Machine learning

SENSOR READING CORRECTION

Absstract of: AU2024243389A1

Disclosed are systems, methods, and devices for correcting or otherwise cleaning sensor data. Sensor readings and metadata or other information about the sensor readings can be collected, and one or more detection rules (e.g., machine learning models or other detection rules) can be automatically generated for modifying subsequent sensor data. Sensor readings can be refined or supplemented by applying applicable detection rules.

REDUCED POWER MACHINE LEARNING SYSTEM FOR ARRHYTHMIA DETECTION

Absstract of: US2025322958A1

Techniques are disclosed for using feature delineation to reduce the impact of machine learning cardiac arrhythmia detection on power consumption of medical devices. In one example, a medical device performs feature-based delineation of cardiac electrogram data sensed from a patient to obtain cardiac features indicative of an episode of arrhythmia in the patient. The medical device determines whether the cardiac features satisfy threshold criteria for application of a machine learning model for verifying the feature-based delineation of the cardiac electrogram data. In response to determining that the cardiac features satisfy the threshold criteria, the medical device applies the machine learning model to the sensed cardiac electrogram data to verify that the episode of arrhythmia has occurred or determine a classification of the episode of arrhythmia.

METHOD FOR DISPLACING DEFECT CENTERS IN A SUBSTRATE FOR QUANTUM APPLICATIONS IN A DEFINED DIRECTION

Absstract of: WO2025215207A1

Method of automated spatial patterning of defect centers (6) in a substrate, particularly a diamond crystal lattice (50), comprising the following steps: - providing a defect center (6) distribution to a machine-learning model, the machine-learning model being particularly trained to determine an output for displacement of at least one defect center (6) based on the provided defect center (6) distribution, the machine learning model providing an output for displacement of individual defect centers (6), - particularly providing a substrate, particularly diamond, comprising defect centers (6) in a bulk structure of the substrate, particularly a bulk diamond crystal lattice (50), - detecting the position of at least one defect center (6) in the bulk structure of the substrate, particularly the bulk diamond crystal lattice (50), and - displacing (61) the at least one defect center (6) in the bulk structure of the substrate, particularly the bulk diamond crystal lattice (50), particularly site-specific, in a defined direction.

METHOD OF AUTOMATED SPATIAL PATTERNING OF DEFECT CENTERS IN A SUBSTRATE

Absstract of: WO2025215209A1

Method of automated spatial patterning of defect centers (6) in a substrate, particularly a diamond crystal lattice (50), comprising the following steps: - providing a defect center (6) distribution to a machine-learning model, the machine- learning model being particularly trained to determine an output for displacement of at least one defect center (6) based on the provided defect center (6) distribution, the machine learning model providing an output for displacement of individual defect centers (6), - particularly providing a substrate, particularly diamond, comprising defect centers (6) in a bulk structure of the substrate, particularly a bulk diamond crystal lattice (50), - detecting the position of at least one defect center (6) in the bulk structure of the substrate, particularly the bulk diamond crystal lattice (50), and - displacing (61) the at least one defect center (6) in the bulk structure of the substrate, particularly the bulk diamond crystal lattice (50), particularly site-specific.

SYSTEMS AND METHODS TO GENERATE SUGGESTED RESPONSES TO CUSTOMER INQUIRIES FOR CUSTOMER RELATIONSHIP MANAGEMENT

Absstract of: WO2025217449A1

The present disclosure relates to generating suggested responses to customer requests using machine learning models. In one example, a method includes: receiving, from a customer, a customer request via a communication channel; displaying in a customer support user interface the customer request; processing the customer request with a machine learning model to determine a suggested response to the customer request; and displaying in an agent assistance user interface element in the customer support user interface: the suggested response to the customer request; a first user interface element configured to implement the suggested response; and a second user interface element configured to dismiss or modify the suggested response.

Automated Data Hierarchy Extraction And Prediction Using A Machine Learning Model

Absstract of: US2025322312A1

Techniques are disclosed for revising training data used for training a machine learning model to exclude categories that are associated with an insufficient number of data items in the training data set. The system then merges any data items associated with a removed category into a parent category in a hierarchy of classifications. The revised training data set, which includes the recategorized data items and lacks the removed categories, is then used to train a machine learning model in a way that avoids recognizing the removed categories.

SYSTEM AND METHOD OF DYNAMICALLY RECOMMENDING ONLINE ACTIONS

Absstract of: US2025322366A1

The present disclosure generally relates to a computer device, method and system utilizing machine learning for capturing and analyzing profile data communicated across a computing environment including but not limited to: each user's profile, online behaviors and career progression path and provides dynamic recommendations of online actions to be performed to reach a desired target state.

