Abstract

Techniques described herein provide for text string comparison for documents identified using automated near-duplicate detection. In one example, a system can receive a pair of documents. The system can extract text strings from the documents. The system can normalize the extracted text strings using a predefined normalization scheme. The system can identify boilerplate text segments in the normalized text strings. The system can remove the boilerplate text segments from the normalized text strings to generate filtered text strings. The system can divide the filtered text strings by identifying section indicators. The system can, for each section, generate groupings of text strings and determine a similarity score between each pair of corresponding groupings to identify matching groupings of text strings. The system can generate an output for display showing the visual indications of the matched groupings of text strings.

IPC Classes  ?

• G06F 16/906 - ClusteringClassification
• G06F 16/355 - Creation or modification of classes or clusters
• G06F 16/93 - Document management systems

Abstract

Techniques described herein provide for automated near-duplicate detection for new text documents given text documents that were previously processed using automated near-duplicate detection for text documents. In one example, a system can receive new documents and documents that were previously processed using a predefined processing technique for automated near-duplicate detection. The system can process the new documents and cluster the new documents into multiple predefined clusters previously identified using the predefined processing technique. For each predefined cluster including at least one new document, the system can generate document groups by determining similarity scores using the predefined processing technique as applied to the documents in the predefined clusters. The system can identify a representative document for each document group and generate an output data structure including the document groups and the representative document for each group.

IPC Classes  ?

• G06F 16/906 - ClusteringClassification
• G06F 16/355 - Creation or modification of classes or clusters
• G06F 16/93 - Document management systems

Abstract

A system and method include dividing training data into training data blocks, determining a support vector subset, distributing the training data blocks and the support vector subset to worker machines, receiving a first set of sub-results from worker machines, combining the first set of sub-results, solving a linear system, distributing a first set of variables to worker machines, receiving a second set of sub-results from worker machines, selecting a step size value and sending the selected step size value to worker machines, receiving updated values of the first set of variables and second set of variables from worker machines, receiving a maximum residual error value from worker machines, selecting a maximum value of the maximum residual error values, responsive to determining that selected maximum value satisfies an optimality condition, outputting a weight value and a bias value, and predicting a label using the weight value and the bias value.

IPC Classes  ?

• G06F 17/12 - Simultaneous equations
• G06F 17/16 - Matrix or vector computation

Abstract

A system and method include forecasting a series of future data points in a long sequence time series data using a decoder-only transformer model by dividing the long sequence time series data into a plurality of sequences, converting each sequence of the plurality of sequences into a first vector to obtain a plurality of first vectors, creating a plurality of second vectors from the time stamps associated with the plurality of data points, combining the first vector with the second vector of each sequence of the plurality of sequences to obtain a plurality of third vectors, computing a context matrix from the plurality of third vectors, performing a convolution operation on the context matrix to forecast the series of future data points, and outputting the series of future data points from the prediction layer.

IPC Classes  ?

• G06F 17/16 - Matrix or vector computation
• G06F 5/01 - Methods or arrangements for data conversion without changing the order or content of the data handled for shifting, e.g. justifying, scaling, normalising
• G06F 17/15 - Correlation function computation

Abstract

A system described herein can generate a graphical user interface (GUI) for a piece of forecasting software. The GUI can include graphical nodes arranged on a drag-and-drop canvas to define an overall forecasting pipeline. The graphical nodes can include a hierarchical modeling node that enables a user to define a time series hierarchy. The hierarchical modeling node can also enable separate level pipelines to be customized for each level of the time series hierarchy. The level pipelines can form subparts of the overall forecasting pipeline. The system can then execute the level pipelines to generate multiple forecasts, where each forecast corresponds to a respective level of the time series hierarchy. In some examples, the system can execute a reconciliation process on the forecasts to generate reconciled forecasts. Each reconciled forecast can correspond to one of the forecasts. The system may then generate one or more visualizations of the reconciled forecasts.

IPC Classes  ?

• G06T 11/20 - Drawing from basic elements, e.g. lines or circles
• G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
• G06F 8/34 - Graphical or visual programming
• G06F 8/38 - Creation or generation of source code for implementing user interfaces

Abstract

Functions representing sequences of values of a time-series dataset measured within a particular time period are accessed. For a current time window of the time period, a first discretized covariance function is computed that represents a relationship between each value measured within the current time window. Eigenanalysis of the first covariance function is performed to estimate first eigenfunctions. The current time window is incremented to obtain a subsequent time window that overlaps a majority of the current time window at a shared window region. A second discretized covariance function is computed for the subsequent time window and eigenanalysis is performed to estimate second normalized eigenfunctions. An angle change is computed between a portion of the first normalized eigenfunctions and a corresponding portion of the second normalized eigenfunctions located within the shared window region. Based on the angle change, an anomaly detection output is generated.

IPC Classes  ?

• H04L 9/40 - Network security protocols
• G16Y 30/10 - Security thereof
• 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

Abstract

A system and method include breaking symmetry in a query graph by converting the query graph into a transformed query graph by generating a symmetry breaking expression that includes detecting one or more orbits in the transformed query graph, selecting an orbit from the one or more orbits having more than one node, generating an automorphism breaking sub-expression for the selected orbit, assigning a node of the selected orbit a unique node attribute, recalculating the one or more orbits in the transformed query graph, repeating the process until each node is in its own orbit, and combining each of the automorphism breaking sub-expressions to obtain the symmetry breaking expression. Using the symmetry breaking expression, the system and method include finding one or more subgraphs of a main graph that match the symmetry breaking expression of the query graph.

IPC Classes  ?

• G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor
• G06F 16/22 - IndexingData structures thereforStorage structures
• G06F 16/2452 - Query translation
• G06F 16/248 - Presentation of query results

Abstract

A system and method include breaking symmetry in a query graph by converting the query graph into a transformed query graph by generating a symmetry breaking expression that includes detecting one or more orbits in the transformed query graph, selecting an orbit from the one or more orbits having more than one node, generating an automorphism breaking sub-expression for the selected orbit, assigning a node of the selected orbit a unique node attribute, recalculating the one or more orbits in the transformed query graph, repeating the process until each node is in its own orbit, and combining each of the automorphism breaking sub-expressions to obtain the symmetry breaking expression. Using the symmetry breaking expression, the system and method include finding one or more subgraphs of a main graph that match the symmetry breaking expression of the query graph.

IPC Classes  ?

• G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor
• G06F 16/22 - IndexingData structures thereforStorage structures
• G06F 16/2452 - Query translation
• G06F 16/248 - Presentation of query results

Abstract

A system and method include breaking symmetry in a query graph by converting the query graph into a transformed query graph by generating a symmetry breaking expression that includes detecting one or more orbits in the transformed query graph, selecting an orbit from the one or more orbits having more than one node, generating an automorphism breaking sub-expression for the selected orbit, assigning a node of the selected orbit a unique node attribute, recalculating the one or more orbits in the transformed query graph, repeating the process until each node is in its own orbit, and combining each of the automorphism breaking sub-expressions to obtain the symmetry breaking expression. Using the symmetry breaking expression, the system and method include finding one or more subgraphs of a main graph that match the symmetry breaking expression of the query graph.

IPC Classes  ?

• G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor
• G06F 16/22 - IndexingData structures thereforStorage structures
• G06F 16/2452 - Query translation
• G06F 16/248 - Presentation of query results

Abstract

A system and method include breaking symmetry in a query graph by converting the query graph into a transformed query graph by generating a symmetry breaking expression that includes detecting one or more orbits in the transformed query graph, selecting an orbit from the one or more orbits having more than one node, generating an automorphism breaking sub-expression for the selected orbit, assigning a node of the selected orbit a unique node attribute, recalculating the one or more orbits in the transformed query graph, repeating the process until each node is in its own orbit, and combining each of the automorphism breaking sub-expressions to obtain the symmetry breaking expression. Using the symmetry breaking expression, the system and method include finding one or more subgraphs of a main graph that match the symmetry breaking expression of the query graph.

IPC Classes  ?

• G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor
• G06F 16/22 - IndexingData structures thereforStorage structures
• G06F 16/2452 - Query translation
• G06F 16/248 - Presentation of query results

Abstract

Techniques described herein provide for generation of structured output for documents identified using automated near-duplicate detection. In one example, a system can receive a set of documents including at least one pair of similar documents determined to be similar to one another based on similarity scores generated using a predefined similarity scoring technique. The system can generate document groups by merging together pairs of documents that share at least one document. The system can, for each of the document groups, identify a representative document for the document group. The system can generate an output for display including a section for each document group, in which each section includes the representative document for the document group and, for each document in the document group, the similarity score relative to the representative document for the document group.

IPC Classes  ?

• G06F 16/906 - ClusteringClassification
• G06F 16/355 - Creation or modification of classes or clusters
• G06F 16/93 - Document management systems

Abstract

A system and method include generating a topological order of a DAG by creating residual series vectors, calculating normality statistic and MSE values for the residual series vectors, comparing the normality statistic values with a critical value, for each normality statistic value that is less than or equal to the critical value, adding a variable index to a temporary order list and the MSE value to an MSE list, counting a number of elements in the temporary order list, if the number of elements in the temporary order list is zero, updating an order list based on the normality statistic values or if the number of elements in the temporary order list is not zero, updating the order list based on at least one of the temporary order list or the MSE list, and outputting the order list as the topological order of the DAG.

IPC Classes  ?

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

Abstract

Techniques described herein provide for automated detection of near-duplicate documents. In one example, a system can cluster documents into a set of clusters based on character frequencies associated with the documents. For a given cluster, the system can generate first similarity scores associated with every pair of documents in the cluster. The system can then select a filtered group of documents associated with first similarity scores that meet or exceed a first predefined similarity threshold. Next, the system can convert the filtered group of documents into matrix representations. The system can generate second similarity scores for every pair of matrix representations. The system can then identify documents, from among the filtered group of documents, associated with second similarity scores that meet or exceed a second predefined similarity threshold. The identified documents can be duplicate or near-duplicate text documents.

