Dynamic Time-to-Completion Estimation via Task Bucketing
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Solution Overview
Problem
There is a lack of visibility in the status of multi-task workflows, making it difficult to accurately estimate the time required for completion, which hampers planning and efficiency.
Innovation Solution
A system and method that utilize historical ticket data to train machine learning engines to predict time-to-completion by bucketing tasks based on common features, allowing for dynamic estimation and adaptation as new information becomes available, and adding resources if service level agreements are breached.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-task workflows are executed without dynamic estimation, then system simplicity is maintained, but time-to-completion accuracy deteriorates
Solution Approach 1:
The patent segments the multi-task workflow into discrete tasks and groups them into buckets based on common features. Each bucket is trained with a separate machine learning model, allowing the system to handle complexity through modular decomposition rather than a monolithic estimation approach.
Solution Approach 2:
The system performs preliminary actions by training machine learning models on historical ticket data before actual workflow execution. This pre-training phase enables the system to have accurate estimation capabilities ready in advance, eliminating the need for complex real-time calculations during workflow execution.
2Adaptability or versatility
If static estimation methods are used, then system complexity is reduced, but adaptability to changing conditions deteriorates
Solution Approach 1:
The patent implements dynamic estimation by continuously updating machine learning models with new historical ticket data as it becomes available. The system adapts to changing conditions by retraining models on updated data, making the estimation process dynamic rather than static while managing complexity through automated learning processes.
Solution Approach 2:
The system incorporates feedback mechanisms where actual workflow completion data is fed back into the machine learning models for continuous improvement. This feedback loop enables the system to learn from past performance and adapt to changing conditions automatically, reducing the need for manual intervention.
3Measurement precision
If detailed tracking of all tasks is implemented, then time-to-completion estimation accuracy is improved, but information processing overhead increases
Solution Approach 1:
The patent extracts only the most relevant features from historical ticket data for model training, rather than processing all available information. By identifying and extracting key predictive features, the system achieves accurate estimation while minimizing information processing overhead.
Solution Approach 2:
The system merges multiple tasks into buckets based on common features, processing them as grouped entities rather than individual tasks. This consolidation reduces the overall information processing burden while maintaining estimation accuracy through the collective learning from similar tasks.
Data Source
AI summary
A method for dynamic estimation of time-to-completion may include: receiving historical ticket data for historical tickets, the historical ticket data comprising historical tasks for each historical ticket; assigning each of the historical tasks into one of a plurality of buckets based on a common feature; training, for each bucket, a trained machine learning engine to predict a time-to-completion for the tasks in the bucket; receiving a current ticket; determining a plurality of tasks and an order of executing the tasks for the current ticket; identifying one of the buckets for each of the tasks in the current ticket; predicting, for each task in the current ticket, the time-to-completion for the current using the trained machine learning engine for the bucket for the task; combining the time-to-completion for the tasks for the current ticket into a current ticket time-to-completion; and returning the current ticket time-to-completion for the current ticket.