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METHOD OF ACCELERATING THERMODYNAMIC PROCESS PARAMETER COMPUTATION FOR CARBON CAPTURE, UTILIZATION, AND STORAGE SYSTEMS

Resumen de: WO2025159758A1

Certain aspects of the disclosure provide a method for carbon capture, utilization, and storage (CCUS) process simulation. The method generally includes processing, with a first sub-model of a machine learning (ML) model, input features to generate a first thermodynamic process parameter for a first thermodynamic component, wherein the input features comprise one or more thermodynamic properties; processing, with a second sub-model of the ML model, the input features to generate a second thermodynamic process parameter predicted for the first thermodynamic component; selecting, by the ML model, a final thermodynamic process parameter for the first thermodynamic component as: the first thermodynamic process parameter when the first thermodynamic process parameter is greater than a first threshold; or the second thermodynamic process parameter when the first thermodynamic process parameter is less than the first threshold; and providing as a first output from the ML model the selected final thermodynamic process parameter.

SYSTEM AND METHOD FOR ESTABLISHING CONTEXTUAL LINKS BETWEEN DATA IN A CONSTRUCTION ENVIRONMENT

Resumen de: WO2025159979A1

A method for establishing and generating contextual links between data from a plurality of data sources is described. The method includes receiving data and decomposing the received data into a decomposed data set; parsing and analyzing the decomposed data set based on a set of attribute analyzers to associate one or more attributes to the decomposed data set; determining an intent of data from the decomposed data set; generating a semantic graph of the decomposed data set based on the intent of data to evaluate data relatability between the decomposed data set; generating atomic knowledge units (AMUs) based on the parsed decomposed data set and the semantic graph; analyzing the AMUs corresponding to the received data by applying trained machine learning models to generate links between the AMUs and processing the generated links by a model ensemble to establish contextual links between data.

SYSTEMS AND METHODS FOR USING ARTIFICIAL INTELLIGENCE FOR RULES-BASED MODELING OF ELECTRONIC TRANSACTIONS

Resumen de: WO2025159851A1

A method for rules-based modeling may include capturing a plurality of historical transaction data of a client account. The method may further include extracting a plurality of item level features from the plurality of historical transaction data. The method may further include providing the plurality of item level features to a predictive machine-learning model. The predictive machine-learning model may be trained to identify patterns within the plurality of item level features and generate a projected balance for the client account based on the identified patterns. The method may further include transmitting the projected balance to a user interface.

DOCUMENT STRUCTURE EXTRACTION, DOCUMENT TEMPLATE GENERATION, AND DOCUMENT GENERATION RULES

Resumen de: WO2025159880A1

A method, a system, and a computer program product for generation of document rules. A structural arrangement of one or more portions of each electronic document in a plurality of electronic documents is determined using one or more machine learning models. One or more parameters associated with each electronic document in the plurality of electronic documents are identified. One or more document generation rules are generated based on one or more parameters and the structural arrangement of one or more portions. One or more document generation rules are generated for each type of electronic document in the plurality of electronic documents. One or more document generation rules are stored in a storage location.

CLINICAL DATA ANALYSIS

Resumen de: WO2025157774A1

Computer-implemented methods of providing a clinical predictor tool are described, comprising: obtaining training data comprising, for each of a plurality of patients, values for a plurality of clinical variables comprising a variable indicative of a diagnosis or prognosis and one or more further clinical variables; and training a clinical predictor model to predict the variable indicative of a diagnosis or prognosis using said training data, wherein obtaining the training data comprises obtaining synthetic clinical data comprising values for a plurality of clinical variables for one or more patients by obtaining a directed acyclic graph (DAG) edges corresponding to conditional dependence relationships inferred from real clinical data comprising values for the plurality of clinical variables for a plurality of patients, and obtaining values for each node of the DAG using a machine learning model and multivariate conditional probability table. Computer-implemented methods of obtaining synthetic clinical data are also described.

MODELLING CAUSATION IN MACHINE LEARNING

Resumen de: US2024104338A1

A method comprising: sampling a temporal causal graph from a temporal graph distribution specifying probabilities of directed causal edges between different variables of a feature vector at a present time step, and from one variable at a preceding time step to another variables at the present time step. Based on this there are identified: a present parent which is a cause of the selected variable in the present time step, and a preceding parent which is a cause of the selected variable from the preceding time step. The method then comprises: inputting a value of each identified present and preceding parent into a respective encoder, resulting in a respective embedding of each of the present and preceding parents; combining the embeddings of the present and preceding parents, resulting in a combined embedding; inputting the combined embedding into a decoder, resulting in a reconstructed value of the selected variable.

