Abstract: An online system receives information describing a set of items requested by a user and an indication via a chat interface that a particular item needs replacement. The online system generates one or more prompts configured to request a machine learned language model to identify the particular item that needs replacement and to identify one or more replacement items for the particular item. The online system receives a set of item identifiers from the machine learned language model and selects a replacement item from a database based on the set of item identifiers. The online system may also receive an order and a communication history associated with a user including a message with a request to modify the a. The online uses the machine-learning language model to map the request type to the set of API requests for updating the order to reflect the request from the user.

Abstract: Systems and methods are provided for providing recommendations of safe driving routes that are tailored to the driving habits of particular drivers. A machine learning model (e.g., an artificial neural network) may be trained using data indicative of insurance claim severity, road conditions, and/or vehicle telematics data associated with vehicle-related incidents, such as vehicle collisions. The machine learning model may be trained to identify road types and conditions that are predictive of claim frequency and severity. Any given driving route(s) may be provided to the trained machine learning model, and a risk value may be computed for the route(s). By further applying a personalized driver profile to the calculations of risk, personalized risk values may be computed for the route(s), and a safest route may be recommended to a driver.

Abstract: A smart ring includes a body. The body includes a first part including at least a part of a sensor unit. The body also can include a second part including at least a part of an output unit and removably connected to the first part of the body. The body can further include a first pair of removable portions. The first pair of removable portions can include a first removable portion connected to the first part of the body. The first pair of removable portions also can include a second removable portion connected to the second part of the body. The first removable portion can be removable from the body. The second removable portion can be removable from the body. Other embodiments are disclosed.

Abstract: Method and system for generating virtual maps. For example, the method includes determining first real-world driving characteristics based upon first real-world telematics data of a first real-world user, determining first real-world geolocation characteristics based upon first real-world geolocation data of the first real-world user, generating a first virtual map based upon the first real-world driving characteristics and the first real-world geolocation characteristics, presenting the first virtual map in a virtual game, determining second real-world driving characteristics based upon second real-world telematics data of a second real-world user, determining second real-world geolocation characteristics based upon second real-world geolocation data of the second real-world user, generating a second virtual map based upon the second real-world driving characteristics and the second real-world geolocation characteristics, and presenting the second virtual map in the virtual game.

Abstract: A system for displaying information via a smart ring device can include a smart ring device. The smart ring device also can include a processor disposed within a ring band. The processor can be configured to collect data to identify one or more conditions. The one or more conditions can include one or more of a biometric of a user of the smart ring device, a state of the user of the smart ring device, or a condition of an environment of the user of the smart ring device. The smart ring device further can include a display at a surface of the ring band. The processor can be configured to cause the display to present the information indicative of the one or more conditions. The processor also can be configured to determine whether a termination condition has been met for the one or more conditions.

Abstract: An online concierge shopping system fulfills orders using workers who pick items at a warehouse to complete an order and workers to deliver the orders to a customer's location. To optimize the staffing of workers for each task, the system uses a trained model to predict the number of workers needed to achieve an optimal outcome based on an input set of contextual information. The system also schedules specific workers to various shifts using the predicted number of workers needed and then searching a feasibility space for an optimal solution. The trained model may be updated based on performance observations.

Abstract: A computer system for providing reimbursement controls to insurance policies is provided. The computer system may include a processor in communication with a memory device.

Abstract: Embodiments relate to automatic determination of an alternative item for an item of original set of items included into an order of a user of an online system. The online system accesses a first computer model trained to identify a set of candidate replacement items for the item and selects a subset of the candidate replacement items from the identified set based on a constraint that each candidate replacement item in the subset has a smaller monetary value than the item. The online system accesses a second computer model trained to select a candidate replacement item from the subset based on a predicted likelihood of conversion by the user for each candidate replacement item. The online system causes a device of the user to display a user interface with the selected candidate replacement item for inclusion into the order instead of the item from the original set of items.

Abstract: An online system receives orders from users and dispatches pickers to fulfill the orders by obtaining ordered items at a retailer. If an ordered item cannot be found by a picker, the picker may refund the item or attempt to find a replacement item. While obtaining a replacement item may increase revenue to the online system, it can also cause a bad outcome for user experience (e.g., an unacceptable replacement item, a refund request of the replacement item, etc.). To balance these interests, the online system trains a model to predict an outcome metric comprising a likelihood of a bad outcome from replacing an item or an expected amount of profit to the online system from a replacement item. The online system compares the outcome metric to a threshold to determine whether to promote or dissuade the picker from replacing a not-found item.

Abstract: An online system generates text-based representations of various types of data for processing using a large language model. The online system extracts location data from a map of a source location and converts the location data into a text-based representation of the location data. The online system receives a set of item identifiers from a client device of a user and generates an LLM prompt based on the set of item identifiers and the text-based representations of the location data. The online system receives a response from the LLM and parses the response for a text-based description of related items. The online system maps the text-based description of the related items to item identifiers and transmits a notification to the client device that includes item data associated with the related items.

