Alerta

DIFFERENTIALLY PRIVATE FEDERATED EXTREME GRADIENT BOOSTING

Absstract of: US20260195639A1

Training a differential privacy-aware (DP-aware) machine learning model includes transmitting epsilon hyperparameters to federated learning (FL) nodes. A differential privacy-aware (DP-aware) machine learning model is generated based on noise-infused surrogate histograms received from the FL nodes, each noise-infused surrogate histogram based on an epsilon hyperparameter and representing a node-specific dataset. The DP-aware machine learning model is transmitted to the FL nodes. A DP-aware aggregate histogram is generated by merging DP-aware gradients and DP-aware Hessians determined by the FL nodes based on each FL node generating predictions by applying the DP-aware machine learning model to a node-specific dataset therein. A decision tree of the DP-aware machine learning model is expanded by dividing data in one or more decision tree nodes. The machine learning model is iteratively trained by successively merging further DP-aware gradients and DP-aware Hessians generated by FL nodes based on updated versions of the DP-aware machine learning model.

METHOD FOR SCHEDULING PEER DEMANDS IN A CONSENSUS PROCESS FOR A BLOCKCHAIN

Absstract of: WO2026146097A1

The collected information is distributed (306) between input data and output data with a view to training (308) a machine learning model in order to obtain predictions identifying which peers are most likely to transmit missing blocks and at what time. The machine learning model thus trained enables each peer to determine a scheduling of demands made by said peer on the other peers in the consensus process for the elaboration of the blockchain, according to the predictions obtained. The consensus process is therefore more efficient.

PARAMETER TUNING METHOD AND APPARATUS, AND DEVICE

Absstract of: US20260195119A1

0000 This application provides example parameter tuning methods. In one example method, a high-dimensional parameter space is divided based on one or more groups of software configuration parameters that are configured for software and corresponding software performance parameters, to form M high-dimensional parameter subspaces, where the M high-dimensional parameter subspaces satisfy: a similarity between data in any one of the high-dimensional parameter subspaces is greater than a similarity threshold, and a difference between amounts of data included in any two of the high-dimensional parameter subspaces is not greater than an amount threshold. M machine learning models are invoked to learn the M high-dimensional parameter subspaces. A target high-dimensional parameter subspace is selected from the M high-dimensional parameter subspaces, and a to-be-configured software configuration parameter is determined by using a machine learning model corresponding to the target high-dimensional parameter subspace.

A SELF-ADAPTIVE FAULT CORRELATION SYSTEM BASED ON CAUSALITY MATRICES AND MACHINE LEARNING

Absstract of: US20260195611A1

The present invention describes a self-adaptive system capable of extracting correlations between multiple faults from net-work topologies, with the innovative component being the data preprocessing phase generating causality matrices to provide as an input to ML models. The proposed fault correlation system is responsible for, without any configuration, identifying the hierarchical relationships be-tween the multiple alarms, allowing for a better understanding of the causality and impact of each malfunction, hence assisting the implementation of RCA rules. This allows, not only for a huge dimensionality reduction of alarms needed to be processed by a TO's, but also significantly increases the knowledge about the topology, thus reducing downtime and increasing the quality of service of the network and services.

MACHINE-LEARNING SYSTEM FOR CONTRACT INTELLIGENCE AUTOMATION USING CONTRACT DIGITIZATION INTO MACHINE PARSEABLE OBJECTS AND METHOD THEREOF

Absstract of: WO2026145883A1

Proposed is a novel machine learning system (1) for contract intelligence automation, and corresponding method for training the machine learning system for automated text analysis and for applying it. A plurality of contracts (2) with a plurality of clauses (22) is received by the system (1), wherein wordings of equivalent clauses (22) vary across contracts (2); contract text (21) is read into a data processing system (15); clause text chunks (121) are identified, that are equivalent across contracts (2), and assigned a contract term category (122). Considering a respective semantic context (131) of each of the contracts (2), a semantic meaning (132) of each of the clause text chunks (121) is determined and encoded in a clause embedding space (141) and stored in vector database (14); data automation tasks can be performed using entries of the vector database (14), particularly automatic consistency monitoring (197) and outlier detection across contracts (2) and a monitoring of clause (22) nuances across contracts (2) e.g. via a graphical representation (16).

