Alerta

PREDICTING PERFORMANCE OF CLINICAL TRIAL FACILITATORS USING PATIENT CLAIMS AND HISTORICAL DATA

Resumen de: US20260260719A1

0000 A clinical trial site evaluation system applies a machine learning technique to predict recruitment performance of a candidate clinical trial facilitator (such as a clinical trial site or a clinical trial investigator) for a clinical trial based on patient claims data or other data associated with the candidate clinical trial facilitator. In a training phase, a training system trains the machine learning model based on historical recruitment data associated with historical clinical trials and patient claims data (or other data) associated with the clinical trial facilitators associated with those trials. In a prediction phase, the machine learning model is applied to claims data (or other data) associated with candidate clinical trial facilitators to predict recruitment performance.

CROSS-DEVICE DATA SYNCHRONIZATION BASED ON SIMULTANEOUS HOTWORD TRIGGERS

Resumen de: US20260260649A1

Techniques are described herein for cross-device data synchronization based on simultaneous hotword triggers. A method includes: executing a first instance of an automated assistant in an inactive state at least in part on a first computing device operated by a user; while in the inactive state, receiving, via one or more microphones of the first computing device, audio data that captures a spoken utterance of the user; processing the audio data using a machine learning model to generate a predicted output that indicates a probability of one or more hotwords being present in the audio data; determining that the predicted output satisfies a threshold that is indicative of the one or more hotwords being present in the audio data; in response to determining that the predicted output satisfies the threshold, performing arbitration with at least one other computing device that is executing at least in part at least one other instance of the automated assistant; and in response to performing arbitration with the at least one other computing device, initiating synchronization of user data or configuration data between the first instance of the automated assistant on the first computing device and the at least one other instance of the automated assistant on the at least one other computing device, the user data comprising data that is based on one or more interactions with the user at the first computing device, the one or more interactions occurring prior to the receiving of the au

DYNAMICALLY DIVIDING ACTIVATIONS AND KERNELS FOR IMPROVING MEMORY EFFICIENCY

Resumen de: US20260260115A1

Embodiments are generally directed to dynamically dividing activations and kernels for improving memory efficiency. An embodiment of a method in a compute engine performing machine learning comprises: receiving, by a convolutional layer of a convolutional neural network (CNN) implemented on the compute engine, a plurality of activation groups contained in an input data, wherein the convolutional layer includes one or more kernel groups and the one or more kernel groups each include a plurality of kernels; determining a plurality of memory efficiency metrics based on the number of activation groups of the plurality of activation groups and the number of kernels of the plurality of kernels; selecting a first optimal number of activation groups and a second optimal number of kernels that are associated with an optimal memory efficiency metric in the plurality of memory efficiency metrics; and performing a convolutional operation on the input data based on the first optimal number and the second optimal number.

Hellinger Decision Trees for Fraud Detection

Resumen de: US20260260174A1

A Hellinger decision tree can detect fraudulent transactions in a data set of financial transactions. Applying the Hellinger decision tree uses a Hellinger distance. The Hellinger decision tree can be part of a machine learning algorithm. In an example, the Hellinger decision tree is a positive and unbalanced Hellinger decision tree used with an imbalanced positive and unlabeled data.

DATA COLLECTION ACROSS MULTIPLE USER EQUIPMENT CAPABILITY TYPES

Resumen de: WO2026180870A1

Example embodiments of the present disclosure are directed to data collection across multiple user equipment (UE) capability types. A method comprises providing, to a second apparatus, results of measurements associated with data collection for a machine learning functionality and ground truth information for the machine learning functionality; receiving, from the second apparatus, an indication for an adjustment of measurement performance at the first apparatus; and adjusting the measurement performance based on the indication.

