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VIRTUAL WORLD ENVIRONMENTS FOR ENTERPRISE DECISION-MAKING

Resumen de: US2025157088A1

In an example embodiment, machine learning is utilized to create a virtual world where a user can view and interact with data in a graphical environment. This virtual world may be termed a “Story Verse” environment, which can create multiple different virtual world universes capable of segmenting the traditional complexities of Enterprise system data into an easily usable and holistic set. In a further example embodiment, the virtual world is presented in a way that data is represented as real world objects, such as amusement park rides, clouds, etc.

HIGH-FIDELITY PREDICTION WITH TRUST REGIONS

Resumen de: US2025156747A1

According to one embodiment, a method, computer system, and computer program product for making high-fidelity predictions with trust regions is provided. The embodiment may include identifying a data set. The embodiment may also include partitioning the data set into two or more clusters. The embodiment may further include creating two or more disjoint polytopic regions in a multi-dimensional space, wherein a cluster from the two or more disjoint polytopic regions corresponds to a trust region from the two or more polytopic regions. The embodiment may also include training a machine learning model based on the two or more disjoint polytopic regions. The embodiment may further include drawing a conclusion based on the trained machine learning model.

SYNTHETIC DATA GENERATION USING LARGE LANGUAGE MODELS

Resumen de: US2025156644A1

In various examples, synthetic question-answer (QA) pairs may be generated using question and answer generation models comprising corresponding language models (e.g., autoregressive LLMs). A repository of textual data representing a particular knowledge base may be used to source synthetic QA pairs by partitioning textual data from the repository into units of text (e.g., paragraphs) that represent context. For each unit of text, the question generation model may be prompted to generate a synthetic question from that unit of text, and the answer generation model may be prompted to generate a synthetic answer to the synthetic question. Textual entailment and/or human evaluations may be used to filter out low quality, incorrect, and/or non-productive QA pairs that may be a result of hallucinations. As such, the synthetic QA pairs may be used as, and/or may be used to generate, training data for one or more machine learning models.

Suggesting Actions Based on Machine Learning

Resumen de: US2025156203A1

This document describes techniques for suggesting actions based on machine learning. These techniques determine a task that a user desires to perform, and presents a user interface through which to perform the task. To determine this task, the techniques can analyze content displayed on the user device or analyze contexts of the user and user device. With this determined task, the techniques determine an action that may assist the user in performing the task. This action is further determined to be performable through analysis of functionalities of an application, which may or may not be executing or installed on the user device. With some subset of the application's functionalities determined, the techniques presents the subset of functionalities via the user interface. By so doing, the techniques enable a user to complete a task more easily, quickly, or using fewer computing resources.

Technologies for Predictive Management of Customer Account Balance Attrition

Resumen de: US2025156939A1

Technologies for predictive management of customer account balance attrition include a compute device. The compute device includes circuitry configured to obtain data indicative of one or more attributes of a customer of a financial institution. The circuitry may also be configured to generate, from the obtained data, a feature set for use by an ensemble of machine-learning models trained to predict customer behavior, provide the feature set to the ensemble of machine-learning models to produce a prediction of whether the customer of the financial institution will be lost to (e.g., at least a portion of the customer's money will be transferred to) a competitor financial institution, obtain the prediction from the ensemble of machine-learning models, and perform, in response to a determination that the that the customer is predicted to be lost, a remedial action to reduce a likelihood of losing the customer.

MACHINE LEARNING-BASED AUTOMATED ABNORMALITY DETECTION IN MEDICAL IMAGES AND PRESENTATION THEREOF

Resumen de: US2025156677A1

The presently disclosed technology relates to medical image processing. An example method includes receiving medical image data which represents an anatomical structure and processing the received image data through convolutional neural network (CNN) to generate predictions. The predictions can include abnormality location proposals and abnormality class probabilities associated with each abnormality location proposals.

MACHINE LEARNING-BASED AUTOMATED ABNORMALITY DETECTION IN MEDICAL IMAGES AND PRESENTATION THEREOF

Resumen de: US2025156676A1

The presently disclosed technology relates to medical image processing. An example method includes receiving medical image data which represents an anatomical structure and processing the received image data through convolutional neural network (CNN) to generate predictions. The predictions can include abnormality location proposals and abnormality class probabilities associated with each abnormality location proposals.

SYSTEM AND METHOD FOR AUTOMATED MAPPING OF DATA TYPES FOR USE WITH DATAFLOW ENVIRONMENTS

Resumen de: US2025156160A1

In accordance with various embodiments, described herein is a system (Data Artificial Intelligence system, Data AI system), for use with a data integration or other computing environment, that leverages machine learning (ML, DataFlow Machine Learning, DFML), for use in managing a flow of data (dataflow, DF), and building complex dataflow software applications (dataflow applications, pipelines). In accordance with an embodiment, the system can provide support for auto-mapping of complex data structures, datasets or entities, between one or more sources or targets of data, referred to herein in some embodiments as HUBs. The auto-mapping can be driven by a metadata, schema, and statistical profiling of a dataset; and used to map a source dataset or entity associated with an input HUB, to a target dataset or entity or vice versa, to produce an output data prepared in a format or organization (projection) for use with one or more output HUBs.

