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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.

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

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.

FORECASTING USING DIFFERENTIAL-BASED MACHINE-LEARNING ARCHITECTURE

Resumen de: WO2025108940A1

Conventional forecast models assume that the underlying data are stationary. In the case of non-stationary data, the conventional forecast model must be increased in complexity, or otherwise, must be frequently retrained. Embodiments are disclosed of a forecast model that accounts for non-stationary data, without increased complexity and without an increase in the frequency of retraining, by recasting the forecasting problem as a matter of tracking differentials in the data. In particular, variables (e.g., covariates) are input into differential determinations and/or intermediate machine-learning models to determine differences in past and forecasted values of a plurality of features. These differences are then input into a machine- learning model to predict a change in the value of the target, which is aggregated with a past value of the target to produce a forecasted value of the target.

LIFELONG MACHINE LEARNING (LML) MODEL FOR PATIENT SUBPOPULATION IDENTIFICATION USING REAL-WORLD HEALTHCARE DATA

Resumen de: US2025173563A1

A deep learning model implements continuous, lifelong machine learning (LML) based on a Bayesian neural network using a framework including wide, deep, and prior components that use available real-world healthcare data differently to improve prediction performance. The outputs from each component of the framework are combined to produce a final output that may be utilized as a prior structure when the deep learning model is refreshed with new data in a deep learning process. Lifelong learning is implemented by dynamically integrating present learning from the wide and deep learning components with past learning from models in the prior component into future predictions. The Bayesian deep neural network-based LML model increases accuracy in identifying patient profiles by continuously learning, as new data becomes available, without forgetting prior knowledge.

ACTIONABLE INSIGHT GENERATION FOR INCIDENTS GENERATED FROM EVENTS

Resumen de: US2025173607A1

A plurality of resolved incident tickets of a technology landscape may be received from an incident handling system. A plurality of events may be received from a metric monitoring system monitoring the technology landscape. An incident cluster having related incidents may be generated from the plurality of resolved incident tickets, and a correlated event of the plurality of events may be identified for the incident cluster. The correlated event may be stored with an incident resolution obtained from the incident cluster, to obtain labeled training data. A machine learning (ML) model may be trained with the labeled training data to obtain an incident prediction model. A new event may be processed with the incident prediction model to provide a predicted incident and a predicted resolution.

SYSTEM AND METHOD FOR IDENTIFYING NATURAL ALTERNATIVES TO SYNTHETIC ADDITIVES IN FOODS

Resumen de: US2025173661A1

A method of modifying a food item to contain plant-based ingredients includes identifying plant-based substances to replace an ingredient of the food item. The plant-based substances are clustered, via a machine learning model, into a plurality of clusters according to an objective based on properties of the plant-based substances. The plant-based substances of a selected cluster are classified into a plurality of classes, via a machine learning classifier, based on the objective and the properties of the plant-based substances of the selected cluster. A score is determined for each plant-based substance of a selected class based on metrics. A plant-based substance is determined based on the score to produce a modified food item with the determined plant-based substance replacing the ingredient.

GENERATING AND PROCESSING SIMULATED MEDICAL INFORMATION FOR PREDICTIVE MODELING

Resumen de: US2025174362A1

A system receives feature parameters, each identifying possible values for one of a set of features. The system receives outcomes corresponding to the feature parameters. The system generates a simulated patient population dataset with multiple simulated patient datasets, each simulated patient dataset associated with the outcomes and including feature values falling within the possible values identified by the feature parameters. The system may train a machine learning engine based on the simulated patient population dataset and optionally additional simulated patient population datasets. The machine learning engine generates predicted outcomes based on the training in response to queries identifying feature values.

AI-CONTROLLED SENSOR NETWORK FOR THREAT MAPPING AND CHARACTERIZATION AND RISK ADJUSTED RESPONSE

Resumen de: US2025175456A1

A system and method for an AI-controlled sensor network for threat mapping and characterization. The system deploys a network of honeypots and sensors across various geographic locations and network segments, collecting and aggregating data on network traffic and potential threats. An AI orchestrator analyzes this data using advanced machine learning models, generating dynamic honeypot profiles and a comprehensive threat landscape. The system can adapt in real-time to emerging threats, optimize resource allocation, and provide actionable intelligence. By correlating data across multiple points, the system offers enhanced threat detection capabilities and proactive cybersecurity measures, surpassing traditional security information and event management (SIEM) tools.

Methods And Systems For Quantifying Partitioning Of Agents In Vivo Based On Partitioning Of Agents In Vitro

Resumen de: US2025172540A1

Small molecule therapeutics can concentrate in distinct intracellular environments, some bounded by membranes, and others that may be formed by membrane-less biomolecular condensates. The chemical environments within biomolecular condensates have been proposed to differ from those outside these bodies, but the internal chemical environments of diverse condensates have yet to be explored. Here we use small molecule probes to demonstrate that condensates formed in vitro with the scaffold proteins of different biomolecular condensates harbor distinct chemical solvating properties. The chemical rules that govern selective partitioning in condensates, which we term condensate chemical grammar, can be ascertained by deep learning, allowing efficient prediction of the partitioning behavior of small molecules. The rules learned from in vitro condensates were adequate to predict the partitioning of small molecules into nucleolar condensates in living cells. Different biomolecular condensates harbor distinct chemical environments, that the chemical grammar of condensates can be ascertained by machine learning.

