Alerta

DYNAMIC VISUALIZATION FOR THE PERFORMANCE EVALUATION OF MACHINE LEARNING MODELS AND ANY OTHER TYPE OF PREDICTIVE MODEL

Absstract of: WO2025080778A1

A universal system and method for dynamically evaluating and visualizing the performance of any predictive model, including machine learning models. The system and method compute performance metrics based on test set data and display visual representations in real-time, allowing users to interactively explore model performance by adjusting parameters that reflect model-deployment scenarios. Key features include model-agnostic design, support for both technical and business metrics, and the ability to compare multiple models. The system and method's extensible architecture enables custom metrics and visualizations, making them scalable across various modeling use cases and industries. By providing intuitive, real-time visual feedback, embodiments of the invention empower both technical and non-technical stakeholders to gain deeper insights into model behavior, leading to more informed decisions about deployment and optimization.

TECHNIQUES FOR VERIFYING VERACITY OF MACHINE LEARNING OUTPUTS

Absstract of: US11966704B1

The techniques described herein relate to techniques for verifying a veracity of machine learning outputs. An example method includes receiving a first output generated by a first model responsive to a first input, the first output comprising one or more verifiable statements in text, verifying, using a second model and first reference data stored in at least one first datastore, the one or more verifiable statements to produce first verification results indicating which of them has been verified, when it is determined that at least one of them remains unverified based on the first verification results, identifying, using at least one of the first or second models, at least one second datastore having second reference data attesting to veracity of the first output; and verifying, using the second model and the second reference data, the at least one unverified statement to produce second verification results to be provided as output.

GENERATING SOCIAL MEDIA CONTENT FOR A USER ASSOCIATED WITH AN ENTERPRISE

Absstract of: US20260236730A1

0000 Systems and methods disclosed herein relate to fine-tuning machine learning (ML) chatbots for an enterprise. The systems and methods may use ML chatbots and/or generative ML to generate social media content for a user associated with an enterprise. The systems and methods may fine-tune a base ML model, and use the fine-tuned ML model for the ML chatbot. A user profile may indicate user attributes, and a fine-tuned ML model may be loaded for the ML chatbot based upon an identified user profile.

IMAGE AND VIDEO CODING METHOD WITH PREDICTION USING SUCCESSIVE TRAINING OF A MACHINE-LEARNING MODEL

Absstract of: US20260237100A1

Methods of processing at least one of image or video data, and a decoder are provided. The method includes successively decoding a plurality of fragments of an image or video. The decoding includes, for one or more fragment in the plurality of fragments: decoding a respective encoded residual fragment to obtain a decoded residual fragment; generating, using a machine-learning model, a predicted fragment from a respective reference fragment, where the respective reference fragment is selected from one or more reference fragments stored in a set of reference fragments; generating, based on the predicted fragment and the decoded residual fragment, a reconstructed fragment; storing the reconstructed fragment in the set of reference fragments; and updating parameters of the machine-learning model based on the respective reference fragment and the reconstructed fragment.

BIOMASS UTILIZATION SUPPORT DEVICE, METHOD, AND PROGRAM

Absstract of: US20260237002A1

A biomass utilization support device: acquires biomass information relating to a biobased material and product information for each of a plurality of products including information about materials configuring the products; uses a machine learning model, which has been trained to estimate appropriate values for replacement amounts in a case of replacing a portion of the materials configuring the products with the biobased material, and the acquired biomass information and product information to estimate the appropriate values for each of the plurality of products; calculates, for each of the plurality of products, environmental impact indicators in a case in which a portion of the materials configuring the products has been replaced with the biobased material at the replacement amounts represented by the estimated appropriate values; and outputs support information listing the estimated appropriate values and the calculated environmental impact indicators.

DATA MODIFICATION OPERATORS FOR REDUCING BIAS IN MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS

Absstract of: US20260236854A1

0000 A system generates data modification operators that reduce bias or distortions in artificial intelligence (AI) models. The system uses a first artificial intelligence (AI) model to generate outputs based on a set of corresponding inputs to the first AI model. First measurement values of one or more model output metrics in the outputs generated by the first AI model are received. Based on the first measurement values, the system generates a set of data modification operators that specifies one or more operations for modifying inputs to a second AI model. Inputs to the second AI model can be modified using the set of data modification operators to generate a modified set of corresponding inputs. The second AI model can then be applied to the modified set of corresponding inputs to the second AI model.

