Conversational Digital Assistant for Corporate Network Interface Translation
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing user requests across diverse applications in a corporate network environment, particularly in determining the correct interface and generating appropriate network requests without requiring technical knowledge from users.
Innovation Solution
A digital assistant utilizes a language model trained for conversational interaction to interpret user prompts, determine the relevant application interface, obtain the interface's requirement description, and generate a network request definition, thereby facilitating seamless communication with various applications in the corporate network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users directly interact with corporate network applications, then resource access is achieved, but technical expertise is required and operation complexity increases
Solution Approach 1:
A conversational interface agent is introduced as an intermediary between users and corporate network applications. The agent receives natural language user inputs, translates them into appropriate application commands, and returns results to users. This mediator handles the complexity of interfacing with multiple applications having different technical requirements, while users only need to provide simple natural language requests.
2Adaptability or versatility
If multiple applications with different technical requirements are integrated, then system versatility improves, but integration complexity increases
Solution Approach 1:
The conversational interface agent is designed as a universal system that can handle multiple different applications through a single unified interface. It maintains knowledge of various application interfaces and requirements, allowing it to adapt to different applications without requiring separate integration mechanisms for each one.
Solution Approach 2:
The system segments the complexity by separating the interface translation function from the application logic. The conversational agent handles the translation layer, while underlying applications maintain their own technical implementations. This segmentation allows each component to be developed and maintained independently.
3Ease of operation
If natural language processing is implemented, then user interaction simplicity improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by maintaining precompiled knowledge of application interfaces, data structures, and command formats. When a user request arrives, the conversational agent can quickly match the natural language input against known patterns and templates, rather than performing full semantic analysis from scratch each time.
Data Source
AI summary
The present disclosure relates to computer-implemented methods, software, and systems that can use a language model trained for conversational interaction using natural language prompts. A prompt is received to request a resource from a corporate network environment. First input can be provided to a language model to determine an interface of an application from a set of applications associated with the corporate network environment. In response to determining the interface by the language model, a requirement description of the interface is obtained. Second input is provided to the language model that comprises the obtained requirement description of the interface and the received prompt, and is used as context for generating a network request definition for a request to be directed to the interface. The request is sent to the interface based on the network request definition provided by the language model to obtain the requested resource.