Collective Intelligence Labeling for Robot Operation Video Inference
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Solution Overview
Problem
Existing systems fail to effectively leverage collective intelligence for labeling and learning on raw data to improve artificial intelligence inference capabilities, particularly in reconstructing operation videos of humans, avatars, or items into robot operation videos.
Innovation Solution
An information processing system and method that utilizes a terminal and server to perform selective labeling, machine learning, and additional labeling on raw data and reconstructed robot operation videos using classification and prediction models, generating aggregated videos through collective intelligence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If selective labeling and machine learning are performed on raw data to generate classification values, then the inference capability of artificial intelligence is improved, but the time and computational resources required for processing increase
Solution Approach 1:
The system performs preliminary selective labeling on raw data before full machine learning processing. By pre-labeling specific portions of data that are most relevant for classification, the system prepares data in advance to accelerate subsequent inference processes while maintaining high accuracy.
Solution Approach 2:
The machine learning process is segmented into multiple stages: initial selective labeling, first machine learning to generate classification values, generation of first images/videos, additional labeling, second machine learning to generate second images/videos. This segmentation allows parallel processing and optimizes resource allocation at each stage.
2Manufacturing precision
If additional labeling and learning are performed on generated images to produce second images, then the quality and accuracy of output is improved, but the complexity of the system increases
Solution Approach 1:
The system implements a feedback loop where generated first images/videos are fed back through additional selective labeling and second machine learning processes. The classification values from the first learning round inform the additional labeling, which then refines the output through second learning, creating a self-improving system.
Solution Approach 2:
The system uses its own generated outputs (first images/videos) as input for further processing. The classification values and generated content automatically feed into the next learning stage without external intervention, allowing the system to self-refine its outputs through iterative learning.
3Reliability
If collective intelligence from multiple terminals is aggregated to improve AI inference, then the intelligence and accuracy are enhanced, but the device complexity and data management requirements increase
Solution Approach 1:
The server aggregates classification values, raw data, and generated images from multiple terminals into a unified learning process. By merging data and computational resources across distributed terminals through a central server, the system achieves collective intelligence that improves inference reliability while managing complexity through coordinated processing.
Solution Approach 2:
The server performs multiple functions: receiving data from terminals, performing selective labeling, executing machine learning to generate classification values, producing images/videos, and facilitating additional learning rounds. This multi-functional design consolidates complexity into a single versatile component rather than requiring separate specialized systems.
Data Source
AI summary
The present invention provides an information processing system using collective intelligence, and a method therefor. The present invention labels one or more pieces of raw data related to specific content provided from a user, performs a learning function on the labeled raw data through a preset classification model and prediction model, additionally labels a first image, which is an output value of the prediction model, performs an additional learning function on the additionally-labeled first image through the classification model and the prediction model, so as to output a second image, and thus provides an avatar and/or an item related to the raw data to the user, and can improve the reasoning ability of artificial intelligence through labeling of the raw data.