FORWARD-FORWARD TRAINING FOR MACHINE LEARNING

Resumen de: WO2024112887A1

Example implementations provide a computer-implemented method for training a machine-learned model, the method comprising: processing, using a layer of the machine-learned model, positive input data in a first forward pass; updating one or more weights of the layer to adjust, in a first direction, a goodness metric of the layer for the first forward pass; processing, using the layer, negative input data in a second forward pass; and updating the one or more weights to adjust, in a second direction, the goodness metric of the layer for the second forward pass.

LOAD BALANCING FOR MIXTURE OF EXPERTS MACHINE LEARNING

Resumen de: EP4592895A1

Aspects of the disclosure are directed to improving load balancing for serving mixture of experts (MoE) machine learning models. Load balancing is improved by providing memory dies increased access to computing dies through a 2.5D configuration and/or an optical configuration. Load balancing is further improved through a synchronization mechanism that determines an optical split of batches of data across the computing die based on a received MoE request to process the batches of data. The 2.5D configuration and/or optical configuration as well as the synchronization mechanism can improve usage of the computing die and reduce the amount of memory dies required to serve the MoE models, resulting in less consumption of power and lower latencies and complexity in alignment associated with remotely accessing memory.

TRAINING A MACHINE-LEARNING MODEL FOR CONSTRAINT-COMPLIANCE PREDICTION USING AN ACTION-BASED LOSS FUNCTION

Resumen de: WO2024081965A1

An online system trains a constraint prediction machine-learning model using an action-based loss function. The action-based loss function computes a weighted sum of (1) an accuracy of the constraint prediction model in predicting whether a user's interaction complies with a set of constraints and (2) an action score representing a number of actions taken by users to determine whether the user's action complies with the set of constraints. The online system may apply this trained constraint prediction model to future interaction data received from third-party systems to predict whether user interactions with those third-party systems comply with the set of constraints.

MODELLING CAUSATION IN MACHINE LEARNING

Resumen de: US2024104370A1

A method comprising: sampling a first causal graph from a first graph distribution modelling causation between variables in a feature vector, and sampling a second causal graph from a second graph distribution modelling presence of possible confounders, a confounder being an unobserved cause of both of two variables. The method further comprises: identifying a parent variable which is a cause of a selected variable according to the first causal graph, and which together with the selected variable forms a confounded pair having a respective confounder being a cause of both according to the second causal graph. A machine learning model encodes the parent to give a first embedding, and encodes information on the confounded pair give a second embedding. The embeddings are combined and then decoded to give a reconstructed value. This mechanism may be used in training the model or in treatment effect estimation.

SYSTEMS AND METHODS FOR PROVIDING AUTOMATED NATURAL LANGUAGE DIALOGUE WITH CUSTOMERS

Resumen de: US2025239253A1

A system includes one or more memory devices storing instructions, and one or more processors configured to execute the instructions to perform steps of providing automated natural dialogue with a customer. The system may generate one or more events and commands temporarily stored in queues to be processed by one or more of a dialogue management device, an API server, and an NLP device. The dialogue management device may create adaptive responses to customer communications using a customer context, a rules-based platform, and a trained machine learning model.

SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR MANAGING AUTOMATED HEALTHCARE DATA APPLICATIONS USING ARTIFICIAL INTELLIGENCE

Resumen de: US2025239366A1

Provided is a system for managing automated healthcare data applications using artificial intelligence (AI) that includes at least one processor programmed or configured to receive healthcare data from a data source, determine a classification of the healthcare data using a machine learning model, wherein the machine learning model is configured to provide a predicted classification of an automated healthcare data analysis application of a plurality of automated healthcare data analysis applications based on an input, and provide the healthcare data to an automated healthcare data analysis application based on the classification of the healthcare data. Methods and computer program products are also disclosed.

