Machine Learning Process Flow Engine for Document Actions
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Solution Overview
Problem
Creating bespoke process flows for enterprises can be prohibitively expensive, and existing technologies lack efficient methods to generate recommendations for document objects and actions that conform to specific enterprise needs, industry standards, and user objectives.
Innovation Solution
A machine-learning model, trained using reinforcement learning techniques such as Q-learning and Markov Decision Processes, processes existing process flows represented as directed graphs to recommend actions and document objects that maximize the overall conformation metric, thereby generating recommendations for forming process flows that are consistent with existing processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If bespoke process flows are created for enterprises, then process flow efficiency and compliance with enterprise needs are improved, but costs become prohibitively expensive
Solution Approach 1:
The system creates simplified copies of existing process flows from the training data and uses machine learning to generate recommendations based on these copies, rather than manually creating bespoke process flows from scratch. This reduces costs while maintaining compliance with enterprise needs.
Solution Approach 2:
The machine learning model automatically generates process flow recommendations by processing training data and applying learned patterns, eliminating the need for expensive manual creation and configuration by experts. The system serves itself by learning from existing data and autonomously generating optimized recommendations.
2Measurement precision
If machine-learning models are trained on existing process flows, then recommendation accuracy and conformation to enterprise standards are improved, but training time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing training data into structured formats (adjacency matrices, environment matrices) and pre-training the machine learning model on historical process flows before deployment. This preliminary preparation enables faster, more accurate recommendations during actual use while distributing the time investment across the training phase.
Solution Approach 2:
The system transforms process flow data into different parameter representations (adjacency matrices for graph structures, environment matrices for state transitions) that are optimized for machine learning processing. This parameter transformation enables efficient training and accurate recommendations by matching the data structure to the algorithm requirements.
3Reliability
If process flows are optimized for conformation metric, then compliance with industry standards is improved, but process flow complexity increases
Solution Approach 1:
The machine learning model uses feedback from the conformation metric calculation to iteratively improve process flow recommendations. The system evaluates recommended processes against industry standards and enterprise requirements, using this feedback to refine future recommendations while maintaining compliance without excessive complexity.
Solution Approach 2:
The system develops universal process flow recommendations that can serve multiple enterprise needs and industry standards simultaneously. By learning common patterns from diverse training data, the model generates multi-functional recommendations that comply with various standards without requiring separate complex processes for each requirement.
Data Source
AI summary
A method for machine-learning based process flow recommendation is provided. The method may include training a machine-learning model by at least processing training data with the machine-learning model. The training data may include a matrix representing one or more existing process flows by at least indicating actions that are performed on a document object to generate a subsequent document object. An indication that a first document object is created as part of a process flow may be received. In response to the indication, the trained machine-learning model may be applied to generate a recommendation to perform, as part of the process flow, an action to generate a second document object. Related systems and articles of manufacture, including computer program products, are also provided.