Alerta

APPARATUS AND METHOD FOR DETERMINING A COMPOSITION OF A REPLACEMENT THERAPY TREATMENT

Resumen de: US2025149142A1

An apparatus and method for determining a composition of a replacement therapy treatment is presented, the apparatus at least a processor and a memory communicatively connected to the processor, the memory containing instructions configuring the at least a processor to receive a user input wherein the user input comprises at least an identifier and a constitutional history of the user, generate a first condition descriptor as a function of the user input, determine a composition of a replacement therapy treatment as a function of the first condition descriptor, wherein the determination comprises training a first machine-learning process using user training data, wherein the user training data correlates user inputs to compositions of the replacement therapy treatment and determining the composition as a function of the user input and the first machine learning process, and output the composition of the replacement therapy treatment as a function of the determination.

MACHINE LEARNING MODEL PUBLISHING SYSTEMS AND METHODS

Resumen de: US2025148373A1

A machine learning (ML) model publisher can, responsive to an indication that a ML model is ready for publication, generate a publication request form or page on a user device. The ML model publisher can be invoked from within a ML modeling application. Responsive to an instruction received through the publication request form or page, the ML model publisher can access a data structure in memory used in training the ML model and populate the publication request form or page with attributes required by the ML model to run. Responsive to activation of a single-click publication actuator, the ML model publisher can publish the ML model directly from the ML modeling application to a target computing system by providing, to the target computing system, a path to a repository location where the ML model is stored and information on the attributes required by the ML model to run.

MAXIMIZING BANDWIDTH UTILIZATION BY SELECTING APPROPRIATE MODE OF OPERATION FOR PCIE CARD

Resumen de: WO2025093940A1

A computer-implemented method, system, and computer program product for maximizing bandwidth utilization of PCIe links. The bandwidth utilization of a PCIe link involving a PCIe card is measured. A bandwidth utilization of the PCIe link at a future time is predicted based on the measured bandwidth utilization of the PCIe link using a machine learning model trained to predict bandwidth utilization of PCIe links. If the predicted bandwidth utilization of the PCIe link exceeds a threshold value, then the PCIe card is configured to implement a first mode of operation that utilizes more bandwidth if not implementing the first mode of operation at the future time. If the predicted bandwidth utilization of the PCIe link does not exceed a threshold value, then the PCIe card is configured to implement a second mode of operation that utilizes less bandwidth if not implementing the second mode of operation at the future time.

END-TO-END WORKFLOW FOR AUTOMATED NARRATIVE CREATION

Resumen de: WO2025096822A1

Certain aspects of the present disclosure provide techniques for narrative creation. A method generally includes receiving a selection of a first narrative type for generation, obtaining: a plurality of user responses to a plurality of prompts associated with the first narrative type; and at least one of: one or more stories from one or more users stored in a repository; or one or more insights associated with one or more documents stored in the repository, and processing, by one or more machine learning (ML) models, the plurality of user responses and at least one of the one or more stories or the one or more insights to generate an output associated with the first narrative type.

REDUCING CARBON FOOTPRINT OF MACHINE LEARNING MODELS

Resumen de: WO2025093915A1

A machine learning platform operating at a server is described. The machine learning platform accesses a dataset from a datastore. A task that identifies a target of a machine learning algorithm from the machine learning platform is defined. The machine learning algorithm forms a machine learning model based on the dataset and the task. The machine learning platform deploys the machine learning model and monitors a performance of the machine learning model after deployment. The machine learning platform updates the machine learning model based on the monitoring.

AI-DRIVEN INTEGRATION PLATFORM AND USER-ADAPTIVE INTERFACE FOR BUSINESS RELATIONSHIP ORCHESTRATION

Resumen de: US2025150457A1

A system, method and tangible non-transitory storage medium are disclosed. The system includes an integration platform configured to generate in interactive graphical user interface (GUI) that simultaneously displays and provides access to a combination of services, internal resources and external resources. Responsive to receiving input from a user device, the interactive GUI provide access to one or more selected services, internal resources and/or external resources. The integration platform may also monitor and capture interaction data associated with activity between the user device and the integration platform, execute machine learning model(s) to predict user-specific interaction tendencies, and revise one or more aspects of interactive GUI based on the predicted user-specific interaction tendencies.