SYSTEMS FOR CHEMISTRY TASKS BASED ON ARTIFICIAL INTELLIGENCE METHODS WITH IMPROVED REASONING USING CHEMISTRY FEEDBACK

Absstract of: WO2025215117A1

Methods and systems are disclosed in which the trustworthiness of predictive models, e.g., machine learning models, is enhanced by incorporating feedback regarding training set reactions is used to train the model so that the model is adapted so that subsequent predictions align with or account for the feedback. The feedback may include, e.g., process level reasoning, mechanism level reasoning, outlines of mechanistic reasoning, suggestions of reference reactions, or estimates of a probability of success of a given reaction. The feedback may itself be generated or proposed by a machine learning model. The model may direct an automated laboratory to perform reactions from which feedback is extracted and used to train the model.

USING MACHINE LEARNING TO PREDICT CELL THERAPY CHARACTERISTICS

Absstract of: WO2025217397A1

Disclosed are systems and methods for improving processes for developing cell therapies by applying machine learning to data including manufacturing process data and clinical measurements (e.g., patient response and treatment data) to determine parameters and settings for a manufacturing process for engineering cells for use in cell therapy. Parameters and settings for a manufacturing process for genetically engineered T-cells including, but not limited to, Chimeric Antigen Receptor (CAR) T cells can be determined. A method can include receiving a set of process parameters of a cell engineering process, predicting a clinical response associated with an output of the cell engineering process by applying a machine learning model on the received set of process parameters, where the machine learning model is trained on process parameter data and clinical response data, and generating a visualization for use in a graphical user interface of the predicted clinical response.

SYSTEM FOR OPTIMAL DECISION-MAKING AND METHODS THEREOF

Absstract of: WO2025215419A1

The present disclosure provides a system and method for optimal decision-making in multi-criteria decision-making (MCDM) problems. The invention addresses limitations of conventional approaches, which rely heavily on subjective expert inputs and biased preprocessing techniques, by introducing a statistically driven framework based on distribution normalization and data-driven weight assignment. The system comprises modules for preprocessing, evaluation, assessment, and output generation, wherein input data is normalized, criteria constraints inverted where necessary, and statistical weights optimally assigned. Decision alternatives are then computed, evaluated, and ranked to derive one or more optimal decisions. This framework ensures unbiased, efficient, and replicable outcomes across applications including Geographic Information Systems (GIS), Data Analysis, Artificial Intelligence, and Machine Learning.

SYSTEMS AND METHODS FOR MACHINE LEARNING-BASED SITE-SPECIFIC THREAT MODELING AND THREAT DETECTION

Absstract of: US2025322269A1

Systems and methods for implementing a threat model that classifies contextual events as threats. The method can include: accessing a threat model; identifying a set of contextual events, wherein each contextual event comprises a set of semantic primitives predicted from a plurality of sensor streams; and determining a threat level for each contextual event based on threat probabilities.

RESULT SET RANKING ENGINE FOR A MACHINE LEARNING BASED QUESTION AND ANSWER (Q&A) ASSISTANT

Absstract of: US2025322272A1

A multimodal content management system having a block-based data structure can include a question and answer (Q&A) assistant (e.g., a chatbot). The system can receive a natural language prompt and generate a result set. The result set can include blocks (e.g., blocks that include responsive content, including content in different modalities). The system can apply a set of authority signals to items in the result set to generate a ranked result set. The authority signals can be generated using aspects of the block-based data structure, such as block properties. The system can cause the Q&A assistant to return a set of hyperlinks to the ranked result set items. The hyperlinks can be operable to enable navigation to block content without closing the Q&A assistant.

Item Weight Prediction with Machine Learning

Absstract of: US2025322289A1

A smart shopping cart includes a load sensor to measure the weight of items added to the cart. To avoid waiting for the load sensor to converge, a detection system predicts the weight of items added to the storage area of a smart shopping cart based on the shape of a load curve output by the load sensor when an item is added to the cart. The detection system receives load data from the load sensor, detects that an item was added to the storage area of the shopping cart during a time period and identifies a set of load measurements captured by the load sensor during the time period. The set of load measurements comprise a load curve, to which the detection system applies a weight prediction model to generate a predicted weight of the added item.