SYSTEMS AND METHODS FOR GENERATING CUSTOMIZED TRAINING

Absstract of: US20260195418A1

A system may be configured to perform a method for generating customized training. The system may receive first user interaction data associated with a user. The system may determine, using a machine learning model (MLM), whether the first user interaction data exceeds a predetermined threshold. Based on such determination, the system may assign a training module to the user. The system may access a user profile associated with the user, the user profile comprising a plurality of training modules. The system may generate a training plan based on the training module and the plurality of training modules. The system may receive second user interaction data associated with the user, and may determine an efficacy level of the training plan based on the second user interaction data. The system may dynamically update the training plan based on the efficacy level, and may dynamically display the training plan in the user profile.

SYSTEMS AND METHODS TO GENERATE DATA MESSAGES INDICATING A PROBABILITY OF EXECUTION FOR DATA TRANSACTION OBJECTS USING MACHINE LEARNING

Absstract of: US20260195660A1

A computer system includes a transceiver that receives over a data communications network different types of input data and multiple data transaction objects from multiple source nodes. A pre-processor processes the different types of input data and the data transaction objects to generate an input data structure. Based on the input data structure, one or more predictive machine learning models is trained and used to predict a probability of execution of each of the data transaction objects at a future execution time. Output data messages are then generated for transmission by the transceiver over the data communications network indicating the probability of execution for at least one of the data transaction objects at the future execution time.

MACHINE LEARNING BASED DISAMBIGUATION IN A KNOWLEDGE AWARE CONVERSATION SYSTEM

Absstract of: US20260195357A1

Aspects of the present disclosure provide techniques for machine learning based disambiguation. Embodiments include receiving a query via a user interface; generating an enriched query by rewording the query based on conversation history data associated with the query. Embodiments include retrieving relevant information from a data store based on using an embedding of the enriched query to perform a semantic search. Embodiments include providing the enriched query and the relevant information to a language processing machine learning model along with a prompt that instructs the language processing machine learning model to generate an answer to the enriched query based on the relevant information and to generate a disambiguation question if one or more conditions are met. Embodiments include receiving an output from the language processing machine learning model in response to the prompt. Embodiments include providing a response to the query via the user interface based on the output.

PRECISION TREATMENT WITH MACHINE LEARNING AND DIGITAL TWIN TECHNOLOGY FOR OPTIMAL METABOLIC OUTCOMES

Absstract of: US20260191466A1

A patient health management platform accesses a metabolic profile for a patient and biosignals recorded for the patient during a current time period comprising sensor data and/or lab test data collected for the patient. The platform encodes the biosignals into a vector representation and inputs the vector representation into a patient-specific metabolic model to determine a metabolic state of the patient at a conclusion of the current time period. The patient-specific metabolic model comprises a set of parameter values determined based on labels assigned to the previous metabolic states and a function representing one or more effects of the plurality of biosignals of the personalized metabolic profile. The platform compares the determined metabolic state of the patient to a threshold metabolic state representing a target metabolism. The platform generates a patient-specific treatment recommendation outlining instructions for the patient to improve the determined metabolic state to the functional metabolic state.

System, Method, and Computer Program Product for Early Detection of a Merchant Data Breach Through Machine-Learning Analysis

Absstract of: US20260195760A1

Provided are systems, methods, and computer program products for early detection of a merchant data breach through machine-learning analysis. An example system includes a processor configured to receive transaction authorization request data. The processor is also configured to generate a metric based on security-testing transaction activity. The processor is further configured to generate features for training one or more models. The processor is further configured to generate a first dataset based on the features and associated with a plurality of merchants, and a second dataset based on the features and associated with a previously breached merchant. The processor is further configured to train an ensembled model to associate merchants with a likelihood of data breach. The processor is further configured to determine a breached merchant, automatically freeze a transaction, retrain the ensembled model, and determine another breached merchant based on the updated models.

SYSTEMS AND METHODS FOR AUTHENTICATING A RESOURCE SYSTEM

Absstract of: US20260197315A1

Systems and methods are disclosed for determining authenticity of a resource system. The method includes receiving a dataset that includes a first subset and a second subset associated with a first resource system; down-sampling the first subset but not the second subset; generating a first feature for a machine learning model based on the down-sampled first subset; generating a second feature for the machine learning model based on the second subset; generating, via input of at least one of the first feature or the second feature into the machine learning model that is trained to output a fraudulent measure, one or more data objects indicative of validating the fraudulent measure; and initiating performance of one or more prediction-based actions in response to the generating.

