Machine learning

PLATFORMS, SYSTEMS, AND METHODS TO CORRECT BATCH EFFECTS IN MACHINE LEARNING TRAINING BY ITERATIVE SPLITTING

Absstract of: US20260120809A1

0000 A method may receive experimental data from a plurality of experiments. The method may detect a systematic variation between the experiments that is not related to a factor of interest. The method may apply a data normalization technique to minimize batch-specific systemic variation while preserving underlying signals. The method may generate probability distributions representing experimental outcomes to provide a summary of uncertainty. The method may use a machine learning model to identify and correct batch effects directly from the data without requiring explicit modeling of all possible sources of variation. The method may output normalized data with reduced batch effects.

ARTIFICIAL INTELLIGENCE TECHNIQUES UTILIZING A GENERATIVE RELATIONAL NETWORK

Absstract of: WO2026087924A1

A system and method for identifying relationships using relational networks. A method includes applying a generative relational network (GRN) in order to create a model of relationships between entities. The GRN includes multiple sets of nodes, where each set of nodes includes a respective set of machine learning models. The sets of nodes include dominance factor nodes and evolution of internal component nodes, where the dominance factor nodes define a dominance factor based on change intensity and change frequency, and the evolution of internal component nodes define evolution with respect to changes over time. Relationships among the entities are simulated using the model, and at least a portion of the relationships are eliminated for a target interaction based on the simulation results. The remaining relationships are tested with respect to the target interaction in order to identify the target interaction.

DYNAMICALLY ADJUSTABLE EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI) MODEL

Absstract of: WO2026085843A1

Techniques are disclosed for enhancing the transparency and interpretability of machine learning (ML) models using explainable artificial intelligence (XAI). In some embodiments, a computing system generates an XAI model that provides reasons for the outputs of a first ML model by selecting from a set of predefined reasons based on an aggregation function. This aggregation function combines importance scores for various features associated with the ML model's output, where each feature is mapped to a corresponding reason. The computing system may determine one or more parameters for the aggregation function to improve the accuracy of the selected reason, allowing for adjustments in how the aggregation function processes the importance scores. In certain cases, the system may involve an imitation model that is trained to replicate the first ML model's outputs.

TECHNIQUES FOR TRAINING A GENERATIVE RELATIONAL NETWORK

Absstract of: WO2026087925A1

A system and method for training relational networks. A method includes applying a self-organizing map (SOM) to training data in order to create a visualization. The SOM is a neural network configured to transform relationships between data items. The visualization has a lower dimensionality than the training data. The method also includes training machine learning models of a generative relational network (GRN) based on the visualization, where the GRN includes sets of nodes having respective machine learning models among the machine learning models of the GRN and the sets of nodes include a set of dominance factor nodes and a set of evolution of internal component nodes. The set of dominance factor nodes defines a dominance factor based on change intensity and change frequency, and the set of evolution of internal component nodes defines evolution with respect to changes determined based on values of the dominance factor over time.

SMART AGITATION MONITOR SYSTEMS AND METHODS WITH ARTIFICIAL INTELLIGENCE DRIVEN CLINICAL ASSESSMENT AND REPORTING

Absstract of: WO2026090586A1

Disclosed are smart sensor systems and related methods that integrate machine learning (ML) models to analyze actigraphy data and predict the Richmond Agitation-Sedation Scale (RASS) score. Disclosed systems and related methods continuously track physical movements using ML algorithms to identify patterns in this data and correlate them with RASS scores, thereby providing real-time, data-driven insights that enhance clinical assessments and support informed decision-making about patient care and interventions.

DETERMINING AN OUTPUT FOR AN INDIVIDUAL USER USING A CLINICAL MANIFOLD

Absstract of: WO2026090484A1

The present disclosure relates to methods and systems for determining an output for an individual user using a clinical manifold. An individual user health dataset is received and converted into an individual user temporal graph. The individual user temporal graph is converted to an individual user attributed graph using the clinical manifold. The individual user attributed graph and an initial probability structure are inputted into a deep learning model configured to: convert the individual user attributed graph into a meta graph and determine the output based on the meta graph, the probability distribution, and a prevalence of the output.

