Neural Network Emotion Recognition Media Selection
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Solution Overview
Problem
Current computer systems are limited in their ability to accurately detect and respond to human emotions, leading to unnatural interactions between humans and computers.
Innovation Solution
A computer-implemented method and system that uses a multi-layer neural network trained in a two-step process, first with an unlabeled dataset for pre-training and then with a labeled dataset for fine-tuning, to extract features such as emotions from user input and select appropriate media responses based on classification vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a computer system uses traditional methods to detect and respond to user input, then the system structure remains simple, but the ability to accurately recognize emotions and provide natural responses deteriorates
Solution Approach 1:
The system divides the emotion recognition task into separate functional modules: a neural network-based emotion detection module, a media library module, and a response selection module. This segmentation allows each component to be optimized independently while working together to achieve accurate emotion recognition without requiring the entire system to be overly complex
Solution Approach 2:
The patent introduces a neural network as an intermediary component that bridges the gap between simple user input and appropriate media responses. This intermediary layer processes the input data, extracts emotional features, and selects suitable responses, thereby improving accuracy without requiring direct complex interactions between all system components
2Measurement precision
If a system uses a multi-layer neural network with two-step training process, then emotion recognition accuracy is improved, but training time and computational resources increase
Solution Approach 1:
The system performs preliminary training on a large unlabeled dataset first, then refines the model using a smaller labeled dataset. This preliminary action allows the neural network to learn general patterns from abundant data before focusing on specific emotion classifications, significantly reducing the time needed for final training while maintaining high accuracy
Solution Approach 2:
The patent applies partial training action by using only a subset of labeled data for fine-tuning after pre-training on all available unlabeled data. This approach leverages the benefits of extensive pre-training while avoiding the time-consuming process of training on all labeled data from scratch, achieving a balance between accuracy and training efficiency
3Adaptability or versatility
If a system uses a two-step training process with unlabeled and labeled datasets, then model adaptability is improved, but the complexity of data processing increases
Solution Approach 1:
The data processing pipeline is segmented into two distinct stages: pre-training on unlabeled data to learn general language patterns, and fine-tuning on labeled data to specialize in emotion recognition. This segmentation enables the model to achieve high adaptability across different emotional states while keeping each processing stage relatively simple and manageable
4Ease of operation
If a system selects media responses based on extracted features, then the naturalness of human-computer interaction is improved, but the complexity of media selection logic increases
Solution Approach 1:
The neural network serves as an intermediary that automatically maps user input to appropriate media responses based on extracted emotional features. This intermediary layer handles the complexity of media selection logic internally, presenting a simple and natural interface to the user while managing the sophisticated selection algorithms in the background
Data Source
AI summary
Methods and systems are provided for providing media to a user based on a feature extracted from an input of the user. A communication interface receives the input from the user. Memory is provided for storing a neural network model, media objects and training data, the training data including a first training dataset and a second training dataset. The neural network model is trained in a pre-training step with the first training dataset and is followed by a fine-tuning step with the second training dataset to obtain a multi-layer neural network. Input is provided to the multi-layer neural network to obtain a classification vector. Based on the classification vector, one or more media objects are selected for delivery to the user through the communication interface.