According to GlobalData’s company profile on Royal Bank of Canada, Social commerce was a key innovation area identified from patents. Royal Bank of Canada's grant share as of February 2024 was 90%. Grant share is based on the ratio of number of grants to total number of patents.

Patent granted for variational autoencoder for text sequence machine learning

A recently granted patent (Publication Number: US11914955B2) discloses a computer-implemented system for a variational autoencoder designed for text sequence machine learning. The system includes a memory and a processor that executes machine-interpretable instruction sets to provide the variational autoencoder, consisting of an encoder and a decoder. The encoder reads an input sequence and generates a feature vector for a series of hidden states by performing pooling operations over multiple temporal dimensions. The processor also includes an approximate posterior data structure determination engine to extract mean and variance parameters from the feature vector for the hidden states, encapsulating them for the variational autoencoder to conduct text sequence machine learning. The decoder then generates output data based on the approximate posterior data structure and the feature vector for the hidden states.

Furthermore, the system and method described in the patent involve the encoder and decoder networks within the variational autoencoder to process input sequences, compute latent variables, and generate output sequences based on the properties of the input. The method includes steps for determining hidden representations, generating reconstructed output sequences distinct from the input, and utilizing pooling functions such as average or max pooling over the hidden states. The patent also covers the use of string or character-based tokens as input sequences for the variational autoencoder to conduct text sequence machine learning, showcasing a comprehensive approach to machine learning in the text domain.

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