In some embodiments, a method can include receiving a set of job descriptions and a set of candidate profiles. Each job description is associated with a first subset of candidate profiles from the set of candidate profiles. The method can further include executing a model to identify, from the first subset of candidate profiles, a second subset of candidate profiles that satisfy a fit metric and a third subset of candidate profiles that does not satisfy the fit metric. The method can further include calculating a bias metric based on a true positive value, a false positive value, a true negative value, and a false negative value that were calculated based on auditing the second subset of candidate profiles and the third subset of candidate profiles. The method can further include updating the set of job descriptions based on the bias metric.

A process mining system performs process mining using visual logs generated from video streams of worker devices. Specifically, for a given worker device, the process mining system obtains a series of images capturing a screen of a worker device while the worker device processes one or more tasks related to an operation process. The process mining system determines activity labels for a plurality of images. An activity label for an image may indicate an activity performed on the worker device when the image was captured. The activity label is determined by extracting information from pixels of the image and inferring the activity of the worker device from the extracted information. The process mining system generates event logs from the visual logs of worker devices and uses the event logs for process mining.

In some examples, a server may determine that a case, created to address an issue of a computing device, is closed and perform an analysis of steps in a process used to close the case. The analysis may determine a length of time of each step and determine that a time to close the case or complete a particular step was at least a predetermined amount faster than average. The server may use machine learning to create a survey question to determine a technique used to close the case or complete the particular step faster than average and to determine one or more incentives to provide a technician that closed the case. An answer from the technician to the survey question may include the technique used to close the case or complete the particular step faster than average. The technique may be shared with other technicians.

A remote driving taxi system provides a mobility service using remote driving taxis that are driven by remote drivers. Management information indicates assignment states between the remote driving taxis and the remote drivers. The remote driving taxi system executes an assignment process based on the management information, in response to a request from a user. Specifically, the remote driving taxi system selects one of unassigned taxis to each of which the remote driver has not been assigned, as a first remote driving taxi that provides the service to the user. Further, the remote driving taxi system selects one of remote drivers each of which has not been assigned to the remote driving taxi, as a first remote driver that provides the service to the user. Then, the remote driving taxi system assigns the first remote driver to the first remote driving taxi.

A method for aggregating data to facilitate gamification of employee contributions is provided. The method includes compiling, via an application programming interface, raw data from a source, the raw data including employee contribution data, employee achievement data, and employee engagement data; mapping the compiled raw data based on a predetermined guideline; generating a structured data set based on a result of the mapping; identifying a user record from the structured data set; determining a characteristic for the identified user record; and updating a user profile that corresponds to the identified user record with information relating to the determined characteristic.

A system for evaluating communication of a participant in a team setting including communication data of at least one participant, a computational infrastructure configured to receive the communication data and transpose the communication data into a machine-readable transcription, a communication representation platform configured to receive the machine-readable transcription wherein the communication representation platform selects at least one data set from the machine-readable transcription, applies a linear algebra technique to the at least one data set, and generates at least one score for the at least one data set, and a human-comprehensible output including at the least one score generated by the communication representation platform.

An engineering assistant system 1 includes: an engineering server 10 that issues a work list including information related to work necessary for performing the engineering of a process control system 100; and an at least one engineering client 20 that grants work authority for each worker based on the work list issued by the engineering server 10 and makes it possible to perform work on a constituent apparatus that constitutes the process control system 100 within a range of granted work authority.

A system to assess the current, or future workload of a pilot and automatically providing annunciation when needed to the pilot, as well as to resources that support the pilot. The system may automatically, and without pilot involvement, assess the workload for the pilot based on incoming events and abnormal scenarios. The predicted incoming events may be related to operation data, such as navigation information, weather, traffic avoidance, and so on. The system may also detect abnormal scenarios, such as engine warnings, fuel or other aircraft issues. The system may determine whether the predicted incoming events or the abnormal scenario satisfies one or more pre-defined criteria. Based on the event satisfying one or more criteria, the system may output an annunciation to either the pilot, resources that support the pilot, such as an additional pilot and air traffic control (ATC).

Methods, apparatus, systems, and computer program products are disclosed for utilizing specially configured machine learning models to generate incremental currency value(s) associated with one or more target merchant data objects. Some embodiments, based on one or more market record sets, identify an actual electronic currency value for a total merchant data object set, and include a counterfactual model configured to generate a counterfactual electronic currency value for use in determining a counterfactual incremental electronic currency impact, and in some embodiments for ranking other models. Embodiments, additionally or alternatively, include an incrementality-trained ensemble model for generating a predictive incremental electronic currency impact. The incrementality-trained ensemble model may be trained to predict based on the rankings of the outputs of the counterfactual model. Embodiments may further rank target merchant data objects and perform one or more additional actions, including assigning the target merchant data objects to sales account data structures for management.

Systems and methods to provide training content to equipment operators and determine the success of the training content. Sensor data is captured during a user’s operation of the equipment. The sensor data is analyzed to detect an event in the operation of the equipment. In response to detecting an event, training content for the event is selected and presented to the user. If the user does not consume the training content, the content is re-presented to the user. If the user consumed the training content, but a reoccurrence of the event is detected in additional sensor data, then the training content is labeled as being unsuccessful at training the user to avoid the event. In response to the training content being unsuccessful at training the user, additional training content may be provided to the user or a modification to the equipment may be suggested.