Context-Aware Digital Assistant Actions From Voice Input
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Solution Overview
Problem
Conventional automated digital assistants rely on backend components for functionality due to computational limitations of electronic devices, failing to leverage the context of voice-based inputs to provide personalized and effective actions for users.
Innovation Solution
A method and system that utilizes a digital assistant on a media device to interpret natural language inputs, determine context, calculate priority scores for actions, and generate dynamic action buttons for user selection, enabling efficient task execution based on voice messages or voicemails.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional automated digital assistants rely on backend components for functionality, then computational limitations of electronic devices are addressed, but context of voice-based inputs is not leveraged to provide personalized actions
Solution Approach 1:
The system performs preliminary action by extracting and analyzing context information from voice-based inputs before generating actions. The digital assistant identifies entities, relationships, and contextual metadata from the voice input, preparing this information in advance to enable personalized action selection without requiring continuous backend processing.
Solution Approach 2:
The invention extracts relevant context information from the voice-based input and stores it as contextual metadata on the electronic device. This extraction separates the context analysis from the action execution, allowing the device to leverage stored context for personalized responses without relying solely on backend components.
2Productivity
If digital assistants perform multiple actions based on voice input, then user needs are met more comprehensively, but priority determination becomes complex
Solution Approach 1:
The system changes parameters by assigning numerical priority scores to different actions based on contextual metadata. Actions are evaluated using modifiable weighting factors that can be adjusted based on user profile, time of day, or contextual conditions, transforming complex priority determination into a parameter-based scoring system.
Solution Approach 2:
The digital assistant performs self-service by automatically determining and executing the highest priority action without requiring user intervention for priority selection. The system autonomously evaluates multiple actions against stored context and user preferences, selecting and executing the most appropriate action independently.
3Ease of operation
If dynamic action buttons are generated for user selection, then user control over actions is improved, but interaction time increases
Solution Approach 1:
The system applies partial action by presenting only the top prioritized action through a dynamic button, rather than listing all possible actions. This selective presentation maintains user control for the most important action while reducing the time required to review and respond to multiple options.
Data Source
AI summary
Systems and methods for providing contextual based actions based on a natural language input are disclosed. The method comprises: receiving, on a media device, a natural language input; determining, based on the natural language input, a first context of the natural language input; and determining, based on the first context, a first action. a dynamic action button is generated and configured to be selected by a user to carry out an action, and in response to the user selecting the dynamic action button, the systems and methods describe carrying out the first action.