Alerta

AGENT-BASED MODELING OF MACHINE-LEARNING TASKS

Resumen de: WO2025116908A1

Described is a system for generating an inference output on candidate data based on reference data by identifying a reference subset of the reference data items with a transformation rule shared in common, accessing candidate data that indicates candidate initial states of candidate data items without indicating any transformed states of the candidate data items, and identifying a candidate subset of the candidate data items based on the identified reference subset of the reference data items. The system then transforms the candidate data items in the candidate subset from their candidate initial states to candidate transformed states based on the transformation rule that defines the goal attained by each one of the reference data items in the reference subset by transforming from its reference initial state to its reference transformed state, and generates an output that indicates the candidate transformed states of the candidate subset of the candidate data items.

METHOD, SYSTEM AND COMPUTER PROGRAM PRODUCT FOR DATA MINING WITH ARTIFICIAL INTELLIGENCE (AI) BASED SMART AGENTS

Resumen de: WO2025117883A1

The invention relates to an AI-driven system for data analytics, processing, mining, and user interaction, utilizing large language models (LLMs) and machine learning (ML) techniques. The system enables personalized, real-time access to company data, guided by AI Agents. These Agents handle tasks such as data extraction, transformation, and loading, with a multi-stage processing pipeline that includes raw data ingestion, curation, and modeling. Specialized Agents like Fixing and Modeling Agents ensure data quality, analysis, and visualization. The system also integrates with BI dashboards for generating insights and predictive analytics. Users interact via natural language queries (NLQs) to receive context-aware, AI-generated answers, including various types of plots, graphs and charts, thus improving decision-making and data management efficiency.

CLOSED-LOOP OPTIMIZATION OF GENERAL REACTION CONDITIONS FOR HETEROARYL SUZUKI-MIYAURA COUPLING

Resumen de: AU2023366930A1

Disclosed are systems and methods for rapidly generating general reaction conditions using a closed-loop workflow leveraging matrix down-selection, machine learning, and robotic experimentation. In certain aspects, provided is a method, comprising: selecting a reaction pair comprising a first molecule and a second molecule; wherein the first molecule is selected from a first matrix and the second molecule is selected from a second matrix; selecting one or more reaction conditions for the reaction pair, the selection based on historic use of the one or more reaction conditions and a structural and functional diversity of the selected reaction pair; automatically performing, by a robotic system, an initial round of reactions between the selected reaction pair under the selected one or more reaction conditions.

FEATURE DIMENSIONALITY REDUCTION FOR MACHINE LEARNING MODELS

Resumen de: US2025181991A1

Provided is a method, system, and computer program product for performing automated feature dimensionality reduction without accuracy loss. A processor may determine a first training value associated with a first dataset of a machine learning model. The processor may rank features of the first dataset in relation to the first training value. The processor may compare the ranked features of the first dataset to a predetermined threshold. The processor may generate a second dataset from the first dataset by removing a third dataset, the third dataset having a set of features that did not meet the predetermined threshold. The processor may determine a second training value associated with the second dataset. The processor may compare the first training value to the second training value. In response to the second training value being lower than the first training value, the processor may analyze the third dataset with a dimensionality reduction algorithm.

CONCURRENT OPTIMIZATION OF MACHINE LEARNING MODEL PERFORMANCE

Resumen de: US2025181978A1

Certain aspects of the present disclosure provide techniques for concurrently performing inferences using a machine learning model and optimizing parameters used in executing the machine learning model. An example method generally includes receiving a request to perform inferences on a data set using the machine learning model and performance metric targets for performance of the inferences. At least a first inference is performed on the data set using the machine learning model to meet a latency specified for generation of the first inference from receipt of the request. While performing the at least the first inference, operational parameters resulting in inference performance approaching the performance metric targets are identified based on the machine learning model and operational properties of the computing device. The identified operational parameters are applied to performance of subsequent inferences using the machine learning model.

DYNAMIC FILTER RECOMMENDATIONS

Resumen de: US2025181587A1

A user preference hierarchy is determined from user response to images. Images may be tagged using machine learning models trained to determine values for images. Products are clustered according to product vectors. Images of products within a cluster are clustered according to composition and groups of images are selected from image clusters for soliciting feedback regarding user preference for products of a cluster. Feedback is used to train a user preference model to estimate affinity for a product vector. A user may provide feedback regarding a price point and products are weighted according to a distribution about the price point. The distribution may be asymmetrical according to direction of movement of the price point. Filters may be dynamically defined and presented to a user based on popularity and frequency of occurrence of attribute-value pairs of search results and based on feedback regarding the search results.

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.

SYSTEMS AND METHODS FOR PROGRAMMATIC LABELING OF TRAINING DATA FOR MACHINE LEARNING MODELS VIA CLUSTERING AND LANGUAGE MODEL PROMPTING

Resumen de: AU2023383086A1

Embodiments introduce an approach to semi-automatically generate labels for data based on implementation of a clustering or language model prompting technique and can be used to implement a form of programmatic labeling to accelerate the development of classifiers and other forms of models. The disclosed methodology is particularly helpful in generating labels or annotations for unstructured data. In some embodiments, the disclosed approach may be used with data in the form of text, images, or other form of unstructured data.

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.

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.

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.

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.

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.