| CPC G06N 20/00 (2019.01) | 20 Claims |
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1. A computer-implemented method for conditional data modification, the method comprising:
gathering raw data comprising a plurality of characteristic data samples of a target user group;
categorizing the characteristic data samples into a plurality of user-related classes and triggers;
building an input property graph for each characteristic data sample, wherein the input property graph comprises data relationships associated with characterial triggers, user identifiers, object identifiers and activity identifiers;
augmenting the input property graph by a concept of hierarchies;
determining a modification vector from the augmented input property graph;
training an encoder/decoder combination machine-learning system comprising a machine learning generative model that is a combined model of an encoder and a decoder, wherein the training comprises:
inputting the characteristic data samples into the encoder to generate an embedding vector;
inputting the embedding vector and the modification vector into the decoder to build the machine-learning generative model,
wherein the machine-learning generative model is configured to output modified data samples relating to the characteristic data samples; and
optimizing the machine-learning generative model using target modified samples as ground truth relating pairwise to the modified data samples; and
receiving, at the trained encoder/decoder combination machine-learning system, inference input data and a conditional input property graph, wherein the conditional input property graph includes a request for a target characterial trigger.
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