IPC Classes  ?

• G06F 16/906 - ClusteringClassification
• G06F 16/355 - Creation or modification of classes or clusters
• G06F 16/93 - Document management systems

Abstract

A system, method, and computer-program product includes commencing a parent computer process based on receiving a request to perform an analytical operation on one or more datasets, commencing at least one child computer process that is launched by the parent computer process when the parent computer process initiates an execution of the analytical operation on the one or more datasets, transmitting, by the at least one child computer process, a request to the parent computer process to retrieve the one or more datasets, writing, by the parent computer process, the one or more datasets to a cross-process queue based on the parent computer process receiving the requests, reading, by the at least one child computer process, the one or more datasets from the cross-process queue, and executing, using an analytical application executing on the least one child computer process, the analytical operation based on the one or more datasets.

IPC Classes  ?

• G06F 9/48 - Program initiatingProgram switching, e.g. by interrupt
• G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]

Abstract

A computer-implemented system, computer-implemented method, and computer-program product includes obtaining a text document that includes text describing an action; extracting one or more action tokens from the text document; executing a plurality of linguistic pattern searches that search the text document for one or more likelihood tokens associated with the one or more action tokens; classifying the action to a likelihood category associated with a respective linguistic pattern search of the plurality of linguistic pattern searches that identified the one or more likelihood tokens; classifying the text document to a respective domain; computing a priority value of the action described in the text document based on an input of the likelihood category and the respective domain; and generating a priority summary artifact that visually prioritizes the text document over one or more other text documents when the priority value of the action satisfies a predefined maximum priority threshold value.

IPC Classes  ?

• G06F 16/35 - ClusteringClassification
• G06F 16/34 - BrowsingVisualisation therefor
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates

Abstract

A new value is written from a dataset to a data structure comprising a set of sorted values. The new value replaces an oldest value and is inserted in a sorted position. The data structure is modified by subtracting a median value from each value of the set of sorted values to obtain sorted signed deviation values. The sorted signed deviation values are segmented to obtain data substructures comprising subsets of sorted absolute deviation values. A binary search is performed on the data substructures to identify a median absolute deviation value. A difference is computed between a particular value and the median value, and based on whether the difference is less than a threshold value computed from the median absolute deviation value, an outlier decision output is generated indicative of whether the particular value comprises an outlier value.

IPC Classes  ?

• G06F 16/23 - Updating
• G06F 7/08 - Sorting, i.e. grouping record carriers in numerical or other ordered sequence according to the classification of at least some of the information they carry
• G06F 16/22 - IndexingData structures thereforStorage structures
• G06F 16/2455 - Query execution

Abstract

A system, method, and computer-program product includes receiving, by a worker process, a plurality of chunks of data from a client process; deriving, by the worker process, an input pattern for feeding the plurality of chunks of data to a machine learning model; caching, by the worker process, a subset of data elements of the plurality of chunks of data specified by the input pattern based on a data caching policy; and training the machine learning model by feeding the subset of data elements cached by the worker process and a remainder of data elements in the plurality of chunks of data when requested by the input pattern.

IPC Classes  ?

• G06F 9/54 - Interprogram communication
• G06N 20/00 - Machine learning

Abstract

A system and method include creating a project package for an Event Stream Processing (ESP) project, generating a first manifest file from the project package, creating a first container pod on a cluster based on the first manifest file, executing a container file generator software and a build kit software on the first container pod, executing an ESP server on the container file generator software, executing the ESP project on the ESP server such that data is not streaming to the ESP server, identifying a list of required software components needed to execute the ESP project, creating a container file having a subset of software components based on the list of required software components, generating a ESP project container image for the ESP server based on the container file, and deploying the ESP project using the ESP project container image to analyze data streamed to the ESP project.

IPC Classes  ?

• G06F 9/455 - EmulationInterpretationSoftware simulation, e.g. virtualisation or emulation of application or operating system execution engines
• G06F 8/61 - Installation

Abstract

In some examples, a system can access video data collected from one or more image sensors, the video data showing a region of interest proximate to a machine. The system can execute an object detection model to detect that a person is within the region of interest proximate to the machine based on the video data. The system can detect a motion status of a component of the machine. The system can execute a pose estimation model on the video data to estimate a pose of the person with respect to the machine. The system can detect a safety rule violation based on the pose of the person with respect to the machine, and the motion status of the machine. The system can transmit a signal to a controller of the machine in response to detecting the safety rule violation.

IPC Classes  ?

• G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
• F16P 3/14 - Safety devices acting in conjunction with the control or operation of a machineControl arrangements requiring the simultaneous use of two or more parts of the body with means, e.g. feelers, which in case of the presence of a body part of a person in or near the danger zone influence the control or operation of the machine the means being photocells or other devices sensitive without mechanical contact
• G06Q 50/26 - Government or public services
• G06T 7/00 - Image analysis
• G06T 7/20 - Analysis of motion
• G06T 7/254 - Analysis of motion involving subtraction of images
• G06T 7/70 - Determining position or orientation of objects or cameras
• G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
• G06V 10/56 - Extraction of image or video features relating to colour
• G06V 10/70 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning
• G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
• 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 20/40 - ScenesScene-specific elements in video content
• G06V 20/52 - Surveillance or monitoring of activities, e.g. for recognising suspicious objects
• G06V 40/10 - Human or animal bodies, e.g. vehicle occupants or pedestriansBody parts, e.g. hands
• G06V 40/20 - Movements or behaviour, e.g. gesture recognition
• G08B 21/02 - Alarms for ensuring the safety of persons

Abstract

A computer-implemented system, computer-implemented method, and computer-program product includes receiving a natural language query from a user for executing an analytical task; generating an analytical large language model (LLM) prompt based on the natural language query and, in response to generating the analytical LLM prompt, orchestrating an LLM-directed workflow for handling the natural language query by: automatically prompting, using the analytical LLM prompt, an analytical task-oriented LLM to generate a structured query for querying a data catalog application; querying the data catalog application using the structured query generated by the analytical task-oriented LLM; obtaining query results from the data catalog application, where the query results include metadata associated with at least one element accessible to the data catalog application; prompting the analytical task-oriented LLM to identify a given analytical task associated with a given analytical agent; and automatically executing, by the given analytical agent, the analytical task.

IPC Classes  ?

• G06F 16/242 - Query formulation
• H04L 9/40 - Network security protocols

Abstract

A system and method include breaking symmetry in a query graph by converting the query graph into a transformed query graph by generating a symmetry breaking expression that includes detecting one or more orbits in the transformed query graph, selecting an orbit from the one or more orbits having more than one node, generating an automorphism breaking sub-expression for the selected orbit, assigning a node of the selected orbit a unique node attribute, recalculating the one or more orbits in the transformed query graph, repeating the process until each node is in its own orbit, and combining each of the automorphism breaking sub-expressions to obtain the symmetry breaking expression. Using the symmetry breaking expression, the system and method include finding one or more subgraphs of a main graph that match the symmetry breaking expression of the query graph.

IPC Classes  ?

• G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor
• G06F 16/22 - IndexingData structures thereforStorage structures
• G06F 16/2452 - Query translation
• G06F 16/248 - Presentation of query results

Abstract

A system, method, and computer-program product includes receiving, by a controller node, a request to execute a client process associated with a first programming language and a plurality of threads; launching, by the controller node, a plurality of multi-language worker processes based on a number of threads associated with the client process; and instructing, by the controller node, the plurality of multi-language worker processes to execute the plurality of threads associated with the client process.

IPC Classes  ?

• G06F 9/54 - Interprogram communication
• G06N 20/00 - Machine learning

Abstract

A system, method, and computer-program product includes commencing a parent computer process based on receiving a request to perform an analytical operation on one or more datasets, commencing at least one child computer process that is launched by the parent computer process when the parent computer process initiates an execution of the analytical operation on the one or more datasets, transmitting, by the at least one child computer process, a request to the parent computer process to retrieve the one or more datasets, writing, by the parent computer process, the one or more datasets to a cross-process queue based on the parent computer process receiving the requests, reading, by the at least one child computer process, the one or more datasets from the cross-process queue, and executing, using an analytical application executing on the least one child computer process, the analytical operation based on the one or more datasets.

IPC Classes  ?

• G06F 8/35 - Creation or generation of source code model driven
• G06F 8/52 - Binary to binary

Abstract

A system, method, and computer-program product includes commencing a parent computer process based on receiving a request to perform an analytical operation on one or more datasets, commencing at least one child computer process that is launched by the parent computer process when the parent computer process initiates an execution of the analytical operation on the one or more datasets, transmitting, by the at least one child computer process, a request to the parent computer process to retrieve the one or more datasets, writing, by the parent computer process, the one or more datasets to a cross-process queue based on the parent computer process receiving the requests, reading, by the at least one child computer process, the one or more datasets from the cross-process queue, and executing, using an analytical application executing on the least one child computer process, the analytical operation based on the one or more datasets.

IPC Classes  ?

• G06F 21/00 - Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
• G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]
• G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements
• G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities

Abstract

A system, method, and computer-program product includes identifying a plurality of code synthesis items for a target programming language, generating a code synthesis prompt based on a first sampling of the plurality of code synthesis items, synthesizing, via a large language model, a plurality of raw code segments using the code synthesis prompt, executing the plurality of raw code segments with a code interpreter associated with the target programming language, determining one or more valid code segments of the plurality of raw code segments that the code interpreter successfully executed, aggregating, via a second sampling, the one or more valid code segments into one or more validated code synthesis training samples, and training a code generation model using the one or more validated code synthesis training samples. User interfaces may be provided to allow target coding tasks to be specified via text or speech.