Automatically Recognizing and Surfacing Important Moments in Multi-Party Conversations

Resumen de: US2025157474A1

A system and a method are disclosed for identifying a subjectively interesting moment in a transcript. In an embodiment, a device receives a transcription of a conversation, and identifies a participant of the conversation. The device accesses a machine learning model corresponding to the participant, and applies, as input to the machine learning model, the transcription. The device receives as output from the machine learning model a portion of the transcription having relevance to the participant, and generates for display, to the participant, information pertaining to the portion.

REUSE OF DATA FOR TRAINING MACHINE LEARNING MODELS

Resumen de: US2025156763A1

Example embodiments may relate to systems, methods and/or computer programs for reusing data for training machine learning models. In an example, an apparatus comprises means for receiving a request to collect new user data for training a machine learning model associated with an application. The apparatus may also comprise means for identifying existing stored data suitable for training the machine learning model based upon an ontology. The apparatus may also comprise means for providing access to the identified existing stored data in response to identifying that the data is suitable for training the machine learning model.

Automatically assigning hybrids or seeds to fields for planting

Resumen de: AU2025202916A1

A computer-implemented method comprising: using field assignment instructions in a server computer system, receiving, over a digital data communication network at the server computer system, grower datasets specifying agricultural fields of growers and inventories of hybrid products or seed products of the growers; using the field assignment instructions in the server computer system, obtaining over the digital data communication network at the server computer system, other input data comprising relative maturity values, historic yield values for the fields of the growers, and mean yield values for regions in which the fields of the growers are located; using the field assignment instructions in the server computer system, calculating pair datasets consisting of permutations of product assignments of two (2) products to two (2) fields from among the fields of the growers, and corresponding converse assignments of the same products and fields; inputting features of the pair dataset(s) to a trained machine learning model, to yield predicted probability of success (POS) values for each of the product assignments and its corresponding converse assignment; blending the predicted POS values for all fields with field classification data using an operations research model of other field data, to result in creating and storing score values for each of the product assignments and the corresponding converse assignments; using the field assignment instructions in the server computer syst

High Frequency QRS in Biometric Identification

Resumen de: AU2025202887A1

Abstract Disclosed is sampling HF-QRS signals from a number of subjects (or derived values or features), and using e.g. deep learning-convolutional neural networks to find features or values which are (i) sufficiently similar for the same subject over all samples, yet (ii) sufficiently different among different subjects to allow identification. Also disclosed is finding signatures which are sufficiently stable over a particular period such that these signatures are within a deviation threshold, and then monitoring all subjects to be identified at least as often as the period used to establish the deviation threshold.

FLEXIBLE MACHINE LEARNING MODEL COMPRESSION

Resumen de: WO2025101721A1

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for compresses a machine learning model having a plurality of parameters. In one aspect, one of the methods includes obtaining trained values of a set of parameters for at least a portion of a machine learning model; identifying one or more dense ranges for the trained values; determining a least number of bits required to represent each trained value within the one or more dense ranges; identifying a second format having a range that is smaller than a range of the first format; and generating a compressed version of the at least a portion of the machine learning model.

BIOLOGICAL AGENT AEROSOL CLASSIFICATION/IDENTIFICATION USING MACHINE LEARNING ALGORITHMS

Resumen de: WO2025101490A1

In accordance with various embodiments, a system and a method for identifying a particle as a bioactive stimulant are provided. The system includes a processor configured to execute machine-readable instructions borne by a non-transitory computer-readable memory device to cause the processor to process one or more steps of the method disclosed herein. The system/method include the steps to: receive a dataset comprising scattered light signals and/or fluorescent light signals of the particle; analyze the dataset using one or more machine learning models, wherein the one or more machine learning models is trained using elastic scattering light intensity data and fluorescent light intensity data of a library of biological molecules; generate a probability score that the particle is bioactive based on the analysis of the dataset; determine, via classification of the probability score, that the particle is bioactive; and/or output a result indicating that the particle is the bioactive stimulant.

GENERATING INFORMED PRIORS FOR HYPERPARAMETER SELECTION

Resumen de: WO2025101345A1

A system iteratively evaluates the target machine learning model using evaluation hyperparameter values of the target machine learning model to measure performance of the target machine learning model for different combinations of the evaluation hyperparameter values. The system trains a surrogate machine learning model using the different combinations of the evaluation hyperparameter values as features and the performance of the target machine learning model based on a corresponding combination of the evaluation hyperparameter values as labels. The system generates a feature importance vector of the surrogate machine learning model based on the training of the surrogate machine learning model, generate informed priors based on the feature importance vector, and generates the target hyperparameter values of the target machine learning model based on the informed priors.

