Orchestrated Task Bot Assembly for Immigration Petition Automation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional preparation of immigration petitions is a manual, time-consuming, resource-intensive, and error-prone process that diverts legal staff from client service and case strategy, requiring repetitive work and varying case-specific processes.
Innovation Solution
A system utilizing an orchestrator bot and a plurality of task bots to automate the generation of immigration petitions, including data preprocessing, form population, and document assembly, with machine learning models for error reduction and efficiency improvements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used for preparing immigration petitions, then flexibility in handling diverse case types is maintained, but productivity is reduced and error rates increase
Solution Approach 1:
The patent divides the immigration petition preparation process into discrete, modular task bots that can be independently developed, executed, and maintained. Each bot handles a specific sub-task (e.g., data validation, form population, document assembly), allowing the system to process different case types by assembling appropriate combinations of these modular components rather than requiring complete process redesign for each case type.
Solution Approach 2:
The patent creates a universal task bot framework that can handle multiple case types through a common architecture. The orchestrator bot and task bot design allows the same set of modular components to serve different immigration petition types by configuring which bots are invoked and in what sequence, eliminating the need for separate proprietary software for each case type.
2Adaptability or versatility
If proprietary software is developed for each case type, then specific case requirements are met, but memory storage needs increase and maintenance becomes difficult
Solution Approach 1:
The patent implements a universal task bot framework where a single set of modular components can handle multiple case types. Instead of storing separate proprietary software for each case type, the system stores a library of reusable task bots that can be assembled and configured to meet specific case requirements, significantly reducing memory storage requirements while maintaining versatility.
Solution Approach 2:
The patent enables dynamic loading and unloading of task bots based on the specific case type being processed. Rather than keeping all possible task bots permanently loaded in memory, the system loads only the necessary bots for each case type, executes them, and then releases their memory resources, optimizing memory utilization while maintaining access to diverse functionality.
3Reliability
If separate proprietary software is used for each task, then task-specific functionality is optimized, but error reduction and updates become inefficient
Solution Approach 1:
The patent segments the system into independent task bots that can be individually tested, debugged, and updated without affecting other parts of the system. Each bot is a self-contained unit with defined inputs and outputs, allowing errors to be isolated and fixed in specific bots rather than requiring system-wide testing and validation, thereby improving both reliability and update efficiency.
Solution Approach 2:
The patent implements feedback mechanisms where task bots report their execution status, errors, and performance metrics to the orchestrator. This feedback loop enables automated error detection, tracking, and notification, allowing rapid identification and correction of issues while maintaining system reliability without requiring manual intervention for every error.
4Productivity
If manual preparation methods are used, then resource consumption is high, but implementation complexity is low
Solution Approach 1:
The patent implements self-service automation where the task bot framework automatically performs data validation, form population, document assembly, and quality checks without requiring manual intervention for routine tasks. The system serves itself by automatically invoking the appropriate sequence of bots based on case type classification, eliminating the need for manual coordination while maintaining high productivity and manageable complexity through automated decision-making.
Data Source
AI summary
Systems and methods disclosed herein relate generally to generating an immigration petition. An orchestrator bot may obtain petitioner data and a case type classification value as input, invoke from a case type repository, based on the case type classification value, a task bot chain, wherein the task bot chain identifies one or more task bots selected from a plurality of task bots to create a subset of task bots to implement an execution sequence, and execute the subset of task bots according to the execution sequence. The subset of task bots may pre-process the petitioner data for injection into one or more digital forms, and generate, from the one or more digital forms, the immigration petition configured for printing on a paper of a given size.