Abstract

Disclosed herein are system, method, and computer program product aspects for a schema management platform. An aspect operates by leveraging artificial intelligence for generating and executing schemas. An aspect operates by also providing functionality for generating code for a schema. The schema management platform may be used to assist real-world workflows and procedures performed in particular contexts. In some aspects, the schema management platform can utilize a schema to instruct a multimodal model on how to respond with verifiable subject matter expertise to various types of inputs and use cases. As such, the schema management platform may provide more accurate and reliable results compared to conventional systems.

IPC Classes  ?

• G06F 16/242 - Query formulation
• G06F 16/2452 - Query translation
• G06F 16/248 - Presentation of query results
• G06F 40/30 - Semantic analysis

Abstract

Disclosed herein are system, method, and computer program product aspects for generating a data pipeline. A model prompt including a received natural language description and a prompt template is generated. The prompt template includes action labels and a processing example. Each action label indicates a respective data processing action, and the processing example includes a sample query and a sample answer comprising one or more sample action labels associated with a sample natural language description of a sample data pipeline. A multimodal model (MM) is queried with the model prompt. The MM response includes one or more action labels corresponding to the natural language description of the requested data pipeline in a format guided by the prompt template. A data pipeline project template can then be generated using one or more executable nodes corresponding to the action labels.

IPC Classes  ?

• G06F 16/2455 - Query execution
• G06F 16/242 - Query formulation

Abstract

A method includes obtaining time-series data of at least one type and at least one textual description of the at least one type of time-series data and processing the time-series data and the at least one textual description using a foundation machine learning model. Processing the time-series data and the at least one textual description using the foundation machine learning model includes generating at least one embedding of the at least one textual description, combining the time-series data and the at least one embedding of the at least one textual description to generate combined data, and generating embedding vectors using the combined data.

IPC Classes  ?

• G06F 16/2458 - Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
• G06F 16/22 - IndexingData structures thereforStorage structures
• G06N 3/045 - Combinations of networks
• G06N 3/0475 - Generative networks
• G06N 3/094 - Adversarial learning
• G06N 3/096 - Transfer learning

Abstract

A method includes obtaining (502) time-series data (118, 302) of at least one type and at least one textual description (304) of the at least one type of time-series data and processing the time-series data and the at least one textual description using a foundation machine learning model (116). Processing the time-series data and the at least one textual description using the foundation machine learning model includes generating (506) at least one embedding (314) of the at least one textual description, combining (510) the time-series data and the at least one embedding of the at least one textual description to generate combined data (318), and generating (512) embedding vectors (322) using the combined data.

IPC Classes  ?

• G06N 3/08 - Learning methods
• G06N 20/10 - Machine learning using kernel methods, e.g. support vector machines [SVM]
• G06F 18/22 - Matching criteria, e.g. proximity measures
• G06N 3/0895 - Weakly supervised learning, e.g. semi-supervised or self-supervised learning
• G06N 3/09 - Supervised learning
• G06N 3/091 - Active learning
• G06N 3/092 - Reinforcement learning
• G06N 3/094 - Adversarial learning
• G06N 3/096 - Transfer learning
• G06N 3/0985 - Hyperparameter optimisationMeta-learningLearning-to-learn

Abstract

Disclosed herein are system, method, and computer program product aspects for a schema management platform. An aspect operates by leveraging artificial intelligence for generating and executing schemas. An aspect operates by also providing functionality for generating code for a schema. The schema management platform may be used to assist real-world workflows and procedures performed in particular contexts. In some aspects, the schema management platform can utilize a schema to instruct a multimodal model on how to respond with verifiable subject matter expertise to various types of inputs and use cases. As such, the schema management platform may provide more accurate and reliable results compared to conventional systems.

IPC Classes  ?

• G06F 16/21 - Design, administration or maintenance of databases
• G06F 16/2452 - Query translation

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 profile information associated with the prompt. These enterprise generative AI systems and methods support granular enterprise access controls, privacy, and security requirements. enterprise generative AI providing traceable references and links to source information underlying the generative AI insights. These systems and methods enable dramatically increased utility for enterprise users to information, analyses, and predictive analytics associated with and derived from a combination of enterprise and external information systems.

IPC Classes  ?

• G06F 16/34 - BrowsingVisualisation therefor
• G06F 16/332 - Query formulation
• G06F 16/334 - Query execution
• G06F 16/335 - Filtering based on additional data, e.g. user or group profiles
• G06F 16/338 - Presentation of query results
• G06F 40/20 - Natural language analysis
• G06F 40/40 - Processing or translation of natural language
• G06N 3/092 - Reinforcement learning
• G06N 5/04 - Inference or reasoning models
• G06N 20/00 - Machine learning

Abstract

A method includes curating CRM data by employing a type system of a model-driven architecture and selecting an AI CRM application from a group of applications. Each CRM application may generate one or more use case insights with one or more objectives. The method also includes obtaining one or more data models including an industry-specific data model from the curated CRM data and orchestrating a plurality of machine learning models for the selected CRM application with the obtained data model(s) to determine one or more machine learning models effective for at least one objective of the selected CRM application. The method further includes applying the determined machine learning model(s) and the obtained data model(s) to predict probabilities that optimize the at least one objective and using the predicted probabilities to apply at least one of the one or more use case insights that optimizes the at least one objective.

IPC Classes  ?

• G06Q 30/0201 - Market modellingMarket analysisCollecting market data
• G06Q 30/01 - Customer relationship services
• G06Q 30/0202 - Market predictions or forecasting for commercial activities

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.

IPC Classes  ?

• H04L 41/14 - Network analysis or design
• G06F 16/901 - IndexingData structures thereforStorage structures
• H04L 41/12 - Discovery or management of network topologies

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.

IPC Classes  ?

• H04L 9/40 - Network security protocols
• G06Q 10/0635 - Risk analysis of enterprise or organisation activities
• G06Q 50/06 - Energy or water supply

Abstract

An agent-based website search interface utilizes a multimodal model to enhance enterprise operations. Data agents collect and process diverse inputs, while an orchestrator manages these agents. The system leverages machine learning models to generate insights and automate decision-making processes. It includes tools for data visualization and validation, ensuring accuracy and reliability. By integrating generative AI, the interface provides advanced search functionalities, improving user experience and operational efficiency. This facilitates seamless interaction to answer context specific questions from complex data, offering a robust solution for enterprise-level search and analysis.

IPC Classes  ?

• G06F 16/34 - BrowsingVisualisation therefor
• G06F 16/332 - Query formulation
• G06F 16/334 - Query execution
• G06F 16/335 - Filtering based on additional data, e.g. user or group profiles
• G06F 16/338 - Presentation of query results
• G06F 40/20 - Natural language analysis
• G06F 40/40 - Processing or translation of natural language
• G06N 3/092 - Reinforcement learning
• G06N 5/04 - Inference or reasoning models
• G06N 20/00 - Machine learning

Abstract

An agent-based website search interface utilizes a multimodal model to enhance enterprise operations. Data agents collect and process diverse inputs, while an orchestrator manages these agents. The system leverages machine learning models to generate insights and automate decision-making processes. It includes tools for data visualization and validation, ensuring accuracy and reliability. By integrating generative AI, the interface provides advanced search functionalities, improving user experience and operational efficiency. This facilitates seamless interaction to answer context specific questions from complex data, offering a robust solution for enterprise-level search and analysis.

