Abstract: Methods, systems, and devices for analyzing communication messages (e.g., emails or activities) to determine relationship strength using a distributed graph are described. In some systems, a user may be associated with a specific tenant. A database server of the system may receive communication messages associated with the user and a target user. The server may perform a natural language processing (NLP) analysis on the communication messages to extract metadata, and may generate or update a distributed graph indicating connections between users based on the extracted metadata. Using the connections of the graph, the server may calculate a closeness score between the user and the target user. Additionally, the server may calculate closeness scores between the target and other users associated with the tenant, and may determine the users with the greatest closeness scores. The server may send a suggestion for the determined users to initiate communication with the target.

Abstract: Methods, systems, and devices for analyzing communication messages (e.g., emails) and selecting corresponding actions are described. In some database systems, a user may receive multiple messages at a user device. To efficiently determine responses to these messages, the user device may send the messages to a backend server for analysis. The server may perform natural language processing (NLP) to classify the message with one or more binary classifications and may extract metadata from each message. Based on the classifications and the metadata, the server may determine one or more actions the user device may perform to respond to each message. The server may send instructions to the user device indicating the suggested actions, and the user device may display these actions as options to a user. Additionally, the user device may use the classifications and metadata to automatically generate one or more communication templates in response to the message.

Abstract: Methods, systems, and devices for analyzing communication messages (e.g., emails or activities) to determine relationship strength using a distributed graph are described. In some systems, a user may be associated with a specific tenant. A database server of the system may receive communication messages associated with the user and a target user. The server may perform a natural language processing (NLP) analysis on the communication messages to extract metadata, and may generate or update a distributed graph indicating connections between users based on the extracted metadata. Using the connections of the graph, the server may calculate a closeness score between the user and the target user. Additionally, the server may calculate closeness scores between the target and other users associated with the tenant, and may determine the users with the greatest closeness scores. The server may send a suggestion for the determined users to initiate communication with the target.

Abstract: Methods, systems, and devices for analyzing communication messages (e.g., emails) and selecting corresponding actions are described. In some database systems, a user may receive multiple messages at a user device. To efficiently determine responses to these messages, the user device may send the messages to a backend server for analysis. The server may perform natural language processing (NLP) to classify the message with one or more binary classifications and may extract metadata from each message. Based on the classifications and the metadata, the server may determine one or more actions the user device may perform to respond to each message. The server may send instructions to the user device indicating the suggested actions, and the user device may display these actions as options to a user. Additionally, the user device may use the classifications and metadata to automatically generate one or more communication templates in response to the message.