SYSTEMS AND METHODS FOR MACHINE LEARNING CONTROL OF A SURGICAL DEVICE

Absstract of: US2025322952A1

A computer-implemented method for control of a surgical device includes accessing raw data captured by a sensor of the surgical device during a procedure, filtering the raw data with a filter, generating a difference data based on a difference between the raw data and the filtered data, generating zero-crossing data based on determining a point in time where an amplitude of the difference data last crossed from a non-zero amplitude value through a zero amplitude value to a non-zero amplitude value of the opposite sign, providing the zero-crossing data as an input to a machine learning classifier, and predicting a probability of an end stop point based on the machine learning classifier. The end stop point includes a point in time where a knife of the surgical device ceases to cut tissue.

MACHINE LEARNING OPTIMIZATION OF A PROCESS IN VIEW OF PREDICTED SUSTAINABILITY

Absstract of: US2025322210A1

A method of performing sustainability optimization includes processing a set of inputs using a trained machine learning model to generate a set of outputs, wherein the set of inputs correspond to configuration parameters of a process configured to be performed on a physical machine, and wherein the set of outputs includes a plurality of predicted waste metrics resulting from performance of the process on the physical machine. The method further includes optimizing the set of inputs and the set of outputs for meeting sustainability constraints in view of process constraints and outputting a recommendation for operating the process on the physical machine based on the optimized set of inputs and set of outputs, for avoiding a risk of failure to operate the process, while meeting the sustainability constraints and the process constraints.

AUGMENTING MACHINE LEARNING LANGUAGE MODELS USING SEARCH ENGINE RESULTS

Absstract of: US2025322236A1

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for augmenting machine learning language models using search engine results. One of the methods includes obtaining question data representing a question; generating, from the question data, a search engine query for a search engine; obtaining a plurality of documents identified by the search engine in response to processing the search engine query; generating, from the plurality of documents, a plurality of conditioning inputs each representing at least a portion of one or more of the obtained documents; for each of a plurality of the generated conditioning inputs, processing a network input generated from (i) the question data and (ii) the conditioning input using a neural network to generate a network output representing a candidate answer to the question; and generating, from the network outputs representing respective candidate answers, answer data representing a final answer to the question.

REAL-TIME CONTENT INTEGRATION BASED ON MACHINE LEARNED SELECTIONS

Absstract of: US2025322316A1

A candidate content item is identified for integration into a content collection. The candidate content item is associated with a first value. Using at least one machine learning model, a select value and a skip value are automatically generated for the candidate content item. The select value indicates a likelihood that the user will select the candidate content item, and the skip value indicates a likelihood that the user will bypass the candidate content item. A second value is generated for the candidate content item based on the first value, the select value, and the skip value. The candidate content item is automatically selected from a plurality of candidate content items based on the second value meeting at least one predetermined criterion. The selected candidate content item is then automatically integrated into the content collection, which is caused to be presented on a device of a user.

MITIGATING TEMPORAL GENERALIZATION FOR A MACHINE LEARNING MODEL

Absstract of: US2025322342A1

Mitigation of temporal generalization losses a target machine learning model is disclosed. Mitigation can be based on identifying, removing, modifying, transforming, etc., features, explanatory variables, models, etc., that can have an unstable relationship with a target outcome over time. Implementation of a more stable representation can be initiated. Temporal stability measures (TSMs) for one or more model feature(s) can be determined based on one or more variable performance metrics (VPMs). A group of one or more VPMs can be selected based on features of a model in either a development or production environment. Model feature modification can be recommended based on a TSM, which can prune a feature, transform a feature, add a feature, etc. Temporal stability information can be presented, e.g., via a dashboard-type user interface. Models can be updated based on mutations of a model comprising a feature modification(s), including competitive champion/challenger model updating.

Monitoring a Multi-Axis Machine Using Interpretable Time Series Classification

Absstract of: US2025322037A1

A method for assessing and/or monitoring a process and/or a multi-axis machine includes recording at least one data time series, wherein the at least one data time series includes at least one channel describing at least one parameter of the process and/or of the multi-axis machine, and wherein the data time series is caused by the process. An interpretable result is determined by a machine learning algorithm based on the at least one data time series, wherein the result describes a classification value of a state in the process and/or of a state of the multi-axis machine. A warning is output when determining the result if the classification value of the state in the process and/or of the state of the multi-axis machine is assigned to a value of an error class that is in a warning range or corresponds to a warning range, and an all-clear signal is output if the classification value of the state in the process and/or of the state of the multi-axis machine is assigned to a value of an error class that is in an all-clear range or corresponds to an all-clear range.