LEVELS OF GRANULARITY FOR IDENTIFIERS RELATED TO ARTIFICIAL INTELLIGENCE OR MACHINE LEARNING

Absstract of: US20260239267A1

Methods, systems, and devices for wireless communications are described. A wireless device may obtain, from a network entity, information related to at least one level of granularity of a set of multiple levels of granularity that correspond to one or more identifiers associated with one or more network settings. The one or more network settings may relate to communication of reference signaling for an artificial intelligence or machine learning (AI/ML)-based positioning or sensing procedure or measurement of reference signaling for an AI/ML-based positioning or sensing procedure. The wireless device may obtain, from the network entity, at least one identifier of the one or more identifiers that corresponds to the at least one level of granularity. Moreover, the wireless device may perform an operation to control the AI/ML-based positioning or sensing procedure based on the at least one identifier in accordance with the at least one level of granularity.

THREAT DETECTION PLATFORMS FOR DETECTING, CHARACTERIZING, AND REMEDIATING EMAIL-BASED THREATS IN REAL TIME

Absstract of: US20260238659A1

0000 Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.

COMPUTING SYSTEM AND METHOD FOR CREATING A DATA SCIENCE MODEL HAVING REDUCED BIAS

Absstract of: US20260236986A1

A computing platform may be configured to (i) train an initial model object for a data science model using a machine learning process, (ii) determine that the initial model object exhibits a threshold level of bias, and (iii) thereafter produce an updated version of the initial model object having mitigated bias by (a) identifying a subset of the initial model object's set of input variables that are to be replaced by transformations, (b) producing a post-processed model object by replacing each respective input variable in the identified subset with a respective transformation of the respective input variable that has one or more unknown parameters, (c) producing a parameterized family of the post-processed model object, and (d) selecting, from the parameterized family of the post-processed model object, one given version of the post-processed model object to use as the updated version of the initial model object for the data science model.

AUTOMATIC DISCOVERY OF MACHINE LEARNING MODEL FEATURES

Absstract of: US20260236798A1

0000 A method performed by a processing system including at least one processor includes monitoring interactions of a human user with a platform for building machine learning models, where the human user is using the platform to build a new machine learning model, detecting, within the interactions, an event that triggers a suggestion feature, performing a search of a data source for existing data from existing machine learning models which can be reused to build the new machine learning model, using information about the event, presenting a suggestion to the human user to reuse a portion of the existing data discovered in the search in the new machine learning model, receiving a user feedback in response to the suggestion, and generating an updated suggestion in response to the user feedback.

DEEP-LEARNING BASED PERSONA SPECIFIC INSIGHT GENERATOR

Absstract of: US20260236860A1

0000 A method for generating persona specific insights. The method may include receiving sensor data associated with a device; extracting features from the received sensor data; processing the features using a machine learning model to generate machine learning metrics; ingesting the machine learning metrics and the features to generate insights data associated with the device; generating personas data using the insights data and the features, and mapping the insights to the personas data; generating custom insights using the insights data, the personas data, and the features, wherein the custom insights are text-based summaries; and disseminating each of the custom insights to respective persona of the personas data to place service orders associated with the device.

MODEL USAGE EVALUATION METHOD, MODEL USAGE EVALUATION SYSTEM, AND MODEL USAGE EVALUATION PROGRAM

Absstract of: US20260237190A1

A model usage evaluation method for evaluating use of an inference model that is generated by machine learning training and inputs information based on an image includes: a target image feature amount acquisition step of acquiring a feature amount of a target image used as an input to the inference model; a training image feature amount acquisition step of acquiring a feature amount of a training image used for training for generating the inference model; and an evaluation step of comparing the feature amount of the target image acquired in the target image feature amount acquisition step with the feature amount of the training image acquired in the training image feature amount acquisition step to evaluate the use of the inference model for the target image.

INTERACTIVE MACHINE LEARNING OPTIMIZATION

Absstract of: US20260236847A1

Methods, computer program products, and systems are presented. The method, computer program products, and systems can include, for instance: examining an enterprise dataset, the enterprise dataset defined by enterprise collected data; selecting one or more synthetic dataset in dependence on the examining, the one or more synthetic dataset including data other than data collected by the enterprise; training a set of predictive models using data of the one or more synthetic dataset to provide a set of trained predictive models; testing the set of trained predictive models with use of holdout data of the one or more synthetic dataset; and presenting prompting data on a displayed user interface of a developer user in dependence on result data resulting from the testing, the prompting data prompting the developer user to direct action with respect to one or more model of the set of predictive models.

