Abstract

A value is inserted to a memory location within a data structure stored to a thread-safe shared memory resource. A data transformation interface is invoked from the data structure to perform in-memory processing of the value at the memory location. A set of arguments is passed to the first data transformation interface, including a value pointer that points to the memory location and a function pointer that points to a memory location of a computational function. The value is accessed at the memory location within the data structure based on the value pointer. Based on the function pointer, the value is processed with the computational function to obtain a new value. The new value is written to the memory location within the data structure to overwrite the value.

IPC Classes  ?

• G06F 12/02 - Addressing or allocationRelocation
• G06F 12/0864 - Addressing of a memory level in which the access to the desired data or data block requires associative addressing means, e.g. caches using pseudo-associative means, e.g. set-associative or hashing

Abstract

A system, method, and computer-program product includes obtaining a data query schema that provides instructions for constructing computer-executable search queries according to a search syntax permitted by a target data query language, extracting, from the data query schema, one or more sets of query components that define the search syntax of the target data query language, synthetically generating a plurality of natural language-to-search query training data samples based at least in part on the one or more sets of query components, and configuring a natural language-to-search query machine learning model based on training a machine learning text-to-text transformer model using the plurality of natural language-to-search query training data samples.

IPC Classes  ?

• G06F 16/2452 - Query translation
• G06N 20/00 - Machine learning

Abstract

An agent deployment request is received for an external computing environment. An agentic module is configured for deployment to the external computing environment based on characteristics of the external computing environment, wherein the agentic module comprises a computational agent operable to process information stored within the external computing environment and a corresponding agentic controller. Responsive to the agent deployment request, a first unit of software instructions is generated that, when executed, instantiates an agentic module instance within the external computing environment. The first unit of software instructions is transmitted to the external computing environment. A performance metric related to processing of the information stored within the external computing environment by a computational agent instance of the agentic module instance is received from an agentic controller instance of the agentic module instance.

IPC Classes  ?

• G06F 8/65 - Updates
• H04L 9/40 - Network security protocols

Abstract

A system, method, and computer-program product includes receiving a plurality of vector embeddings having an initial dimensionality and projecting the plurality of vector embeddings into lower-dimensional spaces using at least two different dimension reduction algorithms to generate corresponding sets of projected vector embeddings. Each set of projected embeddings may be quantized and nearest neighbors for the original embeddings and for each quantized set of projected embeddings may be calculated. Additionally, a neighbor preservation metric may be evaluated for each quantized set by comparing its nearest neighbors to those of the original embeddings. Based on the neighbor preservation metrics and a predefined error tolerance, an optimal compression configuration may be selected.

IPC Classes  ?

• G06N 3/0495 - Quantised networksSparse networksCompressed networks
• G06N 3/0985 - Hyperparameter optimisationMeta-learningLearning-to-learn

Abstract

A system and method include receiving real-time streaming data, detecting a change in a distribution of the real-time streaming data by defining a reference window, defining a current window, computing a first weighted cumulative distribution function for the reference window based on a first weight value, computing a second weighted cumulative distribution function for the current window based on a second weight value, computing a maximum difference between the first weighted cumulative distribution function and the second weighted cumulative distribution function, computing a threshold value, and determining that the maximum difference is greater than the threshold value.

IPC Classes  ?

• G06Q 30/0201 - Market modellingMarket analysisCollecting market data

Abstract

A system and method to detect and reduce noise include receiving a request and deploying a linear mixed model in a plurality of iterations to determine, based on one or more parameters of the linear mixed model, that noise is present in an output of the linear mixed model, determine that the noise is not larger than a threshold and that the output is capable of further improvement, adjust at least some of the one or more parameters to obtain one or more adjusted parameters, adjust the output of the linear mixed model based on the one or more adjusted parameters to reduce the noise in the output, determine whether a stopping criterion is reached, and responsive to determining that the stopping criterion has not reached, repeat next iteration with the adjusted output or responsive to determining that the stopping criterion has reached, transmit the output.

IPC Classes  ?

