Flow Graph Navigation Data Generation
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Solution Overview
Problem
The increasing complexity of digital content makes it difficult for users to locate specific functionality, leading to inefficient user interaction and operational inefficiencies in computing devices, as conventional techniques require significant human and computing resources and are static, unable to dynamically address changes in digital content.
Innovation Solution
A flow graph system that automatically generates navigation data from digital content, using natural language processing to respond to user queries and dynamically update based on changes, allowing for real-time adaptation and improved accessibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional manual techniques are used to generate tutorials for each function, then users can receive guidance on specific functions, but significant human and computing resources are required and the system cannot dynamically adapt to changes in digital content
Solution Approach 1:
The system automatically generates navigation data by analyzing digital content markup and generating flow graphs without human intervention. The computational system serves itself by extracting functionality information, creating graph nodes and edges, and generating utterances automatically, eliminating the need for manual tutorial creation while maintaining adaptability to content changes
Solution Approach 2:
The flow graph structure enables dynamic adaptation to changes in digital content. When content is modified, the system can automatically update the flow graph by reprocessing the markup language, adding or removing graph nodes and edges as needed, allowing the navigation system to remain current without manual intervention
2Adaptability or versatility
If digital content complexity increases to add more functionality, then more features are available to users, but it becomes difficult for users to locate particular functionality and repeated interactions are required
Solution Approach 1:
The flow graph acts as an intermediary between the complex digital content and the user. It processes the markup language to extract functionality information, creates a structured representation with graph nodes and edges, and generates natural language utterances that users can query, thereby simplifying access to functionality without reducing content complexity
Solution Approach 2:
The system uses semantic similarity comparison to match user queries with graph node utterances, providing feedback-based navigation. The query processing system compares semantic meaning rather than exact text matches, allowing users to locate functionality through natural language queries that receive meaningful responses based on the flow graph analysis
3Reliability
If static tutorial techniques are used, then tutorials can be created for specific functions, but they cannot address changes made dynamically to digital content requiring additional manual updates
Solution Approach 1:
The system performs preliminary analysis of the digital content markup language to generate the flow graph structure and navigation data in advance. By pre-processing the content and creating the navigational framework before users need it, the system ensures accuracy is built-in from the start rather than requiring subsequent manual corrections when content changes
Data Source
AI summary
Navigation data generation techniques and systems are described to address the complexities of digital content and that overcome the challenges of the conventional techniques. In one example, digital content is received by a flow graph system and used to generate a flow graph that models functionality available via the digital content as graph nodes and connections between the functions as edges between the graph nodes based on a markup language of the digital content. Each of the graph nodes includes a respective utterance that describes functionality available via that node and thus is usable to locate this functionality using semantic similarity to an input query. The flow graph is used as a basis to generate navigation data.