Abstract: Techniques for generating graphical elements via a communication platform are discussed herein. For example, one or more machine-learning models associated with a communication platform may be configured to receive, as input and from a user of the communication platform, a sentiment and/or a graphical element. The machine-learning model may be trained, using prior natural language statements and prior confidence levels associated with previous graphical elements, to output one or more graphical elements associated with the input. The one or more graphical elements may be shared via the communication platform and used to accurately and effectively convey thoughts, emotions, reactions, and ideas, for example.

Abstract: Methods, systems, apparatuses, devices, and computer program products are described. In a group-based communication system, a user may save posts for later (e.g., to reply to a message at a later time, to complete a task associated with a message at a later time). The system may use a machine learning model to determine to automatically mark a post for later for a user, for example, based on a set of features including at least a semantic embedding of the post. Additionally, or alternatively, the system may use a machine learning model to determine an order for displaying items (e.g., posts, reminders, files) within a user view (e.g., a later tab, a drafts tab, a threads tab, a files tab) for a user via a user interface. The system may update one or more machine learning models based on how users interact with the posts, user views, or both.

Abstract: Methods, systems, apparatuses, devices, and computer program products are described. In a group-based communication system, users may post messages discussing potentially patentable concepts. To support automatic identification of patent-relevant messages within the group-based communication system, the system may use a machine learning model including at least an embedding function. The system may generate a set of features for one or more messages and may input the set of features into the machine learning model. Using the embedding function, the model may create a message embedding and may compare the message embedding with embeddings of patent application language to determine a level of similarity. The machine learning model may output an indication of whether the one or more messages are associated with a patentable concept based on the embedding, and the indication may be surfaced to a user associated with managing patents for an organization of the group-based communication system.

Abstract: Techniques for generating user profile data including one or more frequent channels, related users, and/or related topics within a communication platform are discussed herein. In some examples, a machine-learning model can receive user interaction data (messages sent, messages read, channel posts, documents shared, frequent key words used, etc.) associated with the communication platform and output one or more frequent channels, related users, and/or related topics. The communication platform may then associate the one or more frequent channels, related users, and/or related topics with the user's profile data. In some examples, the communication platform may present different frequent channels, related users, and/or related topics associated with a profile page based on interaction action associated with the user account viewing the profile page.

Abstract: Techniques for generating graphical elements via a communication platform are discussed herein. For example, one or more machine-learning models associated with a communication platform may be configured to receive, as input and from a user of the communication platform, a sentiment and/or a graphical element. The machine-learning model may be trained, using prior natural language statements and prior confidence levels associated with previous graphical elements, to output one or more graphical elements associated with the input. The one or more graphical elements may be shared via the communication platform and used to accurately and effectively convey thoughts, emotions, reactions, and ideas, for example.

Abstract: Methods, systems, apparatuses, devices, and computer program products are described. A group-based communication system may determine timing for sending notifications to users of the group-based communication system to improve the likelihood of the users interacting with the notifications, interacting with other users in the system, or both. For example, the group-based communication system may identify a notification to send to a user, where the user is associated with a user identifier and a workspace identifier in the group-based communication system. The system may dynamically determine a time for sending the notification based on activity data for the user corresponding to the user identifier and activity data for multiple other users corresponding to the workspace identifier (e.g., members of a team with the user). The system may send the notification for display in a user interface of a user device operated by the user at the determined time.

Abstract: Techniques for clustering a plurality of virtual space servers based on communication platform data are discussed herein. In some examples, a user may post a message to a virtual space of a communication platform. The communication platform may transmit the message to virtual space servers configured to manage the virtual space within which the message originated. In such examples, the communication platform may identify clusters of virtual space servers and determine within which cluster the corresponding virtual space server is located. Virtual space servers may be clustered based on attribute data associated with communication platform data. In some examples, the communication platform may send the message to the virtual space server which may send the message to subscribing gateway servers. Further, the gateways servers may send the message to the recipient client devices. The recipient client devices may display the message via a user interface of the communication platform.

