Abstract: Embodiments of a system and a method for classifying blocks of text at varying, possibly simultaneous, and possibly interacting levels of scope (e.g., sentence, paragraph, section, document) are disclosed. A system includes a processor coupled to: a data reader to receive an input comprising of a data stream, and convert the data stream into one or more logical data blocks of varying scopes. The system includes a dependency tree generator to create a dependency tree to define the scope and dependencies of each of a plurality of natural language processors (NL Processors) with respect to each other such that the dependency tree identifies when an input of a NL Processor (depender) depends on an output of another NL Processor (dependee). Each NL Processor is configured based on a type of at least one logical data block of the one or more logical blocks to which it applies.

Abstract: A device may receive an application for transforming legacy applications into low-code/no-code applications to be managed by a low-code/no-code platform, and may execute the application for a legacy application of the legacy applications. The device may process the legacy application, with a machine learning model, to identify one or more components of the legacy application to be managed by the low-code/no-code platform, and may transform the one or more components into one or more transformed components to be managed by the low-code/no-code platform. The device may implement the one or more transformed components in the legacy application to generate a transformed legacy application, and may perform one or more actions based on the transformed legacy application.

Abstract: In some examples, user journey carbon footprint reduction may include generating, for a vehicle associated with a user, a carbon emission quota for user journey carbon footprint reduction. A predicted journey carbon emissions may be generated for the vehicle for a specified journey. Based on collaborative filtering, at least one goal-based and conditions-based recommendation may be generated for the user of the vehicle for the specified journey for the user journey carbon footprint reduction. Based on a user behavior model, a user-interface display may be generated for the specified journey for the user journey carbon footprint reduction. Further, based on the user behavior model, and real-time monitoring of the user and the vehicle, a real-time update of the user-interface display may be generated for the specified journey for the user journey carbon footprint reduction.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating customized recommendations for environmentally-conscious cloud computing frameworks for replacing computing resources of existing datacenters. One of the methods involves receiving, through a user interface presented on a display of a computing device, data regarding a user's existing datacenter deployment and the user's preferences for the new cloud computing framework, generating one or more recommendations for environmentally-conscious cloud computing frameworks based on the received data, and presenting such recommendations through the user interface for the user's review and consideration.

Abstract: Implementations include determining a set of components within the connected vehicle ecosystem, components within the set of components representing at least one layer within the connected vehicle ecosystem, for each component in the set of components: providing a set of facts representative of the respective component, and providing a component digital twin using the set of facts, defining a set of digital twins including digital twins of components in the set of components, generating, using the set of digital twins, at least one AAG representative of potential lateral movement between components of the at least one layer within the connected vehicle ecosystem, the at least one AAG representing a contextual digital twin of components operating within the connected vehicle ecosystem, and evaluating the connected vehicle ecosystem using the at least one AAG.

Abstract: In some examples, continuous learning-based application related trade-off resolution and implementation may include generating, based on a plurality of historical tradeoff instances, an application feature matrix. Further, association rules for historical tradeoff instances for which decisions are not known, and a decision tree for historical tradeoff instances for which decisions are known may be generated. Decision rules may be induced, and default rules may be applied to a cold start scenario. The decision rules and the default rules may be refined to generate refined rules, and a confidence level may be determined for the refined rules. The refined rules may be prioritized based on the confidence level and applied to a new tradeoff instance to generate a resolution associated with the new tradeoff instance. The resolution may be implemented with respect to the new tradeoff instance.

Abstract: Systems, methods, devices, and other techniques for placing and rendering virtual objects in three-dimensional environments. The techniques include providing, by a device, a view of an environment of a first user. A first computing system associated with the first user receives an instruction to display, within the view of the environment of the first user, a virtual marker at a specified position of the environment of the first user, the specified position derived from a second user's interaction with a three-dimensional (3D) model of at least a portion of the environment of the first user. The device displays, within the view of the environment of the first user, the virtual marker at the specified position of the environment of the first user.

Abstract: A device may receive input data associated with a legal regulation, and may process the input data to generate a record that includes: the input data in a knowledge representation format and a semantic representation format, data identifying a feature, data identifying an industry classification, or data identifying an entity of interest. The device may process the record, with machine learning models, to determine output data that includes: data indicating that the legal regulation is inconsistent, data indicating that the legal regulation is outdated, data indicating a sentiment for the legal regulation, data indicating a prescriptive nature of the legal regulation, data indicating a complexity of the legal regulation, data indicating a misrepresentation in the legal regulation, data indicating a compliance burden associated with the legal regulation, or data indicating an industry performance impact of the legal regulation. The device may perform actions based on the output data.

Abstract: An upper-extremity prosthetic is adapted to engage with an athletic ball. The prosthetic includes one or more springs that provide energy return as a user is throwing the ball using the prosthetic. The springs can have a conductivity that changes in relation to an amount of strain or deformation of the spring. The change in conductivity can be used to provide haptic feedback to the user so the user can sense the amount of force being applied to throw the ball. In some embodiments, the springs are made by a multi-material 3D printing (additive manufacturing) process and include a first material that is electrically non-conductive and a second material that electrically conductive. In some embodiments, the prosthetic also includes one or more cantilevered springs that are also adapted to engage with the ball and to provide energy return while throwing the ball.