IPC Classes  ?

• G06F 16/34 - BrowsingVisualisation therefor
• G06F 16/332 - Query formulation
• G06F 16/334 - Query execution
• G06F 16/335 - Filtering based on additional data, e.g. user or group profiles
• G06F 16/338 - Presentation of query results
• G06F 40/20 - Natural language analysis
• G06F 40/40 - Processing or translation of natural language
• G06N 3/092 - Reinforcement learning
• G06N 5/04 - Inference or reasoning models
• G06N 20/00 - Machine learning

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.

IPC Classes  ?

• G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
• G06V 10/40 - Extraction of image or video features
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks

Abstract

An agent-based website search interface utilizes a multimodal model to enhance enterprise operations. Data agents collect and process diverse inputs, while an orchestrator manages these agents. The system leverages machine learning models to generate insights and automate decision-making processes. It includes tools for data visualization and validation, ensuring accuracy and reliability. By integrating generative AI, the interface provides advanced search functionalities, improving user experience and operational efficiency. This facilitates seamless interaction to answer context specific questions from complex data, offering a robust solution for enterprise-level search and analysis.

IPC Classes  ?

• G06F 16/34 - BrowsingVisualisation therefor
• G06F 16/33 - Querying
• G06F 16/332 - Query formulation
• G06F 16/335 - Filtering based on additional data, e.g. user or group profiles
• G06F 16/338 - Presentation of query results
• G06F 40/20 - Natural language analysis
• G06F 40/40 - Processing or translation of natural language
• G06N 3/092 - Reinforcement learning
• G06N 5/04 - Inference or reasoning models
• G06N 20/00 - Machine learning

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.

IPC Classes  ?

• G06Q 50/06 - Energy or water supply
• G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling

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.

IPC Classes  ?

• G06N 3/088 - Non-supervised learning, e.g. competitive learning
• G06N 3/045 - Combinations of networks

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.

IPC Classes  ?

• G05B 23/02 - Electric testing or monitoring
• G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators

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.

IPC Classes  ?

• E21B 49/00 - Testing the nature of borehole wallsFormation testingMethods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
• E21B 43/20 - Displacing by water
• G06V 10/84 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using probabilistic graphical models from image or video features, e.g. Markov models or Bayesian networks

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.

IPC Classes  ?

• G06F 16/34 - BrowsingVisualisation therefor
• G06F 16/33 - Querying
• G06F 16/332 - Query formulation
• G06F 16/335 - Filtering based on additional data, e.g. user or group profiles
• G06F 16/338 - Presentation of query results
• G06F 40/20 - Natural language analysis
• G06F 40/40 - Processing or translation of natural language
• G06N 3/092 - Reinforcement learning
• G06N 5/04 - Inference or reasoning models
• G06N 20/00 - Machine learning

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.

IPC Classes  ?

• G06N 20/00 - Machine learning

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.

IPC Classes  ?

• G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals

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.

IPC Classes  ?

• G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
• A61B 5/00 - Measuring for diagnostic purposes Identification of persons
• G06F 16/33 - Querying
• G06F 40/56 - Natural language generation
• G06N 20/00 - Machine learning
• G06N 3/08 - Learning methods
• G06N 5/047 - Pattern matching networksRete networks
• G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
• G16B 5/00 - ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
• G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis

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.

IPC Classes  ?

• G06F 16/2453 - Query optimisation
• G06F 16/248 - Presentation of query results
• G06F 16/532 - Query formulation, e.g. graphical querying
• G06N 5/045 - Explanation of inferenceExplainable artificial intelligence [XAI]Interpretable artificial intelligence
• G06Q 10/047 - Optimisation of routes or paths, e.g. travelling salesman problem
• G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
• G06F 16/242 - Query formulation
• G06F 16/22 - IndexingData structures thereforStorage structures
• G06Q 30/0202 - Market predictions or forecasting for commercial activities

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.

IPC Classes  ?

• G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

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.

IPC Classes  ?

• G06F 8/41 - Compilation
• G06F 8/36 - Software reuse
• G06F 8/38 - Creation or generation of source code for implementing user interfaces
• G06F 16/901 - IndexingData structures thereforStorage structures

Abstract

An anti-hallucination and attribution architecture for enterprise generative AI systems is disclosed herein which increases the accuracy and reliability of generative artificial intelligence content (e.g., responses or answers) by detecting, preventing, and mitigating hallucination. The anti-hallucination and attribution architecture can be added to deployed generative artificial intelligence systems as a separate tool or module, which allows it to work with the deployed systems without having to retool or redesign those systems. The anti-hallucination and attribution architecture can also be deployed with minimal impact on live production systems.

IPC Classes  ?

• G06N 3/0475 - Generative networks

Abstract

A method includes obtaining (802), using at least one processing device (202), data from one or more data sources (110, 118a-118n), where the data is associated with or affects an underlying system to be optimized. The method also includes generating (804), using the at least one processing device, predictions based on the obtained data, where the predictions represent estimated values associated with one or more time-varying parameters associated with the underlying system. The method further includes providing (806), using the at least one processing device, the predictions to an optimizer (114). In addition, the method includes executing (808), using the at least one processing device, the optimizer to generate optimization results based on the predictions, where the optimization results are associated with the underlying system.

IPC Classes  ?

• G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
• G06F 17/11 - Complex mathematical operations for solving equations
• G06N 20/00 - Machine learning

Abstract

A method includes obtaining, using at least one processing device, data from one or more data sources, where the data is associated with or affects an underlying system to be optimized. The method also includes generating, using the at least one processing device, predictions based on the obtained data, where the predictions represent estimated values associated with one or more time-varying parameters associated with the underlying system. The method further includes providing, using the at least one processing device, the predictions to an optimizer. In addition, the method includes executing, using the at least one processing device, the optimizer to generate optimization results based on the predictions, where the optimization results are associated with the underlying system.

IPC Classes  ?

• G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
• G06Q 50/02 - AgricultureFishingForestryMining

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.

IPC Classes  ?

• G06F 8/41 - Compilation
• G06F 8/36 - Software reuse
• G06F 8/38 - Creation or generation of source code for implementing user interfaces
• G06F 16/901 - IndexingData structures thereforStorage structures

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.

IPC Classes  ?

