Abstract: A management system operates in conjunction with an automotive dealership to improve customer engagement with the automotive dealership. The management system creates customer profiles of the automotive dealership systems' customers using features of the customers obtained via various sources. The management system assigns a customer category to a customer based on the features included in the customer profile and may generate a customized recommendation of a product and/or service for the customer to purchase at the dealership based at least in part on the customer category.

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 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 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: 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: 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: In one aspect, a computerized method useful for three-dimensional (3D) model data rendering and information capturing during a vehicle inspection check-in process for service comprising includes the step of providing a vehicle for a check-in inspection. The computerized method includes the step of obtaining a vehicle identification number (VIN) of the vehicle. The computerized method includes the step of, based on the VIN of the vehicle, obtaining a 3D vehicle model data. The 3D vehicle model data matches a type of the vehicle. The 3D vehicle model data is generated from a manufacturer's three-dimensional (3D) computer-aided design (CAD) model data of the type of the vehicle.

Abstract: A management system operates in conjunction with an automotive dealership to improve customer engagement with the automotive dealership. The management system creates customer profiles of the automotive dealership systems' customers using features of the customers obtained via various sources. The management system assigns a customer category to a customer based on the features included in the customer profile and may generate a customized recommendation of a product and/or service for the customer to purchase at the dealership based at least in part on the customer category.

Abstract: In one aspect, a computerized method useful for three-dimensional (3D) model data rendering and information capturing during a vehicle inspection check-in process for service comprising includes the step of providing a vehicle for a check-in inspection. The computerized method includes the step of obtaining a vehicle identification number (VIN) of the vehicle. The computerized method includes the step of, based on the VIN of the vehicle, obtaining a 3D vehicle model data. The 3D vehicle model data matches a type of the vehicle. The 3D vehicle model data is generated from a manufacturer's three-dimensional (3D) computer-aided design (CAD) model data of the type of the vehicle.

Abstract: In one aspect, a computerized method useful for three-dimensional (3D) model data rendering and information capturing during a vehicle inspection check-in process for service comprising includes the step of providing a vehicle for a check-in inspection. The computerized method includes the step of obtaining a vehicle identification number (VIN) of the vehicle. The computerized method includes the step of, based on the VIN of the vehicle, obtaining a 3D vehicle model data. The 3D vehicle model data matches a type of the vehicle. The 3D vehicle model data is generated from a manufacturer's three-dimensional (3D) computer-aided design (CAD) model data of the type of the vehicle.

Abstract: In one aspect, a computerized method useful for implementing location awareness a vehicle coupled with an on-board diagnostics (OBD) pass-through dongle includes the step of providing a plurality of wireless personal area network (WPAN) beacons, wherein plurality of WPAN beacons transmits a low-energy WPAN signal. The computerized method includes the step of pre-mapping a plurality of subareas in the area with a set of signal attributes for each WPAN beacon of the plurality of WPAN beacons. The computerized method includes the step of coupling a vehicle with an OBD pass-through dongle, wherein the OBD pass-through dongle comprises a Low-Power Wide-Area Network (LPWAN) module, and wherein the OBD pass-through dongle comprises a WPAN module. The computerized method includes the step of, with the WPAN module, receiving one or more WPAN signals from the plurality of WPAN beacons.

Abstract: In one aspect, a computerized method implements a vehicle check-in process at an automobile dealership with a mobile device includes the step of detecting the presence of the vehicle. The method includes the step of obtaining a customer information and a vehicle information. The method includes the step of matching the, vehicle with a service consultant. The method includes the step of communicating an electronic message comprising a notification to the mobile device of the service consultant. The method includes the step of implementing a set of vehicle diagnostics to determine a set of vehicle repair actions. The method includes the step of generating an estimate of costs to service the set of vehicle repair actions. The method includes the step of communicating the set of vehicle repair actions and the estimate of the costs to service the set of vehicle repair actions to the mobile device of the service consultant in a format for rendering on a display of the mobile device.

Abstract: In one aspect, a computerized method useful for three-dimensional (3D) model data rendering and information capturing during a vehicle inspection check-in process for service comprising includes the step of providing a vehicle for a check-in inspection. The computerized method includes the step of obtaining a vehicle identification number (VIN) of the vehicle. The computerized method includes the step of, based on the VIN of the vehicle, obtaining a 3D vehicle model data. The 3D vehicle model data matches a type of the vehicle. The 3D vehicle model data is generated from a manufacturer's three-dimensional (3D) computer-aided design (CAD) model data of the type of the vehicle.

Abstract: Online conversion procedures avoid the need to generate a copy of an entire table in order to convert the data type of a column in the table. To convert a table's column to a new data type, a replacement column is created in the table, the replacement column having the new data type. A background process dedicated to converting the column translates values in the column of the old data type to the new data type and stores the converted values in the replacement column. In addition, as transactions of users are applied to rows in the table, the value in the column of the old data type in the rows is converted and stored in the replacement column. Once the background process completes, the replacement column is used in lieu of the converted column.

Abstract: Online conversion procedures avoid the need to generate a copy of an entire table in order to convert the data type of a column in the table. To convert a table's column to a new data type, a replacement column is created in the table, the replacement column having the new data type. A background process dedicated to converting the column translates values in the column of the old data type to the new data type and stores the converted values in the replacement column. In addition, as transactions of users are applied to rows in the table, the value in the column of the old data type in the rows is converted and stored in the replacement column. Once the background process completes, the replacement column is used in lieu of the converted column.