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This invention describes a computer system that collects diverse information about a product's entire life, from its creation to its end. It then builds a 'digital twin graph' – a smart map that connects all this data using three types of models: one for general concepts, one for specific product examples, and one for predicting future events. The system specifically uses 'digital twin units' for the product examples, which hold pointers to external data and a key feature extracted from that data.
Why it matters: Filed before the widespread adoption of advanced graph database technologies and sophisticated AI for data integration. Modern machine learning, particularly graph neural networks, could now significantly enhance the linking and distillation algorithms for heterogeneous data, making the 'causal and predictive reasoning' more robust.
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