• G06F 16/25 - Integrating or interfacing systems involving database management systems
• G06F 8/10 - Requirements analysisSpecification techniques
• G06F 8/35 - Creation or generation of source code model driven
• G06F 8/77 - Software metrics
• G06F 9/54 - Interprogram communication
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
• G06N 20/00 - Machine learning
• H04L 67/10 - Protocols in which an application is distributed across nodes in the network
• H04L 67/565 - Conversion or adaptation of application format or content
• H04L 67/5651 - Reducing the amount or size of exchanged application data

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.

IPC Classes  ?

• G06F 18/2415 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
• G06Q 50/26 - Government or public services

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.

IPC Classes  ?

• G06N 20/00 - Machine learning
• G06F 16/2458 - Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries

Abstract

Systems and methods for a model inference service system that provides a technical solution for deploying and updating trained machine-learning models with support for specific use case deployments and implementations at scale with efficient processing. The model inference service system includes a hierarchical model registry for versioning models and model dependencies for each versioned model, a model inference service for rapidly deploying model instances in run-time environments, and a model processing system for managing multiple instances of deployed models. Changes to deployed models are captured as new versions in the hierarchical model registry.

IPC Classes  ?

• G06N 20/00 - Machine learning
• G06N 5/04 - Inference or reasoning models

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 profile information associated with the prompt. These enterprise generative AI systems and methods support granular enterprise access controls, privacy, and security requirements. enterprise generative AI providing traceable references and links to source information underlying the generative AI insights. These systems and methods enable dramatically increased utility for enterprise users to information, analyses, and predictive analytics associated with and derived from a combination of enterprise and external information systems.

IPC Classes  ?

• G06F 16/24 - Querying
• G06F 16/242 - Query formulation
• G06F 16/2455 - Query execution
• G06F 16/38 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• G06F 16/532 - Query formulation, e.g. graphical querying

Abstract

Systems and methods managing a plurality of agents to generate a response to a query using a multimodal model. An example method uses the plurality of agents to iteratively determine subsequent outputs of the multimodal model satisfies the query. It can generate a respective context associated with a respective output of the multimodal model. And determine, by the multimodal model based on the respective context, whether the respective subsequent output satisfies the query.

IPC Classes  ?

• G06N 3/0475 - Generative networks
• G06N 20/00 - Machine learning
• G06F 16/245 - Query processing
• G06F 16/33 - Querying

Abstract

A plurality of different data domains of an enterprise information environment are scanned. A plurality of data records of multiple enterprise data sources of the different data domains are chunked. The chunking generates one or respective data record segments for each of the plurality of data records. Respective contextual metadata is generated for each of the one or more respective data record segments. Each respective contextual metadata indicates semantic or contextual descriptions of the respective data records segment, and at least one of the respective contextual metadata is capable of facilitating a determination of a relationship between one of the respective data record segments of a particular respective data record and another one the respective data segments of another respective data record. A respective segment embedding is generated for each data record segment based on the respective contextual metadata, and the segment embeddings are stored in an embeddings datastore.

IPC Classes  ?

• G06F 11/14 - Error detection or correction of the data by redundancy in operation, e.g. by using different operation sequences leading to the same result
• G06F 3/06 - Digital input from, or digital output to, record carriers
• G06F 11/20 - Error detection or correction of the data by redundancy in hardware using active fault-masking, e.g. by switching out faulty elements or by switching in spare elements

Abstract

Systems and methods for a model inference service system that provides a technical solution for deploying and updating trained machine-learning models with support for specific use case deployments and implementations at scale with efficient processing. The model inference service system includes a hierarchical model registry for versioning models and model dependencies for each versioned model, a model inference service for rapidly deploying model instances in run-time environments, and a model processing system for managing multiple instances of deployed models. Changes to deployed models are captured as new versions in the hierarchical model registry.

IPC Classes  ?

• G06N 20/00 - Machine learning
• G06N 3/08 - Learning methods
• G06N 5/02 - Knowledge representationSymbolic representation
• G06F 9/48 - Program initiatingProgram switching, e.g. by interrupt
• G10L 15/18 - Speech classification or search using natural language modelling

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.

IPC Classes  ?

• G06F 16/34 - BrowsingVisualisation therefor
• G06F 16/33 - Querying
• G06F 16/332 - Query formulation
• G06F 16/334 - Query execution
• G06F 16/335 - Filtering based on additional data, e.g. user or group profiles
• G06F 16/338 - Presentation of query results
• G06F 40/20 - Natural language analysis
• G06F 40/40 - Processing or translation of natural language
• G06N 3/092 - Reinforcement learning
• G06N 5/04 - Inference or reasoning models
• G06N 20/00 - Machine learning

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.

IPC Classes  ?

• G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor
• G06F 16/33 - Querying
• G06F 16/332 - Query formulation
• G06F 16/335 - Filtering based on additional data, e.g. user or group profiles
• G06F 16/338 - Presentation of query results
• G06F 16/34 - BrowsingVisualisation therefor
• G06F 40/20 - Natural language analysis
• G06F 40/40 - Processing or translation of natural language
• G06N 3/092 - Reinforcement learning
• G06N 5/04 - Inference or reasoning models
• G06N 20/00 - Machine learning

Abstract

Systems and methods managing a plurality of agents to generate a response to a query using a multimodal model. An example method uses the plurality of agents to iteratively determine subsequent outputs of the multimodal model satisfies the query. It can generate a respective context associated with a respective output of the multimodal model. And determine, by the multimodal model based on the respective context, whether the respective subsequent output satisfies the query.

IPC Classes  ?

• G06F 40/40 - Processing or translation of natural language

Abstract

A plurality of different data domains of an enterprise information environment are scanned. A plurality of data records of multiple enterprise data sources of the different data domains are chunked. The chunking generates one or respective data record segments for each of the plurality of data records. Respective contextual metadata is generated for each of the one or more respective data record segments. Each respective contextual metadata indicates semantic or contextual descriptions of the respective data records segment, and at least one of the respective contextual metadata is capable of facilitating a determination of a relationship between one of the respective data record segments of a particular respective data record and another one the respective data segments of another respective data record. A respective segment embedding is generated for each data record segment based on the respective contextual metadata, and the segment embeddings are stored in an embeddings datastore.

IPC Classes  ?

• G06N 3/092 - Reinforcement learning

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.

IPC Classes  ?

• G06F 40/42 - Data-driven translation
• G06F 40/20 - Natural language analysis
• G06F 40/30 - Semantic analysis
• G06N 5/02 - Knowledge representationSymbolic representation
• G06N 20/00 - Machine learning
• G06F 40/00 - Handling natural language data

Abstract

A computer system receives customer records listing customer attributes and an adoption status of the customer, such as whether the customer has enrolled in a particular energy efficiency program. An initial set of patterns are identified among the customer records, such as according to a decision tree. The initial set is pruned to obtain a set of patterns that meet minimum support and effectiveness and maximum overlap requirements. The patterns are assigned to segments according to an optimization algorithm that seeks to maximize the minimum effectiveness of each segment, where the effectiveness indicates a number of customers matching the pattern of each segment that have positive adoption status. The optimization algorithm may be a bisection algorithm that evaluates a linear-fractional integer program (LFIP-F) to iteratively approach an optimal distribution of patterns.