EXPLAINING BLACK BOX NLP MODELS VIA ENSURING INTERMEDIATE HUMAN-UNDERSTANDABLE REPRESENTATIONS WITH PROTOTYPE-BASED LEARNING

Resumen de: WO2025099498A1

A computer-implemented, machine learning method for providing human-understandable intermediate representations using prototype-based learning includes receiving textual input and generating a rationale using the textual input, where the rationale includes an explanation for the textual input. A closest prototype is identified based on the rationale and a defined distance measure. A classification label and a set of rationales for the closest prototype is provided to a user. The method has applications including, but not limited to, use cases in medicine / healthcare (e.g., for disease classification), resource allocation and data security, data integrity, crime investigation, for example, to optimize predictions or support decision-making.

TRANSFORMER-BASED ASSISTANT FOR IDENTIFYING, ORGANIZING, AND RESPONDING TO CUSTOMER CONCERNS

Resumen de: WO2025102019A1

Transformer-based agent assistant systems as machine learning-based customer service tools that analyze past customer-agent conversations to build a knowledge base of problem-resolution steps are disclosed. The system may include a natural language processing (NLP) model and a transfomer-based model to extract and generate customer concerns and resolutions. One embodiment also includes a head-topic and subtopic detection module for identifying trends in customer concerns. Another embodiment uses a question-answering model and a zero-shot-NLI (natural language inference) classifier for entity extraction and detection. The system is designed to be flexible, incorporating new data over time, and can retrieve company documentation or FAQs for the agent based on cosine similarity.

STATE MACHINE DRIVEN AUTOMATED AGENT WITH NESTED AUDITING

Resumen de: US2025156703A1

A system providing a state-driven automated agent. The state machine can include multiple nested state machines to receive, process, and generate responses to a user inquiry received through a chat application. The state machine has a plurality of states, and navigates between states based on one or more machine learning (ML) models. In some instances, each state is associated with a machine learning model that can predict what state the state machine should transition to from its current state. The machine learning model(s) can be implemented using one or more large language models (LLM). The state-driven automated agent includes an auditing state that analyzes a predicted response to a user inquiry and identifies errors with the response. The errors may be identified and automatically self-corrected.

DEVICES, DATA STRUCTURE, AND COMPUTER-IMPLEMENTED METHODS FOR MACHINE LEARNING USING SEMANTIC TECHNOLOGIES

Resumen de: US2025156758A1

Devices, data structure, and computer-implemented methods for machine learning. A method for machine learning includes providing a data structure of a database, which data structure includes a set of nodes and a set of relations, and a set of tuples. Each respective tuple includes at least two nodes, and at least one relation. The method includes predicting a plurality of tuples depending on the data structure, wherein each respective tuple includes at least two nodes and at least one relation, predicting, whether the respective tuples of the plurality of tuples classifies as a member of the set of tuples, selecting a tuple from the plurality of tuples depending on the uncertainties predicted for the respective tuples, acquiring a label that indicates whether the selected tuple classifies as a member of the set of tuples, and adding the selected tuple to the set of tuples based on the label.

DEVICES, DATA STRUCTURE, AND COMPUTER-IMPLEMENTED METHODS FOR MACHINE LEARNING USING SEMANTIC TECHNOLOGIES

Resumen de: EP4553716A1

Devices, data structure, and computer-implemented methods for machine learning, wherein a first method for machine learning comprises providing (402) a data structure of a database, wherein the data structure comprises a set of nodes, in particular a set of entities, or a set of subjects and objects, wherein the data structure comprises a set of relations, in particular a set of edges, or a set of predicates, and wherein the data structure comprises a set of tuples, wherein a respective tuple of the set of tuples comprises at least two nodes of the set of nodes, and at least one relation of the set of relations, in particular two entities of the set of entities and an edge of the set of edges, or a subject, and an object of the set of subjects and object, and a predicate of the set of predicates, wherein the method comprises predicting (404) a plurality of tuples depending on the data structure, wherein a respective tuple of the plurality of tuples comprises at least two nodes of the set of nodes, and at least one relation of the set of relations, in particular two entities of the set of entities and an edge of the set of edges, or a subject, and an object of the set of subjects and objects, and a predicate of the set of predicates, wherein the method comprises predicting (406), for the plurality of tuples, an uncertainty about whether the respective tuple of the plurality of tuples classifies as a member of the set of tuples or not, selecting (408) a tuple from the plurality

INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING SYSTEM, PROGRAM, AND ISING MODEL DEVELOPMENT ASSISTANCE METHOD

Nº publicación: EP4553723A1 14/05/2025

Solicitante:

RESONAC CORP [JP]
Resonac Corporation

Resumen de: EP4553723A1

With respect to an information processing device that supports creation of an Ising model for causing an annealing-type optimization machine to solve an optimum solution search problem, the information processing device includes a transforming unit configured to binarize an explanatory variable included in a training data set created using a trained machine learning model; a training unit configured to train an Ising model by performing machine learning with a relationship between the binarized explanatory variable and a predicted value of the training data set; and an output unit configured to output the trained Ising model.