Conditional Data Generation with Hierarchical Property Graphs
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Solution Overview
Problem
Current techniques for training systems associated with conditional data modification struggle to accurately identify and classify sentiment due to reliance on limited information, such as facial expressions and tone of voice, making it difficult to predict user responses to stimuli.
Innovation Solution
A processor gathers raw data, categorizes it into user-related classes and triggers, builds an input property graph with hierarchies, determines a modification vector, and trains an encoder/decoder combination machine-learning system to generate a trained generative model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If limited information such as facial expressions and tone of voice is used for sentiment identification, then the system complexity is reduced, but the measurement precision of sentiment identification deteriorates
Solution Approach 1:
The patent segments the data processing into distinct components: raw data collection from multiple sources, property graph construction with hierarchical relationships, and encoder-decoder processing. This segmentation allows the system to handle complex multi-source data through modular processing stages, maintaining manageable system complexity while achieving precise sentiment identification through comprehensive data analysis
Solution Approach 2:
The patent introduces hierarchical property graphs that add structural dimensions to the data representation. By organizing data samples, features, and relationships into multi-level hierarchical structures, the system enhances sentiment identification precision without proportionally increasing operational complexity, as the hierarchy provides organized access to complex relationships
2Measurement precision
If multiple data sources and hierarchical property graphs are used, then the measurement precision of user response prediction is improved, but the device complexity increases
Solution Approach 1:
The patent introduces property graphs as intermediary structures that mediate between raw multi-source data and the encoder-decoder model. These graphs serve as structured representations that organize complex relationships from multiple data sources, enabling precise user response prediction while managing system complexity through standardized intermediate representation
Solution Approach 2:
The patent transforms raw data into modified data samples through the encoder-decoder process, changing the parameter representation from raw features to sentiment-predictive features. This parameter transformation enables precise prediction by converting complex multi-source data into optimized feature representations that directly support the prediction task
3Reliability
If comprehensive raw data is collected and processed through encoder-decoder training, then the ability to predict user responses is improved, but the loss of time for data processing increases
Solution Approach 1:
The patent performs preliminary construction of property graphs and hierarchical structures before the actual sentiment prediction task. By pre-organizing data relationships and structures in advance, the system reduces processing time during inference while maintaining high prediction reliability through comprehensive pre-processed data representation
Data Source
AI summary
A processor may gather raw data comprising a plurality of characteristic data samples of a target user group. The processor may categorize the characteristic data samples into a plurality of user-related classes and triggers. The processor may build an input property graph for each characteristic data sample. The processor may augment the input property graph by a concept of hierarchies. The processor may determine a modification vector from the augmented input property graph. The processor may train an encoder/decoder combination machine-learning system. An embedding vector and a modification vector are used as input for the decoder to build a trained machine-learning generative model.