Abstract: Visual search query intent extraction and search refinement is described. In one or more implementations, a visual search query system receives a search query for items listed on an online marketplace, the search query including an image. Using one or more machine learning models, the visual search query system analyzes the image to determine characteristics of an object in the image. Based on the characteristics of the object in the image, the visual search query system automatically generates one or more search terms and searches the online marketplace to locate items matching the one or more search terms. The visual search query system then displays visual indications of the located items matching the one or more search terms in a user interface of the online marketplace.

Abstract: A multimodal embedding modifier generates a modified seed search selection embedding for providing a set of search results. The multimodal embedding modifier enhances the ability and accuracy of identifying a user's true intent when searching the online marketplace. For example, embodiments disclosed herein can allow a user to navigate multiple modalities for an item. In some embodiments, a user may select a search result corresponding to an initial search query, and further modify the selected search result by inputting a modifier (e.g., a textual modifier). The multimodal embedding modifier can be trained using a training dataset including a text embedding, an image embedding, another type of embedding, or a combination thereof.

Abstract: A multimodal embedding modifier generates a modified seed search selection embedding for providing a set of search results. The multimodal embedding modifier enhances the ability and accuracy of identifying a user's true intent when searching the online marketplace. For example, embodiments disclosed herein can allow a user to navigate multiple modalities for an item. In some embodiments, a user may select a search result corresponding to an initial search query, and further modify the selected search result by inputting a modifier (e.g., a textual modifier). The multimodal embedding modifier can be trained using a training dataset including a text embedding, an image embedding, another type of embedding, or a combination thereof.

Abstract: Systems and methods are directed to automatic listing generation and inventory management based on machine-learning analysis. The system trains a time series-based machine learning (ML) model that forecasts sales. During inference time, the system determines one or more potential categories for a user based on custom preferences and previous analytic queries of the user. High demand items in the one or more potential categories are then applied to the ML model, which outputs probabilities of predicted sales for the high demand items. The system then determines items having a potential inventory gap by cross-checking current inventory with items having a probability outputted by the ML model that satisfies a probability threshold.

Abstract: A multimodal embedding modifier generates a modified seed search selection embedding for providing a set of search results. The multimodal embedding modifier enhances the ability and accuracy of identifying a user's true intent when searching the online marketplace. For example, embodiments disclosed herein can allow a user to navigate multiple modalities for an item. In some embodiments, a user may select a search result corresponding to an initial search query, and further modify the selected search result by inputting a modifier (e.g., a textual modifier). The multimodal embedding modifier can be trained using a training dataset including a text embedding, an image embedding, another type of embedding, or a combination thereof.

Abstract: An example operation may include one or more of requesting, by a processor of a device, permissions to provide access to a transport to a diagnostic location from a network of a plurality of diagnostic locations, in response to receiving the permissions from the network of the plurality of the diagnostic locations, authenticating the diagnostic location, by the device, providing a pre-stored one-time access key to the transport to the diagnostic location, and receiving, by the device, diagnostics from the diagnostic location.

Abstract: A method includes measuring biometric data of a user at a monitoring device. The method further includes outputting, at the monitoring device, an alert to a vehicle computing device within a vehicle based upon detection of a biometric event, such that the party outside of a vehicle is notified. The method also includes analyzing and storing, within an application residing on the monitoring device, the biometric data within the application. The method still further includes outputting, within the application, a predetermined timeframe of the biometric data to the party.

Abstract: Methods, systems, and devices for a cross-linked distributed ledger. The cross-referencing system includes multiple computing devices including a first computing device and a second computing device. A computing device of the multiple computing devices is configured to maintain a first cross-linked distributed ledger. The first cross-linked distributed ledger has a first set of multiple linked records that are associated with a first identifier. The first computing device includes a processor. The processor is configured to link or provide a first record associated with the first identifier to the first cross-linked distributed ledger. The first record has a first reference to a second record. The second record is within a second set of multiple cross-linked records of a second cross-linked distributed ledger.

Abstract: A method includes measuring biometric data of a user at a monitoring device. The method further includes outputting, at the monitoring device, an alert to a vehicle computing device within a vehicle based upon detection of a biometric event, such that the party outside of a vehicle is notified. The method also includes analyzing and storing, within an application residing on the monitoring device, the biometric data within the application. The method still further includes outputting, within the application, a predetermined timeframe of the biometric data to the party.

Abstract: An example operation may include one or more of requesting, by a processor of a device, permissions to provide access to a transport to a diagnostic location from a network of a plurality of diagnostic locations, in response to receiving the permissions from the network of the plurality of the diagnostic locations, authenticating the diagnostic location, by the device, providing a pre-stored one-time access key to the transport to the diagnostic location, and receiving, by the device, diagnostics from the diagnostic location.