IPC Classes  ?

• G06Q 50/06 - Energy or water supply
• G06N 5/01 - Dynamic search techniquesHeuristicsDynamic treesBranch-and-bound
• G06N 5/022 - Knowledge engineeringKnowledge acquisition
• G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
• G06N 20/20 - Ensemble learning
• G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals

Abstract

The present disclosure provides systems and methods that may advantageously apply machine learning to accurately identify and investigate potential money laundering. In an aspect, the present disclosure provides a computer-implemented method for anti-money laundering (AML) analysis, comprising: (a) obtaining, by the computer, a dataset comprising a plurality of accounts, each of the plurality of accounts corresponding to an account holder among a plurality of account holders, wherein each account of the plurality of accounts comprises a plurality of account variables, wherein the plurality of account variables comprises financial transactions; (b) applying, by the computer, a trained algorithm to the dataset to generate a money laundering risk score for each of the plurality of account holders; and (c) identifying, by the computer, a subset of the plurality of account holders for investigation based at least on the money laundering risk scores of the plurality of account holders.

IPC Classes  ?

• G06Q 40/12 - Accounting
• G06N 20/00 - Machine learning

Abstract

Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to select a set of signals relating to a plurality of energy usage conditions. Signal values for the set of signals can be determined. Machine learning can be applied to the signal values to identify energy usage conditions associated with non-technical loss.

IPC Classes  ?

• G06F 8/34 - Graphical or visual programming
• G06N 20/00 - Machine learning
• H04W 52/04 - Transmission power control [TPC]
• H04B 17/391 - Modelling the propagation channel
• G06Q 50/06 - Energy or water supply
• G01R 21/00 - Arrangements for measuring electric power or power factor

Abstract

The present disclosure provides systems and methods that may advantageously apply machine learning to detect and ascribe network interruptions to specific components or nodes within the network. In an aspect, the present disclosure provides a computer-implemented method comprising: mapping a network comprising a plurality of islands that are capable of dynamically changing by splitting and/or merging of one or more islands, wherein the plurality of islands comprises a plurality of individual components; and detecting and localizing one or more local events at an individual component level as well as at an island level using a disaggregation model.

IPC Classes  ?

• H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
• H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• G06N 20/00 - Machine learning
• G01R 19/25 - Arrangements for measuring currents or voltages or for indicating presence or sign thereof using digital measurement techniques
• G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks

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.

IPC Classes  ?

• G06Q 10/00 - AdministrationManagement
• G06N 20/00 - Machine learning
• G06Q 10/087 - Inventory or stock management, e.g. order filling, procurement or balancing against orders

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.

IPC Classes  ?

• H04L 12/24 - Arrangements for maintenance or administration
• G06F 16/901 - IndexingData structures thereforStorage structures
• H04L 41/12 - Discovery or management of network topologies
• H04L 41/14 - Network analysis or design

Abstract

A method includes analyzing information to be processed, where analyzing the information includes classifying invalid data contained in the information and substituting replacement data in place of at least some of the invalid data in the information. The method also includes training at least one machine learning model based on some of the analyzed information. The method further includes providing other of the analyzed information to the at least one trained machine learning model, where the at least one trained machine learning model is used to generate one or more recommendations based on the analyzed information. In addition, the method includes translating each of the one or more recommendations into one or more actions and generating one or more control instructions based on the one or more actions for at least one of the one or more recommendations.

IPC Classes  ?

• G05B 13/02 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric

Abstract

A method includes analyzing information to be processed, where analyzing the information includes classifying (1006, 1104) invalid data contained in the information and. substituting (1008, 1114) replacement data in place of at least some of the invalid data in die information. The method also includes training (1010) at least one machine learning model (122, 314) based on some of the analyzed information. The method further includes providing (1012) other of the analyzed information to the at least one framed machine learning model, where the at least one trained machine learning model is used to generate (1014) one or more recommendations based on the analyzed information. In addition, the method includes translating each of the one or more recommendations into one or more actions and generating (1016) one or more control instructions based on the one or more actions tor at least one of the one or more recommendations.

IPC Classes  ?

• G06N 20/00 - Machine learning
• G06N 3/08 - Learning methods
• G06N 3/0985 - Hyperparameter optimisationMeta-learningLearning-to-learn

Abstract

A simulation method includes providing (101) a physics-based simulation model (220a, 220b) including model parameters (220a1, 220a2) for simulating a physical process using input data from different sources of operational data (215) including time series data, the physics-calculated using the model parameters, an artificial intelligence (AI)-based-system (210) including an AI-based proxy model (210a). The AI-based proxy model responsive to receiving an update of the input data processes (102) the updated input data to generate a proxy prediction (211) for at least one selected prediction from the simulated predictions or a variable derived from the simulated prediction as a replacement for or as a supplement to the selected prediction or the variable derived from the selected prediction.

IPC Classes  ?

• G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
• G05B 17/02 - Systems involving the use of models or simulators of said systems electric
• G06N 20/00 - Machine learning
• G06N 20/20 - Ensemble learning

Abstract

A method includes obtaining (802) information associated with multiple collocation events (302, 402). 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 (810) strengths of different relationships involving different ones of the entities based on the information. The strengths are determined using one or more features (406) of the collocation events.

IPC Classes  ?

• G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
• G06N 3/042 - Knowledge-based neural networksLogical representations of neural networks

Abstract

A simulation method includes providing (101) a physics-based simulation model (220a, 220b) including model parameters (220a1, 220a2) for simulating a physical process using input data from different sources of operational data (215) including time series data, the physics-calculated using the model parameters, an artificial intelligence (AI)-based-system (210) including an AI-based proxy model (210a). The AI-based proxy model responsive to receiving an update of the input data processes (102) the updated input data to generate a proxy prediction (211) for at least one selected prediction from the simulated predictions or a variable derived from the simulated prediction as a replacement for or as a supplement to the selected prediction or the variable derived from the selected prediction.

IPC Classes  ?

• G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
• G06N 20/20 - Ensemble learning
• G05B 17/02 - Systems involving the use of models or simulators of said systems electric
• G06N 20/00 - Machine learning

Abstract

A method includes obtaining (601) a first data model (501) from a data exploration phase performed in a first environment (511), where the first data model includes first metadata (531). The method also includes obtaining (603) a second data model (502) from the data exploration phase performed in a second environment (512) different from the first environment, where the second data model includes second metadata (532). The method further includes generating (605) a third data model (503) including one or more software artifacts (545) using the first metadata and the second metadata. Each of the one or more software artifacts is configured as one or more files that are configured for execution of at least one artificial intelligence (AI)/machine learning (ML) application (320).

IPC Classes  ?