NON-RADIO ACCESS NETWORK (RAN)-AIDED TRAINING DATA GENERATION FOR ARTIFICIAL INTELLIGENCE/MACHINE LEARNING (AIML) SIDELINK POSITIONING

Absstract of: WO2026089927A1

Disclosed are techniques for wireless positioning. In some aspects, a first user equipment (UE) may obtain one or more ranging or sidelink positioning measurements. The first UE may obtain one or more non-radio access network (non-RAN) positioning results of the first UE. The first UE may obtain one or more ground truth labels corresponding to the one or more non-RAN positioning results of the first UE. The first UE may train an artificial intelligence/machine learning (AIML) model based on the one or more ranging or sidelink positioning measurements and the one or more ground truth labels.

SYSTEM AND METHOD TO DETECT SYMPTOMS OF IMPENDING CLIMATE CONTROL FAILURES OF TRANSPORT CLIMATE CONTROL SYSTEMS

Absstract of: US20260119923A1

A method for predicting an impending climate control failure for a transport temperature control system (TCCS) is provided. The method includes a backend obtaining one or more operational parameters and/or one or more control parameters of transport temperature control systems including the TCCS. The method also includes obtaining warrantee data and/or service records for the transport temperature control systems. The method further includes training a machine learning model with the warrantee data and/or service records for the transport temperature control systems, and at least one of the operational parameters of the transport temperature control systems or the control parameters of the transport temperature control systems. Also the method includes deploying the trained machine learning model. The method further includes predicting the impending climate control failure for the TCCS based on the trained machine learning model, operational parameters of the TCCS, and/or control parameters of the TCCS.

MACHINE LEARNING BASED DATABASE SEARCH AND KNOWLEDGE MINING

Absstract of: US20260119566A1

Disclosed herein are embodiments of systems, methods, and products comprises a server for database search and knowledge mining. The server may learn different table's semantics, relationships, and usage by parsing historical query logs and analyzing tables' metadata (e.g., table descriptions). The analytic server may generate a graph database based on the table relationships obtained from the parsing. The graph database may be a relationship graph where tables are the nodes and edges represent the relationships among tables. When the server receives a query, the server extract semantics of the query, and return a set of tables that are semantically similar to the query. The set of tables may be a list of tables whose semantic similarities with the query satisfies a threshold. The analytic server may further generate a graph including the list of tables to show the relationships of these tables.

METHOD AND ELECTRONIC DEVICE FOR SECURE ON-DEVICE STORAGE FOR MACHINE LEARNING MODELS

Absstract of: US20260119053A1

A method for performing an inference includes: detecting a context among at least one context associated with at least one application; triggering a model execution command to a smart agent of an electronic device, based on the detected context; loading a machine learning (ML) model into a secure storage of the electronic device, based on the detected context and the triggered model execution command; generating, using the loaded ML model, an inference, based on data associated with the detected context; and sharing the generated inference with each application of the at least one application that is registered for the detected context.

INFORMATION PROCESSING SYSTEM AND PROGRAM

Absstract of: US20260120820A1

A machine learning model includes a processor obtaining information identifying each of the raw materials received from the user and the amount of each of the raw materials, and obtaining a predicted value of a physical property of the property name to be predicted for a composition comprising each of the raw materials by inputting into a first machine learning model at least one of the chemical fingerprints, SMILES strings or chemical graph structure data or product name or substance name corresponding to each of the raw materials and the amount of each of the raw materials, or by inputting into a second machine learning model a set of values based on at least one of the chemical fingerprints, SMILES strings or chemical graph structure data or product name or substance name corresponding to each of the raw materials and the amount of each of the raw materials.

A SYSTEM AND METHOD FOR POTENTIAL BIAS IDENTIFICATION

Absstract of: US20260119911A1

The described invention is directed to systems and methods capable of identifying Machine Learning (ML) models that are potentially biased. The system obtains: (a) a list of potentially problematic labels, and (b) at least one code segment, including a plurality of code lines containing one or more commands associated with generating at least one machine learning model from a given data structure. The system extracts the actual labels of the given data structure and compares them to the list of potentially problematic labels. Upon a match between at least one of the extracted actual labels and at least one of the potentially problematic labels, the system performs an action associated with the knowledge that the ML model is potentially biased.

OPTIMIZATION OF QUANTUM-ACTIVE DEFECTS AND SPINS IN CHEMICAL SYSTEMS USING MACHINE LEARNING

Absstract of: US20260120812A1

0000 Systems and methods for the manufacture of improved quantum materials are provided. The techniques include generating, using a machine learning model, a regression model of a figure of merit describing the quantum material, the regression model being determined based at least in part on the characterized one or more quantum properties of individual samples of the quantum material and associated fabrication parameters. The techniques also include determining improved fabrication parameters using the regression model of the figure of merit and fabricating a new sample of the quantum material using the improved fabrication parameters.

