Abstract: This application relates to automatically scheduling timeslots, such as timeslots for scheduling item pickups and deliveries. The embodiments may employ machine learning process to determine, for each of a plurality of timeslots, a timeslot capacity. The embodiments may further determine a time as to when each of the plurality of timeslots become available for selection, such as on a webpage. In some examples, the time determined to make available for selection each of the plurality of timeslots is based on a predetermined amount of time before the timeslot. Further, the embodiments may provide for display each of the plurality of timeslots at the determined time. The embodiments may further include receiving a selection of one of the plurality of timeslots, and scheduling a pickup or delivery based on the selected timeslot. The machine learning processes may be trained with features generated from historical timeslot data and workforce availability.

Abstract: This application relates to automatically scheduling timeslots, such as timeslots for scheduling item pickups and deliveries. The embodiments may employ machine learning process to determine, for each of a plurality of timeslots, a timeslot capacity. The embodiments may further determine a time as to when each of the plurality of timeslots become available for selection, such as on a webpage. In some examples, the time determined to make available for selection each of the plurality of timeslots is based on a predetermined amount of time before the timeslot. Further, the embodiments may provide for display each of the plurality of timeslots at the determined time. The embodiments may further include receiving a selection of one of the plurality of timeslots, and scheduling a pickup or delivery based on the selected timeslot. The machine learning processes may be trained with features generated from historical timeslot data and workforce availability.

Abstract: This application relates to automatically scheduling timeslots, such as timeslots for scheduling item pickups and deliveries. The embodiments may employ machine learning process to determine, for each of a plurality of timeslots, a timeslot capacity. The embodiments may further determine a time as to when each of the plurality of timeslots become available for selection, such as on a webpage. In some examples, the time determined to make available for selection each of the plurality of timeslots is based on a predetermined amount of time before the timeslot. Further, the embodiments may provide for display each of the plurality of timeslots at the determined time. The embodiments may further include receiving a selection of one of the plurality of timeslots, and scheduling a pickup or delivery based on the selected timeslot. The machine learning processes may be trained with features generated from historical timeslot data and workforce availability.

Abstract: Example systems and methods for resource allocation for infrastructure engineering are described. In one implementation, a method identifies resources for infrastructure engineering with respect to an organization. The method also sorts the resources based on skills and roles associated with the infrastructure engineering, and then determines multiple teams based on the sorted resources.

Abstract: In different embodiments of the present invention, systems, methods, and computer-readable storage media allow a user to analyze software application modules, during development of the software. The present invention may be used to analyze the software application modules, to identify vulnerabilities and to provide the user with actionable intelligence that may be used to improve the security of the software application modules. The actionable intelligence may include a list or groupings of the vulnerabilities ranked based on severability, type, and/or location.

Abstract: In different embodiments of the present invention, systems, methods, and computer-readable storage media allow a user to analyze software application modules, during development of the software. The present invention may be used to analyze the software application modules, to identify vulnerabilities and to provide the user with actionable intelligence that may be used to improve the security of the software application modules. The actionable intelligence may include a list or groupings of the vulnerabilities ranked based on severability, type, and/or location.

Abstract: Example systems and methods for resource allocation for infrastructure engineering are described. In one implementation, a method identifies resources for infrastructure engineering with respect to an organization. The method also sorts the resources based on skills and roles associated with the infrastructure engineering, and then determines multiple teams based on the sorted resources.