Abstract: Disclosed is a platform that manages worker users in a temporary staffing environment via an artificial machine learning model. The temporary staffing platform matches available workers to available shifts/gigs. Additional features include generating provisional or near-miss matches and informing workers how to turn those near-misses into full matches, plotting a gig-career path to develop additional skills, gamify development, and automatically generate resumes. The platform generates a set of skill tags associated with each shift/gig performed by the user. Designing of resume text files by the artificial machine learning model includes procedurally generated descriptions of experience the user has based on the recording of each shift/gig performed by the user and the skill tags associated with each recorded shift/gig, wherein a format of the resume text file is formulated by the artificial machine learning model evaluating a mix of skill tags and employers amassed by the user.

Abstract: Apparatuses, systems, and methods described include receiving data related to an availability for a shift, automatically triggering initiation of a communications session related to the shift, conducting the communications session, and receiving and storing a plurality of audio or audio and visual signals from the communications session. Machine learning (ML) sentiment analysis is performed on data of the communications session and based on the sentiment analysis, a reliability score is determined.

Abstract: Disclosed is a platform that manages worker users in a temporary staffing environment via an artificial machine learning model. The temporary staffing platform matches available workers to available shifts/gigs. Additional features include generating provisional or near-miss matches and informing workers how to turn those near-misses into full matches, plotting a gig-career path to develop additional skills, gamify development, and automatically generate resumes. The platform generates a set of skill tags associated with each shift/gig performed by the user. Designing of resume text files by the artificial machine learning model includes procedurally generated descriptions of experience the a user has based on the recording of each shift/gig performed by the user and the skill tags associated with each recorded shift/gig, wherein a format of the resume text file is formulated by the artificial machine learning model evaluating a mix of skill tags and employers amassed by the user.

Abstract: Disclosed is a platform that manages worker users in a temporary staffing environment via an artificial machine learning model. The temporary staffing platform matches available workers to available shifts/gigs. Additional features include generating provisional or near-miss matches and informing workers how to turn those near-misses into full matches, plotting a gig-career path to develop additional skills, gamify development, and automatically generate resumes. The platform generates a set of skill tags associated with each shift/gig performed by the user. Designing of resume text files by the artificial machine learning model includes procedurally generated descriptions of experience the a user has based on the recording of each shift/gig performed by the user and the skill tags associated with each recorded shift/gig, wherein a format of the resume text file is formulated by the artificial machine learning model evaluating a mix of skill tags and employers amassed by the user.

Abstract: Disclosed is a platform that manages worker users in a temporary staffing environment via an artificial machine learning model. The temporary staffing platform matches available workers to available shifts/gigs. Additional features include generating provisional or near-miss matches and informing workers how to turn those near-misses into full matches, plotting a gig-career path to develop additional skills, gamify development, and automatically generate resumes. The platform generates a set of skill tags associated with each shift/gig performed by the user. Designing of resume text files by the artificial machine learning model includes procedurally generated descriptions of experience the a user has based on the recording of each shift/gig performed by the user and the skill tags associated with each recorded shift/gig, wherein a format of the resume text file is formulated by the artificial machine learning model evaluating a mix of skill tags and employers amassed by the user.

Abstract: Disclosed is a platform that makes use of hybrid model employing both heuristic and machine learning models to adaptively generate recommendations based on requested circumstances in a temporary staffing platform. The hybrid model is based on a set of training data surrounding historical temporary staffing outcomes. The heuristic model portion identifies matches between current queries to past outcomes and the machine learning model portion trains to derive new recommendations where no match exists. Queries are received and executed upon in real-time as opposed to pre-computing based on the frequency of changes to the recommendation to what would otherwise be the same query. The hybrid model is therefore configured to optimize for real-time responses to individual queries. The data surrounding the historical temporary staffing outcomes includes data relating to users, data relating to shifts, and data derived from a combination of both.

Abstract: Disclosed is a platform that manages worker users in a temporary staffing environment via an AI machine learning model. The machine learning model predicts dispatch outcomes of a plurality of pairings of worker users to potential shifts. A dispatch outcome predicts whether a worker will show up for and work a given shift. The machine learning model is based on a set of training data surrounding historical dispatch outcomes. The data surrounding the historical dispatch outcomes includes data relating to users, data relating to shifts, and data derived from a combination of both. An implementation of the machine learning model stitches together multiple shifts for up to a schedule horizon based on predicted dispatch outcomes.

Abstract: Disclosed is a platform that manages worker users in a temporary staffing environment via an artificial machine learning model. The temporary staffing platform matches available workers to available shifts/gigs. Additional features include generating provisional or near-miss matches and informing workers how to turn those near-misses into full matches, plotting a gig-career path to develop additional skills, gamify development, and automatically generate resumes. The platform generates a set of skill tags associated with each shift/gig performed by the user. Designing of resume text files by the artificial machine learning model includes procedurally generated descriptions of experience the a user has based on the recording of each shift/gig performed by the user and the skill tags associated with each recorded shift/gig, wherein a format of the resume text file is formulated by the artificial machine learning model evaluating a mix of skill tags and employers amassed by the user.

