Abstract: A message suggestion service may use clusters of pre-approved messages to improve the quality of messages suggested to users. During a conversation, messages of the conversation may be processed with a neural network to compute a conversation encoding vector. The neural network may also be used to compute pre-approved message encoding vectors of the pre-approved messages. Distances between the conversation encoding vector and the pre-approved message encoding vectors may be used to select one or more clusters. Distances between the conversation encoding vector and the pre-approved message encoding vectors may then be used to select one or more pre-approved messages from the selected clusters. The selected pre-approved messages may then be presented as suggested messages to a user.

Abstract: A graphical user interface (GUI) page may be represented as GUI page encoding to facilitate processing of the GUI page in an application of GUI pages. A GUI page encoding may be computed by processing a GUI with a GUI page encoding model, and a GUI page encoding model may be trained by processing a training corpus of sequences of GUI pages. The training process may include obtaining first and second GUI pages from the training corpus, computing first and second GUI page encodings with the GUI page encoding model, computing a predicted GUI page encoding by processing the first GUI page encoding with a page predictor model, computing an error value be comparing the predicted GUI page encoding and the second GUI page encoding, and updating parameters of the GUI page encoding model by performing back propagation using the error value.

Abstract: A graph model of a graphical user interface (GUI) may be generated by processing usage data of the GUI where the usage data comprises sequences of GUI pages and actions between GUI pages. The nodes of the graph model may be determined by obtaining GUI pages from the usage data, identifying dynamic GUI elements in the GUI pages, generating canonical GUI pages by modifying the GUI pages using the dynamic GUI elements, and creating graph nodes using the canonical GUI pages. The edges of the graph may be determined by processing actions from the GUI data that were performed by users to transition from one GUI page to another GUI page. The graph model of the GUI may be used for any appropriate application, such as determining statistics relating to the GUI or statistics relating to individual users of the GUI.

Abstract: A graph model of a graphical user interface (GUI) may be generated by processing usage data of the GUI where the usage data comprises sequences of GUI pages and actions between GUI pages. The nodes of the graph model may be determined by obtaining GUI pages from the usage data, identifying dynamic GUI elements in the GUI pages, generating canonical GUI pages by modifying the GUI pages using the dynamic GUI elements, and creating graph nodes using the canonical GUI pages. The edges of the graph may be determined by processing actions from the GUI data that were performed by users to transition from one GUI page to another GUI page. The graph model of the GUI may be used for any appropriate application, such as determining statistics relating to the GUI or statistics relating to individual users of the GUI.

Abstract: A message suggestion service may use clusters of pre-approved messages to improve the quality of messages suggested to users. During a conversation, messages of the conversation may be processed with a neural network to compute a conversation encoding vector. The neural network may also be used to compute pre-approved message encoding vectors of the pre-approved messages. Distances between the conversation encoding vector and the pre-approved message encoding vectors may be used to select one or more clusters. Distances between the conversation encoding vector and the pre-approved message encoding vectors may then be used to select one or more pre-approved messages from the selected clusters. The selected pre-approved messages may then be presented as suggested messages to a user.

Abstract: A graph model of a graphical user interface (GUI) may be generated by processing usage data of the GUI where the usage data comprises sequences of GUI pages and actions between GUI pages. The nodes of the graph model may be determined by obtaining GUI pages from the usage data, identifying dynamic GUI elements in the GUI pages, generating canonical GUI pages by modifying the GUI pages using the dynamic GUI elements, and creating graph nodes using the canonical GUI pages. The edges of the graph may be determined by processing actions from the GUI data that were performed by users to transition from one GUI page to another GUI page. The graph model of the GUI may be used for any appropriate application, such as determining statistics relating to the GUI or statistics relating to individual users of the GUI.

Abstract: A graphical user interface (GUI) page may be represented as GUI page encoding to facilitate processing of the GUI page in an application of GUI pages. A GUI page encoding may be computed by processing a GUI with a GUI page encoding model, and a GUI page encoding model may be trained by processing a training corpus of sequences of GUI pages. The training process may include obtaining first and second GUI pages from the training corpus, computing first and second GUI page encodings with the GUI page encoding model, computing a predicted GUI page encoding by processing the first GUI page encoding with a page predictor model, computing an error value be comparing the predicted GUI page encoding and the second GUI page encoding, and updating parameters of the GUI page encoding model by performing back propagation using the error value.