AI-Based Dialog Service Auto-Provisioning From Historical Dialog Data
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Solution Overview
Problem
The high cost and time-consuming manual processes involved in deploying AI-based dialog services, such as chatbots, on platforms like social engagement timelines, smartphone applications, and websites, make automation deployment unaffordable and inefficient for many enterprises, particularly smaller companies.
Innovation Solution
A system that automates the provisioning of AI-based dialog services by credentialing platforms, crawling historical dialog data, assembling an AI corpus, and interfacing with third-party subsystems without manual intervention, enabling automatic deployment, updates, and notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual deployment processes are used for AI-based dialog services, then deployment precision and reliability are improved, but deployment time and cost increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically credentialing target platforms, crawling historical dialog data, and assembling AI corpora before actual deployment. This prepares all necessary components in advance, eliminating the need for time-consuming manual setup during the actual deployment phase while ensuring reliability through systematic preparation.
Solution Approach 2:
The system enables self-service by automating the entire deployment process including platform credentialing, data crawling, corpus assembly, and service deployment without requiring manual intervention. The automated provisioning system serves itself by managing the complete lifecycle of AI-based dialog service deployment, significantly reducing both time and human resource requirements.
2Manufacturing precision
If manual deployment processes are used for AI-based dialog services, then deployment quality is improved, but deployment cost increases significantly
Solution Approach 1:
The system replaces manual mechanical processes with automated digital processes. Instead of manual credentialing, data collection, and service deployment, the system uses automated software agents, crawlers, and provisioning mechanisms to perform these tasks, eliminating the need for expensive human professional services while maintaining high deployment quality through systematic automation.
Solution Approach 2:
The automated provisioning system performs all deployment tasks autonomously, from credentialing platforms to deploying services, without requiring expensive manual professional services. This self-service capability dramatically reduces deployment costs while maintaining quality through consistent, repeatable automated processes.
3Productivity
If automated deployment is implemented, then deployment efficiency is improved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary automated provisioning layer that manages the complexity of deploying AI-based dialog services. This intermediary system handles platform credentialing, data crawling, corpus assembly, and service deployment, shielding users from the underlying complexity while maintaining high deployment efficiency through automation.
4Ease of operation
If full automated provisioning is implemented, then ease of operation is improved, but initial system complexity increases
Solution Approach 1:
The system achieves ease of operation by implementing full automated provisioning that requires no manual intervention. The system automatically credentials platforms, crawls data, assembles corpora, and deploys services, making the process as easy as initiating a single automated workflow while the underlying complexity is managed by the self-service automation system.
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.