Abstract: A document management system manages documents of an entity. The document management system monitors for entries in a document that are suspicious. Entries in the document are classified by the document management system as a “suspicious entry” or a “non-suspicious entry.” In one embodiment, a suspicious entry is indicative of potentially suspicious activity at the entity.

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 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: A document management system manages documents. The documents are managed such that the documents may be shared with multiple users during an online sharing session. During the online sharing session of a document, the document may be annotated. The appearance and location of the annotation is consistent across the difference devices of the users even though the devices are of different device types.

Abstract: Embodiments relate to a document parser module that can receive a document template from a producing entity, parse the template, and generate a mapping between the template and identifiers of the transaction entity. Using the mapping and parsed information, a document generator module may generate a document that includes the required identifier data and meets the submission standards of the producing entity. A configuration file may be saved so that a document can be generated and submitted every reporting period with little or no additional modification by the transaction entity.

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 system and a method are disclosed for receiving, from a source of a plurality of candidate sources, a payload comprising content and metadata. The system selects a destination to which to route the payload based on the source and the content, and generates an entry at the destination based on the content. The system inputs the metadata into a classification engine, and receives, as output from the classification engine, one or more classifications for the payload. The system applies a metadata tag to the entry, the metadata tag indicating the one or more classifications. The system receives a search request from a client device specifying at least one of the one or more classifications, and, in response to receiving the search request, provides the entry to the client device based on a matching classification.

Abstract: A system and a method are disclosed for receiving, by a server, based on input by a user, a request to lock a set of data. Responsively, the server modifies the set of data to be locked, and determines whether an amount of bandwidth required to index the locked data exceeds a threshold. Responsive to determining that the amount of bandwidth exceeds the threshold, the server instructs a secondary server to allocate bandwidth to index a first portion of the locked data. The server indexes a second portion of the locked data in parallel with the secondary server indexing the first portion of the locked data, and generates an index by collating the indexed first and second portions of the locked data. The server receives a search request for a portion of the locked data, and retrieves the portion of the locked data based on referencing the index.

Abstract: A system and a method are disclosed for receiving an entry, the entry comprising first content and a metadata tag corresponding to a classification, the first content populated by a first source. A rules engine determines that the first content comprises a data field associated with at least one of a plurality of reconciliation policies. Responsive to determining that the first content comprises a data field associated with at least one of the plurality of reconciliation policies, the rules engine selects a reconciliation policy based on the metadata tag. The rules engine retrieves, from a second source, second content associated with the data field, inputs the first content and the second content into a model, the model selected based on the reconciliation policy, the model generating an output, and performs a remediation action based on the output.

Abstract: A document management system manages documents of an entity. The document management system monitors for entries in a document that are suspicious. Entries in the document are classified by the document management system as a “suspicious entry” or a “non-suspicious entry.” In one embodiment, a suspicious entry is indicative of potentially suspicious activity at the entity.

Abstract: A physical lock interface system facilitates secure and remote control of assignment and enablement of physical lock systems. The system receives a request to enable a user to disable a physical lock during a particular range of time. The lock is associated with a geofence boundary defined by a geofence. During the range of time, the system monitors for a presence of the user within the geofence. In response to determining that the user is within the geofence during the range of time, the system transmits a communication to an application of a client device of the user. The application is configured to interact electronically with the physical lock and to cause the client device to display information identifying the physical lock based on the communication. The system detects an electronic signal initiated by the application while the user is within the geofence and disables the physical lock.

Abstract: A system and a method are disclosed herein for machine-learned detection of outliers within entity functions. An entity management system uses machine learning to cluster data characterizing functions performed by entities, and determines one or more data clusters that are outliers. The system receives entity function data indicating a metric and a type, and provides the received data into a supervised machine learning model. The model is trained to apply a label to the entity function data, where the label indicates a classification of the data into a cluster (e.g., an outlier cluster). This outlier detection may inform the system's generation of a function monitor to guide rectifying action that addresses the detected outliers. The system may receive user input affirming or rejection the classification of the data into a particular cluster. The system may leverage the user input to retrain the supervised machine learning model.

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: 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 document management system manages documents. The documents are managed such that the documents may be shared with multiple users during an online sharing session. During the online sharing session of a document, the document may be annotated. The appearance and location of the annotation is consistent across the difference devices of the users even though the devices are of different device types.