Alerta

System and Method to Predict Service Level Failure in Supply Chains

Resumen de: US2025225473A1

A system and method are disclosed for a low-touch centralized system to predict service level failure in a supply chain using machine learning. Embodiments include receiving only historical supply chain data from an archiving system for one or more supply chain entities storing items at stocking locations, predicting one or more supply chain events during a prediction period by applying a predictive model to a sample of historical supply chain data, calculating an occurrence risk score for at least one of the one or more supply chain events and indicating a possibility that the at least one of the one or more supply chain events will occur, generating one or more alerts identifying at least one item and at least one alert stocking location, rendering an alert heatmap visualization comprising one or more selectable user interface elements, and provide one or more tools for initiating corrective actions to be undertaken.

MEDICAL INFORMATION PROCESSING DEVICE, MEDICAL INFORMATION PROCESSING METHOD, AND STORAGE MEDIUM

Resumen de: US2025225439A1

A medical information processing device of an embodiment includes processing circuitry. The processing circuitry is configured to acquire a machine learning model and training data used to train the machine learning model, determine an inference basis for each piece of the training data using the machine learning model to generate inference basis visualization results, determine a concept emphasized by the machine learning model during inference based on the inference basis visualization results, calculate a concept reflection degree of each piece of the training data related to the concept, and generate visualization information of a dependency between the concept and a feature interpretable by a user in the training data based on the concept reflection degree and the feature interpretable by the user.

SYSTEMS AND METHODS FOR IMPLEMENTING DRUG MECHANISMS OF ACTION WITH MACHINE LEARNING

Resumen de: US2025225445A1

A computer-implemented method for generating machine learning training data may include obtaining mechanism of action (MOA) data that is indicative of a hierarchical tree structure of relationships between the MOA data; generating linear representations of branches of the hierarchical tree structure; determining association rules for the MOA data by applying one or more frequent pattern mining algorithm to the linear representations; and determining, as at least a portion of the generated machine learning training data, MOA clusters by applying a clustering model to the linear representations and the association rules.

MACHINE LEARNING BASED FUNCTION TESTING

Resumen de: US2025225441A1

A method for determining the performance metric of a function may include interpolating the performance metric of the function relative to a known performance metric of a reference function. The performance metric of the function may be interpolated based on a first difference in a performance of the function measured by applying a first machine learning model and a performance of the function measured by applying a second machine learning model. The performance metric of the function may be further interpolated based on a second difference in a performance of the reference function measured by applying the first machine learning model and a performance of the reference function measured by applying the second machine learning model. The function may be deployed to a production system if the performance metric of the function exceeds a threshold value. Related systems and articles of manufacture, including computer program products, are also provided.

MACHINE LEARNING ENGINE USING FOLLOWING LINK SELECTION

Resumen de: US2025225523A1

A machine learning engine may be trained using artificial intelligence techniques and used according to techniques discussed herein. While an initial electronic transaction for a resource may be permitted, a subsequent related transaction to the initial electronic transaction may be analyzed in view of additional electronic information that was not available at the time of the initial transaction. Analysis of the subsequent related transaction, using the machine learning engine, may indicate a new classification related to the resource and/or the acquisition of the resource. Based on this new classification, usage of the resource may be restricted and/or denied, and the initial transaction for the resource may even be canceled retroactively.

USER INTERFACE FOR MATERIALS AND MATERIAL PROPERTIES PREDICTION USING A MACHINE LEARNING MODEL

Resumen de: AU2023409235A1

Machine learning can be used to predict formulations for an output formulation. The machine learning can be implemented by a machine learning model, which employs a forward model and an inverse model. A user interface can be used to gather raw materials selections and output formulation property selections. The selections can be used to generate formulations that comply with selections using the ML model.

System for adaptive prompt enhancement in generative artificial intelligence

Resumen de: AU2024266910A1

Aspects of the present disclosure relate to generating optimized machine learning model prompts. Embodiments include providing an input prompt to a child machine learning model that directs the child machine learning model to generate an output. Embodiments further include generating a parent model prompt comprising instructions to generate a score for the input prompt based on one or more scoring criteria, the input prompt, and the output of the child machine learning model. Embodiments further include providing the parent model prompt to a parent machine learning model. Embodiments further include generating, by a generative machine learning model, an optimized prompt for the child machine learning model based on the generated score for the input prompt. Aspects of the present disclosure relate to generating optimized machine learning model prompts. Embodiments include providing an input prompt to a child machine learning model that directs the child machine learning model to generate an output. Embodiments further include generating a parent model prompt comprising instructions to generate a score for the input prompt based on one or more scoring criteria, the input prompt, and the output of the child machine learning model. Embodiments further include providing the parent model prompt to a parent machine learning model. Embodiments further include generating, by a generative machine learning model, an optimized prompt for the child machine learning model based on the gene

