Abstract: A device receives current vehicle data that describes a current state of each of a plurality of components of the vehicle. The device accesses historical data describing prior services previously performed on the vehicle and applies the current vehicle data and the historical vehicle data to a trained machine learning model to obtain a set of recommended services, where the model was trained using labeled training data associated with additional vehicles having a threshold similarity to the vehicle. The machine learning model is trained to output a set of recommended services to be performed for a given vehicle based on inputs of given current vehicle data and given historical vehicle data for the given vehicle. The device outputs for display the set of recommended services to be performed on the vehicle to a user.

Abstract: A management system operates in conjunction with entities to provide component recommendations for objects. The management system trains a machine learning model used to generate the component recommendations. The machine learning model is trained based on historical component entries describing components previously provided and identifiers of the components. The management system generates training data by classifying the historical component entries into predetermined component classifications. After the machine learning model is trained, the management system generates a customized recommendation of components for an object based on likelihoods of selection of the predetermined component classifications.

Abstract: A device receives current vehicle data that describes a current state of each of a plurality of components of the vehicle. The device accesses historical data describing prior services previously performed on the vehicle and applies the current vehicle data and the historical vehicle data to a trained machine learning model to obtain a set of recommended services, where the model was trained using labeled training data associated with additional vehicles having a threshold similarity to the vehicle. The machine learning model is trained to output a set of recommended services to be performed for a given vehicle based on inputs of given current vehicle data and given historical vehicle data for the given vehicle. The device outputs for display the set of recommended services to be performed on the vehicle to a user.

Abstract: Systems and methods are disclosed herein for machine-learned vehicle desking operations. A vehicle recommendation system receives a request to determine similarities between vehicles. The request can indicate an identifier of a user-specified vehicle associated with vehicle attribute values (e.g., white color, sedan body style, 2020 manufacturing year, etc.). A machine learning model can determine respective embeddings for the vehicle attribute values and the respective embeddings can be concatenated, where the concatenated embeddings represent the user-specified vehicle in one embedding. The system can determine similarity metrics of the concatenated embeddings against reference embeddings. For example, a cosine similarity value can be determined for the concatenated embedding of the user-specified vehicle and the respective reference embeddings. Each similarity metric can represent a measure of similarity between the user-specified vehicle and a given vehicle.

Abstract: The online system detects a user interaction between a first user and the online system, and retrieves a set of attributes of the user interaction. Based on the retrieved set of attributes of the user interaction, the online system periodically determines, using a first trained model, a lead score indicative of a likelihood that the user interaction will result in a conversion. If the lead score is higher than a threshold value, the online system determines, using a second trained model, based on attributes of the user interaction between the first user and the online system, and user profile information for the first user, a set of communication parameters to communicate with the first user. The second trained model is trained to select the set of communication parameters to maximize the likelihood of conversion with a least number of communications with the first user associated with the user interaction.

Abstract: Embodiments relate to a system for automatically populating fields of an electronic communication with content items, and providing recommendations relating to one or more vehicles to a selected entity. Responsive to receiving a request associated with drafting an electronic communication to a specified entity, the system generates an input vector using metadata associated with the entity comprising at least a context of a current interaction with the entity, and historical data associated with the entity generated based upon one or more previous interactions of the entity relating to one or more vehicles. A trained machine learning model uses the input vector to generate content item recommendations pertaining to a selected vehicle corresponding to the fields of a selected template. Recommended content items are used to populate the fields of the selected template to generate the electronic communication.

Abstract: A management system operates in conjunction with entities to provide component recommendations for objects. The management system trains a machine learning model used to generate the component recommendations. The machine learning model is trained based on historical component entries describing components previously provided and identifiers of the components. The management system generates training data by classifying the historical component entries into predetermined component classifications. After the machine learning model is trained, the management system generates a customized recommendation of components for an object based on likelihoods of selection of the predetermined component classifications.

Abstract: A management system operates in conjunction with entities to provide component recommendations for objects. The management system trains a machine learning model used to generate the component recommendations. The machine learning model is trained based on historical component entries describing components previously provided and identifiers of the components. The management system generates training data by classifying the historical component entries into predetermined component classifications. After the machine learning model is trained, the management system generates a customized recommendation of components for an object based on likelihoods of selection of the predetermined component classifications.

Abstract: A device receives current vehicle data that describes a current state of each of a plurality of components of the vehicle. The device accesses historical data describing prior services previously performed on the vehicle and applies the current vehicle data and the historical vehicle data to a trained machine learning model to obtain a set of recommended services, where the model was trained using labeled training data associated with additional vehicles having a threshold similarity to the vehicle. The machine learning model is trained to output a set of recommended services to be performed for a given vehicle based on inputs of given current vehicle data and given historical vehicle data for the given vehicle. The device outputs for display the set of recommended services to be performed on the vehicle to a user.