Abstract: In association with a communication platform, a machine learning component may determine affordances to provide to users, where affordances describe features provided by the communication platform. The machine learning component is trained using log data representing interactions and features used (or not used) by users. In some examples, the log data is associated with members of one or more groups while, in other examples, the log data is associated with all of the users of the communication platform. To determine an affordance, the machine learning component analyzes an interaction between a user and the communication platform. Based on the analysis, the machine learning component determines a relationship between the interaction and a feature. The machine learning component then generates the affordance to include information about the feature. Additionally, a user interface then provides the affordance to the user, such as in proximity to the feature.

Abstract: Discovery of communication platform features or exposure of such features to the user may include generating embeddings for a variety of types of communication platform content and communications. These embeddings may be used to characterize and compare various communication platform features and ultimately expose these features to a user when the user may not have otherwise encountered them. The embeddings may additionally or alternatively be used to determine a degree of alignment.

Abstract: Systems, methods, and computer-readable media are provided for adding connections for a user. External graphs may be analyzed to identify connections and relationships which may be missing in a group-based communication system. These missing connections may be suggested to the user as a suggested virtual space. A user may then create the virtual space based on the suggestions.

Abstract: Methods, systems, apparatuses, devices, and computer program products are described. A group-based communication system may determine timing for sending notifications to users of the group-based communication system to improve the likelihood of the users interacting with the notifications, interacting with other users in the system, or both. For example, the group-based communication system may identify a notification to send to a user, where the user is associated with a user identifier and a workspace identifier in the group-based communication system. The system may dynamically determine a time for sending the notification based on activity data for the user corresponding to the user identifier and activity data for multiple other users corresponding to the workspace identifier (e.g., members of a team with the user). The system may send the notification for display in a user interface of a user device operated by the user at the determined time.

Abstract: Media, methods, and systems are disclosed for optimizing performance of a running application in connection with a group-based communication system. Log data is collected regarding prior metrics for applications that have encountered performance events. Application state information is monitored and a machine-learning model mapping application metrics to performance outcomes predicts whether the running application will encounter a performance event. The machine-learning model mapping application metrics to performance outcomes is trained based on the collected logs. Based on whether a degradation outcome will be impactful, an application performance parameter may be degraded.

Abstract: Media, methods, and systems are disclosed for optimizing performance of a running application in connection with a group-based communication system. Log data is collected regarding prior metrics for applications that have encountered performance events. Application state information is monitored and a machine-learning model mapping application metrics to performance outcomes predicts whether the running application will encounter a performance event. The machine-learning model mapping application metrics to performance outcomes is trained based on the collected logs. Based on whether a degradation outcome will be impactful, an application performance parameter may be degraded.

Abstract: Discovery of communication platform features or exposure of such features to the user may include generating embeddings for a variety of types of communication platform content and communications. These embeddings may be used to characterize and compare various communication platform features and ultimately expose these features to a user when the user may not have otherwise encountered them. Embeddings may be generated to characterize a user's interactions with a communication platform and/or characterize a channel and the embeddings may be used to expose methods of communicating with the user and/or over the channel.

Abstract: Discovery of communication platform features or exposure of such features to the user may include generating embeddings for a variety of types of communication platform content and communications. These embeddings may be used to characterize and compare various communication platform features and ultimately expose these features to a user when the user may not have otherwise encountered them. Embeddings may be generated to characterize a user's interactions with a communication platform and/or characterize a channel and the embeddings may be used to expose methods of communicating with the user and/or over the channel.

Abstract: Discovery of communication platform features or exposure of such features to the user may include generating embeddings for a variety of types of communication platform content and communications. These embeddings may be used to characterize and compare various communication platform features and ultimately expose these features to a user when the user may not have otherwise encountered them.

Abstract: Discovery of communication platform features or exposure of such features to the user may include generating embeddings for a variety of types of communication platform content and communications. These embeddings may be used to characterize and compare various communication platform features and ultimately expose these features to a user when the user may not have otherwise encountered them. Embeddings may be generated to characterize a user's interactions with a communication platform and/or characterize a channel and the embeddings may be used to expose methods of communicating with the user and/or over the channel.