Abstract: Disclosed is a platform that manages worker users in a temporary staffing environment via an artificial machine learning model. The temporary staffing platform matches available workers to available shifts/gigs. Additional features include generating provisional or near-miss matches and informing workers how to turn those near-misses into full matches, plotting a gig-career path to develop additional skills, gamify development, and automatically generate resumes. The platform generates a set of skill tags associated with each shift/gig performed by the user. Designing of resume text files by the artificial machine learning model includes procedurally generated descriptions of experience the a user has based on the recording of each shift/gig performed by the user and the skill tags associated with each recorded shift/gig, wherein a format of the resume text file is formulated by the artificial machine learning model evaluating a mix of skill tags and employers amassed by the user.

Abstract: Disclosed is a platform that provides temporary staff along with administrative infrastructure to employers who have temporary staffing needs. Administrative issues are monitored via dynamically generated geofences and mobile devices (mobile phones) carried by worker-users. The geofences serve as time clock and behavioral monitor zone.

Abstract: An article vending machine in a network of article vending machines capable of exchanging an inoperable rental article received from a user for an operable replacement article. The article dispensing machine checks customer identification information against one or more customer rental criteria to determine if the customer is eligible to exchange an inoperable article for an operable one. The article vending machine searches for an identical replacement article within an inventory of the machine itself and within inventories of the networked article vending machines. Alternatively, the article vending machine may provide a refund for the inoperable article.

Abstract: An article vending machine in a network of article vending machines capable of exchanging an inoperable rental article received from a user for an operable replacement article. The article dispensing machine checks customer identification information against one or more customer rental criteria to determine if the customer is eligible to exchange an inoperable article for an operable one. The article vending machine searches for an identical replacement article within an inventory of the machine itself and within inventories of the networked article vending machines. Alternatively, the article vending machine may provide a refund for the inoperable article.

Abstract: An article vending machine in a network of article vending machines capable of exchanging an inoperable rental article received from a user for an operable replacement article. The article dispensing machine checks customer identification information against one or more customer rental criteria to determine if the customer is eligible to exchange an inoperable article for an operable one. The article vending machine searches for an identical replacement article within an inventory of the machine itself and within inventories of the networked article vending machines. Alternatively, the article vending machine may provide a refund for the inoperable article.

Abstract: An article vending machine in a network of article vending machines capable of exchanging an inoperable rental article received from a user for an operable replacement article. The article dispensing machine checks customer identification information against one or more customer rental criteria to determine if the customer is eligible to exchange an inoperable article for an operable one. The article vending machine searches for an identical replacement article within an inventory of the machine itself and within inventories of the networked article vending machines. Alternatively, the article vending machine may provide a refund for the inoperable article.

Abstract: An article vending machine in a network of article vending machines capable of exchanging an inoperable rental article received from a user for an operable replacement article. The article dispensing machine checks customer identification information against one or more customer rental criteria to determine if the customer is eligible to exchange an inoperable article for an operable one. The article vending machine searches for an identical replacement article within an inventory of the machine itself and within inventories of the networked article vending machines. Alternatively, the article vending machine may provide a refund for the inoperable article.

Abstract: An article vending machine in a network of article vending machines capable of exchanging an inoperable rental article received from a user for an operable replacement article. The article dispensing machine checks customer identification information against one or more customer rental criteria to determine if the customer is eligible to exchange an inoperable article for an operable one. The article vending machine searches for an identical replacement article within an inventory of the machine itself and within inventories of the networked article vending machines. Alternatively, the article vending machine may provide a refund for the inoperable article.

Abstract: An article vending machine in a network of article vending machines capable of exchanging an inoperable rental article received from a user for an operable replacement article. The article dispensing machine checks customer identification information against one or more customer rental criteria to determine if the customer is eligible to exchange an inoperable article for an operable one. The article vending machine searches for an identical replacement article within an inventory of the machine itself and within inventories of the networked article vending machines. Alternatively, the article vending machine may provide a refund for the inoperable article.

Abstract: A system and method for communicating secondary vending options for a vendible media product having entertainment content therein is provided. The system comprises a transaction network, and a database in communication with the transaction network, wherein the database stores a plurality of digital video disc titles and a plurality of locations and release dates corresponding to the digital video disc titles. A vending controller at a vending location receives a user request signal for a digital video disc, and determines whether the digital video disc is available in a vending inventory at the vending location. A central controller receives a first signal from the vending controller, wherein the first signal comprises a secondary vending option and a user electronic mail address. The central controller receives a second signal from the vending controller, wherein the second signal comprises an indication that the digital video disc is available in the vending inventory.

Abstract: An article vending machine in a network of article vending machines capable of exchanging an inoperable rental article received from a user for an operable replacement article. The article dispensing machine checks customer identification information against one or more customer rental criteria to determine if the customer is eligible to exchange an inoperable article for an operable one. The article vending machine searches for an identical replacement article within an inventory of the machine itself and within inventories of the networked article vending machines. Alternatively, the article vending machine may provide a refund for the inoperable article.

Abstract: An article vending machine in a network of article vending machines capable of exchanging an inoperable rental article received from a user for an operable replacement article. The article dispensing machine checks customer identification information against one or more customer rental criteria to determine if the customer is eligible to exchange an inoperable article for an operable one. The article vending machine searches for an identical replacement article within an inventory of the machine itself and within inventories of the networked article vending machines. Alternatively, the article vending machine may provide a refund for the inoperable article.