Abstract: Techniques are described for automatically generating job descriptions. Training data is used to define job categories, job sections, and selective phrases for each job category and job section. In response to receiving request to generate a job description, a job category or additional information is derived from the request. Such information is used to populate existing templates for a job description based on a skill knowledge graph, which connects skill names based on hypernym trees and enables enhancement of a job description with better or broader coverage.

Abstract: Techniques are described for automatically generating job descriptions. Training data is used to define job categories, job sections, and selective phrases for each job category and job section. In response to receiving request to generate a job description, a job category or additional information is derived from the request. Such information is used to populate existing templates for a job description based on a skill knowledge graph, which connects skill names based on hypernym trees and enables enhancement of a job description with better or broader coverage.

Abstract: Techniques are described for automatically generating job descriptions. Job categories are identified for the documents in the job description dataset based on clustering the documents. Sections within each document in the job description dataset are automatically identified, and each identified section is automatically classified. Each type of section is analyzed independently of the other section types using natural language processing, which allows automatic generation of each section of a new job description based on the section-specific data derived from the dataset. Section- and category-specific selective phrases are identified within the job description dataset, and embeddings are generated for the identified selective phrases. A request to generate a new job description for a particular category is received.

Abstract: A mobile application for execution upon an electronic device, system, or method for an amusement park or waterpark for tracking guests and collecting feedback. The application provides information, entertainment or other data to the user within the park. The application may determine a position within the park and display notifications (discounts, menus, etc.) at nearby vendors or provide recommendations based upon wait times. The application may allow for reservations at vendors. The application may determine ride sequence(s) based upon various characteristics and navigate according to such sequence(s). Puzzles, games, or other entertainment or educational activities may be displayed to the user for providing rewards based upon their participation. Guests may be tracked using a variety of technologies and information determined (dispatch rates, queues, etc.) by such tracking. Feedback (customer satisfaction) may be collected at guest interfaces.

Abstract: A residual benefit program where recipients of non-consumable goods receive a good with benefits, can recycle that good for benefits, and redeem the benefits at some point in the future. The program accumulates credits for a beneficiary elected by a primary purchaser. Credits may accumulate due to purchase of a product, recycling of a product, or via interest over time. Upon redemption of the accumulated credits, the beneficiary receives the reward chosen by the primary purchaser. The reward or the redeemer who provides the reward is set by the primary purchaser for the beneficiary and may be modified over time.