Onboarding State Diagram for Task Path Optimization
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Solution Overview
Problem
The onboarding process for moving services from client-managed computing infrastructure to network computing infrastructure is inefficient due to lengthy task lists with unclear task relevance and order, leading to client disengagement, as existing methods provide little guidance and do not adapt to client feedback or dynamic changes in task execution.
Innovation Solution
The system generates a state diagram based on onboarding information to identify and prioritize tasks with the highest probability of success, continuously updating the task execution path based on client feedback and automated monitoring, and provides real-time solutions to errors, ensuring optimal task order and engagement throughout the onboarding process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a long and exhaustive list of tasks is provided to all client organizations, then completeness of task coverage is improved, but client engagement and ease of operation deteriorate due to irrelevance and lack of guidance
Solution Approach 1:
The patent segments the exhaustive task list into subsets based on client organization characteristics (size, industry, service type) and onboarding stage. The task management system divides tasks into groups that are progressively revealed, preventing information overload while ensuring all necessary tasks are eventually completed. This segmentation allows clients to focus on relevant tasks only, improving engagement while maintaining completeness through structured progression.
Solution Approach 2:
The patent implements dynamic task list generation that adapts in real-time based on client feedback, task completion status, and changing onboarding requirements. The system re-ranks and re-orders tasks dynamically, removing completed tasks and adjusting priorities based on emerging needs. This dynamic approach ensures the task list remains relevant and engaging while systematically covering all necessary tasks through adaptive refinement.
2Ease of manufacture
If a fixed pre-ordered task list is provided, then ease of implementation is improved, but adaptability to client-specific needs and dynamic changes deteriorates
Solution Approach 1:
The patent transforms the static pre-ordered task list into a dynamic system that automatically re-ranks and re-orders tasks based on real-time factors including client feedback, completion status, and changing requirements. The system maintains ease of implementation through automated management while achieving adaptability through continuous adjustment of task priorities and sequences, allowing the same framework to serve diverse client needs effectively.
Solution Approach 2:
The patent incorporates feedback loops where client responses, task completion outcomes, and system performance data are continuously collected and used to adjust the task list. This feedback mechanism enables the system to adapt to client-specific needs by learning from actual onboarding experiences, while maintaining ease of implementation through automated adjustments rather than requiring manual reconfiguration of the task framework.
3Adaptability or versatility
If manual navigation through task lists is required, then flexibility in task selection is improved, but productivity and time efficiency deteriorate due to difficulty in identifying relevant tasks and optimal order
Solution Approach 1:
The patent implements self-service automation where the system automatically identifies, ranks, and presents the optimal subset of tasks for each client organization based on their characteristics and progress. The automated system performs what would otherwise require manual analysis and decision-making, significantly improving productivity by eliminating the time clients spend navigating and selecting tasks. Flexibility is maintained because the automated system tailors task selection to each client's specific needs and context.
Solution Approach 2:
The patent changes the parameters used for task selection and ordering from static, one-size-fits-all criteria to dynamic parameters that reflect individual client organization characteristics, onboarding stage, and real-time progress. This parameter transformation enables the system to automatically determine the optimal task subset and sequence for each client, improving productivity through automated decision-making while preserving flexibility through customized parameter sets that adapt to different client needs.
4Device complexity
If a static task list is provided without updates, then system simplicity is improved, but reliability of task relevance and optimality deteriorates as onboarding progresses
Solution Approach 1:
The patent implements dynamic task list management that automatically updates the task subset and ordering as onboarding progresses. The system monitors completion status, client feedback, and changing requirements, then re-ranks tasks to maintain optimality. This dynamic approach preserves reliability of task relevance throughout the onboarding process while managing complexity through automated algorithms that systematically adjust the task list based on predefined criteria and real-time data.
Data Source
AI summary
Described herein are techniques and systems for onboarding a service from client-managed computing infrastructure to network computing infrastructure. As part of the onboarding, a database that stores onboarding information is accessed and a set of tasks is identified. A state diagram is generated based on the onboarding information. The techniques and systems are configured to calculate, within the state diagram, a task execution path that is associated with a highest probability of success for moving the client organization from a current environment associated with the client-managed computing infrastructure to a target environment associated with the network computing infrastructure. The task execution path can be used to identify and provide subsets of tasks as part of an autonomously guided onboarding process. The task execution path can be re-calculated based on a determination that an individual task has not been completed within an expected amount of time to complete the individual task.