• G06F 30/20 - Design optimisation, verification or simulation
• G06F 17/12 - Simultaneous equations
• G06F 111/02 - CAD in a network environment, e.g. collaborative CAD or distributed simulation

Abstract

A computing system generates a feature for a machine learning model by receiving identification of character data. The character data is stored in a database comprising multiple documents. One or more documents of the multiple documents pertain to a respective entity of multiple entities. The computing system generates structured data from the character data by generating multiple structured data terms; and storing the multiple structured data terms in respective data elements of multiple memory blocks in memory of the computing system. The computing system generates the feature for the machine learning model based on one or more of: at least two structured data terms in different memory blocks of the multiple memory blocks; and data manipulation of a structured data term in a memory block of the multiple memory blocks.

IPC Classes  ?

• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules

Abstract

A system, method, and computer-program product include receiving, via an API server, an upload request that includes a logical table reference parameter and a structured data file. A table metadata schema corresponding to the logical table reference parameter is retrieved from a metadata repository. A plurality of data entries of the structured data file are converted into schema-conforming data records using column metadata definitions extracted from the schema. The conversion includes identifying source column headers matching source column identifiers, parsing associated cell value sets, validating the cell value sets based on criteria defined in the metadata definitions, and associating validated values with database columns. The schema-conforming data records are persisted to a database table designated in the table metadata schema.

IPC Classes  ?

• G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor
• G06F 16/21 - Design, administration or maintenance of databases
• G06F 16/22 - IndexingData structures thereforStorage structures

Abstract

A data processing system and method for validating a language model includes executing the language model to extract a plurality of strings from a set of documents and for each string, parsing the string to generate a plurality of tokens, lemmatizing the plurality of tokens, applying a part-of-speech label to the plurality of tokens, executing a filtering operation to obtain a plurality of filtered tokens, automatically building a weighted categorization rule based on the plurality of filtered tokens, applying the weighted categorization rule to the set of documents to identify one or more text spans from the set of documents, computing a relevancy score for each of the one or more text spans extracted from the set of documents, and selecting the one or more text spans extracted from the set of documents having the relevancy score greater than a predetermined threshold.

IPC Classes  ?

• G06F 40/226 - Validation
• G06F 40/247 - ThesaurusesSynonyms
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
• G06F 40/289 - Phrasal analysis, e.g. finite state techniques or chunking

Abstract

A system and method include learning a topological order of a plurality of variables in a directed acyclic graph based on real data, computing parameter estimate values corresponding to the real data, computing error values based on the real data, the topological order, and the parameter estimate values, generating simulated data from the parameter estimate values and the error values, such that simulated variables in the simulated data preserve a causal relationship between variables in the real data, and the simulated variables in the simulated data preserve a correlation relationship between the variables in the real data, and reorganizing and outputting the simulated data based on the topological order.

IPC Classes  ?

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

Abstract

A system, method, and computer-program product includes receiving, from a third-party identity management system, an authentication response indicating a set of login attributes; obtaining, from an identity resolution service, a set of identity and authorization attributes using the set of login attributes, the set of identity and authorization attributes including a unique user identifier (UID); granting, by the identity resolution service, a session initiation token when the set of identity and authorization attributes satisfy predefined authorization criteria; in response to the identity resolution service granting the session initiation token: allocating a compute session and a persistent storage resource to the unique UID; executing, via the compute session, an operation that modifies files stored in the persistent storage resource; and transmitting, to a version control system, a version control operation that records the files modified in the persistent storage resource to a code repository using the unique UID or group identifiers.

IPC Classes  ?

• G06F 8/71 - Version control Configuration management
• H04L 9/40 - Network security protocols

Abstract

A computing device displays a graphical user interface (GUI) comprising a navigator interface component. The navigator interface component comprises an interactive graphical representation of an asset. The device, responsive to detecting a predefined event, generates an alert. The device updates the navigator interface component to indicate the alert in association with the asset in the interactive graphical representation. The device receives a selection of the asset via the interactive graphical representation. The device displays, in the GUI, a second interactive graphical representation specific to the asset. The second interactive graphical representation of the asset comprises a task control element for controlling tasks specific to management of the asset. The device receives, using the task control element: a task for resolving the alert; and an association with the task and the asset. The device updates the navigator interface component to indicate the association with the task and the asset.

