Abstract: A device may receive a query from a user device, and may process the query using a machine learning model to determine categories related to the query. The device may send a message, instructing a user to select a category, to the user device to cause the user device to display the message. The device may receive, from the user device, a selection related to the categories, and may call, based on the selection, a module to cause the module to process the query. The device may receive from the module a response to the query, and may send the response to the user device, to cause the user device to display the response. The device may send the query, the selection, and the response to one or more servers for storage, and may retrain the machine learning model based on information stored by the one or more servers.

Abstract: A device may provide, to user devices, a set of objects that are to be used by the user devices as part of a collaborative virtual environment. The collaborative virtual environment may support a virtual workshop. The set of objects may include objects capable of being used to solve a problem that is presented to users within the virtual workshop. The device may receive data describing a set of solutions. The device may curate the data describing the set of solutions using one or more natural language processing techniques. The device may rank the set of solutions that has been curated using a ranking technique or a machine learning technique. The device may provide data identifying a highest ranked solution or data identifying the set of solutions for display within the virtual workshop or to additional user devices that are accessible outside of the virtual workshop.

Abstract: A device may receive a feature submission identifying a candidate feature for a software application. The device may pre-process the feature submission to reformat data associated with the candidate feature to match a data format associated with stored information regarding past feature submissions. The device may perform natural language processing on the data associated with the candidate feature to determine a semantic meaning of the candidate feature. The device may compare the semantic meaning of the candidate feature with an index of past feature submissions. The device may provide a response indicating whether the candidate feature matches the past feature submissions wherein the response comprises an indication that the candidate feature does not match any past feature submission or information identifying one or more past feature submissions associated with a threshold semantic similarity to the candidate feature.

Abstract: A device may receive a query from a user device, and may process the query using a machine learning model to determine categories related to the query. The device may send a message, instructing a user to select a category, to the user device to cause the user device to display the message. The device may receive, from the user device, a selection related to the categories, and may call, based on the selection, a module to cause the module to process the query. The device may receive from the module a response to the query, and may send the response to the user device, to cause the user device to display the response. The device may send the query, the selection, and the response to one or more servers for storage, and may retrain the machine learning model based on information stored by the one or more servers.

Abstract: A device may provide, to user devices, a set of objects that are to be used by the user devices as part of a collaborative virtual environment. The collaborative virtual environment may support a virtual workshop. The set of objects may include objects capable of being used to solve a problem that is presented to users within the virtual workshop. The device may receive data describing a set of solutions. The device may curate the data describing the set of solutions using one or more natural language processing techniques. The device may rank the set of solutions that has been curated using a ranking technique or a machine learning technique. The device may provide data identifying a highest ranked solution or data identifying the set of solutions for display within the virtual workshop or to additional user devices that are accessible outside of the virtual workshop.