Abstract: A device receives a query from a user associated with a car dealership and applies the query to a first trained machine learning model configured to predict an intent, and to a second trained machine learning model to predict a set of entities. The device generates a normalized representation of the first query that is database language agnostic based on the predicted intent and the predicted set of entities, and translates the normalized representation into a second query having a format compatible with a language of a database of the car dealership. The device fetches data from the database of the car dealership using the second query, and provides the data for display to the user.

Abstract: Systems and methods are disclosed herein for machine-learned vehicle desking operations. A vehicle recommendation system receives a request to determine similarities between vehicles. The request can indicate an identifier of a user-specified vehicle associated with vehicle attribute values (e.g., white color, sedan body style, 2020 manufacturing year, etc.). A machine learning model can determine respective embeddings for the vehicle attribute values and the respective embeddings can be concatenated, where the concatenated embeddings represent the user-specified vehicle in one embedding. The system can determine similarity metrics of the concatenated embeddings against reference embeddings. For example, a cosine similarity value can be determined for the concatenated embedding of the user-specified vehicle and the respective reference embeddings. Each similarity metric can represent a measure of similarity between the user-specified vehicle and a given vehicle.

Abstract: A management system of a plurality of entities generates secondary object recommendations for primary objects being acquired from the entities. The management system generates training data by classifying historical acquisition entries from the entities that describe acquisitions of primary objects and secondary objects with predetermined secondary object classifications. The generated training data is used to train a machine learning model to predict for each predetermined secondary object classification a likelihood of selection of a secondary object associated with the predetermined secondary object classification. Responsive to a primary object being acquired from any one of the plurality of entities, the management system may generate a recommendation for one or more secondary objects to provide with the primary object.

Abstract: A management system operates in conjunction with entities to provide component recommendations for objects. The management system trains a machine learning model used to generate the component recommendations. The machine learning model is trained based on historical component entries describing components previously provided and identifiers of the components. The management system generates training data by classifying the historical component entries into predetermined component classifications. After the machine learning model is trained, the management system generates a customized recommendation of components for an object based on likelihoods of selection of the predetermined component classifications.

Abstract: Usage-limited passcodes support authentication when onboarding new employees, when recovering access after an enrolled device is lost or temporarily unavailable, or when registering passwordless authentication methods for new devices during an out of the box setup, among other scenarios. Usage-limited passcodes are also referred to as “temporary access passes” or TAPs. TAP usage may be limited to a specific number of uses, particular kinds of uses, certain time periods, or a combination thereof. A TAP includes a code string and an implementation of corresponding tokens, rights, and other identity aspects within an enhanced access control infrastructure. TAP usage may supplement or replace other authentication, and in particular may replace authentication through a username and password combination, thereby enhancing both usability and security. Self-service identity confirmation may be used to obtain a TAP. Redirection to a federated domain identity provider may be avoided during TAP authentication.

Abstract: A management system operates in conjunction with entities to provide service recommendations for objects of users. The management system trains a machine learning model used to generate the service recommendations for the objects. The machine learning model is trained based on historical service entries including descriptions of services previously performed and identifiers used by entities to categorize types of services. The management system generates training data by classifying the historical service entries into predetermined service classifications based on text in the historical service entries. After the machine learning model is trained, the management system generates a recommendation of services for an object based on likelihoods of selection of the predetermined service classifications.

Abstract: Systems and methods are disclosed herein for machine-learned vehicle desking operations. A vehicle recommendation system receives a request to determine similarities between vehicles. The request can indicate an identifier of a user-specified vehicle associated with vehicle attribute values (e.g., white color, sedan body style, 2020 manufacturing year, etc.). A machine learning model can determine respective embeddings for the vehicle attribute values and the respective embeddings can be concatenated, where the concatenated embeddings represent the user-specified vehicle in one embedding. The system can determine similarity metrics of the concatenated embeddings against reference embeddings. For example, a cosine similarity value can be determined for the concatenated embedding of the user-specified vehicle and the respective reference embeddings. Each similarity metric can represent a measure of similarity between the user-specified vehicle and a given vehicle.

Abstract: Managing an authenticated user session. A method includes a resource provider computer system subscribing to a conditional access termination service for an entity configured to obtain resources from the resource provider computer system through a user session. The resource provider computer system receives an event, related to resource requests, for the entity from the conditional access termination service. The resource provider computer system receives a request for resources from the entity. The resource provider computer system evaluates the request with respect to the event. The resource provider computer system responds to the request based on evaluating the request with respect to the event.

Abstract: Managing an authenticated user session. A method includes a resource provider computer system subscribing to a conditional access termination service for an entity configured to obtain resources from the resource provider computer system through a user session. The resource provider computer system receives an event, related to resource requests, for the entity from the conditional access termination service. The resource provider computer system receives a request for resources from the entity. The resource provider computer system evaluates the request with respect to the event. The resource provider computer system responds to the request based on evaluating the request with respect to the event.