Abstract: Systems and methods are provided for building personal protection equipment (PPE) rules. A live image may be received from a camera, where the live image is of a human body or a portion of the human body. A machine learning model may identify any body parts in the image for which PPE is usable. An augmented reality image may include the live image and an identifier object superimposed on any identified body parts, enabling a selection of a user-selected body part in the augmented reality image. PPE items usable for the user-selected body part may be added to the set of PPE required for a team. Systems and methods of safety compliance are provided that determine, by a machine learning model, a compliance score from the image, the compliance score indicating a degree to which the person is in compliance with PPE rules.

Abstract: Aspects map values of skills data for candidates to skills metadata representations stored within a metadata repository that includes skills metadata representation data dimensions for other candidates; filter via machine learning a top-trending subset of job classifications that have better career opportunity values from a universe of job classifications defined within the repository dimensional data values; determine via machine learning career path viability values for the top-trending subset job classifications as a function of strength of match to candidate dimensional values; project likely future values of mapped candidate values at the end of a future time period within a simulated work market scenario; and prioritize the top-trending subset job classifications as potential career paths for candidates as a function of the career path viability values and the projected future values of the dimensional data mapped for the candidates within the repository.

Abstract: A system includes one or more processors to identify a first plurality of attributes associated with a first opening and an entity structure, identify a second plurality of attributes associated with a plurality of positions and one or more entity structures, identify a target item missing from a metadata representation of a candidate for a second opening within the entity structure, execute an automated interview process for the candidate, generate an updated metadata representation of the candidate based on a value for the target item, and provide data of the candidate for display via an interface of a device of the entity structure.

Abstract: Aspects identify target dimensional data value items via machine learning that are most strongly correlated to successful hires for job opportunities within employment data that are similar to a new job opportunity. In response to determining that the target item value for a candidate is deficient to qualify for the new job opportunity, aspects engage the candidate in an automated artificial intelligence chat bot agent interview process that acquires interview audio and image response data from the candidate; extract data relevant to the target item from interview audio and image data; determine an objective value for the target item as a function of the extracted data; and qualify the candidate for suitability for the new job opportunity as a function of resume data mapped to the metadata representation of the candidate and the objective value determined for the target item.

Abstract: Systems, methods, and computer-readable storage media for responding to a query using a neural network and natural language processing. If necessary, the system can request disambiguation, then parse the query using a trained machine-learning classifier, resulting in at least one of an identified subject or an identified domain of the text query. The system can determine if the user is authorized to retrieve answers to the query and, if so, retrieve factual data associated with the query. The system can then retrieve a response template, and fill in the template with the retrieved facts. The system can then determine, by executing a machine comprehension model on the filled response template, a probable readability token, portion of text, of at least a portion of the filled response template and, upon identifying that the probable readability is above a threshold, reply to the text query with the at least a portion of the filled response template.

Abstract: A system for providing study plans to a user includes a topic catalog storing multiple topics and multiple keywords associated with each topic. The system also includes a plan generator configured to receive multiple sample study plans, each sample study plan having one or more resources, each resource having one or more portions, and each portion being assigned a duration. The plan generator uses the sample study plans and the topic catalog to train a topic model to identify which topics are associated with each resource, resulting in a trained topic model. The plan generator receives a profile of a student from a user, the profile having one or more selected topics the student desires to study and further having multiple preferences associated with the student.

Abstract: Systems, methods, and computer-readable storage media for responding to a query using a neural network and natural language processing. If necessary, the system can request disambiguation, then parse the query using a trained machine-learning classifier, resulting in at least one of an identified subject or an identified domain of the text query. The system can determine if the user is authorized to retrieve answers to the query and, if so, retrieve factual data associated with the query. The system can then retrieve a response template, and fill in the template with the retrieved facts. The system can then determine, by executing a machine comprehension model on the filled response template, a probable readability token, portion of text, of at least a portion of the filled response template and, upon identifying that the probable readability is above a threshold, reply to the text query with the at least a portion of the filled response template.

Abstract: Intelligent management of employee sabbaticals is provided. An analysis of a profile of an employee requesting a sabbatical along with historical data and profiles corresponding to other employees of an employer is performed using an artificial intelligence component. Proposed sabbatical plan options for the employee are generated using the artificial intelligence component based on the analysis of the profile of the employee and the historical data and profiles corresponding to the other employees of the employer. Selections from the proposed sabbatical plan options by the employee are received via a sabbatical planner graphical user interface displayed on a client device of the employee. A sabbatical plan for the employee is generated using the artificial intelligence component based on the selections from the proposed sabbatical plan options by the employee. A set of action steps corresponding to the sabbatical for the employee is performed.

Abstract: Aspects map values of skills data for candidates to skills metadata representations stored within a metadata repository that includes skills metadata representation data dimensions for other candidates; filter via machine learning a top-trending subset of job classifications that have better career opportunity values from a universe of job classifications defined within the repository dimensional data values; determine via machine learning career path viability values for the top-trending subset job classifications as a function of strength of match to candidate dimensional values; project likely future values of mapped candidate values at the end of a future time period within a simulated work market scenario; and prioritize the top-trending subset job classifications as potential career paths for candidates as a function of the career path viability values and the projected future values of the dimensional data mapped for the candidates within the repository.

Abstract: Aspects identify target dimensional data value items via machine learning that are most strongly correlated to successful hires for job opportunities within employment data that are similar to a new job opportunity. In response to determining that the target item value for a candidate is deficient to qualify for the new job opportunity, aspects engage the candidate in an automated artificial intelligence chat bot agent interview process that acquires interview audio and image response data from the candidate; extract data relevant to the target item from interview audio and image data; determine an objective value for the target item as a function of the extracted data; and qualify the candidate for suitability for the new job opportunity as a function of resume data mapped to the metadata representation of the candidate and the objective value determined for the target item.

Abstract: Aspects map, without association to job description data, candidate skills and activity data values to a metadata representation within a metadata repository; determine, without association to the job description data, via a machine learning process, a plurality of employability values for the candidate for top-trending jobs as a function of strength of match of the mapped activity and skills values to respective skills and activity data values that are associated within the repository to top-trending jobs without association to values of the job description data that are associated to the top trending jobs; generate a prioritized subset of the top trending jobs that omits jobs that have employability values failing to meet a minimum threshold employability value; and drive a graphical user interface display to present the prioritized subset of the top trending jobs to the candidate ranked as a function of their determined employability values.

Abstract: Aspects map candidate resume data values to a resume metadata representation of the candidate defined by data dimensions stored within a metadata repository that includes resume metadata representation data dimensions of a plurality of candidates; learn via a machine learning process different trending demand values for job classifications within the dimensional data as a function of employment data; identify via the machine learning process an upwardly trending job position skill missing from the candidate data dimensions and most likely to match a current skill set of the candidate; add the identified skill to the first candidate data dimensions; and generate a resume for the first candidate as a function of the first candidate data dimensions to include the added skill.

Abstract: Aspects of the present invention provide devices that analyze employee health by determining a probability from reported measures of variances to normal work hours for at least one measure of expected employee health that includes expected sick leave or expected termination, and displaying the determined probability of expected employee health on a display device.