IPC Classes  ?

• G06F 9/451 - Execution arrangements for user interfaces
• G06F 9/54 - Interprogram communication

Abstract

A mapping file for a relational database comprising a plurality of tables is accessed. The tables comprise a plurality of data elements. The mapping file comprises predefined join operations that each define a join between two of the tables and a corresponding join type. The mapping file further includes classification information that associates the data elements with element classes. A selection of target data elements is received, the target data elements being associated with a common element class and from at least two different tables. A minimum set of join operations necessary to retrieve the target data elements is dynamically selected from the mapping file in response to receiving the selection. A unit of software instructions is generated and executed to retrieve the target data elements and a structured data object comprising the target data elements is output. A mapping file for a relational database comprising a plurality of tables is accessed. The tables comprise a plurality of data elements. The mapping file comprises predefined join operations that each define a join between two of the tables and a corresponding join type. The mapping file further includes classification information that associates the data elements with element classes. A selection of target data elements is received, the target data elements being associated with a common element class and from at least two different tables. A minimum set of join operations necessary to retrieve the target data elements is dynamically selected from the mapping file in response to receiving the selection. A unit of software instructions is generated and executed to retrieve the target data elements and a structured data object comprising the target data elements is output. SAS Institute Inc.

IPC Classes  ?

• G06F 16/2453 - Query optimisation

Abstract

A system and method include learning a topological order of a DAG by setting an initial index value of a first index, setting an initial score value of a score, setting an initial order list, computing an initial SSCP matrix, sweeping the initial SSCP matrix based on the first index, incrementing the first index to obtain an updated index value of the first index, determining an index value of a second index, computing an updated SSCP matrix, computing an updated score value as a sum of an initial score value and a value identified from the updated SSCP matrix, and computing an updated order list from an initial order list based on the updated index value of the first index and the index value of the second index.

IPC Classes  ?

• G06F 17/16 - Matrix or vector computation
• G06F 16/901 - IndexingData structures thereforStorage structures

Abstract

A data processing system and method include receiving a set of documents having unstructured data, executing the unsupervised machine learning model for outputting topics, selecting a first subset of topic terms, computing an inverse document frequency weight value for each topic term in the first subset of topic terms, computing a second weight value for each topic term in the first subset of topic terms, selecting a second subset of topic terms from the first subset of topic terms, generating a compressed representation of the set of documents from the second subset of topic terms to include in a prompt, inputting the prompt into a language model, and executing the language model based on the prompt to generate the topic label and the topic description.

IPC Classes  ?

• G06F 18/2431 - Multiple classes
• G06F 40/47 - Machine-assisted translation, e.g. using translation memory
• G06N 3/088 - Non-supervised learning, e.g. competitive learning

Abstract

A data processing system and method include receiving a set of documents to summarize a trend across the set of documents, inputting the set of documents into an information extraction model, executing the information extraction model to extract a first plurality of text segments, determining a second plurality of text segments based on the first plurality of text segments, determining a third plurality of text segments from the second plurality of text segments, generating a compressed representation of the set of documents from the third plurality of text segments to include in a prompt for a language model, inputting the prompt into the language model, and executing the language model to generate the summary based on the prompt for the set of documents.

IPC Classes  ?

• G06F 16/34 - BrowsingVisualisation therefor
• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
• G06F 40/289 - Phrasal analysis, e.g. finite state techniques or chunking

Abstract

A data processing system and method include receiving a set of documents having unstructured data, executing the unsupervised machine learning model for outputting topics, selecting a first subset of topic terms, computing an inverse document frequency weight value for each topic term in the first subset of topic terms, computing a second weight value for each topic term in the first subset of topic terms, selecting a second subset of topic terms from the first subset of topic terms, generating a compressed representation of the set of documents from the second subset of topic terms to include in a prompt, inputting the prompt into a language model, and executing the language model based on the prompt to generate the topic label and the topic description.

IPC Classes  ?

