Multimodal AI Entry Points for Fewer Interaction Steps
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Solution Overview
Problem
Existing human-machine interaction systems lack efficient methods to quickly combine AI with electronic devices, limiting their ability to adapt to user needs and provide personalized responses, and often require multiple operation steps for information input and output.
Innovation Solution
An interaction processing method that invokes modality entry points of AI applications through trigger instructions, allowing for the acquisition and processing of interaction information using various input modalities, and outputs results through associated modalities, thereby simplifying the interaction process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional human-machine interaction methods are used, then system complexity is reduced, but interaction efficiency and responsiveness deteriorate
Solution Approach 1:
The patent segments the interaction process into distinct modalities (voice, text, image, video) with dedicated entry points and processing pathways. Each modality has its own interface and processing pipeline, allowing parallel processing and reducing bottlenecks, thereby improving interaction efficiency without requiring complete system redesign
Solution Approach 2:
The patent introduces an intermediary layer that translates various input modalities into unified processing formats and coordinates between different applications and services. This mediator handles the complexity of multi-modal integration, allowing the system to process diverse inputs efficiently without exposing complexity to end users
2Adaptability or versatility
If AI applications are integrated into electronic devices, then adaptability to user needs is improved, but operation steps and interaction complexity increase
Solution Approach 1:
The patent creates a universal multi-modal interaction framework that can handle voice, text, image, and video inputs through a common architecture. This framework provides personalized AI responses across different applications without requiring separate interaction flows for each application, reducing operation steps while maintaining adaptability
Solution Approach 2:
The patent implements preliminary action by pre-configuring multiple modality entry points and establishing translation rules before user interaction. The system proactively prepares processing pipelines and coordinates application interfaces in advance, allowing users to interact naturally without navigating complex setup procedures
3Adaptability or versatility
If multiple modality entry points are provided, then input flexibility is improved, but system complexity and processing overhead increase
Solution Approach 1:
The patent segments the processing of each modality into independent modules with dedicated entry points. Each modality (voice, text, image, video) has its own processing pipeline that can operate independently or in parallel, reducing the overhead of coordinating multiple modalities while maintaining input flexibility
Solution Approach 2:
The patent dynamically adjusts processing parameters based on the input modality type. When a voice input is detected, voice-specific parameters are activated; when text is detected, text-processing parameters are used. This parameter switching mechanism allows the system to handle multiple modalities efficiently without maintaining all processing capabilities at full complexity simultaneously
Data Source
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AI summary
An interaction processing method and related products are provided. In the method, at least one modality entry point of an artificial intelligence (AI) application is invoked in response to a trigger instruction detected; interaction information to-be-processed is acquired, and the interaction information to-be-processed is input to the AI application through the modality entry point; and according to an input modality type of the interaction information to-be-processed, a processing result corresponding to the interaction information to-be-processed is output by the AI application through an output modality type associated with the input modality type.