AI-Based Dialog Service Auto-Provisioning From Historical Data
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Solution Overview
Problem
The high cost and manual-intensive nature of deploying AI-based dialog services, such as chatbots, on platforms like social engagement timelines, smartphone applications, and websites, along with the time and maintenance expenses, make automation deployment unfeasible for many enterprises, especially smaller companies.
Innovation Solution
A system for automated provisioning of AI-based dialog services that eliminates manual intervention by credentialing platforms, crawling historical data, assembling AI-based corpora, interfacing with third-party subsystems, and scheduling updates, enabling automatic deployment and maintenance of dialog services across various platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual deployment processes are used for AI-based dialog services, then deployment can be customized and controlled, but deployment cost and time increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically credentialing platforms, crawling historical data, and assembling AI-based corpora before deployment is needed. This pre-processing eliminates the need for manual discovery and programming work, reducing both cost and time while maintaining customization capabilities.
Solution Approach 2:
The system enables self-service deployment by automatically interfacing with third-party subsystems and scheduling updates without requiring consulting or technical professional services. The automated provisioning process serves itself, eliminating the need for expensive manual intervention while maintaining deployment quality.
2Reliability
If comprehensive discovery and programming services are provided, then automation quality and accuracy improve, but deployment cost increases to hundreds of thousands or millions of dollars
Solution Approach 1:
The system achieves universality by using standardized automated processes that can deploy AI-based dialog services across multiple platforms (social engagement timelines, smartphone applications, websites) without requiring platform-specific manual programming. This multi-functional approach maintains quality while reducing complexity and cost.
Solution Approach 2:
The system replaces the mechanical manual process of discovery and programming with automated computational processes. AI-based algorithms automatically analyze historical data, generate dialog scripts, and configure deployments, substituting human expert labor with automated intelligence while maintaining or improving quality.
3Productivity
If automated provisioning is implemented, then deployment cost and time are reduced, but the complexity of system integration increases
Solution Approach 1:
The system uses an intermediary automated provisioning layer that manages the complexity of system integration. This intermediary automatically handles credentialing, data crawling, corpus assembly, and platform interfacing, shielding users from integration complexity while enabling rapid deployment across diverse platforms.
Data Source
AI summary
A method of auto-provisioning AI-based dialog services for a plurality of target applications includes storing a plurality of dialog templates, generating a deployment object associating one or more of the dialog templates with a target application from among the plurality of target applications, extracting textual data from the target application, assembling the extracted textual data into inquiries or inquiry responses according to the one or more dialog templates associated with the deployment object, and deploying an AI-based dialog service to the target application based on the assembled inquiries or inquiry responses. Each of the dialog templates may include one or more sets of common inquiries or common inquiry responses.