MULTI-PHASE CLOUD SERVICE NODE ERROR PREDICTION BASED ON MINIMIZATION FUNCTION WITH COST RATIO AND FALSE POSITIVE DETECTION

Resumen de: US2025147851A1

Systems and techniques for multi-phase cloud service node error prediction are described herein. A set of spatial metrics and a set of temporal metrics may be obtained for node devices in a cloud computing platform. The node devices may be evaluated using a spatial machine learning model and a temporal machine learning model to create a spatial output and a temporal output. One or more potentially faulty nodes may be determined based on an evaluation of the spatial output and the temporal output using a ranking model. The one or more potentially faulty nodes may be a subset of the node devices. One or more migration source nodes may be identified from one or more potentially faulty nodes. The one or more migration source nodes may be identified by minimization of a cost of false positive and false negative node detection.

CONCEPT SYSTEM FOR A NATURAL LANGUAGE UNDERSTANDING (NLU) FRAMEWORK

Resumen de: US2025148213A1

A natural language understanding (NLU) framework includes a concept system that performs concept matching of user utterances. The concept system generates a concept cluster model from sample utterances of an intent-entity model, and then trains a machine learning (ML) concept model based on the concept cluster model. Once trained, the concept model receives semantic vectors representing potential concepts extracted from utterances, and provides concept indicators to an ensemble scoring system. These concept indicators include indications of which concepts of the concept model that matched to the potential concepts, which intents of the intent-entity model are related to these concepts, and concept-relationship scores indicating a strength and/or uniqueness of the relationship between each concept-intent combination. Based on these concept-related indicators, the ensemble scoring system may determine and apply an ensemble scoring adjustment when determining an ensemble artifact score for each of the artifacts extracted from an utterance.

METHODS AND SYSTEMS FOR TRAINING A DECISION-TREE BASED MACHINE LEARNING ALGORITHM (MLA)

Resumen de: US2025148301A1

Methods and servers for of training a decision-tree based Machine Learning Algorithm (MLA) are disclosed. During a given training iteration, the method includes generating prediction values using current generated trees, generating estimated gradient values by applying a non-convex loss function, generating a first plurality of noisy estimated gradient values based on the estimated gradient values, generating a plurality of noisy candidate trees using the first plurality of noisy estimated gradient values, applying a selection metric to select a target tree amongst the plurality of noisy candidate trees, generating a second plurality of noisy estimated gradient values based on the plurality of estimated gradient values, generating an iteration-specific tree based on the target tree and the second plurality of noisy estimated gradient values, and storing, the iteration-specific tree to be used in combination with the current generated trees.

SYSTEM AND METHOD FOR DETERMINING EXPECTED LOSS USING A MACHINE LEARNING FRAMEWORK

Resumen de: US2025148321A1

A computing device for predicting an expected loss for a set of claim transactions is provided. The computing device predicts, at a first machine learning model, a claim frequency of the set of claim transactions over a given time period and trained using historical frequency data and based on a segment type defining a type of claim, each type of segment having peril types. The computing device also predicts, at a second machine learning model, claim severity of the set of claim transactions during the given time period, the second machine learning model trained using historical severity data and based on the segment type and the corresponding peril types. The computing device then determines the expected loss for the set of claim transactions over the given time period by applying a product of prediction of the first machine learning model and the second machine learning model.

HYBRID MODELING PROCESS FOR FORECASTING PHYSICAL SYSTEM PARAMETERS

Resumen de: US2025148319A1

A method includes receiving first input values for a first parameter of a physical system, calculating first modeled values for a second parameter using a model that represents the physical system, based on the first input values, receiving measured values for the second parameter, training a machine learning model to adjust modeled values generated by the model based on a difference between the first modeled values and the measured values, receiving second input values for the first parameter, calculating second modeled values for the second parameter using the model, generating adjusted values for the second parameter by adjusting the second modeled values using the trained machine learning model, and visualizing the adjusted values for the second parameter as representing operation of the physical system.