METHOD AND APPARATUS FOR PREDICTING INJURIES

Absstract of: US20260195821A1

0000 The present system provides a method and apparatus for predicting a likelihood of injury of an individual. The system generates a frailty score that represents the likelihood of a person being injured. The frailty score is generated by using Artificial Intelligence (AI) and machine learning using a specialized data set. The frailty score can then trigger actions to reduce the possibility of injury or to determine whether to engage in the injury risking behavior at all.

Network Packet Capture Analysis Using Machine Learning Model

Absstract of: US20260197254A1

0000 Embodiments relate to analyzing network packets in a telecommunication networks using machine learning models. The network packets are correlated and then labeled to indicate successes or failures in a subtask of communication flow. Features are extracted based on the labels and correlated network packets. The extracted features are applied to a machine learning model to predict or infer success or failure of the entire communication flow. The result from the machine learning model may again be applied to subsequent machine learning models to predict root cause of a failure or to predict or infer the type of success. In this way, more accurate diagnosis of network issues in the telecommunication networks may be made in a more expedient manner.

DATA DIGITIZATION VIA CUSTOM INTEGRATED MACHINE LEARNING ENSEMBLES

Absstract of: US20260195338A1

0000 Data digitization via custom integrated machine learning ensembles is provided. For example, a system integrates multiple trained machine learning ensembles to identify, extract, and map data. The system receives a data set from sources. The system identifies ensembles can include machine learning models that can determine an outcome. The system filters a subset of data from the data set. The system identifies a layout for the data set based on a vendor type, data type, and the data set. The system executes a block detection module to identify blocks of the layout. The system executes a header detection module. The system executes a policy detection module to identify the headers as policies. The system transforms, based on the headers, the layout, the blocks, and the policies, the data set into a second file type, and presents the transformed data set for integration into a capital management system.

A SYSTEM FOR PROCESSING, ANALYZING, AND CLASSIFYING GRAPH DATA IN MACHINE LEARNING AND DATA SCIENCE

Absstract of: WO2026147368A1

The invention relates to a system for processing, analyzing, and classifying graph data in the fields of machine learning and data science, and an operation method of said system.

Forward-Forward Training for Machine Learning

Absstract of: US20260195643A1

0000 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.

METHOD AND SYSTEM FOR PREDICTING ABDOMINAL AORTIC ANEURYSM (AAA) GROWTH

Absstract of: US20260196354A1

0000 There are provided methods, systems and non-transitory storage mediums for predicting growth of an abdominal aortic aneurysm (AAA) of a patient having been diagnosed with AAA. Segmented regions of interest (ROI) comprising the aorta and adjacent structures are received by segmenting a set of images. A wall shear stress parameter and intraluminal thickness parameter is determined. A 3D parametric mesh comprising a plurality of concentric 3D mesh layers is generated, where each concentric 3D mesh layer includes a same predetermined number of nodes. The generation includes encoding the segmented ROIs, the wall shear stress parameter and the intraluminal thickness parameter as features at respective node locations in the 3D parametric mesh. A trained growth prediction machine learning model predicts, based at least on a subset of features of the 3D parametric mesh, if the given patient will show AAA growth. The training of the growth prediction model is also disclosed.

SYSTEMS AND METHODS FOR ENHANCED MACHINE LEARNING TECHNIQUES FOR KNOWLEDGE MAP GENERATION AND USER INTERFACE PRESENTATION

Absstract of: US20260195614A1

Systems and methods for extracting information from documents and constructing corresponding knowledge maps with respect to defined knowledge models. Deep-learning based models for Natural Language Processing (NLP) are applied to tokenize words, tag, parse, and lemmatize sentences of input documents. Then an information extractor traverses the dependency tree of NLP object to recursively extract the entities of interest to the knowledge models. Finally, a knowledge map constructor traverses the dependency tree of NLP object to determine the relationships among the extracted entities and construct knowledge maps recursively following the defined knowledge models.

CONTROL VARIABLE OPTIMIZATION METHOD, BIORESOURCE PRODUCTION METHOD, AND BIORESOURCE PRODUCTION SYSTEM

Absstract of: EP4773049A1

0001 Provided are a control variable optimization method capable of determining improved culture conditions using a predictive model based on machine learning, and a bioresource production method and a bioresource production system using the same. 0002 A bioresource production system S according to another aspect of the present invention includes: a cultivation system B for performing bioresource production; and a control variable optimization system A for optimizing control variables obtained from the cultivation system. 0003 The control variable optimization system separates the control variables into initial variables and manipulated variables, creates predictive models adapted to the initial variables and the manipulated variables, respectively, and optimizes the control variables by combining the predictive models.