• G06F 8/35 - Creation or generation of source code model driven
• G06F 8/77 - Software metrics
• G06F 9/54 - Interprogram communication
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06N 3/08 - Learning methods
• G06N 20/00 - Machine learning

Abstract

A method includes identifying (402) a sequence of transformations to be performed on an input dataset (308) via a user interface (310). The method also includes identifying (404) a first context associated with the input dataset. The method further includes selecting (406) a first one of multiple execution engines (332) to be used to perform the sequence of transformations on the input dataset based on the first context. In addition, the method includes providing (416) first code (334) implementing the sequence of transformations to the first execution engine and executing (418) the first code using the first execution engine to perform the sequence of transformations on the input dataset.

IPC Classes  ?

• G06F 3/048 - Interaction techniques based on graphical user interfaces [GUI]
• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• G06F 40/151 - Transformation
• H04W 12/02 - Protecting privacy or anonymity, e.g. protecting personally identifiable information [PII]

Abstract

A method includes identifying (414) one or more transformations (320) to be applied in order to generate one or more features or feature sets (308). The method also includes generating (416) metadata (306) identifying the one or more features or feature sets and the one or more transformations. The method further includes using (420) the metadata to determine the one or more features or feature sets for specified data and storing (420) the one or more determined features or feature sets in a feature store (314). In addition, the method includes outputting (424) at least some of the one or more determined features or feature sets or data associated with the at least some of the one or more determined features or feature sets from the feature store to at least one machine learning model (310).

IPC Classes  ?

• G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor
• G06F 16/16 - File or folder operations, e.g. details of user interfaces specifically adapted to file systems
• G06N 5/02 - Knowledge representationSymbolic representation
• G06N 20/00 - Machine learning

Abstract

A method includes identifying one or more transformations to be applied in order to generate one or more features or feature sets. The method also includes generating metadata identifying the one or more features or feature sets and the one or more transformations. The method further includes using the metadata to determine the one or more features or feature sets for specified data and storing the one or more determined features or feature sets in a feature store. In addition, the method includes outputting at least some of the one or more determined features or feature sets or data associated with the at least some of the one or more determined features or feature sets from the feature store to at least one machine learning model.

IPC Classes  ?

• G06N 20/00 - Machine learning
• G06N 5/00 - Computing arrangements using knowledge-based models

Abstract

A method includes obtaining training data having at least one training engineering schematic containing multiple known symbols, where different ones of the known symbols represent different types of equipment. The method also includes augmenting the training data with at least one additional training engineering schematic having at least one synthetic schematic, where the at least one synthetic schematic contains additional known symbols. The method further includes training at least one machine learning model using the training data including the at least one training engineering schematic and the at least one additional training engineering schematic.

IPC Classes  ?

• G06N 20/00 - Machine learning
• G06F 30/17 - Mechanical parametric or variational design

Abstract

A method includes using templates to identify constraints and terms of at least one objective function associated with at least a portion of one or more processing targets At least one of the templates is based on a resource-task network (RTN) representation of resource nodes and task nodes associated with at least the portion of the one or more processing targets. The method also includes generating one or more optimization problems, where the constraints and the at least one objective function represent at least part of the one or more optimization problems. The method further includes generating at least one candidate production schedule for at least the portion of the one or more processing targets using the one or more optimization problems.

IPC Classes  ?

• G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]

Abstract

A method includes generating an authoring representation of a machine learning pipeline based on a received input, where the authoring representation is configured to manage one or more machine learning operations. The method also includes receiving an indication of an operation to be performed on the authoring representation. The method further includes translating the authoring representation to an intermediate representation based on the operation and optimizing the intermediate representation. In addition, the method includes translating the intermediate representation to an execution representation that is understood by one or more machine learning executors.

IPC Classes  ?

• G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks

Abstract

A method includes identifying (402) a sequence of transformations to be performed on an input dataset (308) via a user interface (310). The method also includes identifying (404) a first context associated with the input dataset. The method further includes selecting (406) a first one of multiple execution engines (332) to be used to perform the sequence of transformations on the input dataset based on the first context. In addition, the method includes providing (416) first code (334) implementing the sequence of transformations to the first execution engine and executing (418) the first code using the first execution engine to perform the sequence of transformations on the input dataset.

IPC Classes  ?

• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• G06F 40/151 - Transformation
• G06F 3/048 - Interaction techniques based on graphical user interfaces [GUI]
• H04W 12/02 - Protecting privacy or anonymity, e.g. protecting personally identifiable information [PII]

Abstract

A method includes obtaining (902) an engineering schematic (102) containing multiple symbols (300, 302) 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 (904) visual features of the engineering schematic. The method further includes processing (906) the visual features using at least one trained machine learning model (106, 108, 110) to (i) identify boundaries (204) around the symbols in the engineering schematic and (ii) classify the symbols in the engineering schematic into multiple classifications (206), where different ones of the classifications are associated with different types of symbols.

IPC Classes  ?

• G06N 3/08 - Learning methods
• G06N 20/00 - Machine learning
• G06V 10/00 - Arrangements for image or video recognition or understanding
• G06T 7/00 - Image analysis

Abstract

A method includes obtaining (1002) training data having at least one training engineering schematic (102, 500, 600, 702) containing multiple known symbols (300, 302), where different ones of the known symbols represent different types of equipment. The method also includes augmenting (1004, 1006, 1008) the training data with at least one additional training engineering schematic (504, 602, 702) having at least one synthetic schematic, where the at least one synthetic schematic contains additional known symbols (300, 302). The method further includes training (1010) at least one machine learning model (106, 108, 110) using the training data including the at least one training engineering schematic and the at least one additional training engineering schematic.

IPC Classes  ?

• G06N 3/08 - Learning methods
• G06N 20/00 - Machine learning
• G06V 10/00 - Arrangements for image or video recognition or understanding
• G06T 7/00 - Image analysis

Abstract

A method includes identifying (414) one or more transformations (320) to be applied in order to generate one or more features or feature sets (308). The method also includes generating (416) metadata (306) identifying the one or more features or feature sets and the one or more transformations. The method further includes using (420) the metadata to determine the one or more features or feature sets for specified data and storing (420) the one or more determined features or feature sets in a feature store (314). In addition, the method includes outputting (424) at least some of the one or more determined features or feature sets or data associated with the at least some of the one or more determined features or feature sets from the feature store to at least one machine learning model (310).

IPC Classes  ?

• G06N 5/02 - Knowledge representationSymbolic representation
• G06N 20/00 - Machine learning
• G06F 16/16 - File or folder operations, e.g. details of user interfaces specifically adapted to file systems
• G06N 5/00 - Computing arrangements using knowledge-based models
• G06N 99/00 - Subject matter not provided for in other groups of this subclass
• G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor

Abstract

A method includes generating (701) an authoring representation (304) of a machine learning pipeline (308) based on a received input, where the authoring representation is configured to manage one or more machine learning operations. The method also includes receiving (703) an indication of an operation (310) to be performed on the authoring representation. The method further includes translating (705) the authoring representation to an intermediate representation (306) based on the operation and optimizing (707) the intermediate representation. In addition, the method includes translating (709) the intermediate representation to an execution representation (307) that is understood by one or more machine learning executors.

