Disclosed is a method of controlling a robot system, the method including a first robot receiving user input including a request for delivery of a predetermined article, the first robot transmitting information based on the user input to a server, the server identifying a supporting robot capable of supporting a task corresponding to the delivery request, the server requesting the task from a second robot identified as the supporting robot, the second robot receiving the article, and the second robot moving to a delivery destination included in the delivery request, thereby improving use convenience through cooperation between the robots.

A method of facilitating predictive intent-based routing of service requests. Accordingly, the method may include receiving a service request data from a user device, initiating a chatbot, retrieving a service portfolio based on the receiving of the service request data, processing the service portfolio and the service request data, determining a service level, analyzing the service request data using a first machine learning model, determining an intent based on the analyzing, generating an adjusted service level based on the intent using a second machine learning model, assigning an agent to a user of the at least one user based on the adjusted service level, generating a service notification for the agent based on the assigning, transmitting the service notification to the user device and an agent device associated with the agent, and storing the service request data, the service notification, and the adjusted service level.

Systems and methods for automatically throttling active tasks based on the cognitive load of members and assigned representatives are provided. A system receives, in real-time, a set of messages between a member and a representative as the set of messages are being exchanged. The system, based on these messages, automatically detects an indication of a change in the member's cognitive load and identifies the cognitive load based on the tasks being performed for the member, the presentation of these tasks, and the set of messages. If the member's cognitive load exceeds a threshold, the system dynamically selects one or more active tasks for migration to a suspended state. The remaining active tasks are performed for the benefit of the member.

A method includes (a) receiving and storing interaction time data associated with processes of a communication distributor server for an end-user network having an associated contact center with agent instances; (b) determining whether a trigger specified in a first logical directive is initiated; (c) upon determining that the trigger specified in the first logical directive is initiated, determining whether a metric related to the customer communications with the end-user network satisfies the condition in the first logical directive; and (d) upon determining that the metric related to the customer communications with the end-user network satisfies the condition in the first logical directive, providing the operation to at least one of the end-user network or the communication distributor server.

A customer is detected at a drive-thru and a natural language voice dialogue session is established with the customer. The customer provides voice inquiries and order details via speech during the session, the speech is translated to text sentences, and commands are issued to a transaction system through an Application Programming Interface (API) based on the text of the sentences. The transaction system updates a display associated with the drive-thru based on the commands processed for the session and places an order for the customer with a Point-Of-Sale (POS) terminal associated with the drive-thru based on the order details.

A virtual assistant system for communicating with customers uses human intelligence to correct any errors in the system AI, while collecting data for machine learning and future improvements for more automation. The system may use a modular design, with separate components for carrying out different system functions and sub-functions, and with frameworks for selecting the component best able to respond to a given customer conversation.

A method of facilitating predictive intent-based routing of service requests. Accordingly, the method may include receiving a service request data from a user device, initiating a chatbot, retrieving a service portfolio based on the receiving of the service request data, processing the service portfolio and the service request data, determining a service level, analyzing the service request data using a first machine learning model, determining an intent based on the analyzing, generating an adjusted service level based on the intent using a second machine learning model, assigning an agent to a user of the at least one user based on the adjusted service level, generating a service notification for the agent based on the assigning, transmitting the service notification to the user device and an agent device associated with the agent, and storing the service request data, the service notification, and the adjusted service level.

The embodiments of the present disclosure provide methods and Internet of Things system for smart gas installation management. The method may be implemented based on the Internet of Things system for smart gas installation management. The method may include: obtaining user installation information, wherein the user installation information includes at least one of user information or property information; determining whether to accept a gas installation based on the user installation information and an acceptance condition; in response to acceptance of the gas installation, determining, based on a user requirement and a staff availability degree, overall service intensity of the Internet of Things system for smart gas installation management; and determining a door-to-door service plan based on the overall service intensity.

The embodiments of the present disclosure provide methods and Internet of Things system for smart gas installation management. The method may be implemented based on the Internet of Things system for smart gas installation management. The method may include: obtaining user installation information, wherein the user installation information includes at least one of user information or property information; determining whether to accept a gas installation based on the user installation information and an acceptance condition; in response to acceptance of the gas installation, determining, based on a user requirement and a staff availability degree, overall service intensity of the Internet of Things system for smart gas installation management; and determining a door-to-door service plan based on the overall service intensity.

To enable information on a combination having favorable compatibility of a drink and a food to be presented from a wide range. The item evaluation values of a drink and a food, of the relevant item and the relevant category as a pairing object, are extracted depending on a presentation request respectively from a drink item evaluation value storage section and a food item evaluation value storage section, and a drink and a food in a combination in which a correspondence relationship between the item evaluation values extracted of a drink and a food is matched to a correspondence relationship indicated by a compatibility pattern stored in a compatibility pattern storage section are determined to provide a combination having favorable compatibility. Information on a correspondence relationship between the item evaluation values of such drink and food determined to provide a combination having favorable compatibility is presented.