Network Learning Model Vocal Intent Prediction
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Solution Overview
Problem
Current content platforms are complex to navigate and configure, especially when using vocal commands, which can be as slow as traditional workflows in tasks like inviting players to games, due to the need for manual selection and scrolling through lists.
Innovation Solution
Network-based learning models that capture and interpret vocal utterances to identify user intent and predict corresponding workflows, utilizing a system with a microphone, network server, and processor to streamline interactions by analyzing user interaction data and contextual information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If vocal commands are used for navigation and configuration, then ease of operation is improved, but time required for tasks increases due to complex workflows
Solution Approach 1:
The system performs automatic workflow prediction and execution based on interpreted user intent, eliminating the need for manual selection and scrolling through lists. The prediction engine autonomously determines the appropriate workflow sequence, and the system automatically executes it, allowing the interface to serve itself rather than requiring continuous user guidance through each step.
Solution Approach 2:
The system pre-configures and stores multiple workflows with their associated steps and parameters. When a user provides a vocal command, the system has already prepared the possible workflow options in advance, enabling rapid prediction and selection without requiring real-time manual configuration or navigation through complex menus.
2Reliability
If traditional workflow with manual selection is used, then task completion is reliable, but productivity decreases due to scrolling and manual configuration
Solution Approach 1:
The system incorporates feedback loops where user interactions are continuously monitored and analyzed. The prediction engine learns from past user behavior patterns and feedback to improve workflow predictions over time, ensuring reliable task completion while increasing productivity. The system adjusts its predictions based on feedback regarding user preferences and actual workflow outcomes.
Solution Approach 2:
The patent replaces the mechanical interaction model (manual scrolling, clicking, and selection through interface elements) with an intelligent system that uses natural language processing and machine learning to interpret user intent and automatically execute workflows. This substitution eliminates the need for manual navigation through lists and menus while maintaining reliable task completion through intelligent prediction.
3Adaptability or versatility
If content platforms provide wide variety of content and options, then adaptability is improved, but device complexity increases making navigation complicated
Solution Approach 1:
The system extracts and separates the complexity of workflow configuration from the user interaction model. Instead of requiring users to navigate and configure complex workflow details manually, the system extracts this complexity and handles it automatically through intelligent prediction and execution, presenting users with simplified vocal command interfaces while maintaining access to diverse content and options.
Solution Approach 2:
The prediction engine serves multiple functions: it interprets user intent from vocal commands, predicts appropriate workflows, selects the best matching workflow, and executes it automatically. This multi-functional approach allows the system to handle diverse content and options across different contexts while maintaining a consistent, simplified user interface that adapts to various tasks without increasing perceived complexity.
Data Source
AI summary
Systems and methods of network-based learning models for natural language processing are provided. Information may be stored information in memory regarding user interaction with network content. Further, a digital recording of a vocal utterance made by a user may be captured. The vocal utterance may be interpreted based on the stored user interaction information. An intent of the user may be identified based on the interpretation, and a prediction may be made based on the identified intent. The prediction may further correspond to a selected workflow.