IPC Classes  ?

• G06F 11/36 - Prevention of errors by analysis, debugging or testing of software
• G06F 8/35 - Creation or generation of source code model driven
• G06F 11/3604 - Analysis of software for verifying properties of programs

Abstract

In one example, a computer system can generate a graphical user interface (GUI) for forecasting software including a drag-and-drop canvas with a set of rearrangeable nodes defining a forecasting pipeline. The computer system can detect a user interaction for attaching an external-language execution node to the pipeline, which can be used to insert custom code defined using an external programming language. The computer system can receive the custom code. The computer system can receive a user input to initiate execution of the pipeline. The computer system can generate wrapped custom code by augmenting the custom code with additional program code including shared variables. The computer system can provide the wrapped custom code to a set of execution threads configured to execute the wrapped custom code as part of the pipeline to generate one or more forecasts. The computer system can output the forecasts in the GUI.

IPC Classes  ?

• G06F 8/34 - Graphical or visual programming
• G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
• G06F 3/0486 - Drag-and-drop
• G06F 8/41 - Compilation
• G06F 9/54 - Interprogram communication
• G06F 16/25 - Integrating or interfacing systems involving database management systems
• G06F 16/38 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• G06F 40/30 - Semantic analysis
• H04L 65/1101 - Session protocols
• H04L 67/01 - Protocols

Abstract

A system, method, and computer-program product includes obtaining a decisioning dataset comprising a plurality of favorable decisioning records and at least one unfavorable decisioning record; detecting, via a machine learning algorithm, a favorable decisioning record of the plurality of favorable decisioning records that has a vector value closest to a vector value of the unfavorable decisioning record; executing a counterfactual assessment between the favorable decisioning record and the unfavorable decisioning record; generating an explainability artifact based on one or more bias intensity metrics to explain a bias in a machine learning-based decisioning model; and in response to generating the explainability artifact, displaying the explainability artifact in a user interface.

IPC Classes  ?

• G06N 3/08 - Learning methods
• G06F 17/16 - Matrix or vector computation
• G06N 3/0475 - Generative networks
• G06N 5/045 - Explanation of inferenceExplainable artificial intelligence [XAI]Interpretable artificial intelligence

Abstract

A system, method, and computer-program product includes obtaining a decisioning dataset comprising a plurality of favorable decisioning records and at least one unfavorable decisioning record; detecting, via a machine learning algorithm, a favorable decisioning record of the plurality of favorable decisioning records that has a vector value closest to a vector value of the unfavorable decisioning record; executing a counterfactual assessment between the favorable decisioning record and the unfavorable decisioning record; generating an explainability artifact based on one or more bias intensity metrics to explain a bias in a machine learning-based decisioning model; and in response to generating the explainability artifact, displaying the explainability artifact in a user interface.

IPC Classes  ?

• G06N 3/0895 - Weakly supervised learning, e.g. semi-supervised or self-supervised learning
• G06N 3/042 - Knowledge-based neural networksLogical representations of neural networks

Abstract

A computer-implemented method includes receiving a natural language input including a natural language request for executing an analytical task and processing the natural language input by a language model, where the processing may include translating the natural language input to an analytical function call for calling an analytical function of a set of distinct analytical functions of an analytics computing server. Additionally, the computer-implemented method includes calling the analytical function at the analytics computing server using the analytical function call, receiving a technical output in response to calling the analytical function, and outputting a response to the natural language input that includes the technical analytical output.

IPC Classes  ?

• G06F 17/00 - Digital computing or data processing equipment or methods, specially adapted for specific functions
• G06F 40/183 - Tabulation, i.e. one-dimensional positioning
• G06F 40/58 - Use of machine translation, e.g. for multi-lingual retrieval, for server-side translation for client devices or for real-time translation
• G06F 9/451 - Execution arrangements for user interfaces

Abstract

A system, method, and computer-program product includes selecting, by a controller node, a plurality of hyperparameter search points from a hyperparameter search space; instructing, by the controller node, one or more worker nodes to concurrently train a plurality of machine learning models for a target number of epochs using the plurality of hyperparameter search points; receiving, from the one or more worker nodes, a plurality of performance metrics that measure a performance of the plurality of machine learning models during the target number of epochs; and removing, by the controller node, one or more underperforming hyperparameter search points from the plurality of hyperparameter search points according to a pre-defined performance metric ranking criterion associated with the plurality of performance metrics.

IPC Classes  ?

• G06N 20/00 - Machine learning

Abstract

In one example, a system can receive, from application code including an analysis operation performed on a set of data, an indication to access the set of data included in a tabular data structure using an application programming interface (API), in which the tabular data structure is associated with a memory allocation and a type. The system can determine that the type of the tabular data structure is the native type, the native type characterizing data structures that are accessed using a first programming language and a second programming language. The system can identify a proxy data table that shares the memory allocation, the proxy data table accessed using the API based on the second programming language. The system can issue one or more read commands to the proxy data table to cause the set of data to be read from the tabular data structure.

IPC Classes  ?

• G06F 16/22 - IndexingData structures thereforStorage structures
• G06F 9/54 - Interprogram communication
• G06F 16/21 - Design, administration or maintenance of databases

Abstract

In one example, a system can receive information about a tabular data structure in a memory including a set of data and a first memory allocation. The system can determine a type of the tabular data structure, the type selected from among two types including a native type and a non-native type. The system can, in response to the type being the native type, identify a first proxy data table usable as a proxy for the tabular data structure that shares the first memory allocation. The system can receive a first indication to access the set of data from application code. The system can issue one or more first read commands to the first proxy data table to cause the set of data to be read from the tabular data structure.

IPC Classes  ?

• G06F 16/22 - IndexingData structures thereforStorage structures
• G06F 9/54 - Interprogram communication
• G06F 16/21 - Design, administration or maintenance of databases

Abstract

A system, method, and computer-program product includes obtaining, via a graphical user interface: an input of a measured unit distribution derived from measurements, by a measuring device, of a plurality of instances of a physical unit; an input of characteristics of the measuring device used in the measurements of the plurality of instances of the physical unit; and an input of a type of distribution for fitting a set of measurement values of the physical unit to a target distribution; computing, via a unit distribution estimation algorithm, an estimated true unit distribution of the plurality of instances of the physical unit based on (a) the input of the measured unit distribution, (b) the input of the characteristics of the measuring device, and (c) the input of the type of distribution; and using quantitative characteristics of the estimated true unit distribution to mitigate binning classification error of the physical units.

IPC Classes  ?

• G05B 23/02 - Electric testing or monitoring
• G06F 17/18 - Complex mathematical operations for evaluating statistical data

Abstract

A computing device obtains a computer model that predicts a predicted output for a studied system. The device obtains an initial predicted state for an applied system according to initial inputs to the computer model. The device receives a request for derived inputs that will generate, for the applied system, a user-requested change in the initial predicted state. The device generates decision deltas for the computer model. The device determines allowable function inputs to a computer function. The allowable function inputs are derived based on the decision deltas, the user-requested change, and the computer model. The device computes, using one or more of the allowable function inputs, at least one minimum or maximum value for the computer function. The device outputs output information based on the derived inputs that, according to the computer model, will affect the user-requested change in the initial predicted state.

IPC Classes  ?

• G06N 20/00 - Machine learning

Abstract

A system, method, and computer-program product includes implementing a cross-process queue within a single computer that is configured to transfer a data block between an operating system process executing a write operation and an operating system process executing a read operation, initializing in-memory cell indices within the cross-process queue that include a write operation index tracking index values of one or more cells within the cross-process queue that are available to write and a read operation index tracking index values of one or more cells within the cross-process queue that are available to read, and implementing a cell synchronization data structure tracking states of a plurality of cells of the index of cells of the cross-process queue.

IPC Classes  ?

• G06F 3/06 - Digital input from, or digital output to, record carriers

Abstract

A system, method, and computer-program product includes implementing a cross-process queue within a single computer that is configured to transfer a data block between an operating system process executing a write operation and an operating system process executing a read operation, initializing in-memory cell indices within the cross-process queue that include a write operation index tracking index values of one or more cells within the cross-process queue that are available to write and a read operation index tracking index values of one or more cells within the cross-process queue that are available to read, and implementing a cell synchronization data structure tracking states of a plurality of cells of the index of cells of the cross-process queue.

IPC Classes  ?

• G06F 3/06 - Digital input from, or digital output to, record carriers

Abstract

A system, method, and computer-program product includes receiving an input comprising a plurality of pre-defined factor matrices and an implicit feedback dataset partitioned into a plurality of implicit feedback data subsets; distributing the input across a controller node and a plurality of worker nodes implemented in a distributed computing environment; and training a model using the controller node and the plurality of worker nodes, wherein training the model includes: initializing, by the controller node, a controller-specific user parameters matrix and a controller-specific item parameters matrix, broadcasting, by the controller node, the controller-specific user parameters matrix and the controller-specific item parameters matrix to each worker node of the plurality of worker nodes, and concurrently executing an aggregation model training algorithm at the controller node and a plurality of localized model training algorithms across the plurality of worker nodes until a training termination condition is satisfied.

IPC Classes  ?

• G06N 20/00 - Machine learning

Abstract

A system, method, and computer-program product includes implementing a cross-process queue within a single computer that is configured to transfer a data block between an operating system process executing a write operation and an operating system process executing a read operation, initializing in-memory cell indices within the cross-process queue that include a write operation index tracking index values of one or more cells within the cross-process queue that are available to write and a read operation index tracking index values of one or more cells within the cross-process queue that are available to read, and implementing a cell synchronization data structure tracking states of a plurality of cells of the index of cells of the cross-process queue.

IPC Classes  ?

