The present invention may include parsing an API schema. The present invention may include generating sentences for the conversational bot from the parsed API schema. The present invention may include constructing the conversational bot by training a deep learning model. The present invention may include receiving a natural language expression from a user. The present invention may include determining whether the natural language expression is enough to activate the bot.