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EXPLAINABILITY ANALYSIS IN REAL TIME FOR OPERATOR ASSURANCE, FEEDBACK, AND MACHINE LEARNING MODEL REFINEMENT

Resumen de: US20260056864A1

A computer-implemented method includes transforming sensor data into a first spatial representation, transforming a graphical user interface to display the first spatial representation, transforming the sensor data into a second spatial representation, providing the second spatial representation as input features to a machine learning model to generate inference data, providing the input features, parameters of the machine learning model, and the inference data to an explainability model to generate explainability data, transforming the explainability data into a third spatial representation, the third spatial representation being in a same space as the first spatial representation, and transforming the graphical user interface to overlay the third spatial representation on the first spatial representation.

VIRTUAL METROLOGY APPARATUS, VIRTUAL METROLOGY METHOD, AND VIRTUAL METROLOGY PROGRAM

Resumen de: US20260056009A1

A virtual metrology apparatus, a virtual metrology method, and a virtual metrology program that allow a highly accurate virtual metrology process to be performed is provided. A virtual metrology apparatus includes an acquisition unit configured to acquire a time series data group measured in association with processing of a target object in a predetermined processing unit of a manufacturing process, and a training unit configured to train a plurality of network sections by machine learning such that a result of consolidating output data produced by the plurality of network sections processing the acquired time series data group approaches inspection data of a resultant object obtained upon processing the target object in the predetermined processing unit of the manufacturing process.

SYSTEM AND METHOD FOR OPERATING SYSTEM DISTRIBUTION AND VERSION IDENTIFICATION USING COMMUNICATIONS SECURITY FINGERPRINTS

Resumen de: US20260058949A1

A system and method for inferring an operating system version for a device based on communications security data. A method includes identifying a plurality of sequences in communications security data sent by the device; determining an operating system type of an operating system used by the device based on the identified plurality of sequences; applying a version-identifying model to the identified plurality of sequences, wherein the version-identifying model is a machine learning model trained to output a version identifier, wherein the applied version-identifying model is associated with the determined operating system type; and determining the operating system version of the device based on the output of the version-identifying model.

BALANCED TRAINING DATASETS FOR PREDICTING AIRCRAFT COMPONENT FAULTS

Resumen de: US20260054855A1

The present disclosure provides a method of generating a balanced training dataset for a machine learning model in one aspect, the method including: receiving flight sensor data corresponding to a plurality of flights, and applying one or more criteria to the flight sensor data to generate a training dataset including a plurality of first instances corresponding to flights of the plurality of flights. The method further includes assigning, using component fault data, respective labels to the plurality of first instances, and generating, for groups of one or more labels of the respective labels, a respective plurality of flight series. Each flight series includes a respective sequence of second instances that is based on some of the plurality of first instances, and that concludes with a second instance that is assigned a label included in the group.

SYSTEM FOR PREPARING MACHINE LEARNING TRAINING DATA FOR USE IN EVALUATION OF TERM DEFINITION QUALITY

Resumen de: EP4700604A1

A system for preparing machine learning training data for use in evaluation of term definition quality. The system can include a server having at least one server processor and at least one server memory for storing a plurality of terms with corresponding definitions, and a plurality of client devices each having at least one client memory device and at least one client processor. The client processor programmed to receive at least one of the plurality of terms and its corresponding definition from the server, display the term and its corresponding definition, and receive an indication of whether the definition satisfies one or more definition quality guidelines. The server memory includes instructions for causing the at least one server processor to receive the indications from the plurality of client devices and label each definition as satisfying each of the definition quality guidelines or not based on the received indications.

MACHINE LEARNING (ML)-BASED METHOD FOR DETERMINING A CONTROL CHANNEL ELEMENT (CCE) AGGREGATION LEVEL FOR A USER EQUIPMENT (UE) IN A PHYSICAL DOWNLINK CONTROL CHANNEL (PDCCH)

Resumen de: WO2024218535A1

The disclosure relates to a ML-based method for determining a CCE aggregation level for a UE in a PDCCH. The method comprises obtaining RBS traces. The method comprises training, using first data obtained from the traces, a machine learning model to predict a probability of discontinuous transmission (DTX) "isDTX probability". The method comprises inputting second data obtained from the traces into the machine learning model, obtaining the isDTX probability and expanding the second data with the isDTX probability. The method comprises, for each of a plurality of probability thresholds (PTs) and for each of a plurality of strategies, selecting a data having an isDTX probability greater or equal to the PT and best satisfying the strategy and using the data to train a classifier. The method comprises selecting one classifier and using the classifier for determining the CCE aggregation level for the UE in the PDCCH.