• G06F 16/93 - Document management systems

Abstract

A data processing system and method include receiving a set of documents having unstructured data, executing the unsupervised machine learning model for outputting topics, selecting a first subset of topic terms, computing an inverse document frequency weight value for each topic term in the first subset of topic terms, computing a second weight value for each topic term in the first subset of topic terms, selecting a second subset of topic terms from the first subset of topic terms, generating a compressed representation of the set of documents from the second subset of topic terms to include in a prompt, inputting the prompt into a language model, and executing the language model based on the prompt to generate the topic label and the topic description.

IPC Classes  ?

• G06F 16/93 - Document management systems

Abstract

A data processing system and method include receiving a set of documents from which to generate at least one topic, creating at least one information extraction model for the set of documents, executing the at least one information extraction model to extract a plurality of text segments from the set of documents by applying one or more rule sets defined for at least one entity, word list, or grammatical pattern and extracting a plurality of text portions from the set of documents based on the one or more rule sets as the plurality of text segments, inputting at least a subset of the plurality of text segments into an unsupervised machine learning model, and executing the unsupervised machine learning model to output the at least one topic for the set of the documents.

IPC Classes  ?

• G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
• G06F 40/253 - Grammatical analysisStyle critique

Abstract

A system and method include receiving a plurality of elements in a data array to be sorted, assigning the elements a sequential index value, computing an amount of additional memory needed to sort the elements, creating a Linked-Array Tournament Tree (LATT), storing the LATT in the allocated amount of additional memory, initializing the LATT, initializing a number of iterations, initializing a next sorted index position in the data array, determining from the LATT, the index value of a champion element from all the winners of the pairwise comparisons, swapping the champion element with an element at the next sorted index position in the data array, updating the LATT responsive to the swapping, updating the next sorted index position, repeating a plurality of times, and outputting the data array having the elements in a sorted order.

IPC Classes  ?

• G06F 7/24 - Sorting, i.e. extracting data from one or more carriers, re-arranging the data in numerical or other ordered sequence, and re-recording the sorted data on the original carrier or on a different carrier or set of carriers
• G06F 12/02 - Addressing or allocationRelocation
• G06F 16/22 - IndexingData structures thereforStorage structures

Abstract

A system and method include generating synthetic data by generating a first set of hyperparameters for a first trained machine learning model and a second set of hyperparameters for a second trained machine learning model, generating a plurality of synthetic data vectors using the first and second trained machine learning models, computing an error function for the first and second set of hyperparameters using a third machine learning model, computing an objective function value, responsive to determining that the objective function value is not an optimal value, updating the first set of hyperparameters and the second set of hyperparameters or responsive to determining that the objective function value is an optimal value outputting the plurality of synthetic data vectors as a set of synthetic data.

IPC Classes  ?

• G06F 17/11 - Complex mathematical operations for solving equations
• G06F 17/18 - Complex mathematical operations for evaluating statistical data
• G06N 3/0985 - Hyperparameter optimisationMeta-learningLearning-to-learn
• G06N 20/00 - Machine learning

Abstract

A computing device receives a request to generate computing operations to effectuate a response for a function to an input set. The device generates a hashmap data structure. The hashmap data structure indexes respective data storage associated with a respective key of one or more keys. The device generates a first set of computing instructions to solve a first component for the function, executes the first set of computing instructions, and generates a first hash key. The first hash key represents multiple executions of the first set of computing instructions. The device stores the first hash key in the hashmap data structure to index a storage location in the hashmap data structure, generates a second set of computing instructions to solve a second component for the function, and executes the second set of computing instructions by using the first hash key to retrieve data indexed by the first hash key.

IPC Classes  ?

• G06F 16/901 - IndexingData structures thereforStorage structures

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
• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules

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

Techniques are provided for persisting validated hierarchical data in a datastore. In one example, a system can identify, in a dataset having a dependency hierarchy, records that are independent from any other records in the dataset and store the records in a datastore. The system can identify records in the dataset that are dependent on other records in the dataset and store the records in a temporary memory. The system can process the records using an iterative hierarchical processing scheme, in which each iteration of the scheme involves identifying a subset of records corresponding to a particular level of the dependency hierarchy. For each record in the subset of records, the system can obtain previously processed records on which the record depends, process each record using the previously processed records, store each processed record in the datastore, and remove the record from the temporary memory.

IPC Classes  ?