AUTO-CAPTURE OF INTERESTING MOMENTS BY ASSISTANT SYSTEMS

Absstract of: EP4733927A2

In one embodiment, a method includes receiving, at a microphone of the headset associated with a first user, a user request to record an activity associated with the first user; determining a trigger condition based on the user request; accessing, from the headset, one or more sensor signals captured by one or more sensors comprising a camera and the microphone; detecting, based on one or more machine learning models and using at least one of the one or more sensor signals, a change in a context associated with the activity of the first user, wherein the change in the context satisfies the trigger condition associated with the activity; and automatically capturing, responsive to the detected change in the context, visual data by the camera.

EXPLAINABLE DEEP LEARNING SYSTEM

Absstract of: WO2026085086A1

Provided are methods for an explainable deep learning system. Some methods include encoding a state and a trajectory of an autonomous vehicle into features and generating concept predictions based on the features. An explanation is generated based on the concept predictions. Systems and computer program products are also provided.

STRESS-STRAIN PREDICTION METHOD FOR GAP-GRADED SOILS BASED ON COUPLING OF DISCRETE ELEMENT METHOD AND MACHINE LEARNING

Absstract of: US20260110609A1

0000 The present disclosure relates to stress-strain prediction method for gap-graded soils based on coupling of the discrete element method and machine learning. The method includes the following steps: S1. obtaining baseline stress-strain data; S2. establishing and verifying a discrete element model; S3. establishing discrete element specimens of gap-graded soils with different gradations; S4. obtaining stress-strain data of the different gap-graded soils; S5. establishing a raw database; S6. partition the raw database; S7. establishing a random forest model; S8. training the random forest model to obtain a stress prediction model; S9. evaluating the trained random forest model; S10. predicting stress data of the gap-graded soils. This method can quickly predict the stress-strain curve of a gap-graded soil directly with its particle size ratio and fines content, improving efficiency.

Automatic Retraining Of Machine Learning Models Upon Data Deletion

Absstract of: US20260111787A1

Machine learning models trained using personal data are automatically retrained upon deletion of the personal data. A system identifies a first data set including personal data and used to train a machine learning model. The system deletes the personal data from a data store associated with the machine learning model. The system also automatically retrains, based on deleting the personal data, the machine learning model using a second data set that excludes the personal data.

CATALYSTS FOR GROWTH OF SUPERINTELLIGENCE

Absstract of: US20260111727A1

Data is the “fuel” that powers the machine learning “engine” for Artificial Intelligence. However, identifying high quality data that can catalyze smarter AI, AGI, and SuperIntelligent systems is becoming an increasingly challenging bottleneck for machine learning. This invention not only describes novel methods for identifying the most valuable data, but it also presents an entirely new framework for understanding the information content of AI-relevant datasets. The methods can be used by intelligent systems autonomously or in collaboration with humans. Novel methods for accelerating AI learning, and for updating the knowledge of AI systems in real-time, are also disclosed. Consistent with the view that human survival may depend on the fastest path to AGI also being the safest path, the invention describes catalysts which help maximize alignment between the values of AGI and humans. These innovative catalysts increase not only the intelligence, but also the safety, of AI systems.

MPPT PHOTOVOLTAIC POWER OPTIMIZATION METHOD BASED ON PHOTOVOLTAIC MODULES, AND SYSTEM

Absstract of: WO2026081162A1

An MPPT photovoltaic power optimization method based on photovoltaic modules, and a system, which relate to the technical field of photovoltaic power optimization. The method comprises: collecting photovoltaic signals, and analyzing the collected signals; on the basis of a deep learning algorithm, learning historical data and real-time environmental data to predict future illumination and temperature change trends, and generating a corresponding power point prediction model; using an edge computing device to locally process the data and implement control, and adjusting the output power of a photovoltaic module in real time on the basis of a generated MPPT control strategy; and by means of a reinforcement learning algorithm, optimizing an installation layout of photovoltaic modules. By means of introducing a deep learning model combined with dynamic step size adjustment and perturb-and-observe strategies, accurate and efficient tracking of a maximum power point for a photovoltaic system in a complex environment is realized, thus effectively improving the stability and response speed of power output. Power fluctuations and losses are reduced, thereby significantly improving the overall energy efficiency of a photovoltaic power generation system.

NETWORK PACKET CAPTURE ANALYSIS USING MACHINE LEARNING MODEL

Absstract of: US12592860B2

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.