SYSTEM FOR SIMPLIFIED GENERATION OF SYSTEMS FOR BROAD AREA GEOSPATIAL OBJECT DETECTION

Resumen de: US2025148283A1

A system for broad area geospatial object detection includes a processor configured to retrieve training data including a first plurality of orthorectified geospatial training images each including at least one labeled instance of the object of interest, and a second plurality of orthorectified geospatial images each including at least one labeled instance of the object of interest and/or at least one unlabeled instance of the object of interest, and apply at least one type of image correction to the training data. The processor is also configured to train a plurality of machine learning classifier elements, based on the first plurality of orthorectified geospatial training images and subsequently based on the second plurality of orthorectified geospatial images, each of the plurality of machine learning classifier elements being defined by a machine learning protocol parameterized based on one or more visually unique features of the object of interest.

Method, System, and Computer Program Product for Machine Learning Using Decoupled Knowledge Graphs

Resumen de: US2025148353A1

Described are a method, system, and computer program product for machine learning using decoupled knowledge graphs. The method includes generating a graph including nodes connected by edges based on data of entities in a network. Generating the graph includes generating entity nodes, determining a distribution of values for an attribute of the entities, generating a lower attribute node associated with a lower subset of values for the attribute, generating a higher attribute node associated with a higher subset of values for the attribute, and generating edges connecting the nodes. The method also includes initializing node embeddings, and generating representations of the nodes by repeating, until convergence, updating the embeddings of the entity nodes while holding other embeddings static, and updating the embeddings of the non-entity nodes while holding other embeddings static. The method further includes executing a machine learning model using the representations.

SYSTEMS AND METHODS FOR LEARNING DATA PATTERNS PREDICTIVE OF AN OUTCOME

Resumen de: US2025148259A1

System and methods for learning data patterns predictive of an outcome are described. An example system may include a plurality of input sensors communicatively coupled to a controller; a data collection circuit structured to collect output data from the plurality of input sensors; and a machine learning data analysis circuit structured to receive the output data, learn received output data patterns indicative of an outcome, and learn a preferred input data collection band among a plurality of available input data collection bands. The machine learning data analysis circuit may be structured to learn received output data patterns by being seeded with a model based on industry-specific feedback. The outcome may be at least one of: a reaction rate, a production volume, or a required maintenance.

FLEXIBLE MACHINE LEARNING MODEL COMPRESSION

Resumen de: US2025148357A1

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.

POWER FEATURE AIDED MACHINE LEARNING TO REDUCE NON-LINEAR DISTORTION

Resumen de: US2025148376A1

A computer-implemented method performed by a device configured with a power feature aided machine learning, ML, model is provided that models a behavior of a DPD to reduce non-linear distortion of an output signal of a non-linear device. The method includes extracting a plurality of power features from an input signal destined to be input to the DPD. The method further includes labelling the extracted plurality of power features to obtain at least one labelled average power level; inputting the at least one labelled average power level to the input of the ML model to obtain an output signal from the ML model having characteristics to reduce the non-linear distortion of the output signal of the non-linear device; and providing the output signal from the ML model as an input to the non-linear device.

SYSTEM AND METHOD FOR GENERATING A PATH LOSS PROPAGATION MODEL THROUGH MACHINE LEARNING

Resumen de: WO2024003856A1

The present disclosure provides a system and a method for generating a path loss propagation model through machine learning. The system generates a path loss propagation model for fifth generation (5G) networks for network planning. The path loss model predicts a reference signal received power/ signal to noise interference ratio (RSRP/SINR) by leveraging a fourth generation (4G) user data.

MACHINE-LEARNING-BASED UNSUPERVISED DATA CORRECTION

Resumen de: US2025139533A1

Technologies are described for correcting data, such as master data, in an unsupervised manner using supervised machine learning. Correction of master data can involve receiving a table containing unlabeled master data. Machine learning models are applied to the fields of one or more columns of the table to predict values of the fields, and the machine learning models use unsupervised learning. For example, a machine learning model can be applied to a particular field of a particular column to predict the value of the particular field. The machine learning model uses the fields of other columns as features. Results of applying the machine learning models include indications of recommended values, indications of probabilities of the recommended values, and indications of which original values do not match their respective recommended values. The results can be used to perform manual and/or automatic correction of the master data.

PRIVACY ENHANCED MACHINE LEARNING

Resumen de: US2025139530A1

A method of selecting data for privacy preserving machine learning comprises: storing training data from a first party, storing a machine learning model, and storing criteria from the first party or from another party. The method comprises filtering the training data to select a first part of the training data to be used to train the machine learning model and select a second part of the training data. The selecting is done by computing a measure, using the criteria, of the contribution of the data to the performance of the machine learning model.