IPC Classes  ?

• G06N 20/00 - Machine learning
• G06N 3/02 - Neural networks
• G06N 3/08 - Learning methods
• G06N 5/04 - Inference or reasoning models

Abstract

A method includes generating (701) an authoring representation (304) of a machine learning pipeline (308) based on a received input, where the authoring representation is configured to manage one or more machine learning operations. The method also includes receiving (703) an indication of an operation (310) to be performed on the authoring representation. The method further includes translating (705) the authoring representation to an intermediate representation (306) based on the operation and optimizing (707) the intermediate representation. In addition, the method includes translating (709) the intermediate representation to an execution representation (307) that is understood by one or more machine learning executors.

IPC Classes  ?

• G06N 3/02 - Neural networks
• G06N 3/08 - Learning methods
• G06N 5/04 - Inference or reasoning models
• G06N 20/00 - Machine learning

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.

IPC Classes  ?

• G06V 30/422 - Technical drawingsGeographical maps
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• G06V 30/412 - Layout analysis of documents structured with printed lines or input boxes, e.g. business forms or tables
• G06V 30/413 - Classification of content, e.g. text, photographs or tables
• G06V 30/414 - Extracting the geometrical structure, e.g. layout treeBlock segmentation, e.g. bounding boxes for graphics or text

Abstract

A simulation method includes providing a physics-based simulation model including model parameters for simulating a physical process using input data from different sources of operational data including time series data, the physics-based simulation model generating output data including simulated predictions that are calculated using the model parameters, an artificial intelligence (AI)-based-system including an AI-based proxy model. The AI-based proxy model responsive to receiving an update of the input data processes the updated input data to generate a proxy prediction for at least one selected prediction from the simulated predictions or a variable derived from the simulated prediction as a replacement for or as a supplement to the selected prediction or the variable derived from the selected prediction.

IPC Classes  ?

• G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model

Abstract

A method includes obtaining a first data model from a data exploration phase performed in a first environment, where the first data model includes first metadata. The method also includes obtaining a second data model from the data exploration phase performed in a second environment different from the first environment, where the second data model includes second metadata. The method further includes generating a third data model including one or more software artifacts using the first metadata and the second metadata. Each of the one or more software artifacts is configured as one or more files that are configured for execution of at least one artificial intelligence (AI)/machine learning (ML) application.

IPC Classes  ?

• G06F 8/36 - Software reuse
• G06F 8/41 - Compilation

Abstract

A method includes obtaining (601) a first data model (501) from a data exploration phase performed in a first environment (511), where the first data model includes first metadata (531). The method also includes obtaining (603) a second data model (502) from the data exploration phase performed in a second environment (512) different from the first environment, where the second data model includes second metadata (532). The method further includes generating (605) a third data model (503) including one or more software artifacts (545) using the first metadata and the second metadata. Each of the one or more software artifacts is configured as one or more files that are configured for execution of at least one artificial intelligence (AI)/machine learning (ML) application (320).

IPC Classes  ?

• G06F 8/77 - Software metrics
• G06F 9/54 - Interprogram communication
• G06F 8/35 - Creation or generation of source code model driven
• G06N 3/08 - Learning methods
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06N 20/00 - Machine learning

IPC Classes  ?

• G06F 21/50 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules

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.

IPC Classes  ?

• H04L 9/40 - Network security protocols
• G06N 20/00 - Machine learning

Abstract

A method includes obtaining (1102) data associated with operation of a monitored system (100). The method also includes using (1106) one or more first machine learning models (302) to identify anomalies in the monitored system based on the obtained data, where each anomaly identifies an anomalous behavior. The method further includes using (1108) one or more second machine learning models (304) 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 (116a-116n) to the monitored system, and the identified anomalies are classified based on risk scores (806) determined using the one or more second machine learning models. In addition, the method includes identifying (1112), 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.

IPC Classes  ?

• G06F 21/50 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules

Abstract

A method includes using (2506) templates (406, 412) to identify constraints and terms of at least one objective function associated with at least a portion of one or more processing targets (370, 390, 1200, 1332, 1502-1506, 1602-1604, 1700). At least one of the templates is based on a resource-task network (RTN) representation (350) of resource nodes (352) and task nodes (354) associated with at least the portion of the one or more processing targets. The method also includes generating (2506) one or more optimization problems, where the constraints and the at least one objective function represent at least part of the one or more optimization problems. The method further includes generating (2508) at least one candidate production schedule for at least the portion of the one or more processing targets using the one or more optimization problems.

IPC Classes  ?

• G06F 16/21 - Design, administration or maintenance of databases
• G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations

Abstract

A method includes using (2506) templates (406, 412) to identify constraints and terms of at least one objective function associated with at least a portion of one or more processing targets (370, 390, 1200, 1332, 1502-1506, 1602-1604, 1700). At least one of the templates is based on a resource-task network (RTN) representation (350) of resource nodes (352) and task nodes (354) associated with at least the portion of the one or more processing targets. The method also includes generating (2506) one or more optimization problems, where the constraints and the at least one objective function represent at least part of the one or more optimization problems. The method further includes generating (2508) at least one candidate production schedule for at least the portion of the one or more processing targets using the one or more optimization problems.

IPC Classes  ?

• G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
• G06F 16/21 - Design, administration or maintenance of databases

Abstract

A method includes identifying, using at least one processor, uncertainty distributions for multiple variables. The method also includes identifying, using the at least one processor, one or more hyperparameters. The method further includes performing, using the at least one processor, multiple simulations to simulate effects of future requests using the one or more hyperparameters and at least one of the uncertainty distributions. The simulations involve sampling of the at least one uncertainty distribution to simulate at least one uncertainty associated with at least one of the variables on the future requests. In addition, the method includes selecting, using the at least one processor, one or more of the simulated future requests.

IPC Classes  ?

• G06Q 10/08 - Logistics, e.g. warehousing, loading or distributionInventory or stock management
• G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
• G06Q 30/02 - MarketingPrice estimation or determinationFundraising

Abstract

A method includes identifying (2402), using at least one processor (2305), uncertainty distributions for multiple variables. The method also includes identifying (2404), using the at least one processor, one or more hyperparameters. The method further includes performing (2406), using the at least one processor, multiple simulations to simulate effects of future requests using the one or more hyperparameters and at least one of the uncertainty distributions. The simulations involve sampling of the at least one uncertainty distribution to simulate at least one uncertainty associated with at least one of the variables on the future requests. In addition, the method includes selecting (2408), using the at least one processor, one or more of the simulated future requests.

IPC Classes  ?

