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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.

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.

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.

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.

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.

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

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.

System and Method for Providing Advisory Notifications to Mobile Applications

Resumen de: US2025217175A1

A system and method are provided for providing advisory notifications to mobile applications. The method includes interfacing the server device with at least one endpoint within an enterprise system and storing a model trained by a machine learning engine to automatically determine advisory notifications relevant to client data sets stored by the endpoint(s) and/or the at least one endpoint. The method also includes determining a current state of a client account, using the model to determine an advisory notification for the client account based on the current state, referring to a set of rules to determine when to provide the advisory notification in the mobile application, and in what portion of the mobile application to display the notification; and sending the advisory notification via the communications module to a client device to display the advisory notification in the mobile application.

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.

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.

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.

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.