• G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
• G06F 16/22 - IndexingData structures thereforStorage structures
• G06F 16/23 - Updating

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

Abstract

A system, method, and computer-program product includes selecting, by a controller node, a plurality of candidate 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 candidate hyperparameter search points; identifying, by the controller node, a collection of intermediate candidate hyperparameter search points that outperform a remainder of the plurality of candidate hyperparameter search points by evaluating a performance of the plurality of machine learning models after training for the target number of epochs; and performing, by the controller node, a crossover operation with the collection of intermediate candidate hyperparameter search points to identify one or more new candidate hyperparameter search points to test in the hyperparameter search space.

IPC Classes  ?

• G06N 20/00 - Machine learning
• G06N 20/20 - Ensemble learning

Abstract

A computing device receives a trained computer model. A computing device obtains a selection of an assessment type. The assessment type comprises more than one component for determining an overall assessment of the trained computer model according to the assessment type. The computing device generates, responsive to receiving the trained computer model, an overall training assessment of the trained computer model according to the assessment type. For example, the computing device can generate by obtaining multiple component assessments. The multiple component assessments comprise at least one assessment for each of the more than one components for determining the overall assessment. The computing device can generate by generating the overall training assessment based on the multiple component assessments. The computing device generates a graphical representation comprising visual representation of both the overall training assessment and the multiple component assessments. The computing device displays the graphical representation in a model card visualization.

IPC Classes  ?

• G06F 11/34 - Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation
• G06F 9/451 - Execution arrangements for user interfaces

Abstract

A system and method include generating synthetic data by generating a first set of hyperparameters for a first trained machine learning model and a second set of hyperparameters for a second trained machine learning model, generating a plurality of synthetic data vectors using the first and second trained machine learning models, computing an error function for the first and second set of hyperparameters using a third machine learning model, computing an objective function value, responsive to determining that the objective function value is not an optimal value, updating the first set of hyperparameters and the second set of hyperparameters or responsive to determining that the objective function value is an optimal value outputting the plurality of synthetic data vectors as a set of synthetic data.

IPC Classes  ?

• G06F 17/11 - Complex mathematical operations for solving equations

Abstract

A system and method include generating synthetic data by generating a first set of hyperparameters for a first trained machine learning model and a second set of hyperparameters for a second trained machine learning model, generating a plurality of synthetic data vectors using the first and second trained machine learning models, computing an error function for the first and second set of hyperparameters using a third machine learning model, computing an objective function value, responsive to determining that the objective function value is not an optimal value, updating the first set of hyperparameters and the second set of hyperparameters or responsive to determining that the objective function value is an optimal value outputting the plurality of synthetic data vectors as a set of synthetic data.

IPC Classes  ?

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

Abstract

A system and method include generating synthetic data by generating a first set of hyperparameters for a first trained machine learning model and a second set of hyperparameters for a second trained machine learning model, generating a plurality of synthetic data vectors using the first and second trained machine learning models, computing an error function for the first and second set of hyperparameters using a third machine learning model, computing an objective function value, responsive to determining that the objective function value is not an optimal value, updating the first set of hyperparameters and the second set of hyperparameters or responsive to determining that the objective function value is an optimal value outputting the plurality of synthetic data vectors as a set of synthetic data.

IPC Classes  ?

• G06N 3/0985 - Hyperparameter optimisationMeta-learningLearning-to-learn
• G06F 18/241 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches

Abstract

A system, method, and computer-program product includes receiving a machine-generated alert indicating a data outlier for a physical asset; obtaining, from a computer database, a dataset including observed data for the physical asset within a predefined temporal window of the machine-generated alert; generating, via a description generator, a contextual description of the data outlier based at least on the observed data for the physical asset; searching an embedding-based knowledgebase for a subset of embedding representations of the plurality of embedding representations within a similarity threshold of an embedding representation of the contextual description; obtaining, from the computer database, a subset of the plurality of reference artifacts that correspond to the subset of embedding representations; and generating, via a large language model, a resolution suggestion for resolving the data outlier based at least on the subset of reference artifacts obtained from the computer database.

IPC Classes  ?