DATA COLLECTION ACROSS MULTIPLE USER EQUIPMENT CAPABILITY TYPES

Resumen de: WO2026180869A1

Example embodiments of the present disclosure are directed to data collection across multiple user equipment (UE) capability types. A method comprises providing, to a second apparatus, results of measurements associated with data collection for a machine learning functionality and ground truth information for the machine learning functionality; receiving, from the second apparatus, further results of the measurements obtained by a third apparatus; and adjusting a measurement performance based on a comparison between the results and the further results.

MITIGATING CYBER-THREATS IN A NETWORK

Resumen de: WO2026180318A1

A method of mitigating cyber-threats in a target network comprising a plurality of nodes, the method comprising: deploying a decentralised multi-agent model to the target network, wherein the decentralised multi-agent model comprises a plurality of local models; and at each of the plurality of nodes: monitoring a local region of the network to obtain local network information; and using the local model to predict threat mitigation actions, based on the local network information The decentralised multi-agent model may be a machine learning model trained using reinforcement learning wherein, for each of one or more training networks: the local models are deployed in respective nodes of the training network; and a trainer system iteratively evaluates a performance of the multi-agent model and adjusts the local models.

SCALABLE DETECTION OF DECENTRALIZED GENETIC BIOMARKERS IN HUMAN MICROBIAL DATA TO PERSONALIZE MEDICAL TREATMENT

Resumen de: WO2026181071A1

Detecting biomarkers in human microbiome DNA to predict a biological condition and administer its treatment. A microbiome network may be generated comprising nodes representing microbiome DNA sequences and edges representing a co-occurrence of each pair of microbiome DNA sequences in a same DNA sample or sub-length. Nodes may be bundled into distinct groups based on the node's degree quantifying a number of its edges indicating a number of unique microbiome DNA sequences that co¬ occur with the microbiome DNA sequence represented by the node in the same DNA sample or sub-length. Groups of bundled microbiome DNA sequences may be validated having an internal connectivity that satisfies an anomaly condition indicating the group's sequences co-occur with a probability that is unlikely randomly statistical, e.g., deviating from a power law distribution. A machine learning model may be trained with the validated groups to predict a biological condition correlated therewith to administer its treatment.

USING MACHINE LEARNING TECHNIQUES TO GENERATE SPATIO-TEMPORAL PREDICTIONS RELEVANT TO NOVEL GEOGRAPHIC AREAS

Resumen de: WO2026178648A1

In some embodiments, a computer-implemented method is provided. A computing system ingests historical data for a first geographic area and causal priors from a Bayesian model to create a knowledge graph. The computing system trains a spatial-temporal model to represent relationships in the knowledge graph over time and space. The computing system trains, using the knowledge graph and the spatial-temporal model, an imputation model to generate latent representations usable to infer missing data and to represent causal relationships. The computing system trains a generative model to generate counterfactual scenarios based on latent representations generated by the imputation model.

Embedded Artificial Intelligence (AI) Superintelligence (ASI) Social Exchange Application

Resumen de: US20260260297A1

The present invention provides a method and apparatus for a thinking system using an ASI distributed Application State Machine that connects to real, accurate, dedicated absolute data, providing privacy, security, and eliminating hallucinated AI. Specifically, one embodiment of the present invention discloses an ASI Command, Control, Communications and Computer Intelligence (C4I) system for independent, intelligent AI Prompting and ASI Browsing. The ASI Application State Machine comprises: ASI Application data structure network; ASI Enterprise Interface State Machine, said data structures interfacing with a back-end channel, a Finite State Machine embedded in each data structure; and Application Service Information Base providing a uniform interface for ASI Application data structure identities. Additional embodiments disclose ASI Browser displaying ASI Applications; ASI Agents; ASI Application Network Nodes; ASI Operating System; ASI Switch; ASI Database of real, accurate, dedicated absolute data, for Machine Learning to train AI Models, for performing real-time, bi-directional transactions, connecting to billing.

