A method and system include identifying, by a processor, departing flight information that designates departure airports and departure times in payment card transaction data of a plurality of users. Airport-specific data for a departure airport before a departure time of a departing flight of a user from the plurality of users is received. The airport-specific data is inputted into a machine learning model that outputs a user-specific airport processing time for the user to reach a departure gate upon arriving to the departure airport. A travel time from a geographical location of the computing device of the user to the departure airport is received from a navigation system. The computing device displays a time for the user to start travel to the departure airport based on the user-specific airport processing time and the travel time to the departure airport for the user to reach the departure gate by the departure time.

The SOCIAL MATCH PLATFORM APPARATUSES, METHODS AND SYSTEMS (“SMP”) transforms platform join requests, social network info, and SMP network info inputs via SMP components NJ, JIP, CIP, OP, CN-SGU and CN-UPSOG into job info, candidate info, offer info, and social meetup info outputs. A job information request for a candidate may be obtained. Social data associated with the candidate may be determined. A social job relevancy rating for various jobs may be calculated using the social data. A job may be selected using the social job relevancy rating for the job, and information regarding the selected job may be provided.

The SOCIAL MATCH PLATFORM APPARATUSES, METHODS AND SYSTEMS (“SMP”) transforms platform join requests, social network info, and SMP network info inputs via SMP components NJ, JIP, CIP, OP, CN-SGU and CN-UPSOG into job info, candidate info, offer info, and social meetup info outputs. A job information request for a candidate may be obtained. Social data associated with the candidate may be determined. A social job relevancy rating for various jobs may be calculated using the social data. A job may be selected using the social job relevancy rating for the job, and information regarding the selected job may be provided.

A data programming method is provided for supporting artificial intelligence systems, wherein shareable labeling functions for labeling data are used. The method includes: providing at least two shareable labeling functions with their profile across domains, wherein each of the at least two shareable labeling function profiles includes at least one training-related performance metric; selecting at least one of these shareable labeling functions by a selecting domain, wherein the selecting is based on the labeling functions' at least one performance metric; grouping unlabeled data of the selecting domain for providing at least one group, wherein this grouping step is based on a definable degree of coverage of the selected shareable labeling function per unlabeled data, and training a preferably generative machine learning model of the selecting domain per at least one group with the labeling functions' respective at least one performance metric for producing labeled data or labels.

A method of creating a workflow on a smart device, comprising: presenting to a user for selection a plurality of input devices, each representing a data input mechanism of said smart device; receiving from the user a selection of one or more of said input devices; presenting to the user for selection a plurality of action devices, each representing a possible action to be taken by the smart device; receiving from the user a selection of one or more of said action devices; wherein each input device has one or more input device outputs, and wherein each action device has one or more action device inputs, and wherein the method further comprises forming one or more input-action associations each comprising a selected input device output associated with a selected action device input. The software provides a new system and method for operating a computer, allowing the user to create workflows linking several different inputs and actions, while avoiding or reducing the need to switch between different applications and different contexts. This results in significant improvements in efficiency and reliability during operation and allows specific workflows to be generated without the need for a skilled programmer. Combinations and sequences of input-action associations can also be generated for enhanced workflow creation.

A method of creating a workflow on a smart device, comprising: presenting to a user for selection a plurality of input devices, each representing a data input mechanism of said smart device; receiving from the user a selection of one or more of said input devices; presenting to the user for selection a plurality of action devices, each representing a possible action to be taken by the smart device; receiving from the user a selection of one or more of said action devices; wherein each input device has one or more input device outputs, and wherein each action device has one or more action device inputs, and wherein the method further comprises forming one or more input-action associations each comprising a selected input device output associated with a selected action device input. The software provides a new system and method for operating a computer, allowing the user to create workflows linking several different inputs and actions, while avoiding or reducing the need to switch between different applications and different contexts. This results in significant improvements in efficiency and reliability during operation and allows specific workflows to be generated without the need for a skilled programmer. Combinations and sequences of input-action associations can also be generated for enhanced workflow creation.

An electronic device providing a message service includes: a communication unit configured to exchange a message with another device; a display configured to display a message received from the another device, a message input by the electronic device, and at least one response message generated by the electronic device, all together on a message service screen of the display; and a processor configured to determine whether the received message includes an inquiry, and provide at least one piece of recommended content based on the inquiry in response to the received message being determined as including the inquiry, and transmit the at least one response message comprising the at least one piece of recommended content to the other device.

A computer-implemented method for anonymizing online transaction details may include performing a process to generate an identifier (ID) and reference address for a user, and receiving a request for transaction processing from an e-commerce server. The request may include first data including: information associated with a purchase of a product to be delivered to the user; the identifier; and the reference address. The method may further include: in response to the request, determining whether the ID and reference address are included in the request, and if so, obtaining the accurate address and shipping rate rules; applying, based on the first data and the accurate address, the shipping rate rules to determine accurate shipping rate cost information associated with delivering the product to the accurate address; performing an anonymizing process on the accurate information to obtain anonymous shipping rate information, and transmitting the anonymous shipping rate information to the e-commerce server.

A system for casual driver ride sharing includes an interface and processor. The interface is to receive a request for a ride from a user, wherein the request includes GPS information for the user. The processor is to determine compatibility between the typical route information and the request for the ride; determine a ranked list based at least in part on the compatibility; and provide a ride offer to a driver of the one or more casual drivers based at least in part on the ranked list.

A deterioration estimation device of the present embodiment is provided with a machine learning unit, a stacking function unit and a GWS calculation unit. The machine learning unit estimates deterioration indices respectively using a plurality of estimation models in a first layer created by performing different types of machine learning using environment information and facility information of dispersedly installed facilities as explanatory variables and the deterioration indices as objective variables. The GWS calculation unit calculates geographically weighted statistics of the deterioration indices. The machine learning unit estimates geographically weighted statistics of the deterioration indices using a statistic estimation model created by performing machine learning using the environment information and the facility information of the dispersedly installed facilities as explanatory variables and the geographically weighted statistics of the deterioration indices as objective variables. The stacking function unit estimates deterioration indices from estimation results of the estimation models in the first layer and an estimation result of geographic statistics.