• G06N 20/00 - Machine learning
• G06Q 10/08 - Logistics, e.g. warehousing, loading or distributionInventory or stock management

Abstract

A method includes identifying (2402), using at least one processor (2305), uncertainty distributions for multiple variables. The method also includes identifying (2404), using the at least one processor, one or more hyperparameters. The method further includes performing (2406), using the at least one processor, multiple simulations to simulate effects of future requests using the one or more hyperparameters and at least one of the uncertainty distributions. The simulations involve sampling of the at least one uncertainty distribution to simulate at least one uncertainty associated with at least one of the variables on the future requests. In addition, the method includes selecting (2408), using the at least one processor, one or more of the simulated future requests.

IPC Classes  ?

• G06N 20/00 - Machine learning
• G06N 99/00 - Subject matter not provided for in other groups of this subclass
• G06Q 10/08 - Logistics, e.g. warehousing, loading or distributionInventory or stock management

Abstract

Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to select a set of signals relating toa plurality of energy usage conditions. Signal values for the set of signals can be determined. Machine learning can be applied to the signal values to identify energy usage conditions associated with non-technical loss.

IPC Classes  ?

• G06F 8/34 - Graphical or visual programming
• G06N 20/00 - Machine learning
• H04W 52/04 - Transmission power control [TPC]
• H04B 17/391 - Modelling the propagation channel
• G06Q 50/06 - Energy or water supply
• G01R 21/00 - Arrangements for measuring electric power or power factor

Abstract

A method includes curating CRM data by employing a type system of a model-driven architecture (220) and selecting an AI CRM application from a group of applications (226-238). Each CRM application may generate one or more use case insights with one or more objectives. The method also includes obtaining one or more data models (2806, 2808) including an industry-specific data model (2808) from the curated CRM data and orchestrating a plurality of machine learning models (304, 308, 310, 312, 402, 404, 808, 904, 1010, 1104, 1204, 1308, 1410, 1508, 1610, 2802, 2804) for the selected CRM application with the obtained data model(s) to determine one or more machine learning models effective for at least one objective of the selected CRM application. The method further includes applying the determined machine learning model(s) and the obtained data model(s) to predict probabilities that optimize the at least one objective and using the predicted probabilities to apply at least one of the one or more use case insights that optimizes the at least one objective.

IPC Classes  ?

• G06F 8/35 - Creation or generation of source code model driven
• G06F 8/10 - Requirements analysisSpecification techniques
• G06F 8/77 - Software metrics
• G06F 9/54 - Interprogram communication
• G06F 16/25 - Integrating or interfacing systems involving database management systems
• G06N 20/00 - Machine learning

Abstract

A method includes curating CRM data by employing a type system of a model-driven architecture (220) and selecting an AI CRM application from a group of applications (226-238). Each CRM application may generate one or more use case insights with one or more objectives. The method also includes obtaining one or more data models (2806, 2808) including an industry-specific data model (2808) from the curated CRM data and orchestrating a plurality of machine learning models (304, 308, 310, 312, 402, 404, 808, 904, 1010, 1104, 1204, 1308, 1410, 1508, 1610, 2802, 2804) for the selected CRM application with the obtained data model(s) to determine one or more machine learning models effective for at least one objective of the selected CRM application. The method further includes applying the determined machine learning model(s) and the obtained data model(s) to predict probabilities that optimize the at least one objective and using the predicted probabilities to apply at least one of the one or more use case insights that optimizes the at least one objective.

IPC Classes  ?

• G06F 8/10 - Requirements analysisSpecification techniques
• G06F 8/35 - Creation or generation of source code model driven
• G06F 8/77 - Software metrics
• G06F 9/54 - Interprogram communication
• G06F 16/25 - Integrating or interfacing systems involving database management systems
• G06N 20/00 - Machine learning

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.

IPC Classes  ?

• H04L 9/40 - Network security protocols
• G06Q 10/0635 - Risk analysis of enterprise or organisation activities
• G06Q 50/06 - Energy or water supply

Abstract

The present disclosure provides systems and methods that may advantageously apply machine learning to accurately identify and investigate potential money laundering. In an aspect, the present disclosure provides a computer-implemented method for anti-money laundering (AML) analysis, comprising: (a) obtaining, by the computer, a dataset comprising a plurality of accounts, each of the plurality of accounts corresponding to an account holder among a plurality of account holders, wherein each account of the plurality of accounts comprises a plurality of account variables, wherein the plurality of account variables comprises financial transactions; (b) applying, by the computer, a trained algorithm to the dataset to generate a money laundering risk score for each of the plurality of account holders; and (c) identifying, by the computer, a subset of the plurality of account holders for investigation based at least on the money laundering risk scores of the plurality of account holders.

IPC Classes  ?

• G06Q 40/00 - FinanceInsuranceTax strategiesProcessing of corporate or income taxes
• G06N 20/00 - Machine learning
• G06Q 40/12 - Accounting

Abstract

A method includes curating CRM data by employing a type system of a model-driven architecture and selecting an AI CRM application from a group of applications. Each CRM application may generate one or more use case insights with one or more objectives. The method also includes obtaining one or more data models including an industry-specific data model from the curated CRM data and orchestrating a plurality of machine learning models for the selected CRM application with the obtained data model(s) to determine one or more machine learning models effective for at least one objective of the selected CRM application. The method further includes applying the determined machine learning model(s) and the obtained data model(s) to predict probabilities that optimize the at least one objective and using the predicted probabilities to apply at least one of the one or more use case insights that optimizes the at least one objective.

IPC Classes  ?

• G06Q 30/02 - MarketingPrice estimation or determinationFundraising
• G06Q 30/00 - Commerce

Abstract

Processing of data relating to energy usage. First data relating to energy usage is loaded for analysis by an energy management platform. Second data relating to energy usage is stream processed by the energy management platform. Third data relating to energy usage is batch parallel processed by the energy management platform. Additional computing resources, owned by a third party separate from an entity that owns the computer system that supports the energy management platform, are provisioned based on increasing computing demand. Existing computing resources owned by the third party are released based on decreasing computing demand.

IPC Classes  ?

• G01R 21/00 - Arrangements for measuring electric power or power factor
• G01R 21/133 - Arrangements for measuring electric power or power factor by using digital technique
• G06Q 50/06 - Energy or water supply
• G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
• G06F 16/2455 - Query execution
• G06F 16/2453 - Query optimisation
• G06F 16/25 - Integrating or interfacing systems involving database management systems
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
• G06F 16/23 - Updating

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.

IPC Classes  ?

• G05B 23/02 - Electric testing or monitoring
• G05B 13/04 - Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators

Abstract

The present disclosure provides systems and methods that may advantageously apply machine learning to detect and ascribe network interruptions to specific components or nodes within the network. In an aspect, the present disclosure provides a computer-implemented method comprising: mapping a network comprising a plurality of islands that are capable of dynamically changing by splitting and/or merging of one or more islands, wherein the plurality of islands comprises a plurality of individual components; and detecting and localizing one or more local events at an individual component level as well as at an island level using a disaggregation model.

IPC Classes  ?

