Next Node Recommender for Directed Acyclic Graphs
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Solution Overview
Problem
Unskilled users face difficulty in constructing computer algorithms using directed acyclic graphs due to indecision about which code blocks to connect next, leading to frustration and potential abandonment of software capabilities for computer-assisted construction.
Innovation Solution
A method and system that utilize a transition probability dictionary to recommend the most suitable next node in a directed acyclic graph, based on probabilities derived from training data, to assist users in building complex algorithms without requiring extensive programming knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If unskilled users attempt to construct computer algorithms using directed acyclic graphs, then they can access software capabilities for computer-assisted construction, but they experience indecision and frustration about which code blocks to connect next
Solution Approach 1:
The patent introduces an automated next node recommender system that acts as an intermediary between the user and the directed acyclic graph. This recommender analyzes the current execution path and suggests the next appropriate node to connect, thereby mediating the interaction and eliminating user indecision while maintaining full access to software capabilities
Solution Approach 2:
The system enables self-service by allowing the software to automatically recommend and even auto-connect next nodes based on analysis of training data and current execution paths. This self-service capability removes the burden of decision-making from unskilled users while still utilizing the full functionality of the directed acyclic graph construction tools
2Adaptability or versatility
If unskilled users are required to make decisions about connecting code blocks, then they can construct custom algorithms, but they require extensive programming knowledge to do so effectively
Solution Approach 1:
The automated recommender serves as an intermediary that bridges the gap between unskilled users and complex algorithm construction tasks. It analyzes the directed acyclic graph structure and current execution path to suggest appropriate next nodes, thereby enabling custom algorithm construction without requiring users to possess extensive programming knowledge
Solution Approach 2:
The system performs preliminary action by pre-analyzing training data to build transition probability dictionaries that encode optimal next node selections. This preliminary analysis allows the system to automatically suggest correct connections without requiring users to understand the underlying programming logic or decision-making criteria
3Ease of operation
If the system provides automated next node recommendations, then non-technical users can construct executable algorithms, but the system requires complex transition probability dictionaries derived from training data
Solution Approach 1:
The system performs the complex work of building transition probability dictionaries in advance, during a training phase that occurs before actual algorithm construction. This preliminary action separates the complexity of data analysis from the simplicity of user interaction, allowing non-technical users to benefit from sophisticated recommendations without encountering the underlying complexity
Solution Approach 2:
The system serves itself by automatically building and maintaining transition probability dictionaries from training data without requiring user intervention. This self-service approach to handling complex data structures allows the system to manage its own complexity internally while presenting a simple, usable interface to non-technical users
Data Source
AI summary
A method includes receiving a set of execution paths for a directed acyclic graph. The directed acyclic graph includes multiple nodes and multiple edges. The nodes include sets of executable code. The edges represent an operational relationship between at least two nodes. The execution paths include a subset of the nodes connected by a sequence of edges. The method further includes setting a current training level to a maximum training level. The method further includes constructing a transition probability set for the current training level and adding the transition probability set to a transition probability dictionary. The method further includes storing the transition probability dictionary as a final transition probability dictionary.