Unified Task Representation for Multi-Modal AI Processing
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Solution Overview
Problem
Current AI technology is limited in processing complex tasks due to its reliance on unimodal data, resulting in weak generalization ability and difficulty in applying AI models to various application scenarios.
Innovation Solution
A system and method for multi-modal multi-task processing that includes a task representation component to define tasks in a unified format, a data conversion component to determine encoding sequences, and a data processing component to process tasks across different modalities, enabling the processing of multiple tasks simultaneously and improving generalization ability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI models are trained based on unimodal data, then the model structure is simple and easy to implement, but the generalization ability is weak and difficult to apply to various complex application scenarios
Solution Approach 1:
The patent applies universality by designing a unified task representation framework that can handle multiple modalities (text, image, audio, video) and multiple task types through a single common structure. The framework uses a standardized task definition format with task description, input information, and output information elements that work across different modalities, allowing one model to perform diverse functions without requiring separate specialized models for each modality or task type.
Solution Approach 2:
The patent segments the complex multi-modal multi-task processing into distinct functional components: task representation component for defining tasks, data conversion component for encoding different modalities, and data processing component for executing tasks. This segmentation allows each component to specialize in specific functions while working together as an integrated system, making the overall complexity manageable and the model easier to implement despite handling diverse modalities and tasks.
2Adaptability or versatility
If AI technology deals with simple tasks of single tasks, small tasks or similar tasks, then the task processing is straightforward and efficient, but the application field is limited and cannot handle complex application scenarios
Solution Approach 1:
The patent implements dynamics by enabling the system to adaptively handle different task complexities and modalities through a flexible task representation framework. The framework can dynamically adjust to various task types (classification, generation, detection, segmentation) and modalities (text, image, audio, video) without requiring rigid predefined structures for each scenario, allowing the system to maintain efficiency while expanding application fields to complex scenarios.
Solution Approach 2:
The patent introduces a data conversion component as an intermediary that bridges different modalities and task types. This intermediary layer converts various input modalities into a unified representation format that the processing component can handle consistently, enabling the system to efficiently process complex multi-modal tasks while maintaining the straightforward processing efficiency of simpler tasks through the standardized conversion and processing pipeline.
3Productivity
If multiple tasks in different modalities are processed simultaneously, then the system achieves high productivity and resource utilization, but the system complexity and difficulty of implementation increase
Solution Approach 1:
The patent merges multiple task processing functions into a single unified system through the common task representation framework and shared processing component. Instead of having separate systems for different modalities and tasks, the framework combines text, image, audio, and video processing capabilities along with various task types (classification, generation, detection, segmentation) into one integrated system that processes multiple tasks simultaneously, thereby achieving high productivity while managing system complexity through unification.
Solution Approach 2:
The patent uses parameter changes by standardizing task representations through a unified format that transforms diverse tasks into a common parameter structure (task description information, task input information, task output information). This parameter standardization allows the system to process multiple different tasks through the same processing pipeline by simply changing the input parameters, enabling high throughput productivity without proportionally increasing system complexity since the same processing logic handles all task types.
Data Source
AI summary
A system for multi-modal multi-task processing includes a task representation component configured to determine a task representation element corresponding to a task representation framework that is used to define a content format for describing a to-be-processed task, and the task representation element including an element used to define task description information, an element used to define task input information, and an element used to define task output information; and based on the task representation element, acquire task description information, task input information, and task output information corresponding to each of to-be-processed tasks in different modalities; a data conversion component configured to determine an encoding sequence corresponding to each of the to-be-processed tasks; and a data processing component configured to process each of the to-be-processed tasks based on the encoding sequence corresponding to each of the to-be-processed tasks to obtain a task processing result corresponding to each of the to-be-processed tasks.