Machine learning for insurance applications is provided to customers, potential customers, underwriters, and/or other insurance industry associates. An insurance application portal receives an insurance application from a customer regarding an insurance line of business of an insurance carrier. The portal can complete another insurance application for the customer for another line of business of the insurance carrier. The other insurance application is completed using information from the received insurance application and machine learning of customer data to infer inputs of information that does not overlap between the two applications. The portal can provide the completed second insurance application to the customer for approval to apply for the second line of business.

A method for predicting service requests volume includes generating a machine learning model predicting a number of service requests in time series data, based upon a plurality of actually received service requests in the time series data. The method recommends service request features for use in predicting the service requests volume. The method receives a determination from an human-in-the-loop indicating whether the generated machine learning model correctly predicts the number of service requests in time series data, based on the plurality of actually received service requests in the time series data and the recommended service request features. The method selectively updates the machine learning model predicting the number of service requests in times series data, based upon the determination from the human-in-the-loop. The method predicts, using the updated machine learning model, a number of service requests in time series data incoming during a future time period.

A computer-implemented is disclosed. The method includes: obtaining current delivery schedules of a plurality of delivery entities, the current delivery schedules including delivery data for pending deliveries of product orders associated with a plurality of merchants; receiving, from a computing device, a first order for a product associated with a first merchant; determining order data for the first order, the order data indicating, at least, an inventory location having available inventory of the product and a delivery destination for the first order; identifying a first set of the pending deliveries, the identifying including comparing delivery routes associated with said pending deliveries with order data for the first order; generating messages for causing delivery entities associated with the first set of pending deliveries to include the first order in their respective delivery schedules; and sending the generated messages to the respective delivery entities.

A micromobility transit vehicle service task management system and related methods are disclosed. In one embodiment, a system determines service tasks to be executed based on a level of availability of one or more micromobility transit vehicles. A value for each of the tasks is determined based on a cost to perform the task and value generated by an increase in availability of the one or more micromobility transit vehicles, for dynamic transportation matching, from execution of the task. The tasks may be prioritized into an order of execution that maximizes a total of the values of the tasks. The determined values of the tasks may be adjusted in the prioritization of the tasks based on the order of execution of the tasks. A navigational task route may be generated based on the prioritized tasks and assigned to a technician device to guide a technician to each of the tasks.

The present disclosure provides method and apparatus for determining a food item from a photograph and a corresponding restaurant serving the food item. An image is received from a user, the image being associated with a consumable item. One or more ingredients of the consumable item in the image is identified along with a location of the user and using a neural network, determining one or more similar images from a database. A restaurant associated with each of the one or more similar images is determined along with a similarity score indicating a similarity between the restaurant and the identified content of the image. The one or more restaurants and/or associated similar food items are ranked based on the similarity score and a list of ranked restaurants is provided to the user.

Order details for an order is evaluated based on real-time data associated with establishments that can satisfy the order, location data, traffic conditions, estimated order preparation times, and available delivery personnel (when the order is associated with a delivery). An optimal establishment that can fulfill the order is selected and the order with the order details is placed with the optimal establishment. In an embodiment, the real-time data continues to be evaluated after the order is placed, and when conditions warrant the order is redirected to a new optimal establishment for fulfillment.

A computer-based method, system, and computer program product for automatically identifying significant events for food traceability. The method may comprise receiving a series of events from an agriculture supply chain entity, automatically determining, at a machine learning model of an event analysis module, one or more events in the series having a significance for food traceability greater than a threshold, and automatically reporting the one or more events to a ledger.

The present disclosure provides a method comprising receiving a ride request from a user having a user profile, the ride request including a requested drop-off location within a service area and the user profile specifying at least one drop-off location preference parameter; querying a database to obtain data regarding a condition of the requested drop-off location, wherein the data is collected by a plurality of vehicles traversing the service area and equipped with at least one sensor and at least one imaging device; determining whether the obtained data satisfies the at least one drop-off location preference parameter; and determining at least one alternative drop-off location within a first distance from the requested drop-off location if the obtained data does not satisfy the at least one drop-off location preference parameter.

The present application discloses systems, methods, and computer-readable media that can dynamically control a number of provider devices operating in a prioritized-dispatch mode by determining a range of prioritized-dispatch-mode slots for a target time based on differences between value metrics received by provider devices operating in multiple dispatch modes and sending availability nudges to provider devices based on the range of prioritized-dispatch-mode slots. For instance, the disclosed systems can generate a threshold-noticeable-value difference between historical value metrics received by provider devices while operating in a prioritized-dispatch mode and in a basic-dispatch mode. Based on the threshold-noticeable-value difference, the disclosed systems determine a range of slots for the prioritized-dispatch mode during a target time period for a geographic area. Based on the determined range of slots, the disclosed systems can transmit prioritized-mode-availability indicators to provider devices to indicate varying levels of availability of the prioritized-dispatch mode for the geographic area.

A device may receive schedule data identifying schedules of appointments for drivers of vehicles and may receive location data identifying geographical locations of the vehicles. The device may receive traffic data identifying traffic conditions, and may process the received data, with a first model, to determine status data identifying estimated statuses of the appointments. The device may process the received data, with a second model, to generate sets of isochrones for destinations of the appointments, and may calculate, based on the sets of isochrones and the location data, estimated travel time data identifying estimated travel times for the appointments. The device may process the status data, the estimated travel time data, and the schedule data, with a third model, to calculate estimated time of arrival data identifying estimated times of arrival for the appointments, and may perform actions based on the estimated time of arrival data.