Abstract: The present disclosure provides systems and methods that may advantageously apply machine learning to accurately manage and predict inventory variables with future uncertainty. In an aspect, the present disclosure provides a system that can receive an inventory dataset comprising a plurality of inventory variables that indicate at least historical (i) inventory levels, (ii) inventory holding costs, (iii) supplier orders, or (iv) lead times over time. The plurality of inventory variables can be characterized by having one or more future uncertainty levels. The system can process the inventory dataset using a trained machine learning model to generate a prediction of the plurality inventory variables. The system can provide the processed inventory dataset to an optimization algorithm. The optimization algorithm can predict one or more inventory management parameters that result in a particular probability of achieving a target service level while minimizing a cost.

Abstract: Described herein are machine learning methods and systems for locating and tracking performance of latent themes in changing data from disparate sources. Themes may be indirect goals or consequential impacts indicated by latent topics. Identifying performance indicators of latent themes in large changing data sets uncovers underlying trends or previously concealed behaviors that may be accelerating or undermining goals.

Abstract: A method includes obtaining data associated with operation of a monitored system. The method also includes using one or more first machine learning models to identify anomalies in the monitored system based on the obtained data, where each anomaly identifies an anomalous behavior. The method further includes using one or more second machine learning models to classify each of at least some of the identified anomalies into one of multiple classifications. Different ones of the classifications are associated with different types of cyberthreats to the monitored system, and the identified anomalies are classified based on risk scores determined using the one or more second machine learning models. In addition, the method includes identifying, for each of at least some of the anomalies, one or more actions to be performed in order to counteract the cyberthreat associated with the anomaly.

Abstract: A method includes obtaining information associated with multiple collocation events. Each collocation event is associated with an occurrence where multiple entities were collocated with one another. The information associated with each collocation event identifies the entities associated with the collocation event, a duration of the collocation event, one or more locations associated with the collocation event, and a time associated with the collocation event. The method also includes identifying strengths of different relationships involving different ones of the entities based on the information. The strengths are determined using one or more features of the collocation events.

Abstract: In some embodiments, an artificial intelligence vision system may be integrated with multiple enterprise software applications and the artificial intelligence vision system and method may transform the human-machine interface (HMI). The artificial intelligence vision system and method may use augmented reality, virtual reality, and/or mixed reality devices to create a new level of data interaction and collaboration for enterprise processes. Synchronization may be enabled between virtual environments presented via multiple computing devices and/or between virtual environments presented via different applications executing on the same user device. The artificial intelligence vision system and method may provide a seamless immersive and interactive experience for users by providing accurate multi-dimensional virtual environments mapped to physical environments.

Abstract: The present disclosure provides systems and methods that may advantageously apply machine learning to accurately manage and predict inventory variables with future uncertainty. In an aspect, the present disclosure provides a system that can receive an inventory dataset comprising a plurality of inventory variables that indicate at least historical (i) inventory levels, (ii) inventory holding costs, (iii) supplier orders, and/or (iv) lead times overtime. The plurality of inventory variables can be characterized by having one or more future uncertainty levels. The system can process the inventory dataset using a trained machine learning model to generate a prediction of the plurality inventory variables. The system can provide the processed in inventory dataset to an optimization algorithm. The optimization algorithm can be used to predict a target inventory level for optimizing an inventory holding cost. The optimization algorithm can comprise one or more constraint conditions.

Abstract: Systems and methods are configured to generate a set of potential responses to a prompt using one or more data models with data from at least a plurality of data domains of an enterprise information environment that includes access controls. A deterministic response is selected from the set of potential responses based on scoring of the validation data and restricting based on access controls in view of profile information associated with the prompt. These enterprise generative AI systems and methods support granular enterprise access controls, privacy, and security requirements, and provide traceable references and links to source information underlying the generative AI insights. These systems and methods enable dramatically increased utility for enterprise users to access information, analyses, and predictive analytics associated with and derived from a combination of enterprise and external information systems.

Abstract: Provided herein are methods and systems for determining a historical state of a dynamic network. The methods may comprise continuously obtaining data associated with a system from a plurality of different data sources; constructing a full history dynamic network (FHDN) of the system using the data; and providing a state of the system for a historical time instance in response to a query of the FHDN for the historical time instance.

Abstract: The disclosed technology can acquire a first set of data from a first group of data sources including a plurality of network components within an energy delivery network. A first metric indicating a likelihood that a particular network component, from the plurality of network components, is affected by cyber vulnerabilities can be generated based on the first set of data. A second set of data can be acquired from a second group of data sources including a collection of services associated with the energy delivery network. A second metric indicating a calculated impact on at least a portion of the energy delivery network when the cyber vulnerabilities affect the particular network component can be generated based on the second set of data. A third metric indicating an overall level of cybersecurity risk associated with the particular network component can be generated based on the first metric and the second metric.

Abstract: A method includes obtaining an engineering schematic containing multiple symbols and connections involving the symbols, where different ones of the symbols in the engineering schematic represent different types of equipment. The method also includes identifying visual features of the engineering schematic. The method further includes processing the visual features using at least one trained machine learning model to (i) identify boundaries around the symbols in the engineering schematic and (ii) classify the symbols in the engineering schematic into multiple classifications, where different ones of the classifications are associated with different types of symbols.