MACHINE LEARNING BASED SEMICONDUCTOR MEASUREMENT MODELS TRAINED USING HISTORICAL DATA

Resumen de: WO2025155771A1

Methods and systems for using historical measurement data to train a present state, machine learning (ML) based measurement model are described herein. This approach takes advantage of the correlation between structural characteristics of measured samples fabricated in accordance with different design revisions, process revisions, or both. In one aspect, a present state, ML based measurement model is trained using training data associated with measurements of a plurality of instances of a current version of a semiconductor structure in a present state of a semiconductor process flow and training data associated with measurements of a plurality of instances of a historical version of the semiconductor structure in the present state of the semiconductor process flow. In some examples, training data also includes prior state measurement data. Historical training data, prior state training data, or both, may be derived from actual reference measurements, in-line, production measurements, or both.

Statistical Network Application Security Policy Generation

Resumen de: US2025240329A1

A method for automatically generating network communication policies employs unsupervised machine learning on unlabeled network data representing communications between applications on multiple computer systems. This approach uniquely derives policy rules without predefined labels or user-defined communication categories, ensuring automated rules complement existing user-generated policies by excluding them during training. The method validates network interactions by enforcing rules that leverage application fingerprints and identified feature clusters to distinguish permitted from prohibited communications. Additional techniques include dynamically adapting policies, utilizing decision trees, frequent itemset discovery, and evolutionary algorithms. Suspicious applications are flagged, and malicious data is excluded from training. The system uses aggregated flows, MapReduce processing, and simulated annealing optimization, providing human-readable, periodically retrained rules for balanced network security management.

AUTOMATED COMPLIANCE VERIFICATION OF REGULATED CONTENT ITEMS IN A CONTENT PAGE

Resumen de: US2025238811A1

Computer-implemented systems and methods are disclosed, including systems and methods for performing compliance testing using language models or other machine learning models. A computer-implemented method may include, for example, accessing a content item; accessing a compliance ruleset; executing a compliance checker that utilizes a set of machine learning models; generating a prompt that includes the content item and the compliance ruleset; processing the prompt using the compliance checker; responsive to receiving a compliance determination dataset that indicates whether the content item satisfies one or more criteria within the compliance ruleset from the compliance checker; and generating an output based at least in part on the compliance determination dataset.

SYSTEMS AND METHODS FOR PREDICTING HYDRAULIC FRACTURING DESIGN PARMATERS BASED ON INJECTION TEST DATA AND MACHINE LEARNING

Resumen de: US2025237131A1

Systems and methods presented herein include systems and methods for receiving data relating to an injection/falloff test performed in a well in fluid communication with a subterranean reservoir; determining operational parameters of a hydraulic fracturing operation using at least a portion of the data; applying the operational parameters to a pre-trained machine learning predictive model to determine an optimal set of control parameters; and issuing one or more commands relating to the control parameters to optimize the hydraulic fracturing operation on the subterranean reservoir.

SYSTEMS AND METHODS FOR MODEL SELECTION USING HYPERPARAMETER OPTIMIZATION COMBINED WITH FEATURE SELECTION

Resumen de: US2025238715A1

Systems and methods for selecting machine learning features using iterative batch feature reduction. In some aspects, the system receives training data intended for generating a machine learning model. Based on a preliminary model trained on the training data, the system defines a hyperparameter search space to generate a set of hyperparameter configurations. For each hyperparameter configuration, the system generates a feature vector by executing a feature selection method. Based on the set of feature vectors and the training data, the system generates a set of candidate models corresponding to the set of hyperparameter configurations. The system ranks the set of candidate models based on a performance metric to select the machine learning model from the set of candidate models.

MODULAR SYSTEMS AND METHODS FOR SELECTIVELY ENABLING CLOUD-BASED ASSISTIVE TECHNOLOGIES

Resumen de: US2025238596A1

Systems and methods are disclosed for manually and programmatically remediating websites to thereby facilitate website navigation by people with diverse abilities. For example, an administrator portal is provided for simplified, form-based creation and deployment of remediation code, and a machine learning system is utilized to create and suggest remediations based on past remediation history. Voice command systems and portable document format (PDF) remediation techniques are also provided for improving the accessibility of such websites.

AUTOMATED METHODS AND SYSTEMS THAT PROVIDE RESOURCE RECOMMENDATIONS FOR VIRTUAL MACHINES

Resumen de: US2025238280A1

The current document is directed to methods and systems that generate recommendations for resource specifications used in virtual-machine-hosting requests. When distributed applications are submitted to distributed-computer-system-based hosting platforms for hosting, the hosting requestor generally specifies the computational resources that will need to be provisioned for each virtual machine included in a set of virtual machines that correspond to the distributed application, such as the processor bandwidth, memory size, local and remote networking bandwidths, and data-storage capacity needed for supporting execution of each virtual machine. In many cases, the hosting platform reserves the specified computational resources and accordingly charges for them. However, in many cases, the specified computational resources significantly exceed the computational resources actually needed for hosting the distributed application. The currently disclosed methods and systems employ machine learning to provide accurate estimates of the computational resources for the VMs of a distributed application.