Abstract: The present disclosure is directed to apparatuses, systems and methods for automatically classifying digital images of occupants inside a vehicle. More particularly, the present disclosure is directed to apparatuses, systems and methods for automatically classifying digital images of occupants inside a vehicle by comparing current image data to previously classified image data.

Abstract: Method and system for selecting a vehicle route. For example, the method includes collecting information for a future vehicle trip that will be made by a user, generating multiple routes for the same future vehicle trip, predicting an amount of fuel consumption for each of the multiple routes based at least in part upon information for the route and user driving features of the user, and selecting a route from the multiple routes based at least in part upon the amounts of fuel consumption predicted for the user, where the selected route corresponds to a least amount among the amounts of fuel consumption predicted for the user.

Abstract: A ring includes a housing. The housing can include an inner surface configured to contact a finger of a user when the housing is worn by the user. The housing also can include a first power source configured to be wirelessly charged by a second power source that is removably coupled to the housing. The housing further can include a component configure to draw energy from the first power source. The component can perform one or more of: sensing an activity of the user wearing the housing or sending data to a communication device. Other embodiments are disclosed.

Abstract: A system serializing and deserializing models configured to (i) store a first model, wherein the first model includes a plurality of functionalities; (ii) generate a human-readable document based on the first model, wherein the human-readable document describes the first model; (iii) generate a second model based on the human-readable document, wherein the second model includes the plurality of functionalities; and (iv) perform at least one execution of the second model.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations: analyzing telematics data and device interaction data to determine vehicle driving instances in which a user interacts with a mobile device during a vehicle trip for a vehicle; and the device interaction data is generated when the user interacts with the mobile device during the vehicle trip for the vehicle; receiving positioning data associated with one or more positions of the mobile device within the vehicle during the vehicle trip for the vehicle; and identifying whether the user interacting with the mobile device is a driver of the vehicle based at least in part upon the positioning data and the vehicle driving instances. Other embodiments are disclosed.

Abstract: A ring includes a controller. The ring can also include a power source configured to power the controller. The ring further can include a charging unit. The charging unit can be configured to convert a first form of energy into a second form of energy. The charging unit also can be coupled to the power source to provide the second form of energy to the power source. The controller can execute instructions to control operation of a user interface. Other embodiments are disclosed.

Abstract: Telematics systems and methods are described for generating interactive animated guided user interfaces (GUIs). A telematics cloud platform is configured to receive vehicular telematics data from a telematics device onboard a vehicle. A GUI value compression component determines, based on the vehicular telematics data, a plurality of GUI position values and a plurality of corresponding GUI time values. A geospatial animation app receives the plurality of GUI position values and the plurality of corresponding GUI time values. The geospatial animation app implements an interactive animated GUI that renders a plurality of geospatial graphics or graphical routes on a geographic area map via a display device. The geospatial graphics or graphical routes are rendered to have different visual forms based on differences between respective GUI position values and corresponding GUI time values.

Abstract: Method, system, device, and non-transitory computer-readable medium for data management. In some examples, a computer-implemented method includes: collecting a plurality of personal data sets continually; collecting a plurality of sensor data sets continually via one or more sensing modules, and generating and continually updating an operator profile, determining and continually updating one or more telematics interferences; generating and continually updating a data profile; and receiving one or more access policies associated with the data profile; and listing and continually updating the data profile onto a telematics marketplace according to the one or more access policies such that only marketplace consumers having one or more consumer classifications satisfying the one or more access polices can access the data profile.

Abstract: An online system may prompt a shopper to capture one or more images of items on a checkout belt of a retailer, wherein the items are for fulfilling orders for one or more users of an online service. An online system may provide the one or more images to a machine learning model configured to classify an item as a product. An online system may classify the items to one or more products by applying the machine learning model to the images. An online system may for each user, matching the classified products to the user's order. An online system may obtain an annotated image of the items highlighting classified products which do not match the user's order. An online system may provide to the shopper the annotated image with a notification of a potential discrepancy.

Abstract: An online system detects an anomaly associated with an item selection made by a picker for fulfilling an order of a user of an online system. The system generates a prompt for execution by a machine-learned model trained as a large language model. The prompt comprises a chat log between the picker and the user. The system provides the prompt to the machine-learned model for execution. The system receives, as output from the machine-learned model and based on the chat log, a description indicating whether the anomaly is attributable to the user. The system determines, based on the output from the machine-learned model, that the item selection is not attributable to the user. Responsive to determining that the item selection is not attributable to the user, the system provides a notification to a client device of the user to confirm whether the item selection is approved by the user.