• G06F 16/3329 - Natural language query formulation
• G06F 16/334 - Query execution

Abstract

A system and method include generating synthetic data by generating a first set of hyperparameters for a first trained machine learning model and a second set of hyperparameters for a second trained machine learning model, generating a plurality of synthetic data vectors using the first and second trained machine learning models, computing an error function for the first and second set of hyperparameters using a third machine learning model, computing an objective function value, responsive to determining that the objective function value is not an optimal value, updating the first set of hyperparameters and the second set of hyperparameters or responsive to determining that the objective function value is an optimal value outputting the plurality of synthetic data vectors as a set of synthetic data.

IPC Classes  ?

• G06F 17/11 - Complex mathematical operations for solving equations
• G06N 3/0985 - Hyperparameter optimisationMeta-learningLearning-to-learn
• G06N 20/00 - Machine learning
• G06F 17/18 - Complex mathematical operations for evaluating statistical data

Abstract

In some examples, a system can access video data collected from one or more image sensors. The system can execute a human detection model to detect a person within a region of interest proximate to the environment based on the video data. The system can also execute an object detection model to detect a presence or absence of personal protection equipment (PPE) based on the video data. The system can associate the PPE with the person to detect an unsafe PPE status. The system can determine that the unsafe PPE status has persisted for a predetermined period of time. In response to determining that the unsafe PPE status has persisted for the predetermined period of time, the system can generate a signal indicating the unsafe PPE status.

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
• G06T 7/70 - Determining position or orientation of objects or cameras
• G06V 10/25 - Determination of region of interest [ROI] or a volume of interest [VOI]
• G06V 10/70 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning
• 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 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
• 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
• G08B 21/02 - Alarms for ensuring the safety of persons
• G08B 21/18 - Status alarms

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 40/183 - Tabulation, i.e. one-dimensional positioning
• G06F 9/451 - Execution arrangements for user interfaces
• 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

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 8/36 - Software reuse
• G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]
• G06F 9/54 - Interprogram communication

Abstract

A system, method, and computer-program product includes computing, by a controller node, a hyperparameter search space for a plurality of hyperparameters; selecting, by the controller node, a plurality of hyperparameter search points from the hyperparameter search space; instructing, by the controller node, one or more worker nodes to concurrently train a plurality of machine learning models based on 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; determining, by the controller node, one or more sets of optimal hyperparameter values based on the plurality of performance metrics; and outputting, by the controller node, the one or more sets of optimal hyperparameter values.

IPC Classes  ?

• G06N 20/20 - Ensemble learning

Abstract

A computer-implemented method includes executing, via a flood simulation model, a computer simulation that simulates flooding within a target spatial area based on an input of a geospatial dataset and the rainfall intensity data of one or more flooding events associated with the target spatial area; determining normalized inundation scores for a set of geographical cells and one or more clusters of interconnected geographical cells based on executing the computer simulation; determining, via an optimization algorithm, a set of optimal sensor locations in the target spatial area based on the normalized inundation scores and the one or more clusters of interconnected geographical cells; and generating a sensor location map that includes the set of geographical cells and a set of geospatial markers identifying the set of sensor locations; and generating a visual output that displays the sensor location map.

IPC Classes  ?

• G01W 1/10 - Devices for predicting weather conditions
• G01W 1/14 - Rainfall or precipitation gauges
• G06F 16/29 - Geographical information databases
• G06F 18/23 - Clustering techniques

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  ?

• G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
• G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
• G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations

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  ?

• G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
• G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
• G06Q 10/087 - Inventory or stock management, e.g. order filling, procurement or balancing against orders

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 3/04 - Architecture, e.g. interconnection topology
• G06F 17/16 - Matrix or vector computation
• G06N 3/042 - Knowledge-based neural networksLogical representations of neural networks
• G06N 3/0475 - Generative networks
• G06N 3/08 - Learning methods
• G06N 3/0895 - Weakly supervised learning, e.g. semi-supervised or self-supervised learning
• G06N 5/045 - Explanation of inferenceExplainable artificial intelligence [XAI]Interpretable artificial intelligence

Abstract

One example described herein can involve a system that receives, from a user, a structured document that describes one or more conditions selected by the user to customize one or more electronic data structures. The system can also query a database to retrieve data from the database. The system can then execute an interpreter program, which can ingest the structured document and the data, identify a subset of the data that satisfies the condition(s), and generate the one or more electronic data structures based on the subset of the data. The electronic data structure(s) can include the identified subset of the data and exclude a remainder of the data. The system can compact the electronic data structure(s) by removing empty fields therein. The system can then provide the compacted data structure(s) to the user.

