AI Agent Content Submission Control via Model Selection
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Solution Overview
Problem
Existing systems lack effective methods for dynamically controlling the submission of user-composed content based on real-time user parameters, such as mood and intended recipients, to provide personalized feedback and optimize content processing.
Innovation Solution
An apparatus and method utilizing an artificial intelligent agent with multiple trained machine learning models, selected based on user parameters, to assess and generate feedback on user content, enabling dynamic control of content submission processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single machine learning model is used for content assessment, then the system is simple to manage, but it cannot adapt to different user contexts and moods
Solution Approach 1:
The system segments the machine learning models into multiple specialized models, each trained on different datasets corresponding to different user moods, contexts, or content types. This segmentation allows the system to select the most appropriate model for each specific situation, improving adaptability while keeping individual models manageable in size and complexity.
Solution Approach 2:
The system dynamically selects which machine learning model to use based on real-time user parameters such as mood, context, or content type. This dynamic selection mechanism allows the system to adapt to changing user needs without requiring all models to be active simultaneously, thus improving versatility while controlling overall system complexity through on-demand model deployment.
2Measurement precision
If multiple machine learning models are selected based on user parameters, then personalized feedback is improved, but computational resources and processing time increase
Solution Approach 1:
The system applies partial action by selecting and deploying only the specific machine learning model needed for the current user context rather than running all models. This approach provides sufficiently accurate personalized feedback for each situation while significantly reducing computational resource usage compared to executing the entire model suite, achieving the right balance between feedback accuracy and energy consumption.
3Loss of information
If real-time parameter monitoring is implemented, then user context awareness is enhanced, but system overhead and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing user parameter data before it is needed for content assessment. User context information is captured and prepared in advance, allowing the system to quickly retrieve and utilize this information when needed without incurring significant processing delays during the actual content assessment workflow, thus reducing real-time overhead while maintaining comprehensive context awareness.
Data Source
AI summary
An apparatus comprising means for: obtaining a value of one or more parameters which vary with actions of a user; accessing an artificial intelligent agent configured to use one or more trained machine learning models selected, from a plurality of differently trained machine learning models, based on the obtained value of one or more parameters, the plurality of differently trained machine learning models being configured to provide respective outputs; providing content composed by the user as an input to the artificial intelligent agent to cause generation of feedback to the user on the content composed by the user, the feedback being dependent on the artificial intelligent agent; controlling a submission based on the content composed by the user; and causing the feedback to be provided to the user.