DYNAMIC CHANNEL STATE INFORMATION COMPRESSION BASED ON INPUT CHANNEL STATE INFORMATION SPECTRAL ENTROPY

Absstract of: WO2026169272A1

Systems and methods for dynamic channel state information (CSI) compression based on input CSI spectral entropy are discussed herein. For example, a user equipment receives, from a base station, a first CSI compression configuration and a reference signal. The UE generates first CSI feedback based on the reference signal received from the base station and generates a PSE value. Then, the UE determines, using the CSI compression configuration, based on the PSE value, a first compression profile for the first CSI feedback. The UE applies the first CSI feedback and the first compression profile at a first encoder of a first artificial intelligence (AI)/machine learning (ML) model to generate compressed CSI feedback, wherein the first encoder generates the first compressed CSI feedback according to the first compression profile for the first CSI feedback and sends the compressed CSI feedback and data corresponding to the compression profile to the base station.

Patient Ventilator Asynchrony Detection

Absstract of: US20260237511A1

A decision support tool is provided for identifying and assisting clinicians with patient ventilator asynchrony. The information used to make the identification may include data from a patient's ventilator including the volume, flow, and pressure associated with that ventilator. At least some of this information may be used to compute one or more features for a time series of the data received for the patient. These features may be used in connection with heuristic rules and machine learning algorithms to identify instances of patient ventilator asynchrony. Based on the identification, one or more intervening actions may be initiated to reduce the impact of patient ventilator asynchrony.

Using Machine Learning to Power a Brand Integrity Platform

Absstract of: US20260236965A1

0000 Transcribed text associated with a predetermined source is accessed by a brand integrity platform. Features of the transcribed text are input into a classifier that is configured to detect presence of sensitive content of a predetermined category in the input features. The features are input into a tonal model that is trained to detect a neutral emotion score. A plurality of neutral score thresholds associated with the predetermined category of sensitive content whose presence is detected in the input features are accessed. The accessed plurality of neutral score thresholds are applied to the detected neutral emotion score to determine, for the predetermined source, a risk level associated with the predetermined category of sensitive content. An action is performed when the risk level for the predetermined category of sensitive content for the predetermined source meets a tolerance threshold associated with a user.

MACHINE LEARNING FEATURE RECOMMENDATION

Absstract of: US20260236829A1

0000 A pre-trained model trained to predict a measure of expected model performance based at least in part on a feature relevance score associated with a text field data type is generated. A specification of a desired target field for machine learning prediction and one or more text fields storing input content is received. A corresponding feature relevance score for each of the one or more text fields storing the input content is calculated. Based on the corresponding calculated feature relevance scores, a corresponding measure of expected model performance for each of the one or more text fields storing the input content is predicted using the pre-trained model. The predicted measures of expected model performance are provided for use in feature selection among the one or more text fields storing the input content for generating a machine learning model to predict the desired target field.

SYSTEMS AND METHODS FOR MINING FREQUENTLY ASKED QUESTION (FAQ) FROM CONVERSATION DATA

Absstract of: US20260236793A1

0000 Systems and methods for mining question-and-answer pairs from conversation data to update knowledge bases are provided. In particular, a computing system may obtain conversation data, extract a question-and-answer pair from the conversation data with meta data using a machine learning model, the question-and-answer pair including a question and an answer corresponding to the question, determine whether a knowledge base includes existing content similar to the question-and-answer pair, provide the question-and-answer pair on a graphical user interface with an indication of a presence of any existing content in the knowledge base that is similar to the question-and-answer pair, receive an input to integrate the question-and-answer pair with the knowledge base, and update the knowledge base to integrate the question-and-answer pair in accordance with the input.

SPATIOTEMPORAL TRANSFER MACHINE LEARNING

Absstract of: US20260236836A1

A computer-implemented, machine learning method for spatiotemporal transfer learning. Sectors of an area are aggregated using preprocessed data from one or more data sources. The sectors are clustered based on different representations obtained for each context feature associated with each of the sectors. One or more context features that have a higher impact on a target feature to be predicted than other context features are identified from a plurality of context features and aggregated to obtain a representation of the area. Using the representation of the area, a particular sector within each of the clustered sectors is selected based on similarity to a respective centroid of the cluster to generate a set of particular sectors. A model associated with a source sector of the set of particular sectors is trained. The method has applications including, but not limited to smart cities, public safety and energy optimization.