• G06F 9/54 - Interprogram communication
• G06F 9/48 - Program initiatingProgram switching, e.g. by interrupt

Abstract

Embodiments described herein relate to the efficient generation of synthetic datasets that represent many-to-many relationships. In particular, certain embodiments implement a particular factorization for many-to-many generative models, which leads to a scalable generation framework by combining random graph theory and representation learning. Further embodiments we extend the framework to establish the notion of differential privacy within the synthetically generated data. The embodiments described herein are therefore able to generate synthetic datasets efficiently while preserving information within and across many-to-many datasets with improved accuracy.

IPC Classes  ?

• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models

Abstract

A computing device learns a directed acyclic graph (DAG). (A) A target variable is defined from variables based on a topological order vector and a first index. (B) Input variables are defined from the variables based on the topological order vector and a second index. (C) A machine learning model is trained with observation vectors using the target variable and the input variables. (D) The machine learning model is executed to compute a loss value. (E) The second index is incremented. (F) (B) through (E) are repeated a first plurality of times. (G) The first index is incremented. (H) (A) through (G) are repeated a second plurality of times. A parent set is determined for each variable based on a comparison between the loss value computed each repetition of (D). The parent set is output for each variable to describe the DAG that defines a hierarchical relationship between the variables.

IPC Classes  ?

• G06F 17/18 - Complex mathematical operations for evaluating statistical data

Abstract

A computing device learns a best topological order vector for a plurality of variables. (A) A topological order vector is defined. (B) A target variable and zero or more input variables are defined based on the topological order vector. (C) A machine learning model is trained with observation vectors using values of the target variable and the zero or more input variables. (D) The machine learning model is executed with second observation vectors using the values of the target variable and the zero or more input variables to compute a loss value. (E) (A) through (D) are repeated a plurality of times. Each topological order vector defined in (A) is unique in comparison to other topological order vectors defined in (A). The best topological order vector is determined based on a comparison between the loss values computed for each topological order vector in (D).

IPC Classes  ?

• G06F 16/22 - IndexingData structures thereforStorage structures
• G06N 3/04 - Architecture, e.g. interconnection topology
• G06N 3/08 - Learning methods

Abstract

A computing device learns a directed acyclic graph for a plurality of variables. (A) A target variable and zero or more input variables are defined based on a predefined topological order vector and a first index. (B) A machine learning model is trained with observation vectors using the target variable and the input variables. (C) The machine learning model is executed using the observation vectors with the target variable and the input variables to compute a residual vector. (D) The first index is incremented. (E) (A) through (D) are repeated a first plurality of times. A parent set is determined for each variable by comparing the residual vector computed each repetition of (C) to other residual vectors computed on other repetitions of (C). The parent set is output for each variable to describe a directed acyclic graph that defines a hierarchical relationship between the variables.

IPC Classes  ?

• G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
• G06F 17/16 - Matrix or vector computation
• G06N 20/00 - Machine learning

Abstract

A computing device learns a directed acyclic graph (DAG). (A) A target variable is defined from variables based on a topological order vector and a first index. (B) Input variables are defined from the variables based on the topological order vector and a second index. (C) A machine learning model is trained with observation vectors using the target variable and the input variables. (D) The machine learning model is executed to compute a loss value. (E) The second index is incremented. (F) (B) through (E) are repeated a first plurality of times. (G) The first index is incremented. (H) (A) through (G) are repeated a second plurality of times. A parent set is determined for each variable based on a comparison between the loss value computed each repetition of (D). The parent set is output for each variable to describe the DAG that defines a hierarchical relationship between the variables.

IPC Classes  ?

• G06F 17/18 - Complex mathematical operations for evaluating statistical data

Abstract

The computing device trains a first model on a first data set using a first graph to predict relevant links between a plurality of nodes. The computing device applies the trained first model to the one or more links between the plurality of nodes from a first node, iteratively connects each node to the one or more first sets of generated networks for each of the relevant links until the relevant links for connection to the plurality of nodes are not present, and outputs the one or more first sets of generated networks. The computing device also applies the trained first model to the one or more links between the plurality of nodes, removes the non-relevant links, connects each node of the plurality of nodes with the relevant links to generate one or more second sets of networks, and outputs the one or more second sets of generated networks.

IPC Classes  ?

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

Abstract

The computing device trains a first model on a first data set using a first graph to predict relevant links between a plurality of nodes. The computing device obtains the first data set or a second data set associated with the plurality of nodes. The computing device determines the one or more features for the one or more links between the plurality of nodes, applies the trained first model to the one or more links between the plurality of nodes, outputs the relevant links and non-relevant links of the one or more links between the plurality of nodes, removes the non-relevant links between the plurality of nodes, connects each node of the plurality of nodes with the relevant links to generate one or more first sets of networks, and outputs the one or more first sets of generated networks.

IPC Classes  ?

• G06N 5/01 - Dynamic search techniquesHeuristicsDynamic treesBranch-and-bound

Abstract

In some examples, a system can store a first array, which is a one-dimensional array of values (e.g., matrix values), in memory. The system can also store a second array in the memory, where the second array is a one-dimensional array of pointers that point to positions of a subset of the values in the first array. The subset of values can be a first entry of each row or column of a matrix. The system can then provide the second array as input to a program routine, which can perform a matrix operation. To do so, the program routine can access the first array and the second array in memory, select a set of values for the matrix from the first array by using the pointers, execute the matrix operation using the using the selected set of values, and output the result.

IPC Classes  ?

• G06F 17/16 - Matrix or vector computation

Abstract

A computer-program product, computer-implemented method, and computer-implemented system includes obtaining a raw dataset; executing an outlier filtration process based on obtaining the raw dataset; training a model using a refined outlier-reduced dataset; and predicting, via the trained model, a value of the target entity at a future time.

IPC Classes  ?

• G06N 20/00 - Machine learning

Abstract

A system and method include receiving a first set of variables associated with a real-time request, extracting a predetermined subset of the first set of variables for generating a second set of variables, identifying historical request data, computing a set of parameters based on the first set of variables and the historical request data, generating a plurality of numeric sequences and a plurality of string sequences for the real-time request, converting each of the plurality of string sequences into an encoded string sequence to obtain a plurality of encoded string sequences, inputting the plurality of numeric sequences and the plurality of encoded string sequences into a trained deep machine learning model, and computing a score from the trained deep machine learning model, the score indicative of a likelihood that the real-time request belongs to an unauthorized classification.

IPC Classes  ?

• G06N 3/086 - Learning methods using evolutionary algorithms, e.g. genetic algorithms or genetic programming
• G06F 18/21 - Design or setup of recognition systems or techniquesExtraction of features in feature spaceBlind source separation
• G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
• G06F 18/22 - Matching criteria, e.g. proximity measures
• G06N 3/045 - Combinations of networks
• G06N 3/084 - Backpropagation, e.g. using gradient descent

Abstract

A graphical user interface (GUI) and pipeline for processing text documents is provided herein. In one example, a system can receive unstructured text documents. The system can determine entity-issue descriptions corresponding to the unstructured text documents. The system can then generate a GUI indicating the entity-issue descriptions. The GUI can also indicate assignments of the unstructured text documents to categories of a predefined schema. The GUI can allow the user to adjust the assignments of the unstructured text documents to the categories. The GUI can also include a table of rows, where each row corresponds to one of the unstructured text documents. Each row can indicate an entity-issue description in the corresponding unstructured text document and the categories assigned to the unstructured text document. Each row can also include a graphical button that is selectable to allow the user to view the unstructured text document corresponding to the row.

IPC Classes  ?

• G06F 16/34 - BrowsingVisualisation therefor

Abstract

A graphical user interface (GUI) and pipeline for processing text documents is provided herein. In one example, a system can receive unstructured text documents. The system can determine entity-issue descriptions corresponding to the unstructured text documents. The system can then generate a GUI indicating the entity-issue descriptions. The GUI can also indicate assignments of the unstructured text documents to categories of a predefined schema. The GUI can allow the user to adjust the assignments of the unstructured text documents to the categories. The GUI can also include a table of rows, where each row corresponds to one of the unstructured text documents. Each row can indicate an entity-issue description in the corresponding unstructured text document and the categories assigned to the unstructured text document. Each row can also include a graphical button that is selectable to allow the user to view the unstructured text document corresponding to the row.

IPC Classes  ?

• G06F 16/34 - BrowsingVisualisation therefor

Abstract

A computing system trains a classification model using distributed training data. A first worker index and a second worker index are received from a controller device and together uniquely identify a segment of a lower triangular matrix. The first and second worker indices have values from one to a predefined block size value. In response to receipt of a first computation request from the controller device, a first kernel matrix block is computed at each computing device based on the first worker index and the second worker index. In response to receipt of a second computation request from the controller device, an objective function value is computed for each observation vector included in an accessed training data subset. The computed objective function value is sent to the controller device. Model parameters for a trained classification model are output.

IPC Classes  ?

• G06N 3/098 - Distributed learning, e.g. federated learning
• G06N 5/04 - Inference or reasoning models
• G06N 20/10 - Machine learning using kernel methods, e.g. support vector machines [SVM]
• G06N 20/00 - Machine learning

Abstract

A computing system is configured to receive, at a service entity, from a data exchange entity, an execution command indicating to store an instance of a data program in a memory portion of the computing system by storing computer instructions based on an external data program of an external computing system. The computing system is configured to receive, at a service entity, from a data exchange entity, an indication of availability of the input data. The input data is available for use by the instance of the data program. The computing system is configured to send from the service entity an indication of availability of the output data. The output data is generated based on execution of the instance of the data program.

IPC Classes  ?