MACHINE LEARNING TO ENHANCE SATELLITE TERMINAL PERFORMANCE

Resumen de: US2024396814A1

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for using machine learning to detect and correct satellite terminal performance limitations. In some implementations, a system retrieves data indicating labels for clusters of network performance anomalies. The system generates a set of training data to train a machine learning model, the set of training data being generated by assigning the labels for the clusters to sets of performance indicators used to generate the clusters. The system trains a machine learning model to predict classifications for communication devices based on input of performance indicators for the communication devices. The system determines a classification for the communication device based on output that the trained machine learning model generates.

TRAINING OF MACHINE LEARNING MODELS FOR WELL TARGET RECOMMENDATION

Resumen de: WO2024064077A1

Disclosed are methods, systems, and computer programs for placing one or more optimal infill well locations within a reservoir. The methods include: generating a first multi-dimensional reservoir model of a first reservoir that is parameterized; assigning well placement data to the first reservoir model to generate a simulation model; applying a stochastic optimization process in a first simulation on the simulation model; determining infill well locations data based on the first simulation; configuring a second multi-dimensional reservoir model based on the infill well locations data; and generating using the second multi-dimensional reservoir model, one or more of: pressure delta data for one or more infill locations associated with a second reservoir, and a simulation opportunity index indicating reservoir properties for the one or more infill locations associated with the second reservoir.

MACHINE LEARNING TRAINING FOR CHARACTERIZING WATER INJECTION AND SEISMIC PREDICTION

Resumen de: WO2024064009A1

A method including receiving a reservoir model of a target underground region. The method also includes extracting, from the reservoir model, a historic pressure distribution in grid cells of the target underground region. The method also includes extracting, from the reservoir model, distances. Each distance represents a distance between a grid cell and a corresponding lineament in the target underground region. The method also includes receiving historic earthquake data of past earthquakes in the target underground region. The method also includes generating a vector. The vector includes features and corresponding values for at least i) the historic pressure distribution, ii) the distances, and iii) the historic earthquake data. The method also includes training a trained machine learning algorithm by recursively executing a machine learning algorithm on the vector until convergence.

AUTOMATED MACHINE LEARNING FAULT MODELING WITH GROUPING

Resumen de: WO2024063797A1

Methods, computing systems, and computer-readable media for a machine learning method of modeling fault-related properties of a geological region are presented. The techniques include: obtaining seismic geological data for a geological region; obtaining from a user identifications of a plurality of faults in the geological region; automatically generating values for descriptors of respective faults of the plurality of faults; automatically partitioning faults of the plurality of faults into a plurality of groups according to the values for the descriptors; obtaining a mapping of respective groups of the plurality of groups to modeling parameter values; applying the mapping to a fault in the geological region outside of the plurality of faults to obtain a modeling parameter value for the fault outside of the plurality of faults; and modeling a fault-related property of the geological region based on the modeling parameter value for the fault outside of the plurality of faults.

MACHINE LEARNING INFERENCE SERVER AND ADAPTIVE BATCH SIZE DETERMINATION METHOD IN MACHINE LEARNING INFERENCE SERVICE

Resumen de: KR20250101648A

본 명세서의 기계학습 추론 서버는, 지난 1초 동안 들어온 요청량을 모니터링하고 상기 요청량에 따른 최적 배치 크기를 최소 배치 크기에서부터 하나씩 증가하면서 탐색하여 결정하는 배치 모니터링 알고리즘을 구비한 배치 모니터; 및 상기 배치 모니터의 결정에 따라 실제 서버의 요청 대기열에 접근하여 배치를 구성하고, GPU에 전달하여 연산이 처리되도록 하는 디스패처를 포함할 수 있다.

COMPUTER-READABLE RECORDING MEDIUM STORING MACHINE LEARNING PROGRAM, COMPUTER-READABLE RECORDING MEDIUM STORING DETERMINATION PROGRAM, AND MACHINE LEARNING DEVICE

Resumen de: US2025217435A1

A non-transitory computer-readable recording medium storing a machine learning program for causing a computer to execute a process including training a machine learning model by machine learning that uses a cost function in which each element of a matrix obtained by relaxing a discrete variable to be optimized to a continuous matrix becomes a discrete optimization problem as a cost function in a search process that performs a search by adopting continuous relaxation into the discrete optimization problem.