BATCH SELECTION POLICIES FOR TRAINING MACHINE LEARNING MODELS USING ACTIVE LEARNING

Resumen de: US2024354655A1

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a machine learning model. In one aspect, a method comprises: generating a set of candidate batches of model inputs; generating, for each candidate batch of model inputs, a respective score for the candidate batch of model inputs that characterizes: (i) an uncertainty of the machine learning model in generating predicted labels for the model inputs in the candidate batch of model inputs, and (ii) a diversity of the model inputs in the candidate batch of model inputs; and selecting the current batch of model inputs from the set of candidate batches of model inputs based on the scores; and training the machine learning model on at least the current batch of model inputs.

METHOD FOR DETERMINING SPARSE INTERACTION EFFECT OF BLACK-BOX ARTIFICIAL INTELLIGENCE MODEL

Resumen de: EP4700652A1

The present application relates to the technical field of machine learning. Disclosed are a method and system for interpreting a sparse interaction effect modeled by a black-box artificial intelligence model. The method and system can automatically analyze an interactive distribution modeled by a model. The implementation of the method and system comprises the following steps: providing data that needs to be assessed; using a black-box model to perform prediction on the data, so as to obtain a prediction result of the model; on the basis of an output of the black-box model, modeling the interaction effect between input units of samples, calculating the interaction intensity between combinations formed by the input units, and expressing the black-box model as an "AND addition relationships" and an "OR addition relationships" between the combinations of the input units; and performing optimization, such that the "AND addition relationships" and the "OR addition relationships" are sparser. The advantages of the present invention lie in that a quantification method for interpreting the interaction modeled by a black-box artificial intelligence model is provided, and in comparison with previous research, a sparser and concise interactive interpretation can be obtained.

BALANCED TRAINING DATASETS FOR PREDICTING AIRCRAFT COMPONENT FAULTS

Resumen de: EP4700611A1

The present disclosure provides a method of generating a balanced training dataset for a machine learning model in one aspect, the method including: receiving flight sensor data corresponding to a plurality of flights, and applying one or more criteria to the flight sensor data to generate a training dataset including a plurality of first instances corresponding to flights of the plurality of flights. The method further includes assigning, using component fault data, respective labels to the plurality of first instances, and generating, for groups of one or more labels of the respective labels, a respective plurality of flight series. Each flight series includes a respective sequence of second instances that is based on some of the plurality of first instances, and that concludes with a second instance that is assigned a label included in the group.

SYSTEMS AND METHODS IMPLEMENTING AN INTELLIGENT OPTIMIZATION PLATFORM

Resumen de: EP4700664A2

A system and method includes receiving a tuning work request for tuning an external machine learning model; implementing a plurality of distinct queue worker machines that perform various tuning operations based on the tuning work data of the tuning work request; implementing a plurality of distinct tuning sources that generate values for each of the one or more hyperparameters of the tuning work request; selecting, by one or more queue worker machines of the plurality of distinct queue worker machines, one or more tuning sources of the plurality of distinct tuning sources for tuning the one or more hyperparameters; and using the selected one or more tuning sources to generate one or more suggestions for the one or more hyperparameters, the one or more suggestions comprising values for the one or more hyperparameters of the tuning work request.

USER PROFILING USING CHAIN-OF-THOUGHT KNOWLEDGE GRAPHS FOR QUERYING A MACHINE LEARNING SYSTEM

Nº publicación: EP4700603A1 25/02/2026

Solicitante:

EQUINIX INC [US]
Equinix, Inc

Resumen de: EP4700603A1

Techniques are disclosed for a machine learning model, such as a large learning model (LLM), that incorporates a model of a chain of thought of a particular user when responding to a query from the user. In one example, a system generates a knowledge graph of a chain of thought of the user. The knowledge graph comprises nodes representing topics present within past queries by the user and edges representing a co-occurrence between the topics. The system determines, based on a topic present within a query from the user and the knowledge graph, a goal query comprising a goal topic. The system provides, to a machine learning model, the user to generate, by the machine learning model, a response. The machine learning model is constrained to include the goal topic of the goal query within the response. The system outputs, for display, the response to the query.