• H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
• H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
• H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• G06N 20/00 - Machine learning
• G01R 19/25 - Arrangements for measuring currents or voltages or for indicating presence or sign thereof using digital measurement techniques
• G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks

Abstract

A method includes obtaining (1602) information defining multiple customer orders for one or more products over time. The method also includes using (1604) machine learning to identify one or more of the customer orders that are likely to change based on classification of the customer orders. The method further includes using (1606) machine learning to estimate one or more lengths of time that the one or more customer orders are likely to change based on regression of the customer orders. In addition, the method includes generating (1608) a consumption plan for a facility based on the one or more estimated lengths of time that the one or more customer orders are likely to change.

IPC Classes  ?

• G06E 1/00 - Devices for processing exclusively digital data

Abstract

A method includes obtaining (1502) information identifying (i) multiple processing units in a facility, (ii) multiple interconnections between the processing units, and (iii) constraints associated with the processing units and the interconnections. The method also includes identifying (1504) an optimization problem associated with production of multiple products by the processing units in the facility, where the optimization problem is associated with a cost function. The method further includes removing (1506) one or more terms from the optimization problem to generate a relaxed optimization problem. In addition, the method includes generating (1508) one or more solutions to the relaxed optimization problem, where each solution represents a proposed production schedule.

IPC Classes  ?

• G06E 1/00 - Devices for processing exclusively digital data

Abstract

A method includes obtaining information identifying (i) multiple processing units in a facility, (ii) multiple interconnections between the processing units, and (iii) constraints associated with the processing units and the interconnections. The method also includes identifying an optimization problem associated with production of multiple products by the processing units in the facility, where the optimization problem is associated with a cost function. The method further includes removing one or more terms from the optimization problem to generate a relaxed optimization problem. In addition, the method includes generating one or more solutions to the relaxed optimization problem, where each solution represents a proposed production schedule.

IPC Classes  ?

• G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
• G06Q 50/04 - Manufacturing

Abstract

A method includes obtaining a solution representing a proposed production schedule associated with multiple processing units in a facility, where the multiple processing units are capable of producing different products. The method also includes performing post-processing of the solution to identify a final production schedule for the processing units in the facility. The post-processing of the solution includes multiple operations each configured to modify the proposed production schedule so that the final production schedule is feasible given constraints associated with the processing units in the facility.

IPC Classes  ?

• G06Q 50/04 - Manufacturing
• G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling

Abstract

A method includes obtaining (1502) information identifying (i) multiple processing units in a facility, (ii) multiple interconnections between the processing units, and (iii) constraints associated with the processing units and the interconnections. The method also includes identifying (1504) an optimization problem associated with production of multiple products by the processing units in the facility, where the optimization problem is associated with a cost function. The method further includes removing (1506) one or more terms from the optimization problem to generate a relaxed optimization problem. In addition, the method includes generating (1508) one or more solutions to the relaxed optimization problem, where each solution represents a proposed production schedule.

IPC Classes  ?

• G06E 1/00 - Devices for processing exclusively digital data

Abstract

A method includes obtaining (1602) information defining multiple customer orders for one or more products over time. The method also includes using (1604) machine learning to identify one or more of the customer orders that are likely to change based on classification of the customer orders. The method further includes using (1606) machine learning to estimate one or more lengths of time that the one or more customer orders are likely to change based on regression of the customer orders. In addition, the method includes generating (1608) a consumption plan for a facility based on the one or more estimated lengths of time that the one or more customer orders are likely to change.

IPC Classes  ?

• G06E 1/00 - Devices for processing exclusively digital data

Abstract

A method includes obtaining information defining multiple customer orders for one or more products over time. The method also includes using machine learning to identify one or more of the customer orders that are likely to change based on classification of the customer orders. The method further includes using machine learning to estimate one or more lengths of time that the one or more customer orders are likely to change based on regression of the customer orders. In addition, the method includes generating a consumption plan for a facility based on the one or more estimated lengths of time that the one or more customer orders are likely to change.

IPC Classes  ?

• G06Q 10/08 - Logistics, e.g. warehousing, loading or distributionInventory or stock management
• G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
• G06N 20/00 - Machine learning

Abstract

The present disclosure provides systems and methods that may advantageously apply machine learning to detect and ascribe network interruptions to specific components or nodes within the network. In an aspect, the present disclosure provides a computer-implemented method comprising: mapping a network comprising a plurality of islands that are capable of dynamically changing by splitting and/or merging of one or more islands, wherein the plurality of islands comprises a plurality of individual components; and detecting and localizing one or more local events at an individual component level as well as at an island level using a disaggregation model.

IPC Classes  ?

• H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
• H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
• H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• G06N 20/00 - Machine learning
• G01R 19/25 - Arrangements for measuring currents or voltages or for indicating presence or sign thereof using digital measurement techniques
• G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks

Abstract

Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to train a Bayesian network model based on a given set of data. Information associated with a user can be received. The information can include aggregated energy consumption data at one or more low frequency time intervals. At least a portion of the information can be inputted into the Bayesian network model. A plurality of energy consumption values for a plurality of energy consumption sources associated with the user can be inferred based on inputting the at least the portion of the information into the Bayesian network model.

IPC Classes  ?

• G06N 20/00 - Machine learning
• G06N 7/00 - Computing arrangements based on specific mathematical models

Abstract

A computer system receives customer records listing customer attributes and an adoption status of the customer, such as whether the customer has enrolled in a particular energy efficiency program. An initial set of patterns are identified among the customer records, such as according to a decision tree. The initial set is pruned to obtain a set of patterns that meet minimum support and effectiveness and maximum overlap requirements. The patterns are assigned to segments according to an optimization algorithm that seeks to maximize the minimum effectiveness of each segment, where the effectiveness indicates a number of customers matching the pattern of each segment that have positive adoption status. The optimization algorithm may be a bisection algorithm that evaluates a linear-fractional integer program (LFIP-F) to iteratively approach an optimal distribution of patterns.

IPC Classes  ?

• G06Q 50/06 - Energy or water supply
• G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals
• G06N 20/20 - Ensemble learning
• G06N 5/01 - Dynamic search techniquesHeuristicsDynamic treesBranch-and-bound
• G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
• G06N 5/022 - Knowledge engineeringKnowledge acquisition

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.

IPC Classes  ?

• G06Q 50/06 - Energy or water supply
• G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling

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.

IPC Classes  ?

• G01V 99/00 - Subject matter not provided for in other groups of this subclass
• G06G 7/48 - Analogue computers for specific processes, systems, or devices, e.g. simulators

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.

IPC Classes  ?

• G06G 7/48 - Analogue computers for specific processes, systems, or devices, e.g. simulators

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.

IPC Classes  ?

• E21B 49/00 - Testing the nature of borehole wallsFormation testingMethods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
• E21B 43/20 - Displacing by water
• G06V 10/84 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using probabilistic graphical models from image or video features, e.g. Markov models or Bayesian networks