Evaluation of Predictions as Individual Probability Density Functions

Resumen de: US20260260180A1

0000 A system and method are disclosed to train machine learning models, generate predictions, and evaluate the predictions as individual probability density functions. Embodiments include a computer comprising a processor and memory and configured to train a first machine learning model to predict a mean demand of one or more items. Embodiments train a second machine learning model to predict a variance associated with the predicted mean demand. Embodiments use the first and second machine learning models and received current sales data to predict a negative binomial variance of demand of the one or more items, comprising a confidence interval specifying a stocking level for the one or more items that will satisfy a defined number of estimated outcomes. Embodiments generate an individual probability density function using the predicted mean demand of one or more items and the predicted negative binomial variance of demand, and evaluate the individual probability density function.

INTELLIGENT PARKING SPACE ALLOCATION SYSTEM AND METHOD WITH DYNAMIC MATCHING, MACHINE LEARNING AND BLOCKCHAIN INTEGRATION

Resumen de: WO2026180016A1

The invention relates to a computer-implemented method and system for dynamically allocating and coordinating parking spaces by means of real-time synchronisation between a departing driver and a driver searching for a parking space. In order to determine a precise departure time, the system uses sensor fusion comprising GNSS data, inertial sensor technology, and OBD-II vehicle data, as well as activity recognition. A hybrid machine-learning model comprising LSTM networks and random-forest regressors predicts the handover probability and generates an optimised assignment of the participants. Transaction security is ensured by a blockchain-based smart contract (preferably a layer-2 solution) which validates arrival, departure and payment by means of zero-knowledge proofs or a cryptographic handshake. By integrating predictive analytics and decentralised validation, traffic caused by drivers searching for parking is proactively reduced and utilisation of parking areas is optimised.

MACHINE LEARNING APPARATUS, MACHINE LEARNING METHOD, AND COMPUTER READABLE MEDIUM

Resumen de: US20260260138A1

An inference unit (113) executes inference on input data for each model parameter set using a machine learning model to which the model parameter set is set, and obtains output data indicating an inference result of the machine learning model when the model parameter set has been set. A comparison unit (114) compares features of the inference results between pieces of the output data and obtains a comparison result. An output unit (115) determines information related to information leakage among information included in the inference result indicated in any of the pieces of output data based on the comparison result, applies a modification to the information related to information leakage on the output data, and outputs the modified output data.

SYSTEM AND METHOD FOR MANAGING AN ON-SENSOR MACHINE LEARNING (ML) MODEL

Resumen de: US20260260170A1

0000 A system and method for managing an on-sensor machine learning (ML) model includes monitoring performance parameters of plurality of on-sensor ML models present in an industrial plant; detecting a degradation of at least one on-sensor ML model based on the monitored ML model performance parameters of the plurality of on-sensor ML models, wherein degradation of the at least one on-sensor ML model comprises at least one of: data distribution change, training serving skew, model drift, occurrence of outlier event, and data quality issue; and updating the at least one on-sensor ML model based on the ML model upgradation parameters retrieved from one of the plurality of sources.

ADVERSARIAL TRAINING OF A MODEL FOR QUESTION ANSWERING

Resumen de: US20260260122A1

Embodiments of the present disclosure provide a solution for adversarial model training. A method includes: generating a prompt input using an adversarial machine learning model; providing the prompt input to a target machine learning model, to generate a response to the prompt input; determining a first reward score for the response with respect to the prompt input; and fine-tuning the target machine learning model according to a first optimization objective, the first optimization objective being configured to increase or maximize the first reward score for the target machine learning model.

INFERENCE DATA SIMILARITY FEEDBACK FOR MACHINE LEARNING MODEL PERFORMANCE MONITORING IN BEAM PREDICTION

Resumen de: US20260260136A1

0000 In an aspect, a UE may obtain a first indication of a first set of characteristics associated with a plurality of reference datasets. The UE may calculate a respective level of similarity between an inference dataset and each of the plurality of reference datasets based on the first set of characteristics and a second set of characteristics associated with the inference dataset. The UE may output at least one second indication of the respective level of similarity between the inference dataset and each of the plurality of reference datasets.