DEVICES AND METHODS FOR OPTIMIZING PARALLEL EXECUTION OF A MACHINE LEARNING MODEL

Absstract of: WO2024255997A1

A data processing apparatus (10) for enhancing, in particular optimizing a machine learning, ML, model for parallelized operation on a plurality of processing devices (20) is disclosed, wherein each processing device (20) comprises a plurality of processing elements, PEs, (21a- d) configured to perform one or more of a plurality of ML model tasks. The data processing apparatus (10) is configured to generate based on the ML model a computational graph, CG, representation (30) of the ML model, wherein the CG representation (30) comprises a plurality of nodes (31) and a plurality of edges (32), wherein each of the plurality of nodes (31) is associated with one or more of the plurality of ML model tasks and wherein the plurality of edges (32) define a plurality of dependencies of the plurality of nodes (32) of the CG representation (30). Furthermore, the data processing apparatus (10) is configured to generate an enhanced CG representation of the ML model by adding to the CG representation (30) of the ML model one or more further dependencies of the plurality of nodes (31) of the CG representation (30) of the ML model. The data processing apparatus (10) is further configured to compile the enhanced CG representation of the ML model for generating code for each of the plurality of processing devices (20).

SYSTEMS AND METHODS FOR RAPID RESPONSE AND REMEDIATION OF DATA PROLIFERATION

Absstract of: EP4730710A1

0001 Systems and methods are disclosed for responding to a data incident. One or more processors receive one or more indicators of a data leak occurring at one or more nodes of a network. One or more processors causes an identification, by a machine-learning model, of one or more compromised nodes within the network based on the one or more indicators of a data leak. One or more processors may receive from the machine-learning model the identification of the one or more compromised nodes. One or more processors modify access permissions at one or more identified compromised nodes based on a user permission schema or pre-determined access rules, in response to the data leak. One or more processors cause a generation of a notification regarding the data leak and the modifications of access permissions to one or more users associated with the network.

SYNTHETIC DATA GENERATION FOR MACHINE LEARNING MODELS

Absstract of: WO2024258464A1

Techniques for generating synthetic data for machine learning (ML) models are described. A system includes a language model that processes a task and a corresponding set of example inputs to generate another input, referred to herein as a machine-generated data. The machine-generated data is processed using a ML model (that data is being generated for) to determine a model output, and the model output is analyzed to determine whether it corresponds to a target output. If the model output corresponds to the target output, then the machine-generated data is added to the set of example inputs and one of the original example inputs is removed to generate an updated set of example inputs. The updated set can be used for various training techniques.

AUTOMATIC APPLICATION PROGRAMMING INTERFACE CONSUMPTION AND INTEGRATION ACROSS THE SYSTEM LANDSCAPE VIA LARGE LANGUAGE MODELS

Absstract of: EP4730132A1

0001 A received query is parsed by a first machine learning (ML) model to retrieve one or more semantic entities. Next, a second ML model is trained with a list of application programming interfaces (APIs) and corresponding first data to generate a second trained ML model which receives the one or more semantic entities as inputs. Based on these inputs, the second trained ML model selects a given API from the list of APIs. Then, a third ML model is trained with the given API and corresponding second metadata to generate a third trained ML model. Next, the third trained ML model receives the one or more semantic entities as inputs and generates a given API call for invoking the given API. Then, the given API call is executed and the query is completed to a first computing system based on invoking the given API.

Verfahren zum Suchen von Objekten in einer Datenbank

Nº publicación: BE1032955A1 20/04/2026

Applicant:

PHOENIX CONTACT GMBH & CO KG [DE]

Absstract of: BE1032955A1

Die Erfindung betrifft ein computerimplementiertes Verfahren zum Suchen von Datenbankobjekten in einer Datenbank 50, umfassend die Schritte: Empfangen (S1), durch eine Eingangsschnittstelle (10), von Objektdaten (DO) eines Suchobjekts. Bestimmen (S2), durch ein Machine learning, ML,- Kodierungsmodul (30), einer vektoriellen Objekt-Kodierung des Suchobjekts unter Verwendung der Objektdaten (DO), wobei die vektorielle Kodierung mindestens einen Merkmalsvektor (VO) umfasst. Bestimmen (S3), durch ein Suchmodul (40), einer Ähnlichkeit des mindestens einen Merkmalsvektors (VO) zu Merkmalsvektoren der Datenbankobjekte (OD). Bestimmen (S4), durch das Suchmodul (40), eines Suchergebnisses (E) aus Datenbankobjekten (OD) abhängig von der bestimmten Ähnlichkeit.