The present disclosure relates generally to systems and processes for position fulfillment and, more particularly, to systems and methods of identifying and matching human resources to an open employment position within an organization. The method includes: obtaining, by a computer system, one or more profiles from one or more data sources; analyzing, by the computer system, the one or more profiles to parse attributes and find similarities and/or recurring occurrences in the parsed attributes; normalizing the parsed attributes based on the at least one similarities and recurring occurrences; and matching the normalized attributes to attributes of an open position.

The disclosed embodiments provide a system for processing data. During operation, the system obtains labels for entities found in portions of text in a first set of jobs. Next, the system inputs the portions of text and the labels as training data for a machine learning model. The system then applies the machine learning model to a second set of jobs to generate predictions of additional entities in additional portions of text in the second set of jobs. Finally, the system creates, based on the predictions, an index containing mappings of the additional entities to subsets of the second set of jobs in which the additional entities are found.

A computerized-method for trading a scheduled-working-shift, is provided herein. The computerized-method includes operating a trading-shifts module. The module includes: communicating with a computerized-device of a source-agent, to receive a trade-request for a scheduled-working-shift, retrieving adequate target-agents, having a day-off, during the scheduled-working-shift, sorting the adequate target-agents, to yield a sorted-list of target-agents, sending the sorted-list of target-agents to the computerized-device of the source-agent, to be presented via a trading-shift-interface. Upon receiving from the computerized-device of the source-agent, a selection of adequate target-agents from the sorted-list of target-agents, presenting a notification for a trade-request, on each computerized-device of the selected adequate target-agents, receiving a trade-action from each notified adequate target-agents, for the trade request, and sending the trade-action to the computerized-device of the source-agent, to be presented, and receiving a selection of a scheduled-working-shift of a target-agent, from the computerized-device of the source-agent, according to the trade-action and the scheduled-working-shift.

A method may include receiving a request from a first user for a task to be completed on an agricultural field including an identification of the agricultural field, a piece of agricultural equipment for use on the agricultural field, and a date range for the task; generating a job data structure with the information in the request; executing a query to identify an operator for an available piece of agricultural equipment during the date range; transmitting a notification to a second user, the notification identifying request information, receiving confirmation the operator will complete the task and updating the job data structure based on the confirmation; in response to receiving a completion indication, transmitting a rating request for the piece of agricultural equipment; receiving, from the computing device, a rating for the piece of agricultural equipment; and updating an equipment listing for the second user with the rating.

Systems and methods are provided for assessing morality of a user. A request comprising an input data is received over a communication network to assess the morality corresponding to the input data. Upon receiving the request, a first vector is generated through deployment of a predefined language model based on the input data. Then a set of common-sense characteristics are extracted from the input data by generating a corresponding second vector for each of the set of common-sense characteristics from the input data by deploying a common-sense model. Upon generation of the first vector and the second vectors, morality value is determined for the input data based on the first vector and the second vectors corresponding to the set of common-sense characteristics, the morality value indicates whether a context of the input data is morally correct.

In accordance with some embodiments of the disclosed subject matter, mechanisms (which can, for example, include systems, methods, and media) for maintaining components of a unit are provided. In some embodiments, a method comprises: receiving an indication that a unit is ready to be turned; generating a customized user interface for assessment of the unit based on a template; receiving a request, from a remote computing device, for the user interface; causing the remote device to present the user interface; receiving, via the remote device, input indicating that a component should be replaced; receiving, from the remote device, an image of the component; receiving, from a second remote computing device associated with a designated user, input indicating that replacement of the first component is approved; and causing a notification to be presented via the remote computing device indicating that replacement of the first component is approved.

Systems and methods of the present invention provide for determining a required skill set for meeting a goal, determining the current skill set of a user, identifying skill gaps between the required skill set and the user's current skill set, assigning skill training objectives to the user corresponding to the skill gaps, monitoring progress of the user through completion of the skill training objectives, and alerting the user upon completion of the skill training objectives. Goals and skill training objectives may be set for multiple users to close skill gaps preventing such users from qualifying for roles of a project to which the users are closely matched. The each user's respective skill training objective progress may be monitored and tracked provided via a user interface at a manager device associated with the project. The manager device may be alerted when the skill training objectives have been completed by the users.

The present disclosure provides a personal introduction information generating method. The method includes: obtaining basic information of an attorney; establishing a communicational connection with a third-party database to extract all patent case data corresponding to the attorney; counting a historical approval rate of the attorney based on all the patent case data to extract to-be-evaluated patent cases; evaluating the to-be-evaluated patent cases to obtain a review result; generating personal introduction information corresponding to the attorney using a preset template according to the historical approval rate of the attorney and the review result. The method realizes the accurate and comprehensive obtaining of information of patent attorneys. In addition, a computing device and a storage medium are also provided.

Systems, methods, and computer-readable media for cooperative decision making in a social network include identifying a potential match between a first individual and a second individual based on interests of the first individual, receiving, from a first client device associated with the first individual, a request for input regarding the potential match, and, in response to the request, identifying other individuals for providing input to the first individual regarding the potential match, supplying profile information for the second individual to client devices associated with the first individual and the other individuals, enabling network communication between the first individual and the other individuals for providing the input to the first individual regarding the potential match, and receiving a vote to connect the first individual with the second individual from the first client device or at least one of the other client devices.

The present disclosure is directed toward systems, methods, and non-transitory computer readable media for identifying and recommending team members for target users from a content management system utilizing a machine learning approach. In particular, the disclosed systems can generate a set of candidate team members from among users of the content management system based on various factors such as access to a common digital content item. In some embodiments, the disclosed systems further determine recommended team members from among the set of candidate team members. For example, the disclosed systems can utilize a machine learning approach to generate or predict recommended team members based on particular features extracted or determined for, or with respect to, the various candidate team members. In certain implementations, the disclosed systems further provide a recommended-team-member notification to notify a target user of a recommended team member.