• G06F 15/173 - Interprocessor communication using an interconnection network, e.g. matrix, shuffle, pyramid, star or snowflake
• G06F 16/11 - File system administration, e.g. details of archiving or snapshots
• G06F 16/178 - Techniques for file synchronisation in file systems
• 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

Abstract

A system, method, and computer-program product includes obtaining an analytical request that specifies an analytical task to be performed using computing resources of an adaptive analytics compute service, determining, by the adaptive analytics compute service, an initial set of compute resources for executing the analytical request based on identifying a type of the analytical request, deploying, by the adaptive analytics compute service, a compute environment for executing the analytical request based on the initial set of compute resources, observing utilization data of the initial set of compute resources during a period of executing the analytical request within the compute environment, and commencing a machine learning-informed feedback sequence for autonomously adapting the compute environment, wherein one iteration of the machine learning-informed feedback sequence includes: generating a proposed set of compute resources, and encoding, based on the proposed set of compute resources, a set of instructions for automatically adapting the compute environment.

IPC Classes  ?

• G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]
• G06F 9/455 - EmulationInterpretationSoftware simulation, e.g. virtualisation or emulation of application or operating system execution engines
• G06F 9/48 - Program initiatingProgram switching, e.g. by interrupt
• G06F 11/30 - Monitoring

Abstract

In one example, a system can receive information about a data structure including a set of data entries. The system can generate a proxy data table including a set of columns. The system can use a data access layer to generate a mapping from the data entries to the columns. The system can receive an input to cause an operation to be performed on the data structure by performing the operation on the data structure. Generating a result can involve issuing read commands to the data access layer to perform the operation on the data structure such that the data access layer obtains the associated data entries and provides them as responses to the read commands by performing a translation between the data entries and the columns based on the mapping. The system can then output the result of the operation.

IPC Classes  ?

• G06F 16/2452 - Query translation
• G06F 8/41 - Compilation

Abstract

In one example, a system can receive an input from a user indicating a target variable to be forecasted over a future time window. The system can then determine independent variables that influence the target variable and generate a set of candidate variables, including combinations of the independent variables. The system can then execute a random forest classifier to identify a subset of candidate variables having a threshold level of influence on the target variable. The system can then construct a machine-learning model configured to receive the identified subset of candidate variables as inputs and generate a forecast of the target variable. After constructing the machine-learning model, the system can train the machine-learning model using historical data and then execute the machine-learning model to generate the forecast.

IPC Classes  ?

• G06N 3/045 - Combinations of networks
• G06N 20/20 - Ensemble learning

Abstract

A graphical user interface (GUI) and pipeline for processing text documents is provided herein. In one example, a system can receive unstructured text documents. The system can determine entity-issue descriptions corresponding to the unstructured text documents. The system can then generate a GUI indicating the entity-issue descriptions. The GUI can also indicate assignments of the unstructured text documents to categories of a predefined schema. The GUI can allow the user to adjust the assignments of the unstructured text documents to the categories. The GUI can also include a table of rows, where each row corresponds to one of the unstructured text documents. Each row can indicate an entity-issue description in the corresponding unstructured text document and the categories assigned to the unstructured text document. Each row can also include a graphical button that is selectable to allow the user to view the unstructured text document corresponding to the row.

IPC Classes  ?

• G06F 16/34 - BrowsingVisualisation therefor

Abstract

Techniques described herein provide for automated detection of near-duplicate documents. In one example, a system can cluster documents into a set of clusters based on character frequencies associated with the documents. For a given cluster, the system can generate first similarity scores associated with every pair of documents in the cluster. The system can then select a filtered group of documents associated with first similarity scores that meet or exceed a first predefined similarity threshold. Next, the system can convert the filtered group of documents into matrix representations. The system can generate second similarity scores for every pair of matrix representations. The system can then identify documents, from among the filtered group of documents, associated with second similarity scores that meet or exceed a second predefined similarity threshold. The identified documents can be duplicate or near-duplicate text documents.

IPC Classes  ?

• G06F 7/02 - Comparing digital values
• G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor
• G06F 16/906 - ClusteringClassification
• G06F 16/93 - Document management systems
• G06F 16/35 - ClusteringClassification

Abstract

A treatment model trained to compute an estimated treatment variable value for each observation vector of a plurality of observation vectors is executed. Each observation vector includes covariate variable values, a treatment variable value, and an outcome variable value. An outcome model trained to compute an estimated outcome value for each observation vector using the treatment variable value for each observation vector is executed. A standard error value associated with the outcome model is computed using a first variance value computed using the treatment variable value of the plurality of observation vectors, using a second variance value computed using the treatment variable value and the estimated treatment variable value of the plurality of observation vectors, and using a third variance value computed using the estimated outcome value of the plurality of observation vectors. The standard error value is output.

IPC Classes  ?

• G06N 3/04 - Architecture, e.g. interconnection topology
• G06N 3/045 - Combinations of networks
• G06N 3/08 - Learning methods
• G06N 3/084 - Backpropagation, e.g. using gradient descent

Abstract

A point estimate value for an individual is computed using a Bayesian neural network model (BNN) by training a first BNN model that computes a weight mean value, a weight standard deviation value, a bias mean value, and a bias standard deviation value for each neuron of a plurality of neurons using observations. A plurality of BNN models is instantiated using the first BNN model. Instantiating each BNN model of the plurality of BNN models includes computing, for each neuron, a weight value using the weight mean value, the weight standard deviation value, and a weight random draw and a bias value using the bias mean value, the bias standard deviation value, and a bias random draw. Each instantiated BNN model is executed with the observations to compute a statistical parameter value for each observation vector of the observations. The point estimate value is computed from the statistical parameter value.

IPC Classes  ?

• G06N 3/04 - Architecture, e.g. interconnection topology
• G06N 3/045 - Combinations of networks
• G06N 3/08 - Learning methods
• G06N 3/084 - Backpropagation, e.g. using gradient descent

Abstract

A method, system, and computer-program product includes identifying a set of heterogeneous sensors, configuring a plurality of model training compositions for each of the set of heterogeneous sensors, computing, for each of the plurality of model training compositions, a first efficacy metric value based on predictive outputs of the at least two machine learning models, identifying, for each sensor of the set of heterogeneous sensors, a champion model training composition of the subject sensor, the champion model training composition having a highest efficacy metric value, and electing, from a plurality of champion model training compositions corresponding to the champion model training compositions identified for each sensor of the set of heterogeneous sensors, an overall champion model training composition corresponding to a champion sensor of the set of heterogeneous sensors based on an assessment of second efficacy metric values of the plurality of champion model training compositions.

IPC Classes  ?

• G06N 20/00 - Machine learning
• G06F 18/20 - Analysing
• G06F 18/2135 - Feature extraction, e.g. by transforming the feature spaceSummarisationMappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
• G06N 3/09 - Supervised learning

Abstract

A system, method, and computer-program product includes receiving speech audio of a multi-turn conversation, generating, via a speech-to-text process, a transcript of the speech audio, wherein the transcript of the speech audio textually segments speech spoken during the multi-turn conversation into a plurality of utterances, generating a speaker diarization prompt that includes contextual information about a plurality of speakers participating in the multi-turn conversation, inputting, to a large language model, the speaker diarization prompt and the transcript of the speech audio, and obtaining, from the large language model, an output comprising an enhanced transcript of the speech audio, wherein the enhanced transcript of the speech audio textually segments the speech spoken during the multi-turn conversation into a plurality of refined utterances and associates a speaker identification value with each of the plurality of refined utterances.

IPC Classes  ?

• G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
• G10L 15/02 - Feature extraction for speech recognitionSelection of recognition unit
• G10L 15/04 - SegmentationWord boundary detection
• G10L 15/26 - Speech to text systems
• G10L 25/30 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the analysis technique using neural networks
• G10L 25/78 - Detection of presence or absence of voice signals

Abstract

In some examples a system can receive sets of usage data from agent computer systems associated with agents. The agents can be associated with service providers that provide services to service users. The system can generate a corresponding set of metric values for a common set of metrics for each agent based on a corresponding set of usage data. The common set of metrics can be used for all of the agents to detect anomalies related to the agents. The system can generate a score for each agent based on the corresponding set of metric values, wherein the score indicates a risk level associated with the agent. The system can compare the scores for the agents to a predefined threshold to identify one or more agents that may be problematic. The system can then generate a graphical user interface indicating the one or more identified agents.

IPC Classes  ?

• H04L 41/06 - Management of faults, events, alarms or notifications
• H04L 41/046 - Network management architectures or arrangements comprising network management agents or mobile agents therefor
• H04L 41/22 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks comprising specially adapted graphical user interfaces [GUI]

Abstract

Parameter values in source code can be automatically validated using the techniques described herein. For example, a system can receive source code that includes a call to an action. The action can have a parameter that is set to a selected value in the source code. The parameter can be defined in definition data. The system can also receive a file that separate from the source code and includes metadata for the parameter. The system can extract the metadata from the file and modify the definition data to include the metadata. The system can then execute a validation process on the selected value for the parameter. The validation process can involve retrieving the metadata from the modified definition data, evaluating the selected value using the metadata to determine whether the selected value is invalid, and if it is invalid, outputting an error notification indicating that the selected value is invalid.

IPC Classes  ?

• G06F 11/36 - Prevention of errors by analysis, debugging or testing of software

Abstract

A method, system, and computer-program product includes identifying a set of heterogeneous sensors, configuring a plurality of model training compositions for each of the set of heterogeneous sensors, computing, for each of the plurality of model training compositions, a first efficacy metric value based on predictive outputs of the at least two machine learning models, identifying, for each sensor of the set of heterogeneous sensors, a champion model training composition of the subject sensor, the champion model training composition having a highest efficacy metric value, and electing, from a plurality of champion model training compositions corresponding to the champion model training compositions identified for each sensor of the set of heterogeneous sensors, an overall champion model training composition corresponding to a champion sensor of the set of heterogeneous sensors based on an assessment of second efficacy metric values of the plurality of champion model training compositions.