IPC Classes  ?

• G06F 16/2455 - Query execution
• G06F 16/248 - Presentation of query results
• G06F 16/81 - Indexing, e.g. XML tagsData structures thereforStorage structures

Abstract

A system and method include tuning hyperparameters for an ARIMA model using a derivative free approach by determining a set of initial hyperparameter values, fitting an ARIMA model to the set of initial hyperparameter values, selecting a tuning method for the set of hyperparameters, responsive to selecting a single-objective method, computing a first objective function value from time-series data applied to the ARIMA model based on the set of initial hyperparameter values, or responsive to selecting a multi-objective method, computing at least a second objective function value and a third objective function value from the time-series data applied to the ARIMA model based on the set of initial hyperparameter values, determining whether a stopping criterion for tuning the set of hyperparameters has reached, responsive to determining that the stopping criteria has reached, outputting a set of tuned hyperparameter values.

IPC Classes  ?

• G06N 20/00 - Machine learning

Abstract

In one example, a computer system performs operations including accessing a cluster including a node pool that includes nodes executing deployments. The cluster includes an auto-scaler configured to scale the nodes in the node pool based on a configuration. The configuration includes a minimum setting for the number of nodes for the node pool. The computer system determines that a number of deployments are executing in the node pool that meets or exceeds a threshold. In response, the computer system updates the minimum setting of the auto-scaling configuration for the node pool to a first predefined value sufficient for executing a predefined maximum number of deployments. The computer system later determines that a different number of deployments are executing on the node pool, now below the threshold. In response, the computer system updates the minimum setting of the auto-scaling configuration for the node pool to a second predefined value.

IPC Classes  ?

• G06F 3/00 - Input arrangements for transferring data to be processed into a form capable of being handled by the computerOutput arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
• G06F 9/54 - Interprogram communication

Abstract

A system and method include reformulating an optimization program as a Quadratic Unconstrained Binary Optimization (QUBO) model and in a single iteration, solving the optimization program by inputting the QUBO model into a quantum computing solver, instructing the quantum computing solver to generate a plurality of solutions to the optimization program based on the QUBO model, receiving the plurality of solutions from the quantum computing solver, inputting each of the plurality of solutions into a nonquantum computing solver, wherein the nonquantum computing solver uses each of the plurality of solutions as a starting point to continue solving the optimization program, and outputting an optimal solution to the optimization program from the nonquantum computing solver.

IPC Classes  ?

• G06N 10/20 - Models of quantum computing, e.g. quantum circuits or universal quantum computers
• G06F 17/11 - Complex mathematical operations for solving equations

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 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 16/355 - Creation or modification of classes or clusters

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 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 16/355 - Creation or modification of classes or clusters

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 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 16/355 - Creation or modification of classes or clusters

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-program product obtains a text document that includes text describing an action. The computer-program product extracts one or more action tokens from the text document. The computer-program product executes 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. The computer-program product classifies the action to a likelihood category associated with a respective linguistic pattern search of the plurality of linguistic pattern searches. The computer-program product classifies the text document to a respective domain. The computer-program product computes a priority value of the action described in the text document. The computer-program product generates 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
• G06F 40/30 - Semantic analysis

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
• G06F 17/16 - Matrix or vector computation
• G06N 3/042 - Knowledge-based neural networksLogical representations of neural networks
• G06N 3/0475 - Generative networks
• G06N 3/08 - Learning methods
• G06N 5/045 - Explanation of inferenceExplainable artificial intelligence [XAI]Interpretable artificial intelligence

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 3/098 - Distributed learning, e.g. federated learning
• G06F 16/9536 - Search customisation based on social or collaborative filtering
• G06F 17/16 - Matrix or vector computation
• G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
• 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 3/06 - Digital input from, or digital output to, record carriers
• 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 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

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

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