Service Application Generator for Conversational Interfaces
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Solution Overview
Problem
Current conversational assistants require users to manually process data, leading to inefficiencies and mistakes, as they often need to cut and paste information to achieve desired outcomes, especially when handling queries that cannot be handled by existing service applications.
Innovation Solution
A service application generator that interacts with users to define parameters, intents, and data sources, allowing users to generate service applications with minimal coding, using natural language inputs and intent handlers to derive intents and entities from queries, and associate them with data communication paths and value paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually process data by cutting and pasting information, then they can obtain desired outcomes from conversational assistants, but this leads to inefficiencies and mistakes
Solution Approach 1:
The patent introduces an intermediary component that automatically extracts and processes data between the conversational assistant and the user's workflow. This mediator handles the data transformation and integration tasks that previously required manual cutting and pasting, thereby improving efficiency while reducing errors through automated, consistent processing.
Solution Approach 2:
The system enables self-service by allowing the conversational assistant to automatically perform data extraction, transformation, and integration operations without requiring manual user intervention. The assistant autonomously completes tasks such as extracting entities from queries, formatting data, and integrating results into the user's workflow, eliminating the need for manual data processing while maintaining accuracy.
2Adaptability or versatility
If conversational assistants handle queries that cannot be serviced by existing applications, then they can provide broader functionality, but users require manual data processing to achieve desired outcomes
Solution Approach 1:
The system performs preliminary actions by pre-configuring integration templates and data transformation rules for various query types. When a new query is received, the system automatically matches it against predefined templates and executes the appropriate data processing workflow in advance, eliminating the need for users to manually process data for each query while expanding the system's ability to handle diverse query types.
Solution Approach 2:
The patent implements a universal data processing framework that can handle multiple types of queries and integrate with various external systems through a common interface. This multi-functional approach allows the conversational assistant to service diverse queries (weather, news, transactions, etc.) while automatically performing the necessary data extraction and integration, reducing user effort across all query types rather than requiring manual processing for each specific case.
3Reliability
If service applications are generated with predefined parameters, then they can handle specific intents effectively, but they lack flexibility to handle diverse natural language inputs
Solution Approach 1:
The system employs dynamic parameter configuration where the service application's parameters are not fixed but can be automatically adjusted based on the incoming query. The system uses machine learning models to analyze natural language inputs, dynamically identify the appropriate intent, and automatically configure or select the corresponding service application parameters, thereby maintaining high intent handling accuracy while adapting to diverse natural language inputs.
Solution Approach 2:
The patent implements parameter changes by allowing the service application's configuration parameters to be dynamically modified based on the query characteristics. When a new type of natural language input is detected, the system automatically adjusts parameters such as data extraction patterns, integration targets, and response formatting to match the new intent, enabling the same service application framework to reliably handle both predefined and novel query types through parameter adaptation rather than requiring separate applications for each query type.
Data Source
AI summary
Technologies are described herein for generating a service application. A service application generator can be used to generate a service application upon receiving a prompt to generate the service application. The service application generator can interface with a user or other entity to determine information used to build a service application.