SPATIOTEMPORAL TRANSFER MACHINE LEARNING

Absstract of: WO2025046310A1

A computer-implemented, machine learning method for spatiotemporal transfer learning. Sectors of an area are aggregated using preprocessed data from one or more data sources. The sectors are clustered based on different representations obtained for each context feature associated with each of the sectors. One or more context features that have a higher impact on a target feature to be predicted than other context features are identified from a plurality of context features and aggregated to obtain a representation of the area. Using the representation of the area, a particular sector within each of the clustered sectors is selected based on similarity to a respective centroid of the cluster to generate a set of particular sectors. A model associated with a source sector of the set of particular sectors is trained. The method has applications including, but not limited to smart cities, public safety and energy optimization.

MACHINE LEARNING MODEL POSITIONING PERFORMANCE MONITORING AND REPORTING

Absstract of: EP4773657A2

0001 Disclosed are techniques for wireless communication. In an aspect, a network entity receives a provide location information message from a user equipment (UE), the provide location information message including one or more positioning estimates derived by the UE during one or more positioning inference occasions of a machine learning model, wherein the machine learning model is applied to one or more measurements of a wireless channel between the UE and a network node during each of the one or more positioning inference occasions, and transmits a performance report indicating a performance of the machine learning model at least in deriving the one or more positioning estimates during the one or more positioning inference occasions.

SYSTEM AND METHOD FOR EXTRACTING HIDDEN CUES IN INTERACTIVE COMMUNICATIONS

Absstract of: US20260188340A1

Disclosed herein are system, method, and computer program product embodiments for machine learning systems to process interactive communications between at least two participants. Speech and text, within the interactive communications, are analyzed using machine learning classifiers to extract prosodic, semantic and key phrase cues located within the interactive communications to identify changes to emotion, sentiments and key phrases. A summary of the interactive communications between a first participant and a second participant is generated at least, in-part, based on the extracted prosodic, semantic and key phrase cues and the summary is highlighted based on any of the changes to emotion, the sentiments or the key phrases.

OPERATIONALIZING MACHINE LEARNING MODELS AN INFORMATION TECHNOLOGY AND SECURITY OPERATIONS APPLICATION

Absstract of: US20260187518A1

Techniques are described for providing a ML data analytics application including guided ML workflows that facilitate the end-to-end training and use of various types of ML models, where such guided workflows may also be referred to as ML “experiments.” For example, the ML data analytics application may enable users to create experiments related to prediction of numeric fields (for example, using linear regression techniques), predicting categorical fields (for example, using logistic regression), detecting numerical outliers (for example, using various distribution statistics), detecting categorical outliers (for example, using probabilistic statistics), forecasting time series data, and clustering numeric events (for example, using k-means, density-based spatial clustering of applications with noise (DBSCAN), spectral clustering, or other techniques), among other possible uses of various types of ML models to analyze data.

BOOSTING AND MATRIX FACTORIZATION

Absstract of: US20260187197A1

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for presenting a new machine learning model architecture. In some aspects, the methods include obtaining a training dataset with a plurality of training samples that includes feature variables and output variables. A first matrix is generated using the training dataset which is a sparse representation of the training dataset. Generating the first matrix can include generating a categorical representation of numeric features and an encoded representation of the categorical features. The methods further include generating a second, third and a fourth matrix. Each feature of the first matrix is then represented using a vector that includes a multiple adjustable parameters. The machine learning model can learn by adjusting values of the adjustable parameters using a combination of a loss function the fourth matrix, and the first matrix.

DECISION TREE OF MODELS: USING DECISION TREE MODEL, AND REPLACING THE LEAVES OF THE TREE WITH OTHER MACHINE LEARNING MODELS

Nº publicación: US20260187484A1 02/07/2026

Applicant:

FORD GLOBAL TECH LLC [US]
Ford Global Technologies, LLC

Absstract of: US20260187484A1

Described are techniques of generating and training a neural network that include training multiple models and constructing multiple decision trees with said models. Each decision tree may include additional decision trees at various levels of that decision tree. Each decision tree has a different accuracy indicator due to the unique structuring of each decision tree, and by testing each tree through a testing dataset, the tree with the highest accuracy can be determined.