MACHINE LEARNING AND RULES-BASED RECOMMENDATIONS FOR USER INTERFACE WORKFLOWS

Resumen de: US2025238705A1

Various embodiments of the present disclosure provide machine learning and rules-based recommendations for user interface workflows. In one example, an embodiment provides for generating a set of recommendation data objects for a user identifier associated with a user interface based on a set of predefined rules associated with input data provided via a user interface workflow associated with the user interface, generating a ranked version of the set of recommendation data objects using a machine learning model, and initiating a rendering of a set of selectable graphical elements via the user interface based on the ranked version of the set of recommendation data objects.

MANAGING A MODEL TRAINED USING A MACHINE LEARNING PROCESS

Resumen de: US2025238720A1

A computer implemented method of managing a first model that was trained using a first machine learning process and is deployed and used to label medical data. The method comprises determining (202) a performance measure for the first model, and if the performance measure is below a threshold performance level, triggering (204) an upgrade process wherein the upgrade process comprises performing further training on the first model to produce an updated first model, wherein the further training is performed using an active learning process wherein training data for the further training is selected from a pool of unlabeled data samples, according to the active learning process, and sent to a labeler to obtain ground truth labels for use in the further training.

DYNAMIC AUTOMATED CONTENT COMPLIANCE VERIFICATION

Resumen de: US2025238723A1

Computer-implemented systems and methods are disclosed, including systems and methods for performing compliance testing using language models or other machine learning models. A computer-implemented method may include, for example, accessing a content item; accessing a compliance ruleset; executing a compliance checker that utilizes a set of machine learning models; generating a prompt that includes the content item and the compliance ruleset; processing the prompt using the compliance checker; responsive to receiving a compliance determination dataset that indicates whether the content item satisfies one or more criteria within the compliance ruleset from the compliance checker; and generating an output based at least in part on the compliance determination dataset.

LANGUAGE MODEL-ASSISTED CONTENT COMPLIANCE ANALYSIS SYSTEM

Resumen de: US2025238812A1

Computer-implemented systems and methods are disclosed, including systems and methods for performing compliance testing using language models or other machine learning models. A computer-implemented method may include, for example, accessing a content item; accessing a compliance ruleset; executing a compliance checker that utilizes a set of machine learning models; generating a prompt that includes the content item and the compliance ruleset; processing the prompt using the compliance checker; responsive to receiving a compliance determination dataset that indicates whether the content item satisfies one or more criteria within the compliance ruleset from the compliance checker; and generating an output based at least in part on the compliance determination dataset.

SYSTEM AND METHOD FOR ML-BASED ENGINEERING LIBRARY TRANSLATION AND INTEGRATION, AND FOR SCHEMA & FILE FORMAT MAPPING

Resumen de: EP4589435A1

The present invention relates to a method, comprising: using an artificial intelligence/machine learning, AI/ML, model to map content between an interface of a first entity for interaction with other entities and an interface of a second entity for interaction with other entities, and/or classify content of the interface of the first entity and/or of the interface of the second entity; and obtaining, from the AI/ML model, a first output indicative of a result of the mapping of the content, and/or of a result of the classification of the content.

IMPROVED CODE GENERATION USING TREE STRUCTURE FOR TRAINING AND TRIMMING OF GENERATIVE MODEL OUTPUT

Nº publicación: EP4589421A1 23/07/2025

Solicitante:

GOOGLE LLC [US]
Google LLC

Resumen de: EP4589421A1

A method implemented using one or more processors comprises selecting a starting location in an original code snippet, processing the original code snippet to generate a tree representation of the original code snippet, identifying a subtree of the tree representation that contains the starting location in the original code snippet, identifying a ground truth portion of the original code snippet that corresponds to at least a portion of the subtree of the tree representation, and training a machine learning model to generate a predicted code snippet that corresponds to the portion of the subtree. The training includes processing a remainder of the original code snippet outside of the ground truth portion using the machine learning model.