FRAMEWORK FOR FOCUSED TRAINING OF LANGUAGE MODELS AND TECHNIQUES FOR END-TO-END HYPERTUNING OF THE FRAMEWORK

Resumen de: US2025218428A1

Techniques are disclosed herein for focused training of language models and end-to-end hypertuning of the framework. In one aspect, a method is provided that includes obtaining a machine learning model pre-trained for language modeling, and post-training the machine learning model for various tasks to generate a focused machine learning model. The post-training includes: (i) training the machine learning model on an unlabeled set of training data pertaining to a task that the machine learning model was pre-trained for as part of the language modeling, and the unlabeled set of training data is obtained with respect to a target domain, a target task, or a target language, and (ii) training the machine learning model on a labeled set of training data that pertains to another task that is an auxiliary task related to a downstream task to be performed using the machine learning model or output from the machine learning model.

Data source mapper for enhanced data retrieval

Resumen de: AU2024266907A1

Aspects of the present disclosure provide techniques for enhanced electronic data retrieval. Embodiments include receiving a natural language query and identifying one or more electronic data sources indicated in the natural language query using a named entity recognition (NER) machine learning model trained through a supervised learning process based on training natural language strings associated with labels indicating entity names. Embodiments include determining one or more additional electronic data sources related to the one or more electronic data sources using a knowledge graph that maps relationships among electronic data sources. Embodiments include retrieving data related to the natural language query by transmitting requests to the one or more electronic data sources and the one or more additional electronic data sources and providing a response to the natural language query based on the data related to the natural language query. Aspects of the present disclosure provide techniques for enhanced electronic data retrieval. Embodiments include receiving a natural language query and identifying one or more electronic data sources indicated in the natural language query using a named entity recognition (NER) machine learning model trained through a supervised learning process based on training natural language strings associated with labels indicating entity names. Embodiments include determining one or more additional electronic data sources related to the one or mor

GENERATIVE ATOMISTIC DESIGN OF MATERIALS

Resumen de: US2025218551A1

A system and method are provided for generative atomistic design of materials. The disclosure herein includes a machine learning system for generating a new material, using multiple predictive machine learning models for atomic level properties to create training data, and/or then using the same predictive machine learning models to refine the output of a generative machine learning system. In use, one or more datasets are received at at least one computing device corresponding to a desired material. Additionally, using at least two machine learning models associated with the at least one computing device, a new dataset is created for the desired material. Further, the at least two machine learning models are trained, using at least semi-supervised learning, based on the one or more datasets, to model properties of the desired material. Still yet, using the at least one computing device, a prediction is outputted comprising the desired material.

USING MACHINE LEARNING TO DECODE NEURAL ACTIVITY, AND APPLICATIONS THEREOF

Resumen de: WO2025141520A1

In an embodiment, a computer-implemented method for decoding neural activity is provided. In the method, at least one machine learning model is trained using a training data set of EEG data and concurrently collected environmental data collected from data collection participants. Once the at least one machine learning model is trained, EEG data measured from sensors attached to or near a user's head is received. Environmental data describing stimulus the user is exposed to concurrently with the measurement of the EEG data is also received. The EEG data and the environmental data is input into the at least one machine learning model to determine an inference related to the neural activity. Based on the inference, an operation of a computer program is altered.

BIG DATA INTELLIGENT DECISION ANALYSIS METHOD AND SYSTEM BASED ON MACHINE LEARNING

Resumen de: WO2025139187A1

The present invention relates to the technical field of data analysis, and specifically relates to a big data intelligent decision analysis method and system based on machine learning. The system comprises an interaction layer, an analysis layer, and a decision layer. Real-time position information of users and building parameters of an area where the users are located are captured by the interaction layer, and seismic information is synchronously collected by the interaction layer in real time; and the analysis layer receives the seismic information collected by the interaction layer in real time. In the present invention, on the basis of the real-time position information of the users and the seismic information, a better earthquake risk mitigation and maintenance effect is brought to the users, so as to ensure that an unorganized user population, when an earthquake strikes, can more orderly evacuate from a building on the basis of data provided by the system, thereby effectively improving the escape probability of indoor users when an earthquake strikes; and moreover, when indoor users cannot escape, data support is provided for rescue personnel in a position information feedback mode, so that rescue work can be carried out more efficiently, and a safety guarantee is further provided for the users.

DATA PRIVACY PRESERVATION IN MACHINE LEARNING TRAINING

Resumen de: WO2025144544A1

A first computing system includes a data store with a sensitive dataset. The first computing system uses a feature extraction tool to perform a statistical analysis of the dataset to generate feature description data to describe a set of features within the dataset. A second computing system is coupled to the first computing system and does not have access to the dataset. The second computing system uses a data synthesizer to receive the feature description data and generate a synthetic dataset that models the dataset and includes the set of features. The second computing system trains a machine learning model with the synthetic data set and provides the trained machine learning model to the first computing system for use with data from the data store as an input.