MACHINE LEARNING OPTIMIZATION OF A PROCESS IN VIEW OF PREDICTED SUSTAINABILITY

Absstract of: EP4632619A1

A method of performing sustainability optimization includes processing a set of inputs using a trained machine learning model to generate a set of outputs, wherein the set of inputs correspond to configuration parameters of a process configured to be performed on a physical machine, and wherein the set of outputs includes a plurality of predicted waste metrics resulting from performance of the process on the physical machine. The method further includes optimizing the set of inputs and the set of outputs for meeting sustainability constraints in view of prospcess constraints and outputting a recommendation for operating the process on the physical machine based on the optimized set of inputs and set of outputs, for avoiding a risk of failure to operate the process, while meeting the sustainability constraints and the process constraints.

INFORMATION PROCESSING METHOD, PROGRAM, AND INFORMATION PROCESSING DEVICE

Absstract of: EP4632637A1

Provided is an information processing method, etc. that assists a user in interpreting behavior of a generated machine learning model. In the information processing method, a computer executes processing of recording a plurality of sets of an explanatory data vector xn input to an existing machine learning model (21) and an objective data vector yn output from the machine learning model (21) in association with each other, calculating an interpretation matrix A† which is a vector product of an explanatory matrix X in which a plurality of sets of the explanatory data vector xn is arranged and a generalized inverse matrix of an objective matrix Y in which the objective data vector yn is arranged in an order corresponding to the explanatory data vector X, and outputting a chart (41, 42, and 43) related to the interpretation matrix A†.

Machine-learning model(s) for estimating ran functionality machine learning model impact on performance measurement counters

Absstract of: GB2640229A

An apparatus 100 comprising: means for receiving a network configuration 106 derived from a plurality of machine-learning, ML models, each ML model directed towards a respective one or more radio access network, RAN functionalities; means for receiving a plurality of predicted performance, PM measurement counters output 108 from a plurality of ML performance measurement models, each ML prediction measurement model corresponding to one of the plurality of ML models; and means for processing, using a common ML performance measurement counter model 102, the network configuration and the plurality of predicted performance measurement counters to determine a model output comprising, for one or more performance measurement counters, a respective plurality of impact scores 112, wherein each impact score is indicative of a predicted impact of a corresponding ML model in the plurality of ML models on the respective performance measurement counter of said impact score for the network configuration. The apparatus may further comprise means for executing the plurality of ML models on respective measurement data to generate a plurality of respective RAN functionality predictions; and means for generating, from the plurality of respective RAN functionality predictions, the network configuration.

AN INTELLIGENT CENTRALIZED AGENT FOR AUTONOMOUSLY ORCHESTRATING MULTIPLE DATA TOOLS

Absstract of: WO2025212608A1

An intelligent centralized agent comprising: a dynamic planner; a context short-term memory specific to an interaction session; and at least one data tool that enables the intelligent centralized agent to interact with the application programming interface, the external long-term memory, the machine learning model, and the user interface; wherein the dynamic planner receives input from the application programming interface to make decisions regarding subsequent actions based on the interaction session from the context short term memory, the data tool, and the machine learning model. This unique system provides the intelligent centralized agent with vast access to externally stored data which enables users to resolve questions or queries quickly and reliably in milliseconds.

Method, System, and Computer Program Product for Ensemble Learning With Rejection

Absstract of: US2025315740A1

Methods, systems, and computer program products are provided for ensemble learning. An example system includes at least one processor configured to: (i) generate a rejection region for each baseline model of a set of baseline models (ii) generate a global rejection region based on the rejection regions of each baseline model; (iii) train an ensemble machine learning model; (iv) update, based on a baseline model predictive performance metric for each baseline machine learning model, the set of baseline machine learning models; and (iv) repeat (i)-(iv) until there is a single baseline model in the set of baseline models or a predictive performance or global acceptance ratio of the ensemble model satisfies a threshold.

CANCER CLASSIFIER MODELS, MACHINE LEARNING SYSTEMS AND METHODS OF USE

Nº publicación: US2025316339A1 09/10/2025

Applicant:

COHEN JONATHAN [US]
DOSEEVA VICTORIA [US]
SHI PEICHANG [US]
Cohen Jonathan,
Doseeva Victoria,
Shi Peichang

Absstract of: US2025316339A1

Disclosed herein are classifier models, computer implemented systems, machine learning systems and methods thereof for classifying asymptomatic patients into a risk category for having or developing cancer and/or classifying a patient with an increased risk of having or developing cancer into an organ system-based malignancy class membership and/or into a specific cancer class membership.