Bot Controller Framework for Enterprise Task Automation
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Solution Overview
Problem
Current technologies lack an efficient framework for managing and controlling multiple bots within an enterprise network, particularly those performing natural language processing, which hinders real-time monitoring and automation of enterprise tasks.
Innovation Solution
A framework that initializes a bot controller application instance to receive registration information from bot hosts, retrieve configuration data, and display summaries of registered bot hosts, allowing for real-time control, script execution, and monitoring, including natural language processing capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple bots are deployed within an enterprise network to automate tasks, then productivity and task automation capability are improved, but device complexity and difficulty of managing/monitoring bots increase
Solution Approach 1:
The patent introduces a bot controller as an intermediary component that manages multiple bot hosts. The bot controller receives instructions, processes them through natural language processing, and distributes appropriate tasks to relevant bot hosts. This intermediary layer abstracts the complexity of managing multiple bots, allowing centralized control while maintaining the productivity benefits of distributed bot execution across the enterprise network.
2Reliability
If real-time monitoring and control of bot hosts is implemented, then reliability and task execution accuracy are improved, but use of energy and computational resources increase
Solution Approach 1:
The bot controller implements continuous monitoring and control mechanisms that operate throughout the task execution lifecycle. Rather than periodic checks, the system maintains continuous awareness of bot host status, task progress, and execution accuracy through ongoing communication channels. This continuous action ensures high reliability while optimizing resource usage by maintaining steady-state monitoring rather than intensive intermittent sampling.
Solution Approach 2:
Bot hosts are equipped with self-reporting capabilities that automatically transmit status information, execution progress, and error conditions to the bot controller. This self-service approach to monitoring reduces the computational burden on the central controller, as bot hosts independently manage their own status tracking and communication, thereby maintaining reliability without proportionally increasing overall system resource consumption.
3Adaptability or versatility
If natural language processing capabilities are added to bots, then adaptability and ease of operation are improved, but device complexity and processing time increase
Solution Approach 1:
The bot controller is designed with a universal natural language processing module that handles diverse input types and task descriptions through a single integrated system. Rather than implementing separate specialized processors for different language tasks, the NLP module provides multi-functional capabilities including intent recognition, entity extraction, and task parameter parsing. This universal approach enhances adaptability to various natural language inputs while avoiding the complexity proliferation that would result from multiple specialized processing components.
4Ease of operation
If centralized control through bot controller is implemented, then ease of operation and coordination are improved, but loss of time in communication and coordination increases
Solution Approach 1:
The bot controller pre-processes incoming natural language instructions and identifies task assignments before bot hosts are fully engaged. By performing preliminary parsing, intent recognition, and bot selection in advance, the system reduces the communication round-trip time during actual task execution. This preliminary action allows the controller to have instructions ready for immediate distribution, minimizing coordination delays while maintaining centralized control benefits.
Data Source
AI summary
A framework is described for editing, assigning, controlling, and monitoring multiple bots within an enterprise network, including bots that perform natural language processing. In one implementation, a method includes: initializing a bot controller application instance; receiving, at the bot controller application instance, registration information from bot hosts; retrieving, from a web services gateway, configuration information for each of the of bot hosts; and using at least the retrieved registration information and configuration information for each of the bot hosts, displaying at a graphical user interface of the bot controller application instance a summary of the registered bot hosts and data relating to scripts executed by each of the bot hosts.