Abstract: The present disclosure provides systems, methods, and computer program products for generating a digital representation of a system from engineering documents of the system comprising one or more schematics and a components table. An example method can comprise (a) classifying, using a deep learning algorithm, (i) each of a plurality of symbols in the one or more schematics as a component and (ii) each group of related symbols as an assembly, (b) determining connections between the components and the assemblies, (c) associating a subset of the components and the assemblies with entries in the components table; and (d) generating the digital representation of the system from the components, the assemblies, the connections, and the associations. The digital representation of the system can comprise at least a digital model of the system and a machine-readable bill of materials.

Abstract: The present disclosure provides system, methods, and computer program products for predicting and detecting anomalies in a subsystem of a system. An example method may comprise (a) determining a first plurality of tags that are indicative of an operational performance of the subsystem. The tags can be obtained from (i) a plurality of sensors in the subsystem and (ii) a plurality of sensors in the system that are not in the subsystem. The method may further comprise (b) processing measured values of the first plurality of tags using an autoencoder trained on historical values of the first plurality of tags to generate estimated values of the first plurality of tags; (c) determining whether a difference between the measured values and estimated values meets a threshold; and (d) transmitting an alert that indicates that the subsystem is predicted to experience an anomaly if the difference meets the threshold.

Abstract: Aspects of this disclosure are directed to enterprise systems and methods that provide machine learning and artificial intelligence (AI) driven software that generates baseline predictions and optimizations for production capacity to reduce waste and harmful byproducts. Baseline predictions can include resource baseline predictions that can help estimate (or, predict) how much lower or higher an asset's resource inputs (e.g., fuel) and/or outputs (e.g., emissions) could be in comparison to the asset's current resource inputs and/or outputs. AI generated baseline predictions can be broken down into several different levels (e.g., facility level down to the asset level) so that it is clear where the most significant opportunities for resource savings lie, and the system can perform optimizations (e.g., trigger corrective actions) to achieve those resource savings.

Abstract: Machine learning model optimization explainability application is provides explanations (e.g., natural language explanations) for the operations and decisions associated with an optimization model (e.g., optimization algorithm) used to solve an optimization problem. More specifically, the software application for machine learning model optimization explainability enables explainability for a query that determine how the solution was generated. The system can provide a query response (e.g., natural language explanation), and perform a variety of different actions to address any issues surfaced in the query response.

Abstract: Embodiments provide systems and methods for supporting a medical assessment of a target digital entity. To facilitate the medical assessment, a numerical representation of a target digital entity is generated based on at least a portion of source data associated with the target digital entity, and the numerical representation of the target digital entity is compared to numerical representations of a plurality of digital entities to generate similarity values. Each of the similarity values representing a correspondence between the numerical representations of the target digital entity and the plurality of digital entities. Based on the similarity values, one or more candidate digital entities that are similar to the target digital entity are identified. In some aspects, keywords associated with the target digital entity are used to identify an article associated with a diagnosis or treatment of the target digital entity.

Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to identify a set of features associated with at least one of a collection of residences or an energy billing period. Measured energy consumption information and a plurality of feature values can be acquired for each residence in the collection of residences. Each feature value in the plurality of feature values can correspond to a respective feature in the set of features. A regression model can be trained based on the measured energy consumption information and the plurality of features values for each residence in the collection of residences. At least one expected consumption value and at least one efficient consumption value can be determined based on the regression model.

Abstract: Systems and methods managing, by an orchestrator, a plurality of agents to generate a response to an input. The orchestrator employs one or more multimodal models such as a large language models to process or deconstruct the prompt into a series of instructions for different agents. Each agent employs one or more machine-learning models to process disparate inputs or different portions of an input associated with the prompt. The system generates, by the orchestrator, a natural language summary of the structured and unstructured data records. The system formulates output and transmits the natural language summary of the data records.

Abstract: A method of waterflood management for reservoir(s) having production hydrocarbon-containing well(s) including injector well(s). A reservoir model has model parameters in a mathematical relationship relating a water injection rate to a total production rate of the production well including at least one of a hydrocarbon production rate and water production rate. A solver implements automatic differentiation utilizing training data regarding the reservoir including operational data that includes recent sensor and/or historical data for the water injection rate and the hydrocarbon production rate, and constraints for the model parameters. The solver solves the reservoir model to identify values or value distributions for the model parameters to provide a trained reservoir model. The trained reservoir model uses water injection schedule(s) for the injector well to generate predictions for the total production rate.

Abstract: A method includes identifying a sequence of transformations to be performed on an input dataset via a user interface. The method also includes identifying a first context associated with the input dataset. The method further includes selecting a first one of multiple execution engines to be used to perform the sequence of transformations on the input dataset based on the first context. In addition, the method includes providing first code implementing the sequence of transformations to the first execution engine and executing the first code using the first execution engine to perform the sequence of transformations on the input dataset.

Abstract: Systems, methods, and devices for a cyberphysical (IoT) software application development platform based upon a model driven architecture and derivative IoT SaaS applications are disclosed herein. The system may include concentrators to receive and forward time-series data from sensors or smart devices. The system may include message decoders to receive messages comprising the time-series data and storing the messages on message queues. The system may include a persistence component to store the time-series data in a key-value store and store the relational data in a relational database. The system may include a data services component to implement a type layer over data stores. The system may also include a processing component to access and process data in the data stores via the type layer, the processing component comprising a batch processing component and an iterative processing component.