Abstract: Systems and methods for optimizing a transportation plan for a transportation network are described. In an example, a computer system determines a forecasted demand for transporting items between a source node and a destination node of a transportation network and a set of constraints associated with a handling node of the transportation network. The computer system generates a transportation plan that comprises a network path including the handling node and a type of item handling at the handling node. In a first stage of optimization, an optimization model of the computer system selects the network path from network paths. In a second stage of optimization, the optimization model selects the type of item handling from types of item handling available at the handling node. The optimization model uses the network paths and the types of item handling as variables.

Abstract: A device may receive log data from application logs associated with applications, service logs associated with services, and server logs associated with server devices. The device may store the log data. The device may perform natural language processing on the log data to convert the log data into event data identifying events associated with categories. The device may process the event data, with a first machine learning model, to identify patterns in the event data and to generate an alert based on the patterns. The device may process the event data, with a second machine learning model, to generate a correlation matrix for the event data and to predict an event based on the correlation matrix. The device may process the event data, with a third machine learning model, to classify the event data based on the categories and to generate a recommendation based on classifying the event data.

Abstract: An example method includes detecting, using sensors, packets throughout a datacenter. The sensors can then send packet logs to various collectors which can then identify and summarize data flows in the datacenter. The collectors can then send flow logs to an analytics module which can identify the status of the datacenter and detect an attack.

Abstract: Routines are used to connect bot queries entered via a bot interface and database queries executed on a database. Each routine is associated with routine configurations, including (a) query attributes associated with bot queries that trigger the routine, (2) a database query executed or to be executed for the routine, and/or (3) display settings for displaying information returned from the database query in a bot response. Each routine is configured to generate an output structured data object (SDO) indicating information returned from the database query and the display settings applicable to the information. The output SDOs from the various routines are transmitted to the bot application via a single API endpoint. The routine configurations are entered by a design user through one or more design user interfaces rendered by an integrative configuration application module.

Abstract: An Artificial Intelligence (AI) based document digitization, transformation and validation system extracts fields from digital documents via different document digitization processes. A document packet with a plurality of documents is initially accessed and any non-digital documents in the document packet are digitized. The errors in the digitized documents are corrected and non-English documents are translated into English. Each of the documents is provided to a plurality of digitization services for the extraction of fields by a plurality of field extraction models. If a field has multiple field instances extracted by more than one digitization service, then a field instance with the highest confidence score is selected for inclusion into the consolidated results. The consolidated results produced in different JavaScript Object Notation (JSON) formats are converted into a common JSON format which may be further validated and provided to downstream processes.

Abstract: Methods and apparatus for bioprocess monitoring are disclosed. An example apparatus includes a controller to monitor a first bioprocess instrument, a data logger to collect data for the first bioprocess instrument, the collected data including data collected while the first bioprocess instrument is transferred from a first location to a second location, a configurator to configure the first bioprocess instrument to operate in a first mode at the first location and in a second mode at the second location, the first or second mode determined based on a type of processing at the first or second location, and a user interface to display the collected data to a user, the collected data including real-time bioprocess monitoring data, the controller to adjust a setting of the first bioprocess instrument based on the monitoring data, the monitoring data used to maintain controlled environmental conditions within a vessel of the instrument.

Abstract: An Artificial Intelligence (AI)-based data transformation system receives an input package and enables automatic execution of one or more processes in a robotic process automation system (RPA). The input package includes a plurality of documents and metadata required for the execution of the automated processes. The plurality of documents are categorized into a domain. Entities with their corresponding name-value pairs and entity relationships are extracted from the plurality of documents. An ontology is selected based on the domain. The entities are mapped to output fields identified from the selected ontology. The mappings thus generated are transmitted to the RPA system which employs the mappings to automatically execute the one or more processes.

Abstract: An Artificial Intelligence (AI) Driven multi-platform, multi-lingual translation system analyzes the speech context of each audio stream in the received audio input, selects one of a plurality translation engines based on the speech context, and provides translated audio output. If audio input from multiple speakers is provided in a single channel then it is diarized into multiple channels so that each speaker transmitted on a corresponding channel to improve audio quality. A translated textual output received from the selected translation engine is modified with sentiment data and converted into an audio format to be provided as the audio output.

Abstract: A vehicle immobilizer system comprised of at least one deployable, remotely actuated lift system. The lift system is transported by a movable frame which can be remotely operated. The movable frame suspends the lift system during transport. However, during the lifting operation, the lift system bears the weight of the vehicle, while the movable frame does not. As a result, the movable frame can be of lightweight construction while the lift system can be robust. The lift system supports a universal docking plate which is designed to contact the suspect vehicle and suspend it without damaging it.

Abstract: An authentication and applications access system provides access to a plurality of backend applications via a social media application installed on a user device. Metadata associated with a request to access one of the plurality of backend applications is initially extracted. The metadata is used to authenticate the request via a plurality of validation steps. Upon authentication, the information from the request is provided to the backend application to receive the results responsive to the request. Any sensitive data included in the results is suppressed via data substitution steps from the transmission to the user device. The output from the applications access system with the sensitive data occluded is provided for display on a social media user interface (UI) on a user device.