PREDICTIVE, MACHINE-LEARNING, EVENT-SERIES COMPUTER MODELS WITH ENCODED REPRESENTATION

Resumen de: US2025139528A1

Provided is a process including: obtaining, for a plurality of entities, entity logs, wherein: the entity logs comprise events involving the entities, a first subset of the events are actions by the entities, at least some of the actions by the entities are targeted actions, and the events are labeled according to an ontology of events having a plurality of event types; training, with one or more processors, based on the entity logs, a predictive machine learning model to predict whether an entity characterized by a set of inputs to the model will engage in a targeted action in a given duration of time in the future; and storing the trained predictive machine learning model in memory.

SYNTHETIC DATA TESTING IN MACHINE LEARNING APPLICATIONS

Resumen de: US2025139500A1

Determining whether synthetic data is sufficient for utilization in connection with one or more machine learning models. The computing device accesses a protected batch of data associated with a machine learning model. The computing device accesses a simulated batch of data, the simulated batch of data based upon but anonymizing the protected batch of data. The computing device accesses one or more comparisons of one or more variables in the protected batch of data and the simulated batch of data to obtain a similarity value. The computing device performs a machine learning function utilizing at least in-part the simulated batch of data if the similarity value exceeds a similarity threshold.

MITIGATING BIAS IN MACHINE LEARNING WITHOUT POSITIVE OUTCOME RATE REGRESSIONS

Resumen de: WO2025090145A1

A computer obtains multipliers of a sensitive feature. From an input that contains a value of the feature, a probability of a class is inferred. Based on the value of the feature in the input, one of the multipliers of the feature is selected. The multiplier is specific to both of the feature and the value of the feature. The input is classified based on a multiplicative product of the probability of the class and the multiplier that is specific to both of the feature and the value of the feature. In an embodiment, a black-box tri-objective optimizer generates multipliers on a three-way Pareto frontier from which a user may interactively select a combination of multipliers that provides a best three-way tradeoff between fairness and accuracy. The optimizer has three objectives to respectively optimize three distinct validation metrics that may, for example, be accuracy, fairness, and favorable outcome rate decrease.

METHODS AND APPARATUS FOR MACHINE LEARNING PREDICTIONS OF MANUFACTURING PROCESSES

Resumen de: US2025138507A1

The subject technology is related to methods and apparatus for training a set of regression machine learning models with a training set to produce a set of predictive values for a pending manufacturing request, the training set including data extracted from a set of manufacturing transactions submitted by a set of entities of a supply chain. A multi-objective optimization model is implemented to (1) receive an input including the set of predictive values and a set of features of a physical object, and (2) generate an output with a set of attributes associated with a manufacture of the physical object in response to receiving the input, the output complying with a multi-objective condition satisfied in the multi-objective optimization model.

METHOD AND SYSTEM FOR DIAGNOSING MACHINE LEARNING MODEL UNDERPERFORMANCE OR FAILURE

Resumen de: WO2025088179A1

There is provided a computer-implemented method for diagnosing machine learning model underperformance or failures, comprising: embedding validation data samples and text string phrase candidates in a joint embedding space to generate data embeddings and text embeddings; receiving a trained machine learning model; conducting inference with the validation dataset using the trained machine learning model to obtain numerical metrics per validation data sample; generating a spatio-temporal relationship representation for the validation data samples; determining a plurality of data slices; and generating a semantic description for each data slice based on the text embeddings. There is further provided a computing system for diagnosing machine learning model underperformance or failures, computer programs, machine-readable storage media, data carrier signals and use of the computer-implemented method.

REDUCING CARBON FOOTPRINT OF MACHINE LEARNING MODELS

Nº publicación: US2025139501A1 01/05/2025

Solicitante:

MIND FOUNDRY LTD [GB]
Mind Foundry Ltd

Resumen de: US2025139501A1

A machine learning platform operating at a server is described. The machine learning platform accesses a dataset from a datastore. A task that identifies a target of a machine learning algorithm from the machine learning platform is defined. The machine learning algorithm forms a machine learning model based on the dataset and the task. The machine learning platform deploys the machine learning model and monitors a performance of the machine learning model after deployment. The machine learning platform updates the machine learning model based on the monitoring.