IPC Classes  ?

• G06N 3/09 - Supervised learning
• G06F 18/20 - Analysing
• G06F 18/2135 - Feature extraction, e.g. by transforming the feature spaceSummarisationMappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis

Abstract

A computing device learns a best topological order vector of a plurality of variables. A target variable and zero or more input variables are defined. (A) A machine learning model is trained with observation vectors using the target variable and the zero or more input variables. (B) The machine learning model is executed to compute an equation loss value. (C) The equation loss value is stored with the identifier. (D) The identifier is incremented. (E) (A) through (D) are repeated a plurality of times. (F) A topological order vector is defined. (G) A loss value is computed from a subset of the stored equation loss values based on the topological order vector. (F) through (G) are repeated for each unique permutation of the topological order vector. A best topological order vector is determined based on a comparison between the loss value computed for each topological order vector in (G).

IPC Classes  ?

• G06F 17/18 - Complex mathematical operations for evaluating statistical data

Abstract

A computing device trains a fair prediction model while defining an optimal event cutoff value. (A) A prediction model is trained with observation vectors. (B) The prediction model is executed to define a predicted target variable value and a probability associated with an accuracy of the predicted target variable value. (C) A conditional moments matrix is computed based on fairness constraints, the predicted target variable value, and the sensitive attribute variable value of each observation vector. The predicted target variable value has a predefined target event value only when the probability is greater than a predefined event cutoff value. (D) (A) through (C) are repeated. (E) An updated value is computed for the predefined event cutoff value. (F) (A) through (E) are repeated. An optimal event cutoff value is defined from the predefined event cutoff values used when repeating (A) through (E). The optimal value and prediction model are output.

IPC Classes  ?

• G06N 3/08 - Learning methods
• G06N 5/022 - Knowledge engineeringKnowledge acquisition

Abstract

A system, method, and computer-program product includes detecting, via a localization machine learning model, a target object within target image data of a scene, classifying, via an object classification machine learning model, the target object to a probable object class of a plurality of distinct object classes, routing, via the one or more processors, the target image data of the scene to a target object-condition machine learning classification model of a plurality of distinct object-condition machine learning classification models based on a mapping between the plurality of distinct object classes and the plurality of distinct object-condition machine learning classification models, classifying, via the target object-condition machine learning classification model, the target object to a probable object-condition class of a plurality of distinct object-condition classes, and displaying, via a graphical user interface, a representation of the target object in association with the probable object-condition class.

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 10/94 - Hardware or software architectures specially adapted for image or video understanding

Abstract

The computing device receives data for a plurality of events that includes a timestamp associated with a digital traffic campaign in an event processing system. Based on the timestamp of the data for each event, the computing device executes operations comprising: applying filtering using digital signal processing to the event count for the combined data for each of the one or more intervals, executing a model to compute one or more backward difference approximations for the one or more candidate systems time constants from the evaluated exponential curve, and selecting a system time constant that predicts a first time instant wherein the data for the plurality of events approaches a point on a horizontal asymptote for the evaluated exponential curve. The computing device determines an epoch for the selected system time constant and outputs the determined epoch for the selected system time constant in the graphical user interface.

IPC Classes  ?

• G06Q 30/0242 - Determining effectiveness of advertisements
• G06Q 30/0201 - Market modellingMarket analysisCollecting market data

Abstract

A computer determines a solution to a nonlinear optimization problem. A conjugate gradient (CG) iteration is performed with a first order derivative vector and a second order derivative matrix to update a CG residual vector, an H-conjugate vector, and a residual weight vector. A CG solution vector is updated using a previous CG solution vector, the H-conjugate vector, and the residual weight vector. An eigenvector of the second order derivative matrix having a smallest eigenvalue is computed. A basis matrix is defined that includes a cubic regularization (CR) solution vector, a CR residual vector, the CG solution vector, the CG residual vector, and the eigenvector. A CR iteration is performed to update the CR solution vector. The CR residual vector is updated using the first order derivative vector, the second order derivative matrix, and the updated CR solution vector. The process is repeated until a stop criterion is satisfied.

IPC Classes  ?

• G06N 3/08 - Learning methods
• G06F 17/16 - Matrix or vector computation

Abstract

A system, method, and computer-program product includes displaying a plurality of factor-setting user interface (UI) control elements configured to receive an input of characters for specifying a set of design of experiment factors for creating a design of experiment (DOE), displaying a plurality of factor type UI control elements configured to receive input for specifying a factor type of a plurality of factor types, displaying a plurality of dynamic rows of editable UI control elements configured to receive inputs of experimental values for the set of DOE factors, and displaying a composite factor UI control component configured to receive inputs for generating one or more control signals that add or remove one or more DOE factors of the set of DOE factors.

IPC Classes  ?

• G06F 30/12 - Geometric CAD characterised by design entry means specially adapted for CAD, e.g. graphical user interfaces [GUI] specially adapted for CAD
• G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
• G06F 3/04847 - Interaction techniques to control parameter settings, e.g. interaction with sliders or dials

Abstract

One example described herein involves a system that can receive a set of data records and execute an automated entity resolution (AER) process configured to assign the set of data records to a set of entities. For each entity in the set of entities, the system can generate a respective consistency score for the entity, generate a respective confidence score for the entity based on the respective consistency score for the entity, and determine a respective visual indicator based on the respective confidence score for the entity. The respective visual indicator can indicate a risk of record misassignment to a user. The system can then generate a graphical user interface that includes the respective visual indicator for each of the entities.

IPC Classes  ?

• G06F 16/21 - Design, administration or maintenance of databases
• G06F 16/215 - Improving data qualityData cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
• G06F 16/23 - Updating

Abstract

One example described herein involves a system that can receive a set of data records and execute an automated entity resolution (AER) process configured to assign the set of data records to a set of entities. For each entity in the set of entities, the system can generate a respective consistency score for the entity, generate a respective confidence score for the entity based on the respective consistency score for the entity, and determine a respective visual indicator based on the respective confidence score for the entity. The respective visual indicator can indicate a risk of record misassignment to a user. The system can then generate a graphical user interface that includes the respective visual indicator for each of the entities.

IPC Classes  ?

• G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor
• G06F 16/215 - Improving data qualityData cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
• G06F 16/23 - Updating

Abstract

A heart-rate detection system can receive heartbeat data generated by a wearable heart-rate sensor worn by a wearer. The system can then execute a noise-reduction process for reducing noise in the heartbeat data. The noise-reduction process can involve applying a lowpass filter to the heartbeat data, generating wavelet coefficients by applying a wavelet transform to the filtered heartbeat data, and generating a reduced set of wavelet coefficients by thresholding the wavelet coefficients. An inverse wavelet signal can then be generated by applying an inverse wavelet transform to the reduced set of wavelet coefficients. R-peaks can be identified by performing peak detection on the instantaneous amplitudes of the data points in the inverse wavelet signal. A heart rate curve can then be generated based on the R-peaks and modified by applying a Hampel filter. Heartbeat data can then be generated based on the modified heart rate curve for output.

IPC Classes  ?

• A61B 5/00 - Measuring for diagnostic purposes Identification of persons
• A61B 5/024 - Measuring pulse rate or heart rate
• G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
• 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

Abstract

A system, method, and computer-program product includes displaying a plurality of factor-setting user interface (UI) control elements configured to receive an input of characters for specifying a set of design of experiment factors for creating a design of experiment (DOE), displaying a plurality of factor type UI control elements configured to receive input for specifying a factor type of a plurality of factor types, displaying a plurality of dynamic rows of editable UI control elements configured to receive inputs of experimental values for the set of DOE factors, and displaying a composite factor UI control component configured to receive inputs for generating one or more control signals that add or remove one or more DOE factors of the set of DOE factors.

IPC Classes  ?

• G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
• G06F 3/04847 - Interaction techniques to control parameter settings, e.g. interaction with sliders or dials
• G06F 30/20 - Design optimisation, verification or simulation

Abstract

A computing device determines a node traversal order for computing a computational parameter value for each node of a data model of a system that includes a plurality of disconnected graphs. The data model represents a flow of a computational parameter value through the nodes from a source module to an end module. A flow list defines an order for selecting and iteratively processing each node to compute the computational parameter value in a single iteration through the flow list. Each node from the flow list is selected to compute a driver quantity for each node. Each node is selected from the flow list in a reverse order to compute a driver rate and the computational parameter value for each node. The driver quantity or the computational parameter value is output for each node to predict a performance of the system.

IPC Classes  ?

• G06F 15/82 - Architectures of general purpose stored program computers data or demand driven
• G06F 11/34 - Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation

Abstract

A computing device identifies an anomaly among a plurality of observation vectors. An observation vector is projected using a predefined orthogonal complement matrix. The predefined orthogonal complement matrix is determined from a decomposition of a low-rank matrix. The low-rank matrix is computed using a robust principal component analysis algorithm. The projected observation vector is multiplied by a predefined demixing matrix to define a demixed observation vector. The predefined demixing matrix is computed using an independent component analysis algorithm and the predefined orthogonal complement matrix. A detection statistic value is computed from the defined, demixed observation vector. When the computed detection statistic value is greater than or equal to a predefined anomaly threshold value, an indicator is output that the observation vector is an anomaly.

IPC Classes  ?