METHOD AND APPARATUS FOR SUPPORT OF ARTIFICIAL INTELLIGENCE/MACHINE LEARNING (AI/ML)-BASED POSITIONING

Resumen de: GB2639745A

According to various examples of the present disclosure, there is provided a location management function (LMF) entity configured to: subscribe to or request artificial intelligence/machine learning (AI/ML) -related services from a network data analytics function (NWDAF) entity in relation to an AI/ML model for determining positioning of a user equipment (UE); and receive, from the NWDAF entity, an indication that training has been performed for the AI/ML model. According to various examples of the present disclosure, there is provided a network data analytics function (NWDAF) entity configured to: receive, from a location management function (LMF) entity, a subscription to or request for artificial intelligence/machine learning (AI/ML) -related services in relation to an AI/ML model for determining positioning of a UE; obtain data for training the AI/ML model from at least one other entity; train the AI/ML model based on the obtained data; and transmit, to the LMF entity, an indication that training has been performed for the AI/ML model; wherein the NWDAF entity includes a model training logical function (MTLF).

Methods for identifying biological material by microscopy

Resumen de: NZ768408A

The present invention relates generally to the field of computer-based image recognition. More particularly, the invention relates to methods and systems for diagnosing infections by ovum-producing parasites in non-human animals using microscopic image analysis. The invention may be embodied in the form of a method of diagnosing an infection by an ovum-producing parasite in a non-human animal. The method may include generating a computer-readable image of a faecal sample by light microscopy and inputting the image into a trained computer configured to identify a genus or species of a parasite ovum. One or more image features are extracted and compared with features extracted from labelled cropped training images. Where sufficient similarity is identified, the computer outputs a genus or species identification to provide an infection diagnosis. The computer is trained using microscopy images containing parasite ova and non-parasite ovum material, including a first human-supervised machine-learning stage for distinguishing parasite ova from non-parasite ovum material and a second stage in which cropped parasite ovum images are labelled with genus or species identifications and associated with extracted image features for subsequent parasite identification.

ESTIMATION METHOD, COMPUTER PROGRAM, AND INFORMATION PROCESSING APPARATUS

Resumen de: US20260252973A1

0000 An estimation method for estimating a retention time of a specific target component comprises a step of acquiring an HSP of the specific target component, a step of acquiring respective HSPs of a specific stationary phase and a specific mobile phase, and a step of acquiring an estimation result of an index related to the retention time of the specific target component by inputting the respective HSPs of the specific target component, the specific stationary phase, and the specific mobile phase into a machine learning model. The machine learning model may have been subjected to a machine learning process with respective HSPs of a target component, a stationary phase, and a mobile phase as explanatory variables and an index related to the retention time of the target component as an objective variable.

Transformer-Based Assistant for Identifying, Organizing, and Responding to Customer Concerns

Resumen de: US20260252607A1

0000 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 transformer-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.

CONTINUOUS-FLOW DYNAMIC TITRATION CALORIMETER INCORPORATING MICROFLUIDICS AND MACHINE LEARNING-BASED CORRECTION, AND METHOD FOR USING SAME

Resumen de: WO2026174682A1

The present invention provides a continuous-flow dynamic titration calorimeter incorporating microfluidics and machine learning-based correction, and a method for using same. An apparatus comprises: a thermal insulation apparatus, used for accommodating a calorimetric structure, performing thermal insulation on the calorimetric structure, collecting the ambient temperature of the calorimetric structure, and transmitting the ambient temperature to a control and signal processing system; and the calorimetric structure, comprising a material preheating aluminum block and a constant-temperature aluminum block that are arranged at the left and right sides, wherein a material inlet/outlet connection block A is provided in a front slot of the material preheating aluminum block, a material inlet/outlet connection block B is provided in a rear slot of the material preheating aluminum block, a thermoelectric generator sheet A is provided at a front part of the constant-temperature aluminum block, a thermoelectric generator sheet B is provided at a rear part of the constant-temperature aluminum block, and a thin film heating sheet is provided above the thermoelectric generator sheet A. The technical solution of the present invention integrates microfluidics, thermoelectric power-generation measurement, and machine learning-based correction, and provides a low-cost, miniaturized and high-precision continuous-flow dynamic titration calorimetry method.

