Abstract: A beverage dispensing system is disclosed that includes a container assembly configured to store two liquids (e.g., liquor and a mixer) that is couplable to a head assembly. The head assembly includes one or more pumps configured to draw the different liquids from the assembly, to mix variable ratios of the two liquids together, and to dispense the drink. In some configurations, the drink may be dispensed from the beverage dispenser into a glass or directly to the mouth of an individual.

Abstract: A beverage dispensing system is disclosed that includes a container assembly configured to store two liquids (e.g., liquor and a mixer) that is couplable to a head assembly. The head assembly includes one or more pumps configured to draw the different liquids from the assembly, to mix variable ratios of the two liquids together, and to dispense the drink. In some configurations, the drink may be dispensed from the beverage dispenser into a glass or directly to the mouth of an individual.

Abstract: A method for collecting activity-data of a user executing a task including at least one activity includes tracking a user-device to determine a location-data of the user-device associated with the user; retrieving, at a server-arrangement from a database-server, a client-data; determining a change in the location-data of the user-device; and generating the activity-data, based on the change in the location-data, by one of correlating the location-data with the client-data by employing machine learning techniques, or processing a prompt-response generated by a manual-input at the user-device, wherein the prompt-response is generated based on a prompt provided by the server-arrangement to the user-device when the machine learning techniques fail to correlate the location data with the client-data.

Abstract: A method for automatically optimizing a travel itinerary and automatically implementing for the travel itinerary, using a machine learning model executed on a server, the method includes: obtaining a first set of data; generating a first database from the first set of data; determining, using the machine learning model, one or more travel options that are specific to a planned travel by analyzing the first set of data, wherein the machine learning model is generated by generating a second database with a first set of data associated with travellers, generating a third database with a travel itinerary of the travellers, processing an expert input on the travel itinerary of the travellers, and providing (a) the first set of data associated with the travellers, (b) the travel itinerary of the travellers, and (c) the expert input on the travel itinerary of the travellers, to a machine learning algorithm as training data; obtaining an itinerary plan request from a user; generating, using the machine learning model

Abstract: A method for collecting activity-data of a user executing a task including at least one activity includes tracking a user-device to determine a location-data of the user-device associated with the user; retrieving, at a server-arrangement from a database-server, a client-data; determining a change in the location-data of the user-device; and generating the activity-data, based on the change in the location-data, by one of correlating the location-data with the client-data by employing machine learning techniques, or processing a prompt-response generated by a manual-input at the user-device, wherein the prompt-response is generated based on a prompt provided by the server-arrangement to the user-device when the machine learning techniques fail to correlate the location data with the client-data.