Abstract: Systems and methods for providing a digital human in a virtual space are disclosed herein. A system receives, from a user via a digital platform, a selection of a persona, for a digital human, from a plurality of personas. Further, the system receives, in real-time, an input from the user via the digital platform, and identifies a set of parameters associated with the input. Furthermore, the system determines, from a knowledge database, a response information based on the context of the input, and generates, using a repository, a set of attributes for the response information based at least on the set of parameters associated with the input and the persona selected by the user. Additionally, the system aggregates the set of attributes and the response information to generate a personalized response to the input, and renders the personalized response by the digital human on the digital platform.

Abstract: Systems and methods for profile service management are disclosed. A system generates user data corresponding to plurality of users in an electronic meeting (e-meeting), based on meta data received from one or more data sources. The generated user data is stored in data lake. The system retrieves, from data lake, the user data. Each of plurality of users has corresponding user profile. The system determines, from user data, factual user information for each user of plurality of users from corresponding user profile. Further, the system compares factual user information of plurality of users, to determine set of commonalities between plurality of users. Furthermore, system generates, for each user, integration data comprising factual user information and set of commonalities. Further, system displays, for each user, plurality of meeting objects in a meeting interface associated with the e-meeting, based on the integration data, to provide one or more profile services.

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: An Artificial Intelligence (AI) based resource identification system enables identifying available resources in response to receiving a request for resources. The request attributes are mapped to the attributes of the resources in a resource pool. Matching index scores are calculated and resources that match the request can be selected based on the matching index scores. If no resources are available suitable alternate resources with lower matching index scores are suggested so that a user has the choice to filter this alternate resources based on a threshold score. Particular suggestions to change one or more of the request attributes can be provided based on analysis of the mapping of the request and the resource attributes which enables the resource identification system to suggest changes to one or more of the skill attributes, level attributes, time attributes or location attributes of the request.

Abstract: An Artificial Intelligence (AI) based resource identification system enables identifying available resources in response to receiving a request for resources. The request attributes are mapped to the attributes of the resources in a resource pool. Matching index scores are calculated and resources that match the request can be selected based on the matching index scores. If no resources are available suitable alternate resources with lower matching index scores are suggested so that a user has the choice to filter this alternate resources based on a threshold score. Particular suggestions to change one or more of the request attributes can be provided based on analysis of the mapping of the request and the resource attributes which enables the resource identification system to suggest changes to one or more of the skill attributes, level attributes, time attributes or location attributes of the request.

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.