Abstract: A system and method for automating and improving data extraction from a variety of document types, including both unstructured, structured, and nested content, is disclosed. The system and method incorporate an intelligent machine learning model that is designed to intelligently identify chunks of text, map the fields in the document, and extract multi-record values. The system is designed to operate with little to no human intervention, while offering significant gains in accuracy, data visualization, and efficiency. The architecture applies customized techniques including density-based adaptive text clustering, tabular data extraction based on hierarchical intelligent keyword searches, and natural language processing-based field value selection.

Abstract: Electrical input devices can be produced using a multi-material 3D-printing process. The electrical input devices can include a non-conductive material portion and a conductive material portion. The non-conductive and conductive material portions are integrally formed during a single 3D-printing process. Deformation of the electrical input devices cause an electrical variance of the conductive material portion that is responsive to the deformation. Some electrical input devices described provide digital responses, and some electrical input devices described provide analog responses. The described techniques can be used to manufacture complex finished devices in a single 3D-print run, and, in some examples, without the need for post-processing or assembly.

Abstract: A system may receive enterprise information associated with a client enterprise. The system may select, using an industry analysis model, a set of queries associated with obtaining status information that is associated with a technology profile of the client enterprise. The system may generate client data that is associated with the enterprise information and the status information. The system may convert, using a matrix factorization technique, the client data associated with the client enterprise to a client matrix. The system may convert, using the matrix factorization technique, reference data associated with reference enterprises to a reference matrix. The system may determine, based on a comparison of the client matrix and the reference matrix, a set of scores associated with technology metrics of the technology profile. The system may perform an action associated with the client enterprise based on the set of scores.

Abstract: Systems and methods for computing feature contribution and providing hum-interpretable reason codes for a Support Vector Machine (SVM) model are disclosed, A system computes, for each data point from amongst plurality of data points indicative of plurality of features, a feature contribution of each one of the plurality of features for a SVM model used for at least one of classification decision and a regression analysis. Further, the system provides human interpretable reason code for the interpretation corresponding to at least one of classification decision and the regression analysis from the SVM model. The system outputs to the user, a feature contribution output, and the human interpretable reason codes output. The feature contribution output and the human interpretable reason codes output are indicative of an acceptable decision to be taken by the user based on the classification decision and the regression analysis received from the SVM model.

Abstract: An Artificial Intelligence (AI) based document processing and validation system identifies anomalies such as errors, fraud, and duplicates of received documents and enables automatic actions for valid documents using machine learning (ML) techniques. The received documents are processed for determining probabilities for errors, fraud, and duplicates. A validation worklist is generated with the documents arranged in descending order of the probabilities and invalid documents with higher probabilities are flagged for review while the valid documents with lower probabilities are further processed for the execution of automatic actions. The feedback from the invalid document review is used to further train the models in determining the probabilities.

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: In some implementations, a device may receive prescription information associated with a medication in a container. The device may cause a camera to capture first image data associated with the medication while the medication is in the container and the container is positioned on a receptacle. The device may cause an adjusting device to reposition the container on the receptacle. The device may cause the camera to capture second image data associated with the medication while the medication is in the container. The device may process, via a neural network, the first image data and the second image data to identify the medication based on depictions of individual units of the medication included in the first image data and the second image data. The device may verify the medication based on the prescription information and an identifier of the medication provided by the neural network.

Abstract: This document describes a simulation system that simulates robots and other actors performing tasks in an area. In one aspect, a method includes obtaining a graph representing a physical area. The graph includes area nodes that represent regions of the area that are traversed by a set of actors that perform tasks in the area and terminal nodes that represent regions of the facility where the actors perform the tasks. A set of agents that each include a model corresponding to an actor is identified. At least a portion of the agents includes models for robots that perform tasks in the area. The model of an agent represents durations of time for traversing area nodes and performing tasks are terminal nodes during simulations. A sequence of tasks being performed in the area is simulated using the graph and the set of agents.

Abstract: A device may receive historic temporal data identifying events associated with a system, and may perform block bootstrapping of the hierarchical time series data, based on a hyperparameters, to generate blocks of data points of the historic time series data. The device may process the blocks of data points, with a plurality of different machine learning models, to calculate predictions, and may apply weights to the predictions to generate weighted predictions. The device may aggregate the weighted predictions to generate aggregated predictions, and may apply final weights to the aggregated predictions to generate weighted aggregated predictions. The device may aggregate the weighted aggregated predictions to generate a final prediction, and may perform one or more actions based on the final prediction.

Abstract: A device may receive network data, business data, and user configuration data associated with an entity that is a candidate for a private network and may process the business data and the user configuration data, with a classification machine learning model, to determine a network hardware equipment prediction. The device may process the network data and the business data, with a first linear regression machine learning model, to determine a business output prediction and may utilize a second linear regression machine learning model to determine a data consumption prediction based on the network hardware equipment prediction. The device may process the network hardware equipment prediction, the business output prediction, and the data consumption prediction, with a machine learning model, to determine a financial profitability prediction for the private network and may perform one or more actions based on the financial profitability prediction.

Abstract: A device may receive employee data associated with an employee, and may determine, from the employee data, skill data identifying skills of the employee. The device may process the employee data and the skill data, with a similarity model, to determine similarity scores between the skills of the skill data, and may add or remove one or more skills to or from the skill data for predefined target skill profile categories to generate modified skill data. The device may compare the similarity scores and a predefined threshold to determine anchor skill data from the modified skill data, and may group the anchor skill data for corresponding pluralities of the predefined target skill profile categories to generate groups of the anchor skill data. The device may process the groups and the similarity scores, with a clustering model, to generate clustered anchor skill data, and may perform actions based on the clustered anchor skill data.