• G06N 5/022 - Knowledge engineeringKnowledge acquisition
• G06N 20/00 - Machine learning
• G06F 17/16 - Matrix or vector computation

Abstract

A computer-implemented system includes identifying a target hierarchical taxonomy comprising a plurality of distinct hierarchical taxonomy categories; extracting a plurality of distinct taxonomy tokens from the plurality of distinct hierarchical taxonomy categories; computing a taxonomy vector corpus based on the plurality of distinct taxonomy tokens; computing a plurality of distinct taxonomy clusters based on an input of the taxonomy vector corpus; constructing a hierarchical taxonomy classifier based on the plurality of distinct taxonomy clusters; converting a volume of unlabeled structured datasets to a plurality of distinct corpora of taxonomy-labeled structured datasets based on the hierarchical taxonomy classifier; and outputting at least one corpus of taxonomy-labeled structured datasets of the plurality of distinct corpora of taxonomy-labeled structured datasets based on an input of a data classification query.

IPC Classes  ?

• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
• G06F 16/242 - Query formulation
• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models

Abstract

A computer monitors a status of grid devices using sensor measurements. Sensor data is clustered using a predefined grouping distance value to define one or more sensor event clusters. A plurality of monitored devices is clustered using a predefined clustering distance value to define one or more asset clusters. A location is associated with each monitored device of the plurality of monitored devices. A distance is computed between each sensor event cluster and each asset cluster. When the computed distance is less than or equal to a predefined asset/sensor distance value for a sensor event cluster and an asset cluster, an asset identifier of the asset cluster associated with the computed distance is added to an asset event list. For each asset cluster included in the asset event list, an asset location of an asset is shown on a map in a graphical user interface presented in a display.

IPC Classes  ?

• G01R 31/08 - Locating faults in cables, transmission lines, or networks
• G06Q 30/01 - Customer relationship services
• H02J 13/00 - Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the networkCircuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network

Abstract

Anomalies in a target object can be detected and diagnosed using improved Mahalanobis-Taguchi system (MTS) techniques. For example, an anomaly detection and diagnosis (ADD) system can receive a set of measurements associated with attributes of a target object. A Mahalanobis distance (MD) can be determined using a generalized inverse matrix. An abnormal condition can be detected when the MD is greater than a predetermined threshold value. The ADD system can determine an importance score for each measurement of a corresponding attribute. The attribute whose measurement has the highest importance score can be determined to be responsible for the abnormal condition.

IPC Classes  ?

• G06F 17/16 - Matrix or vector computation

Abstract

A system, method, and computer-program product includes detecting, via a localization machine learning model, a target object within a scene based on downsampled image data of the scene, identifying a likely position of the target object within original image data of the scene, extracting, from the original image data of the scene, a target sub-image containing the target object, classifying, via an object classification machine learning model, the target object to a probable object class of a plurality of distinct object classes, routing the target image resolution of the target sub-image to a target object-condition machine learning classification model of a plurality of distinct object-condition machine learning classification models, classifying, via the target object-condition machine learning classification model, the target object to a probable object-condition class, and displaying, via a graphical user interface, a representation of the target object in association with the probable object-condition class.

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
• G06T 3/40 - Scaling of whole images or parts thereof, e.g. expanding or contracting
• G06T 7/70 - Determining position or orientation of objects or cameras
• G06T 7/11 - Region-based segmentation

Abstract

A system, method, and computer-program product includes constructing a transcript adaptation training data corpus that includes a plurality of transcript normalization training data samples, wherein each of the plurality of transcript normalization training data samples includes: a predicted audio transcript that includes at least one numerical expression, an adapted audio transcript that includes an alphabetic representation of the at least one numerical expression, and a transcript normalization identifier that, when applied to a model input comprising a target audio transcript, defines a text-to-text transformation objective causing a numeric-to-alphabetic expression machine learning model to predict an alphabetic-equivalent audio transcript that represents each numerical expression included in the target audio transcript in one or more alphabetic tokens; configuring the numeric-to-alphabetic expression machine learning model based on a training of a machine learning text-to-text transformer model using the transcript adaptation training data corpus; and executing the numeric-to-alphabetic expression machine learning model.

IPC Classes  ?

• G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
• G10L 15/02 - Feature extraction for speech recognitionSelection of recognition unit
• G10L 15/04 - SegmentationWord boundary detection
• G10L 15/26 - Speech to text systems
• G10L 25/30 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the analysis technique using neural networks
• G10L 25/78 - Detection of presence or absence of voice signals

Abstract

A system, method, and computer-program product includes constructing a transcript correction training data corpus that includes a plurality of labeled audio transcription training data samples, wherein each of the plurality of labeled audio transcription training data samples includes: an incorrect audio transcription of a target piece of audio data; a correct audio transcription of the target piece of audio data; and a transcript correction identifier that, when applied to a model input that includes a likely incorrect audio transcript, defines a text-to-text transformation objective causing an audio transcript correction machine learning model to predict a corrected audio transcript based on the likely incorrect audio transcript; configuring the audio transcript correction machine learning model based on a training of a machine learning text-to-text transformer model using the transcript correction training data corpus; and executing the audio transcript correction machine learning model within a speech-to-text post-processing sequence of a speech-to-text service.

IPC Classes  ?

• G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
• G10L 15/02 - Feature extraction for speech recognitionSelection of recognition unit
• G10L 15/04 - SegmentationWord boundary detection
• G10L 15/26 - Speech to text systems
• G10L 25/30 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the analysis technique using neural networks
• G10L 25/78 - Detection of presence or absence of voice signals

Abstract

A computing device trains a fair prediction model. A prediction model is trained and executed with observation vectors. A weight value is computed for each observation vector based on whether the predicted target variable value of a respective observation vector of the plurality of observation vectors has a predefined target event value. An observation vector is relabeled based on the computed weight value. The prediction model is retrained with each observation vector weighted by a respective computed weight value and with the target variable value of any observation vector that was relabeled. The retrained prediction model is executed. A conditional moments matrix is computed. A constraint violation matrix is computed. Computing the weight value through computing the constraint violation matrix is repeated until a stop criterion indicates retraining of the prediction model is complete. The retrained prediction model is output.

IPC Classes  ?

• G06N 3/08 - Learning methods
• G06N 20/00 - Machine learning

Abstract

A computer-implemented system includes identifying a target hierarchical taxonomy comprising a plurality of distinct hierarchical taxonomy categories; extracting a plurality of distinct taxonomy tokens from the plurality of distinct hierarchical taxonomy categories; computing a taxonomy vector corpus based on the plurality of distinct taxonomy tokens; computing a plurality of distinct taxonomy clusters based on an input of the taxonomy vector corpus; constructing a hierarchical taxonomy classifier based on the plurality of distinct taxonomy clusters; converting a volume of unlabeled structured datasets to a plurality of distinct corpora of taxonomy-labeled structured datasets based on the hierarchical taxonomy classifier; and outputting at least one corpus of taxonomy-labeled structured datasets of the plurality of distinct corpora of taxonomy-labeled structured datasets based on an input of a data classification query.

IPC Classes  ?

• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models

Abstract

A parallel processing technique can be used to expedite reconciliation of a hierarchy of forecasts on a computer system. As one example, the computer system can receive forecasts that have a hierarchical relationship with respect to one another. The computer system can distribute the forecasts among a group of computing nodes by time point, so that all data points corresponding to the same time point in the forecasts are assigned to the same computing node. The computing nodes can receive the datasets corresponding to the time points, organize the data points in each of the datasets by forecast to generate ordered datasets, and assign the ordered datasets to processing threads. The processing threads (across the computing nodes) can then execute a reconciliation process in parallel to one another to generate reconciled values, which can be output by the computing nodes.

IPC Classes  ?

• G06F 9/46 - Multiprogramming arrangements
• G06F 9/52 - Program synchronisationMutual exclusion, e.g. by means of semaphores

Abstract

A computing system detects a defective object. An image is received of a manufacturing line that includes objects in a process of being manufactured. Each pixel included in the image is classified as a background pixel class, a non-defective object class, or a defective object class using a trained neural network model. The pixels included in the image that were classified as the non-defective object class or the defective object class are grouped into polygons. Each polygon is defined by a contiguous group of pixels classified as the non-defective object class or the defective object class. Each polygon is classified in the non-defective object class or in the defective object class based on a number of pixels included in a respective polygon that are classified in the non-defective object class relative to a number of pixels included in the respective polygon that are classified in the defective object class.

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 10/24 - Aligning, centring, orientation detection or correction of the image
• G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• G06T 7/00 - Image analysis
• G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion

Abstract

A system, method, and computer-program product includes distributing a plurality of audio data files of a speech data corpus to a plurality of computing nodes that each implement a plurality of audio processing threads, executing the plurality of audio processing threads associated with each of the plurality of computing nodes to detect a plurality of tentative speakers participating in each of the plurality of audio data files, generating, via a clustering algorithm, a plurality of clusters of embedding signatures based on a plurality of embedding signatures associated with the plurality of tentative speakers in each of the plurality of audio data files, and detecting a plurality of global speakers associated with the speech data corpus based on the plurality of clusters of embedding signatures.

IPC Classes  ?

• G10L 17/00 - Speaker identification or verification techniques
• G10L 15/16 - Speech classification or search using artificial neural networks
• G10L 15/26 - Speech to text systems
• G10L 15/04 - SegmentationWord boundary detection
• G10L 25/78 - Detection of presence or absence of voice signals
• G10L 25/30 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the analysis technique using neural networks
• G10L 15/02 - Feature extraction for speech recognitionSelection of recognition unit

Abstract

A computer system can automatically analyze a video of a physical activity and provide corresponding feedback. For example, the system can receive a video file including image frames showing an entity performing a physical activity that involves a sequence of movement phases. The system can generate coordinate sets by performing image analysis on the image frames. The system can provide the coordinate sets as input to a trained model, the trained model being configured to assign scores and movement phases to the image frames based on the coordinate sets. The system can then select a particular movement phase for which to provide feedback, based on the scores and movement phases assigned to the image frames. The system can generate the feedback for the entity about their performance of the particular movement phase, which may improve the entity's future performance of that particular movement phase.

