Training Data Selection for AI Camera Operation Estimation
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Solution Overview
Problem
Collecting learning data for training a machine learning model to generate impressing content is challenging due to the subjective nature of impressiveness and privacy concerns, especially when relying on social network data, and existing methods fail to consider contextual elements that influence impressions.
Innovation Solution
A training system that includes a data collection device and a training device, utilizing content evaluation information, biological information, and context analysis to identify segments that impress, and a re-training mechanism based on influence analysis to refine learning data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If learning data is collected based on subjective impressiveness, then the training model can capture human impression, but data collection becomes difficult and unreliable
Solution Approach 1:
The patent introduces an intermediary approach by using content evaluation information from professional judges or annotated datasets as a mediator between the subjective human impression and the objective data collection process. This intermediary standard allows systematic data collection while maintaining the essence of capturing human impression, resolving the contradiction between measurement precision and collection difficulty.
Solution Approach 2:
The patent applies preliminary action by pre-collecting and annotating learning data with ground truth labels before the main training process. This preliminary data preparation creates a foundation that simplifies subsequent data collection and ensures high quality training data are available from the outset, addressing both the quality and collection difficulty aspects of the contradiction.
2Measurement precision
If social network data is used to reflect subjectivity, then taste information can be captured, but privacy threats arise
Solution Approach 1:
The patent extracts only the necessary evaluation information from social network data while excluding personally identifiable information. By taking out only the relevant content evaluation aspects and discarding privacy-sensitive user identifiers, the system captures subjectivity reflection accuracy without propagating privacy threats through the training data.
Solution Approach 2:
The patent uses content evaluation information as an intermediary that bridges social network data and training data without directly using raw social network data. This intermediary layer filters out privacy threats while preserving the essential taste and preference information needed for accurate subjectivity reflection.
3Ease of manufacture
If only image data is collected, then data collection is simplified, but contextual elements that influence impressions are lost
Solution Approach 1:
The patent merges multiple data types including images, audio tracks, and contextual metadata into a unified learning data structure. This combination maintains data collection simplicity while capturing contextual elements that influence impressions, as all relevant information is integrated into a single comprehensive dataset that can be processed together.
Solution Approach 2:
The patent creates a universal data collection framework that handles multiple data types (visual, auditory, contextual) through a single unified approach. This multi-functional system collects and processes diverse information simultaneously, preventing context loss while maintaining collection simplicity through standardized handling procedures.
4Ease of operation
If existing training methods are used, then training process is straightforward, but data relevance and privacy are compromised
Solution Approach 1:
The patent applies preliminary action by implementing data filtering and selection processes before training begins. Relevant data is pre-identified and privacy-sensitive information is pre-removed, ensuring data relevance and privacy protection are established before the training process starts, thus maintaining simplicity while improving reliability.
Solution Approach 2:
The patent introduces feedback mechanisms that continuously monitor data quality and privacy compliance during the training process. This feedback loop allows for real-time adjustment of data selection and processing, maintaining training simplicity while ensuring data relevance and privacy protection through automated verification and correction processes.
Data Source
AI summary
Provided is a training system that performs training of a machine learning model that generates an impressing content or estimates a camera operation for capturing an impressing content. The training system includes a data collection device that collects data, and a training device that performs training of a machine learning model by using the data collected by the data collection device, in which the training device performs re-training of the machine learning model by using learning data that affects the training of the machine learning model to a predetermined degree or more, insufficient learning data, or data similar thereto, collected on the basis of a result of analyzing learning data that affects the training of the machine learning model.