Abstract: A device may process data associated with a first entity and a second entity, using one or more machine learning techniques, to identify a set of trends associated with a set of documents. The device may receive new documents associated with the first entity and the second entity. The device may generate, based on the set of trends, a first set of exceptions indicating that a new document is associated with a first type of error. The device may generate, using a similarity analysis technique, a second set of exceptions indicating that a new document is associated with a second type of error. The device may communicate with one or more systems to perform one or more actions associated with correction or prevention of processing errors relating to the new document based a claim relating to the first set of exceptions or the second set of exceptions.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, are described that enable identifying an insurance information associated with a property, retrieving one or more compliance rules related to the insurance information, automatically determining, based on the one or more compliance rules, whether the insurance information has a compliant status or a non-compliant status, in response to determining that the insurance information has the non-compliant status, processing the insurance information, based on one or more exception rules, to generate an exception waiver including a plurality of waiver parameters, applying the plurality of waiver parameters to a waiver model to determine a waiver confidence score, and based on the waiver confidence score selectively generate an insurance certificate for the property.

Abstract: An Artificial Intelligence (AI) based document digitization, transformation and validation system extracts fields from digital documents via different document digitization processes. A document packet with a plurality of documents is initially accessed and any non-digital documents in the document packet are digitized. The errors in the digitized documents are corrected and non-English documents are translated into English. Each of the documents is provided to a plurality of digitization services for the extraction of fields by a plurality of field extraction models. If a field has multiple field instances extracted by more than one digitization service, then a field instance with the highest confidence score is selected for inclusion into the consolidated results. The consolidated results produced in different JavaScript Object Notation (JSON) formats are converted into a common JSON format which may be further validated and provided to downstream processes.

Abstract: Routines are used to connect bot queries entered via a bot interface and database queries executed on a database. Each routine is associated with routine configurations, including (a) query attributes associated with bot queries that trigger the routine, (2) a database query executed or to be executed for the routine, and/or (3) display settings for displaying information returned from the database query in a bot response. Each routine is configured to generate an output structured data object (SDO) indicating information returned from the database query and the display settings applicable to the information. The output SDOs from the various routines are transmitted to the bot application via a single API endpoint. The routine configurations are entered by a design user through one or more design user interfaces rendered by an integrative configuration application module.

Abstract: A system for issue prediction based on multidimensional data analysis includes a model generator that receives a resolved data item relating to a service issue. The resolved data item includes different attributes corresponding to multiple data dimensions and adjusts a population of attributes based on a statistical data model and a deep learning data model operating independent of each other. The statistical data model operates on the attributes for providing a predictive feature and the deep learning data model operates on the attributes for providing a predictive label based on performance metrics related to the data dimensions. The predictive feature and the predictive label collectively define training data. The model generator also trains a classification model based on the training data for predicting a potential issue related to an unresolved data item. The trained data model provides a trigger based on the potential issue being related to the performance metrics.

Abstract: A device may receive log data from application logs associated with applications, service logs associated with services, and server logs associated with server devices. The device may store the log data. The device may perform natural language processing on the log data to convert the log data into event data identifying events associated with categories. The device may process the event data, with a first machine learning model, to identify patterns in the event data and to generate an alert based on the patterns. The device may process the event data, with a second machine learning model, to generate a correlation matrix for the event data and to predict an event based on the correlation matrix. The device may process the event data, with a third machine learning model, to classify the event data based on the categories and to generate a recommendation based on classifying the event data.

Abstract: A device may provide, to user devices, a set of objects that are to be used by the user devices as part of a collaborative virtual environment. The collaborative virtual environment may support a virtual workshop. The set of objects may include objects capable of being used to solve a problem that is presented to users within the virtual workshop. The device may receive data describing a set of solutions. The device may curate the data describing the set of solutions using one or more natural language processing techniques. The device may rank the set of solutions that has been curated using a ranking technique or a machine learning technique. The device may provide data identifying a highest ranked solution or data identifying the set of solutions for display within the virtual workshop or to additional user devices that are accessible outside of the virtual workshop.

Abstract: A data narration generating system generates snippets that include representations of data in one of a plurality of formats for inclusion into a data narration. The narration generating system receives selected data to be included into the data narration and provides the selected data to a plurality of ML models. The plurality of ML models are trained in generating snippets in one of the plurality of formats which can include textual format and a tabular format. Snippets in graphical formats can also be generated by rule-based processes. A plurality of snippets are thus generated in one or more of the plurality of formats which can then be presented to a user for selection and inclusion into the data narration. Alternately, a subset of the plurality of snippets can also be selected automatically based on a quality and quantity of data and a voting mechanism. The data narration thus generated is further configured to present different views based on privileges associated with user profiles.

Abstract: A device may receive, from a user device, a request to access a virtual reality (VR) learning environment that includes an identifier associated with a program that supports the VR learning environment. The device may identify a set of objects to use within the VR learning environment by searching a data structure using the identifier associated with the program that supports the VR learning environment. The device may provide the VR learning environment to the user device. The device may receive, from the user device, information associated with interactions within the VR learning environment. The device may identify one or more additional objects to use within the VR learning environment by using one or more natural language processing techniques to analyze the information associated with the interactions within the VR learning environment. The device may provide the one or more additional objects to the user device.

Abstract: A device receives documents from various sources, and processes the documents, with an optical character recognition engine, to generate digitized documents. The device processes the digitized documents, with a first machine learning model, to detect languages utilized in the digitized documents, and processes the digitized documents, in other languages that are different than a common language and with a second machine learning model, to translate the digitized documents, in the other languages, into the common language and to generate translated digitized documents. The device processes the translated digitized documents and untranslated digitized documents, with a classification model, to generate classified documents, and processes the classified documents, with a third machine learning model, to generate extracted information from the classified documents.