IPC Classes  ?

• G06V 40/20 - Movements or behaviour, e.g. gesture recognition
• G06T 9/00 - Image coding
• 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
• G06N 3/042 - Knowledge-based neural networksLogical representations of neural networks
• G06N 3/047 - Probabilistic or stochastic networks
• G06N 3/09 - Supervised learning
• G06T 7/246 - Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
• G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
• G06N 3/0455 - Auto-encoder networksEncoder-decoder networks

Abstract

An apparatus includes at least one node device to host a computing cluster, and at least one processor to generate a UI providing guidance through a set of configuration settings for the computing cluster, wherein, for each configuration setting that is received as an input during configuration, the at least one processor is caused to: perform a check of the set of configuration settings to determine whether the received configuration setting creates a conflict among the set of configuration settings; and in response to a determination that the received configuration setting creates a conflict among the set of configuration settings, perform operations including generate an indication of the conflict for presentation by the UI, and receive a change to a configuration setting as an input from the input device.

IPC Classes  ?

• G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]
• G06F 9/451 - Execution arrangements for user interfaces
• G06F 9/48 - Program initiatingProgram switching, e.g. by interrupt
• G06F 9/455 - EmulationInterpretationSoftware simulation, e.g. virtualisation or emulation of application or operating system execution engines

Abstract

An apparatus includes a processor to receive a request to provide a view of an object associated with a job flow, and in response to determining that the object is associated with a task type requiring access to a particular resource not accessible to a first interpretation routine: store, within a job queue, a job flow generation request message to cause generation of a job flow definition the defines another job flow for generating the requested view; within a task container in which a second interpretation routine that does have access to the particular resource is executed, generate the job flow definition; store, within a task queue, a job flow generation completion message that includes a copy of the job flow definition; use the job flow definition to perform the other job flow to generate the requested view; and transmit the requested view to the requesting device.

IPC Classes  ?

• G06F 9/48 - Program initiatingProgram switching, e.g. by interrupt

Abstract

Groups of connected nodes in a network of nodes can be detected for evaluating and mitigating risks of the network of nodes. For example, a system can process one or more subnetworks of the network of nodes in parallel. For each subnetwork, the system can identify root nodes and their reachable nodes to create rooted groups of connected nodes. The system then can determine outdegrees of the remaining nodes in the network. The system can identify reachable nodes from a remaining node of the highest outdegree to create a nonrooted group of connected nodes. The system can estimate a risk value based on the number of rooted groups and nonrooted groups, the number of nodes in each rooted group and nonrooted group, and the attributes of the nodes in each group. The system can mitigate potential risks by reconfiguring the network of nodes.

IPC Classes  ?

• H04L 41/12 - Discovery or management of network topologies
• H04L 41/0604 - Management of faults, events, alarms or notifications using filtering, e.g. reduction of information by using priority, element types, position or time
• H04L 41/22 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks comprising specially adapted graphical user interfaces [GUI]
• H04L 9/40 - Network security protocols
• H04L 41/0631 - Management of faults, events, alarms or notifications using root cause analysisManagement of faults, events, alarms or notifications using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
• H04L 41/0677 - Localisation of faults

Abstract

Some examples can involve a system that can receive a first user selection of time series data and a second user selection of a type of forecasting model to apply to the time series data. The system can then obtain a first set of candidate values and a second set of candidate values for a first parameter and a second parameter, respectively, of the selected type of forecasting model. The candidate values may be determined based on statistical information derived from the time series data. The system can then provide the first set of candidate values and the second set of candidate values to the user, receive user selections of a first parameter value and a second parameter value, and determine whether a conflict exists between the first parameter value and the second parameter value. If so, the system can generate an output indicating that the conflict exists.

IPC Classes  ?

• G06N 3/04 - Architecture, e.g. interconnection topology
• G06N 3/044 - Recurrent networks, e.g. Hopfield networks
• G06F 3/04847 - Interaction techniques to control parameter settings, e.g. interaction with sliders or dials

Abstract

A data protection system is provided to detect data and execute security actions on the detected data using multiple tiers of parallel processing and incremental processing. For example, the data protection system can employ parallel job-submission and parallel-job execution to cataloging, scanning, searching, and other processes. Only source data that has not already been processed or has modified may be loaded to a cataloging data queue and a scanning data queue to reduce processing time. Scan results can include different data groups and can be used to search for specific data sets.

IPC Classes  ?

• G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements
• G06F 21/60 - Protecting data
• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules

Abstract

The computing device obtains a training data set related to a plurality of historic user inputs associated with preferences of one or more services or items from an entity. For each of the one or more services or items, the computing device executes operations to train a plurality of models using the training data set to generate a plurality of recommended models, apply a validation data set to generate a plurality of predictions from the plurality of recommended models, obtain a weight of each metric of a plurality of metrics from the entity, obtain user inputs associated with user preferences, and determine a relevancy score for each metric. The computing device selects a recommended model based on the relevancy score of the selected metric or a combination of selected metrics, generates one or more recommendations for the users, and outputs the one or more generated recommendations to the users.

IPC Classes  ?

• G06Q 30/00 - Commerce
• G06Q 30/0601 - Electronic shopping [e-shopping]
• G06N 5/04 - Inference or reasoning models

Abstract

An apparatus includes at least one node device to host a computing cluster, and at least one processor to: use at least one of a level of resource observed to be consumed by operation of the computing cluster or a level of performance observed to be provided by operation of the computing cluster as an input to a pre-existing cluster model to derive a predicted level; compare the predicted level to a corresponding observed level of resource consumed or performance provided; and in response to the predicted level not matching the observed level to within a pre-selected degree, derive a new cluster model from observations of the operation of the computing cluster, and generate a prompt to perform repeat the configuration of the computing cluster using the new cluster model in place of the pre-existing cluster model to generate a new set of configuration settings for the computing cluster.

IPC Classes  ?

• G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]
• G06F 9/451 - Execution arrangements for user interfaces
• G06F 9/48 - Program initiatingProgram switching, e.g. by interrupt

Abstract

An apparatus includes a processor to: receive a request to perform a job flow; within a performance container, based on the data dependencies among a set of tasks of the job flow, derive an order of performance of the set of tasks that includes a subset able to be performed in parallel, and derive a quantity of task containers to enable the parallel performance of the subset; based on the derived quantity of task containers, derive a quantity of virtual machines (VMs) to enable the parallel performance of the subset; provide, to a VM allocation routine, an indication of a need for provision of the quantity of VMs; and store, within a task queue, multiple task routine execution request messages to enable parallel execution of task routines within the quantity of task containers to cause the parallel performance of the subset.

IPC Classes  ?

• G06F 9/48 - Program initiatingProgram switching, e.g. by interrupt

Abstract

A computer system can automatically generate a directed graph interface for use in detecting and mitigating anomalies in entity interactions. For example, the system can receive interaction data describing a set of interactions at two entities. The system can then generate a directed network graph based on the interaction data. To do so, the system can identify pairs of interactions associated with the two entities in the set of interactions. The system can classify the pairs of interactions as outbound and/or inbound interaction pairs. The system can then generate one or more directed links in the directed network graph to represent the outbound and/or inbound interaction pairs. The system can further determine a characteristic of the outbound and/or inbound interaction pairs, automatically detect an anomaly that may be suggestive of malicious activity by one or both entities based on the characteristic, and output an indicator of the detected anomaly.

IPC Classes  ?

• G06F 9/00 - Arrangements for program control, e.g. control units
• G06F 21/55 - Detecting local intrusion or implementing counter-measures
• G06F 9/451 - Execution arrangements for user interfaces

Abstract

A computer monitors a state of a system. A time branch is defined for each valid value of each discrete variable. A system model is executed with observed values to update each time branch and determine a probability associated with each time branch. A discrete variable is selected, and a sequence duration value is incremented. When the incremented sequence duration value is greater than a predefined minimum sequence duration value, a probability change value is computed for the discrete variable, and, when the computed probability change value is less than or equal to a synchronization probability change value, a continuous value for each continuous variable for each time branch of the discrete variable is synchronized, and the sequence duration value for the selected discrete variable is reinitialized. The continuous value for at least one non-observed continuous variable is output.

IPC Classes  ?

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

Abstract

A flexible computer architecture for performing digital image analysis is described herein. In some examples, the computer architecture can include a distributed messaging platform (DMP) for receiving images from cameras and storing the images in a first queue. The computer architecture can also include a first container for receiving the images from the first queue, applying an image analysis model to the images, and transmitting the image analysis result to the DMP for storage in a second queue. Additionally, the computer architecture can include a second container for receiving the image analysis result from the second queue, performing a post-processing operation on the image analysis result, and transmitting the post-processing result to the DMP for storage in a third queue. The computer architecture can further include an output container for receiving the post-processing result from the third queue and generating an alert notification based on the post-processing result.

IPC Classes  ?

• G06V 10/94 - Hardware or software architectures specially adapted for image or video understanding

Abstract

A computer trains a neural network. A neural network is executed with a weight vector to compute a gradient vector using a batch of observation vectors. Eigenvalues are computed from a Hessian approximation matrix, a regularization parameter value is computed using the gradient vector, the eigenvalues, and a step-size value, a search direction vector is computed using the eigenvalues, the gradient vector, the Hessian approximation matrix, and the regularization parameter value, a reduction ratio value is computed, an updated weight vector is computed from the weight vector, a learning rate value, and the search direction vector or the gradient vector based on the computed reduction ratio value, and an updated Hessian approximation matrix is computed from the Hessian approximation matrix, the predefined learning rate value, and the search direction vector or the gradient vector based on the reduction ratio value. The step-size value is updated using the search direction vector.

IPC Classes  ?

• G06N 3/08 - Learning methods
• G06F 18/211 - Selection of the most significant subset of features