Machine Learning-Based Synthetic Monitoring

Absstract of: US20260228642A1

Methods, systems, and apparatuses are described herein for using machine learning processes to improve synthetic monitoring of, e.g., websites. Training data may comprise sitemap information for a plurality of different websites and monitoring information that indicates whether, for each of a plurality of different portions of those websites, one or more synthetic monitoring scripts are executed. A machine learning model may be trained using that training data to output whether one or more portions of an input website should be monitored using synthetic monitoring. A sitemap of a first website may be determined and provided as input to the trained first machine learning model. Based on output from the trained first machine learning model, a synthetic monitoring script may be determined and executed to monitor at least a first portion of the first website.

PREDICTIVE MACHINE LEARNING MODELS FOR PARCELS OF REAL PROPERTY

Absstract of: US20260228572A1

0000 Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and applying a machine learning model. One of the methods includes the actions of obtaining a collection of data associated with a specified parcel of real property, wherein the collection of data includes one or more parameters of interest; using a machine learning model to generate a prediction from the input collection of data for each of the one or more parameters of interest, wherein the prediction for each parameter of interest comprises a likelihood value that the parameter satisfies a particular condition, and wherein the machine learning model is trained using a training set comprising a collection of data associated with a labeled set of data, the labels indicating the existence of particular parameters on the parcels; and based on the prediction, classifying each of the one or more parameters of interest.

MONITORING A PERFORMANCE OF A MACHINE LEARNING MODEL

Absstract of: US20260230849A1

Various aspects of the present disclosure relate to monitoring a performance of a machine learning model. An apparatus, such as a UE or an NE, generates a set of probability values based on inputting unlabeled data in a machine learning model. In some examples, each probability value of the set of probability values indicates a probability that a respective label value is a label of at least a portion of the unlabeled data. Further, the apparatus generates a performance metric for the machine learning model based on the set of probability values and communicates in accordance with the performance metric.

Model-based featurization and classification

Absstract of: AU2026206191A1

MARKED-UP COPY MARKED-UP COPY MARKED-UP In various embodiments, an analytics system uses models to determine features and classification of disease states. A disease state can indicate presence or absence of cancer, a cancer type, or a cancer tissue of origin. The models can include a binary classifier and a tissue of origin classifier. The analytics system can process sequence reads from test biological samples to generate data for training the classifiers. The analytics system can also use combinations of machine learning techniques to train the models, which can include a multilayer perceptron. In some embodiments, the analytics system uses methylation information to train the models to determine predictions regarding disease state. ul u l

Systems and methods for interaction stacking detection

Absstract of: AU2026200494A1

A method can include receiving a request associated with an interaction involving a target entity. The method can include receiving identity data about the target entity and historical data about historical interactions associated with the target entity. The method can include providing the identity data to a first machine-learning model to generate first output. The method can include applying the historical data to rules to generate rule outcomes. The method can include providing the rule outcomes to a second machine-learning model to generate second output. The method can include generating a recommendation including a response to the request. The method can include providing a responsive message including a command executable to automatically control the response to the request. an a n E- -L R E C E I V E A R E Q U E S T F R O M A C O M P U T I N G D E V I C E I N V O L V I N G A N I N T E R A C T I O N A N D A T A R G E T E N T I T Y P R O V I D E A F I R S T S U B S E T O F D A T A A S S O C I A T E D W I T H T H E R E Q U E S T T O A A P P L Y A S E C O N D S U B S E T O F T H E D A T A T O A S E T O F R U L E S T O G E N E R A T E A S E T O F R U L E O U T C O M E S P R O V I D E T H E S E T O F R U L E O U T C O M E S T O A S E C O N D M A C H I N E - L E A R N I N G M O D E L T O G E N E R A T E A B I N A R Y I N D I C A T I O N G E N E R A T E A R E C O M M E N D A T I O N B A S E D O N T H E P R O B A B I L I T Y S C O R E A N D T H E B I N A R Y I N D I C A T I O

DIGITAL TWIN FOR TOUCHLESS MONITORING

Nº publicación: WO2026163047A1 06/08/2026

Applicant:

COVIDIEN LP [US]
COVIDIEN LP

Absstract of: WO2026163047A1

Traditionally, synthetic training data is unavailable or minimally available for training one or more machine-learning (ML) models for non-contact monitoring of a specific patient. To address this issue, a digital twin of the specific patient is generated within a virtual environment representative of a physical environment. In aspects, both the digital twin and the virtual environment can be altered in multiple ways to generate synthetic training data, which can be used to train and/or update one or more ML models for the specific patient. Based on depth data captured by a depth camera, the one or more ML models may recognize a posture, presence, and/or movement of the specific patient in the physical environment.