AUTOMATIC REVISIONS TO DOCUMENT CLAUSES BASED ON CLAUSE TYPE

Resumen de: US2025173380A1

A document management system can include an artificial intelligence-based document manager that can perform one or more predictive operations based on characteristics of a user, a document, a user account, or historical document activity. For instance, the document management system can apply a machine-learning model to determine how long an expiring agreement document is likely to take to renegotiate and can prompt a user to begin the renegotiation process in advance. The document management system can detect a change to language in a particular clause type and can prompt a user to update other documents that include the clause type to include the change. The document management system can determine a type of a document being worked on and can identify one or more actions that a corresponding user may want to take using a machine-learning model trained on similar documents and similar users.

Monitoring Machine Learning Models

Resumen de: US2025173585A1

A computer-implemented method for monitoring machine learning models in a distributed setup includes obtaining model activity data relating to activity of the machine learning models in the distributed setup; analyzing the obtained model activity data; and, based on the analysis of the model activity data, outputting model management data for managing the activity of the machine learning models in the distributed setup.

Machine Learning Based Occupancy Forecasting

Resumen de: US2025173660A1

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.

ARTIFICIAL INTELLIGENCE SYSTEM PROVIDING AUTOMATED DISTRIBUTED TRAINING OF MACHINE LEARNING MODELS

Resumen de: US2025173627A1

Multiple distinct control descriptors, each specifying an algorithm and values of one or more parameters of the algorithm, are created. A plurality of tuples, each indicating a respective record of a data set and a respective descriptor, are generated. The tuples are distributed among a plurality of compute resources such that the number of distinct descriptors indicated in the tuples received at a given resource is below a threshold. The algorithm is executed in accordance with the descriptors' parameters at individual compute resources.

COMPUTER-IMPLEMENTED METHOD FOR COMPENSATING FOR AN UNEVEN DISTRIBUTION IN TRAINING DATA DURING THE TRAINING OF A MACHINE LEARNING ALGORITHM

Resumen de: US2025173617A1

A computer-implemented method for compensating for an uneven distribution in training data during the training of a machine learning algorithm. The training data include a plurality of data sets. The machine learning algorithm solves a regression task. The training data have an uneven distribution with regard to their labels. The method includes: defining auxiliary classes for the training data; creating a classification task; ascertaining a classification probability for each auxiliary class; ascertaining a classification loss function for the classification task; weighting the classification loss function; ascertaining an overall loss function; training the machine learning algorithm’ and providing the trained machine learning algorithm.

Integrated Intelligence Platform for Data-Driven Climate Action

Resumen de: US2025173592A1

The present invention relates to an integrated platform to drive climate change mitigation through advanced data analytics and prediction-based policy activation. The system consolidates siloed emissions data from diverse sources into a unified structure for in-depth analysis using artificial intelligence and machine learning techniques. Sophisticated modeling predicts expected temperature changes, extreme weather events, and evolving emissions patterns. Interactive dashboards clearly visualize these predictions to activate targeted sustainability policies and outcomes. Built-in workflow tools enable administrators to instantly translate predictive insights into optimized climate response plans. Designed for flexibility, the cloud-agnostic architecture readily integrates with existing technology stacks for easy adoption. By breaking down data silos to generate actionable intelligence, this invention provides a comprehensive solution to understand complex climate threats and respond with evidence-based actions to create a sustainable future. The platform ultimately enables data-driven climate governance through unprecedented integration of real-time emissions data sources, predictive analytics, and policy activation.

MACHINE LEARNING BASED METHODS OF ANALYSING DRUG-LIKE MOLECULES

Resumen de: US2025174314A1

There is provided a method for a machine learning based method of analysing drug-like molecules by representing the molecular quantum states of each drug-like molecule as a quantum graph, and then feeding that quantum graph as an input to a machine learning system.

SENSOR-BASED MACHINE LEARNING IN A HEALTH PREDICTION ENVIRONMENT

Nº publicación: US2025174364A1 29/05/2025

Solicitante:

EVIDATION HEALTH INC [US]
Evidation Health, Inc

Resumen de: US2025174364A1

A machine learning prediction system can analyze a dataset of users with self-reported symptoms and associated data from a wearable device to impact measure the impact of an acute health condition (such as the flu) at the population level. The machine learning prediction system can train a machine learning model to recognize individual acute health condition patterns based on differences in user activity with respect to the characteristics of determined baseline periods. For example, per-individual normalized change with respect to baseline aggregated at the population level can be used to determine individual acute health condition patterns and predict the onset of certain acute health conditions using a trained machine learning model. In response to predictions, the machine learning prediction system can take interventions to manage the impact of a predicted acute health condition on an individual.