METHODS AND SYSTEMS OF ASSESSING A REPRESENTATION OF A BRAND IN LANGUAGE MODELS

Resumen de: US20260252814A1

0000 The present disclosure provides a method of assessing a representation of a brand in language models. Further, the method may include generating one or more queries, obtaining one or more responses from one or more pre-trained language models based on the one or more queries, retrieving one or brand information associated with the one or more brands, and analyzing each of the one or more responses and the one or more brand information using one or more machine learning (ML) models configured for processing the one or more responses based on the one or more brand information and determining one or more values of one or more metrics based on the processing, generating a representation score for the representation of the one or more brands based on the one or more values of the one or more metrics, and transmitting the representation score to one or more brand devices.

SYSTEMS AND METHODS FOR ONTOLOGY-ORIENTED GRAPH DATASET MODELING, DATA RETRIEVAL, AND HYBRID STRUCTURING OF GENERATIVE ARTIFICIAL INTELLIGENCE OUTPUTS

Resumen de: WO2026175686A1

The present disclosure relates to systems and methods for ontology-oriented graph dataset modeling, data retrieval, and hybrid structuring of generative machine learning outputs, which address the challenges of querying and extracting clinical insights from large, complex datasets. By combining knowledge graphs and large language models, embodiments of the present disclosure enable lay users to perform natural language queries on large, structured datasets and receive evidence-backed results with visual context. Embodiments include an orchestrator module powered by one or more LLMs and an instruction prompt, which selects an appropriate context retrieval tool based on a user's query. The context retrieval tool retrieves results from an ontologically-oriented graph, generates a knowledge graph, and provides a response to the query. This approach improves the efficiency and accuracy, allowing users to discover novel and unknown relationships within the dataset without needing comprehensive schema knowledge, thereby overcoming the high barrier of entry for extracting insights.

SYSTEMS AND METHODS FOR GENERATING AND EXECUTING FUNCTION CALLS USING MACHINE LEARNING

Resumen de: US20260252397A1

0000 The methods, systems, and computer networking apparatuses described herein enable language models to receive input (e.g., a query or a request) from a user or application, and without any additional training data or instructions, determine to generate a function call based on the received input from the user and generate the function call based on the determination. In some embodiments, a language model may further access an external tool or application to request an output as a response to the generated function call. The disclosed methods, systems, and networking apparatuses improve the technical field by incorporating language model capabilities within the function calling process, and allowing for function-related information to be provided to a language model via input received in any number of formats or types, including structured or unstructured input, language or non-language input, or any combination thereof.

Self-Explaining Decision Architecture and Methods of Use

Nº publicación: US20260252951A1 27/08/2026

Solicitante:

THE BOARD OF REGENTS OF THE UNIV OF OKLAHOMA [US]
The Board of Regents of the University of Oklahoma

Resumen de: US20260252951A1

A Self-Explaining Decision Architecture (SEDA) for machine learning-based decision-making systems capable of generating intuitive explanations for its decisions in real time. SEDA makes use of a feature extraction subsystem and a sequence interpretation subsystem to identify patterns in data followed by a decision generation subsystem that determines appropriate actions based on those patterns. Internal state information from each of these subsystems is used to generate explanations of the system's decisions. Using this information to create explanations provides insight as to the data elements the system focused on when making decisions as well as the reasoning that was used. In at least one embodiment the system uses deep learning components including a combined convolutional neural network and long short-term memory network with attention mechanisms.