Abstract: Engagement signals may be generated and analyzed based on user interactions with documents, particularly in a collaboration environment. The user interactions may generate raw collaboration signals that may be received and processed into cleaned collaboration signals. For example, noise may be removed from the raw collaboration signals to generate the cleaned collaboration signals. The cleaned collaboration signals may be grouped into engagement signals, where each engagement signal represents an individual event or engagement event of the user with the document. The grouping may be based on boundary signals, time frames, and/or any other reasonable limiting element. Each of the engagement signals may be classified into one of several engagement types based on the cleaned collaboration signals in the engagement signal. The engagement signals may then be analyzed to make determinations, recommendations, or the like regarding one or more users of the document, the document content, or the like.

Abstract: A data processing system includes: a processor; a network interface; and a memory comprising programming instructions for execution by the processor to: access an electronic workspace; determine a list of changes to the workspace within a period of time; structure a query to a Large Language Model (LLM) or Artificial Intelligence (AI) engine, the query to generate for a user a summary of the changes; obtain a corresponding summary from the LLM or AI engine; and in a user interface, associate a display of the workspace with the corresponding summary to update a user as to a current state of the workspace.

Abstract: A computer-implemented method is presented for selecting a preferred live agent from a plurality of live agents. The method includes constructing, via the processor, a human expertise matrix pertaining to each of the plurality of live agents by determining an average net promoter score (NPS) for each of the plurality of live agents for each category of a plurality of categories, and in response to a voice call by a user, determining, via the processor, a predicted human expertise on average by collectively assessing the human expertise matrix, a predicted NPS derived from a first deep neural network, and a predicted category derived from a second deep neural network. The method further includes, based on the predicted human expertise on average determined, triggering communication via the live agent communication network between the user and the preferred live agent to initiate a conversation between the user and the preferred live agent.

Abstract: Engagement signals may be generated and analyzed based on user interactions with documents, particularly in a collaboration environment. The user interactions may generate raw collaboration signals that may be received and processed into cleaned collaboration signals. For example, noise may be removed from the raw collaboration signals to generate the cleaned collaboration signals. The cleaned collaboration signals may be grouped into engagement signals, where each engagement signal represents an individual event or engagement event of the user with the document. The grouping may be based on boundary signals, time frames, and/or any other reasonable limiting element. Each of the engagement signals may be classified into one of several engagement types based on the cleaned collaboration signals in the engagement signal. The engagement signals may then be analyzed to make determinations, recommendations, or the like regarding one or more users of the document, the document content, or the like.

Abstract: A computer implemented method, computer system and computer program product are provided for transferring in a BOT conversation. According to the method, a user input is received, by a device operatively coupled to one or more processing units, from a user. A first response from a conversation BOT responding to the user input is obtained by the device. The first response is evaluated by the device according to configured rules to determine whether a human agent is needed, wherein the evaluation according to at least one of the configured rules is carried out by a trained engine of a reinforcement learning model. Finally, responding to determine the human agent is needed, a transferring recommendation is sent to the user by the device.

Abstract: A computer implemented method, computer system and computer program product are provided for transferring in a BOT conversation. According to the method, a user input is received, by a device operatively coupled to one or more processing units, from a user. A first response from a conversation BOT responding to the user input is obtained by the device. The first response is evaluated by the device according to configured rules to determine whether a human agent is needed, wherein the evaluation according to at least one of the configured rules is carried out by a trained engine of a reinforcement learning model. Finally, responding to determine the human agent is needed, a transferring recommendation is sent to the user by the device.

Abstract: A computer-implemented method is presented for selecting a preferred live agent from a plurality of live agents. The method includes constructing, via the processor, a human expertise matrix pertaining to each of the plurality of live agents by determining an average net promoter score (NPS) for each of the plurality of live agents for each category of a plurality of categories, and in response to a voice call by a user, determining, via the processor, a predicted human expertise on average by collectively assessing the human expertise matrix, a predicted NPS derived from a first deep neural network, and a predicted category derived from a second deep neural network. The method further includes, based on the predicted human expertise on average determined, triggering communication via the live agent communication network between the user and the preferred live agent to initiate a conversation between the user and the preferred live agent.