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METROLOGY AND PROCESS CONTROL FOR SEMICONDUCTOR MANUFACTURING

Resumen de: US2025181941A1

A semiconductor metrology system including a spectrum acquisition tool for collecting, using a first measurement protocol, baseline scatterometric spectra on first semiconductor wafer targets, and for various sources of spectral variability, variability sets of scatterometric spectra on second semiconductor wafer targets, the variability sets embodying the spectral variability, a reference metrology tool for collecting, using a second measurement protocol, parameter values of the first semiconductor wafer targets, and a training unit for training, using the collected spectra and values, a prediction model using machine learning and minimizing an associated loss function incorporating spectral variability terms, the prediction model for predicting values for production semiconductor wafer targets based on their spectra.

SUPERVISOR MACHINE LEARNING MODELS FOR TRANSFORMING STATES

Resumen de: WO2025116904A1

Described is a system for transforming an arrangement of data items by accessing reference data, inferring an arrangement transformation rule that controlled a first transformation of the reference arrangement of reference data items from the reference initial state to the reference transformed state, accessing candidate data, and causing a supervisor machine learning model to generate a full output that indicates a candidate transformed state of the candidate arrangement based on the arrangement transformation rule that controlled the first transformation, the supervisor machine learning model causing a supervisee machine learning model to generate a partial output based on the item transformation rule the controlled the second transformation, the partial output indicating a candidate data item in the candidate transformed state, the supervisor machine learning model generating the full output based on the partial output generated by the supervisee machine learning model.

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

Resumen de: AU2023380279A1

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.

MACHINE LEARNING MODEL ARRANGEMENT IDENTIFICATION

Resumen de: WO2025116907A1

Described is a system for arrangement identification by accessing reference data that indicates reference initial states of reference data items and indicates reference transformed states of the reference data items, identifying a reference arrangement that exhibits a fixed set of proportional relationships, inferring a transformation rule, accessing candidate data, identifying a candidate arrangement that exhibits the fixed set of proportional relationships, and generating an output that indicates the candidate arrangement of data items in a candidate transformed state determined based on the transformation rule.

PIPELINED EXECUTION OF GENERATIVE ARTIFICIAL INTELLIGENCE MODELS

Resumen de: WO2025111787A1

Techniques and apparatus for efficiently generating a response to an input query using a generative artificial intelligence model in a pipelined execution environment. An example method generally includes loading a first portion of a machine learning model, wherein the first portion of the machine learning model is associated with a first inference; loading a second portion of the machine learning model, wherein the second portion of the machine learning model is associated with a second inference; and while loading the second portion of the machine learning model, generating the first inference based on an input data set and the first portion of the machine learning model.

MACHINE LEARNING BASED OCCUPANCY FORECASTING

Resumen de: WO2025117106A1

Embodiments determine a final occupancy prediction for a check-in date for a plurality of hotel rooms. Embodiments receive historical reservation data including a plurality of booking curves for the hotel rooms corresponding to a plurality of reservation windows, the historical reservation data including a plurality of features. Based on the historical reservation data, embodiments generate a first occupancy prediction for the check-in date using a first model and generate a second occupancy prediction for the check-in date using a second model. Embodiments determine a best performing model from at least the first model and the second model uses a corresponding occupancy prediction corresponding to the best performing model as the final occupancy prediction for the check-in date.

METHODS, SYSTEMS, AND DEVICES TO VALIDATE IP ADDRESSES

Resumen de: US2025184345A1

Aspects of the subject disclosure may include, for example, obtaining a first group of Internet Protocol (IP) addresses from a group of network devices, and determining a second group of IP addresses from the first group of IP addresses includes possible malicious IP addresses utilizing a machine learning application. Further embodiments can include obtaining a first group of attributes of malicious IP addresses from a first repository, and determining a third group of IP addresses from the second group of IP addresses includes possible malicious IP addresses based on the first group of attributes. Additional embodiments can include receiving user-generated input indicating a fourth group of IP addresses from the third group of IP addresses includes possible malicious IP addresses, and transmitting a notification to a group of communication devices indicating that the fourth group of IP address includes possible malicious IP addresses. Other embodiments are disclosed.

UTILIZING MACHINE LEARNING AND A SMART TRANSACTION CARD TO AUTOMATICALLY IDENTIFY OPTIMAL PRICES AND REBATES FOR ITEMS DURING IN-PERSON SHOPPING

Resumen de: US2025182156A1

A device may receive, from a client device of a customer, item data identifying a price of an item and customer data identifying the customer, where the item data may be received by a transaction card from a price tag of the item. The device may receive price data identifying prices associated with multiple items and other data identifying locations, availabilities, and terms of the multiple items, and may process the item data, the price data, and the other data, with a machine learning model, to identify an optimal price for the item. The device may provide, to the client device, data identifying the optimal price and data identifying a merchant associated with the optimal price, and may receive transaction data identifying the item, the optimal price, and the merchant when the customer purchases the item. The device may perform actions based on the transaction data.

MODULAR MACHINE LEARNING SYSTEMS AND METHODS

Resumen de: US2025181676A1

A computer system is provided that is designed to handle multi-label classification. The computer system includes multiple processing instances that are arranged in a hierarchal manner and execute differently trained classification models. The classification task of one processing instance and the executed model therein may rely on the results of classification performed by another processing instance. Each of the models may be associated with a different threshold value that is used to binarize the probability output from the classification model.

Method And System For Key Predictors And Machine Learning For Configuring Cell Performance

Resumen de: US2025183392A1

A method of managing battery performance may include obtaining, via a measurement device, measurements of one or more parameters relating to one or more cells; generating or updating, based on the measurements, a machine learning model; and generating, using the machine learning model, cell performance prediction data for use in managing at least one cell. Each cell includes a cathode, a separator, and a silicon-dominant anode. The measurements of the one or more parameters correspond to a plurality of different types of data. The measurements include one or more of: measurements of cells or cell components before formation or cycling, measurements from formation cycles for one or more cells, measurements from a number of cycles after formation for one or more cells, and measurements of characteristics of cell components prior to cell assembly.

DEEP LEARNING SYSTEMS AND METHODS FOR PREDICTING IMPACT OF CARDHOLDER BEHAVIOR BASED ON PAYMENT EVENTS

Nº publicación: EP4562570A1 04/06/2025

Solicitante:

MASTERCARD INTERNATIONAL INC [US]
Mastercard International Incorporated

Resumen de: WO2024025710A1

A system is configured to retrieve a set of customer raw transaction data, wherein the transactions are devoid of any target transactions of interest. An impact neural network model is applied to the transaction data using a "notTargef ' variable. The "notTargef ' variable indicates that the target transaction of interest is not included in the transaction data. The model predicts a first result based on the "notTargef' variable. The model is applied to the transaction data using an "isTargef ' variable. The "isTargef ' variable indicates that the target transaction of interest is included in the set of customer raw transaction data. The model predicts a second result based on the "isTargef ' variable. The system determines a difference between the second and first results. The difference is a predicted incremental impact on cardholder behavior. The system presents the predicted incremental impact on cardholder behavior to an issuer associated with the transaction data.