Methods and systems for demonstrating a personalized automated teller machine (ATM) presentation
摘要
說(shuō)明書(shū)
At any stage of demonstrating a personalized automated teller machine (ATM) presentation to a user (e.g., step 304), a trained machine learning algorithm may be used. The trained machine learning algorithm may be part of the analysis model 112. The trained machine learning algorithm may include, e.g., a regression-based model that accepts the transaction data, geographic data, ATM data, user feedback data, or presentation data as input data. The trained machine learning algorithm may be of any suitable form, and may include, for example, a neural network. A neural network may include software representing human neural system (e.g., cognitive system). A neural network may, e.g., include a series of layers termed “neurons” or “nodes.” A neural network may comprise an input layer, to which data is presented; one or more internal layers; and an output layer. The number of neurons in each layer may be related to the complexity of a problem to be solved. Input neurons may receive data being presented and then transmit the data to the first internal layer based on the relative weight of connections between input neurons and neurons in the first internal layer. A neural network may include any suitable type of network, such as a convolutional neural network, a deep neural network, or a recurrent neural network.
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