SAVING QUBITS BY OPTIMIZING ONE-HOT ENCODING GRANULARITY, RANGE, AND POINT DISTRIBUTION

Resumen de: US2025217668A1

A machine learning model is trained, using historical data, to generate distributions for integer variables of a problem. When a new problem or problem instance is presented, the model is used to predict a distribution for each of the integer variables. A range is determined from each of the distributions. One-hot encoded binary variables are generated from the ranges. This reduces the number of qubits needed to one-hot encode the problem instance.

MACHINE-LEARNING MODELS FOR TUMOR GRADING USING RANK-AWARE CONTEXTUAL REASONING ON WHOLE SLIDE IMAGES

Resumen de: WO2025145040A1

This specification discloses systems, methods, and techniques for classifying a grade of cancer represented in a tumor tissue sample. The techniques include obtaining a whole-slide image (WSI) of the tumor tissue sample and partitioning the WSI into a collection of patches. For each patch, (i) a contextualized feature representation of the patch is generated based on intrinsic features of the patch, local dependencies between the patch and a subset of local patches of the WSI, and non-local dependencies between the patch and a subset of non-local patches of the WSI; and (ii) an attention weight is determined for the patch. A WSI-level cancer grade for the tumor tissue sample is predicted based on the contextualized feature representations and the attention weights for the plurality of patches.

MACHINE LEARNING SYSTEM USING QUANTUM COMPUTING

Resumen de: WO2025144432A2

Methods and systems for training and using a binary classifier implemented using quantum computing techniques are disclosed. The described approach involves deriving, from an input data set, a plurality of training samples, each training sample comprising a data vector having a plurality of features and a class label. Each data vector is processed using a quantum classification process including: encoding the data vector as an Ising Hamiltonian; implementing the Ising Hamiltonian on a set of real or virtual qubits of a quantum processing unit or an emulation thereof to form a quantum system representing the data vector; executing operations on the (emulation of the) quantum processing unit to prepare the ground state of the quantum system; determining one or more properties of the ground state; and identifying one of a set of possible ground states corresponding to the data vector based on the one or more properties. The system then determines, based on the identified ground states and class labels for the training samples, a mapping that maps ground states to class labels. The mapping is stored and used for classifying further data samples.

MACHINE LEARNING BASED SYSTEMS AND METHODS FOR CREDIT RISK MANAGEMENT

Resumen de: US2025217881A1

A machine learning based computing method for automatic managing credit risks of first users, is disclosed. The machine learning based computing method includes: receiving inputs from electronic devices associated with second users; retrieving data associated with first users from databases; preprocessing the data to remove noises, outliers, and missing values, from datasets; determining, the credit risks of the entities based on the pre-processed data by machine learning models; generating credit decisions for the entities; generating confidence scores for credit decisions to classify the credit decisions, based on correlation between the data and credit decisions; determining recommended credit values, recommended first credit limits, and recommended second credit limits, based on classification of the credit decisions; and providing an output of the credit decisions, the recommended credit values and the recommended credit limits, to the second users on user interfaces associated with electronic devices.

SYSTEMS AND METHODS FOR DETERMINING LIFETIME VALUE OF WEBSITE VISITOR THROUGH MACHINE LEARNING

Resumen de: US2025217826A1

A vehicle data system receives a lead submission through a website supported by the vehicle data system and determines, utilizing a machine learning model, a user value for a lead associated with the lead submission. The user value represents a probability of the lead purchasing a vehicle from a dealer through the website. The vehicle data system determines a user lifetime value for the lead based at least on the user value for the lead. Subsequently, the vehicle data system obtains clickstream identifiers from a search engine and assigns a corresponding user lifetime value to each clickstream identifier. The vehicle data system aggregates the clickstream identifiers and corresponding user lifetime values in a single file and communicates the single file to a search server for consumption. The user lifetime values are utilized by the search engine in search engine marketing processes.

SYSTEM AND METHOD FOR CONSTRUCTING DIGITAL DOCUMENTS

Nº publicación: US2025217579A1 03/07/2025

Solicitante:

INCLOUD LLC [US]
InCloud, LLC

Resumen de: US2025217579A1

In some aspects described herein, a computer-based system that is capable of constructing digital documents is provided. In some implementations, a machine learning system is provided that learns certain terms within a document. The terms may be, for example, part of a document that forms a legally-binding contract between two entities. In one implementation of the machine learning system, the machine learning system interoperates within a user interface to show predictions of certain terms within the document to the user. Further, the machine learning system may capture user answers relating to certain terms and provide feedback into the system that learns during operation of the system, improving user interactions, accuracy and reducing the number of user interactions.