Abstract: Methods and systems for maintaining telematics data. The system includes a standard vehicle. The standard vehicle includes one or more sensors configured to detect telematics data, a memory configured to store a blockchain associated with the standard vehicle, an electronic control unit (ECU) configured to update the blockchain associated with the standard vehicle with the detected telematics data, and a transceiver configured to communicate the blockchain to one or more other vehicles. The system also includes a collection vehicle. The collection vehicle includes a transceiver configured to receive the blockchain from the standard vehicle, and communicate the blockchain from the standard vehicle to a network to update a distributed record of the blockchain associated with the standard vehicle.

Abstract: An example operation may include one or more of requesting, by a processor of a device, permissions to provide access to a transport to a diagnostic location from a network of a plurality of diagnostic locations, in response to receiving the permissions from the network of the plurality of the diagnostic locations, authenticating the diagnostic location, by the device, providing a pre-stored one-time access key to the transport to the diagnostic location, and receiving, by the device, diagnostics from the diagnostic location.

Abstract: In one embodiment, example systems and methods relate to ordering food from a vehicle. A driver or a passenger of a vehicle indicates that they would like to order food from the vehicle by speaking a command, or by making a selection on a display associated with the vehicle. In response to the indication, the vehicle determines one or more restaurants that are near the vehicle, or that are near the route being traveled by the vehicle. After the driver or passenger selects a restaurant, the vehicle loads a restaurant specific natural language understanding model that is associated with the restaurant. As the driver or passenger speaks their order, the restaurant specific natural language understanding model is used to process the audio and generate an order. The order and an estimated time of arrival of the vehicle is provided electronically to the restaurant.

Abstract: An example operation may include one or more of connecting, by a processor of a transport, to a network comprised of a plurality of transports, receiving, by the processor of the transport, an access key request to the transport from a device, broadcasting, by the processor of the transport, the request to the network, collecting, by the processor of the transport, authorizations from a subset of the plurality of the transports, and sending a one-time access key to the device, if the authorizations are received from a minimal required number of the transports.

Abstract: An example operation may include one or more of requesting, by a processor of a device, permissions to provide access to a transport to a diagnostic location from a network of a plurality of diagnostic locations, in response to receiving the permissions from the network of the plurality of the diagnostic locations, authenticating the diagnostic location, by the device, providing a pre-stored one-time access key to the transport to the diagnostic location, and receiving, by the device, diagnostics from the diagnostic location.

Abstract: An example operation may include one or more of connecting, by a processor of a transport, to a network comprised of a plurality of transports, receiving, by the processor of the transport, an access key request to the transport from a device, broadcasting, by the processor of the transport, the request to the network, collecting, by the processor of the transport, authorizations from a subset of the plurality of the transports, and sending a one-time access key to the device, if the authorizations are received from a minimal required number of the transports.

Abstract: Methods and systems for maintaining telematics data. The system includes a standard vehicle. The standard vehicle includes one or more sensors configured to detect telematics data, a memory configured to store a blockchain associated with the standard vehicle, an electronic control unit (ECU) configured to update the blockchain associated with the standard vehicle with the detected telematics data, and a transceiver configured to communicate the blockchain to one or more other vehicles. The system also includes a collection vehicle. The collection vehicle includes a transceiver configured to receive the blockchain from the standard vehicle, and communicate the blockchain from the standard vehicle to a network to update a distributed record of the blockchain associated with the standard vehicle.

Abstract: In one embodiment, example systems and methods relate to ordering food from a vehicle. A driver or a passenger of a vehicle indicates that they would like to order food from the vehicle by speaking a command, or by making a selection on a display associated with the vehicle. In response to the indication, the vehicle determines one or more restaurants that are near the vehicle, or that are near the route being traveled by the vehicle. After the driver or passenger selects a restaurant, the vehicle loads a restaurant specific natural language understanding model that is associated with the restaurant. As the driver or passenger speaks their order, the restaurant specific natural language understanding model is used to process the audio and generate an order. The order and an estimated time of arrival of the vehicle is provided electronically to the restaurant.

Abstract: Methods, systems, and devices for a cross-linked distributed ledger. The cross-referencing system includes multiple computing devices including a first computing device and a second computing device. A computing device of the multiple computing devices is configured to maintain a first cross-linked distributed ledger. The first cross-linked distributed ledger has a first set of multiple linked records that are associated with a first identifier. The first computing device includes a processor. The processor is configured to link or provide a first record associated with the first identifier to the first cross-linked distributed ledger. The first record has a first reference to a second record. The second record is within a second set of multiple cross-linked records of a second cross-linked distributed ledger.