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84
resultados
Última actualización
19/04/2025 [07:21:00]
Resumen de: US2025125034A1
Comprehensive molecular profiling provides a wealth of data concerning the molecular status of patient samples. Such data can be compared to patient response to treatments to identify biomarker signatures that predict response or non-response to such treatments. This approach has been applied to identify biomarker signatures that strongly correlate with response of colorectal cancer patients to FOLFOX. Described herein are data structures, data processing, and machine learning models to predict effectiveness of a treatment for a disease or disorder of a subject having a particular set of biomarkers, as well as an exemplary application of such a model to precision medicine, e.g., to methods for selecting a treatment based on a molecular profile, e.g., a treatment comprising administration of 5-fluorouracil/leucovorin combined with oxaliplatin (FOLFOX) or with irinotecan (FOLFIRI).
Resumen de: US2025121818A1
A processor retrieves data associated with a set of driving sessions and generates a training dataset by labeling a first subset of data that corresponds to driving sessions that included a first event and labeling a second subset of the data that corresponds to driving sessions that included an indication of an airbag activation. The processor then trains an artificial intelligence model using the training dataset, such that trained artificial intelligence model predicts a score indicative of a likelihood of a new driving session associated with a new driver being associated with at least the first event or an airbag activation. Once trained, the processor can augment the score using data retrieved after each driving session. The processor can also notify the driver if the driver's actions has caused their score to increase/decrease and provide an underlying reason.
Resumen de: US2025123572A1
The current disclosure describes techniques for managing vertical alignment or overlay in semiconductor manufacturing using machine learning. Alignments of interconnection features in a fan-out WLP process are evaluated and managed through the disclosed techniques. Big data and machine learning are used to train a classification that correlates the overlay error source factors with overlay metrology categories. The overlay error source factors include tool signals. The trained classification includes a base classification and a Meta classification.
Resumen de: US2025123953A1
Disclosed are systems and methods for scenario planning by using specially programmed software engines to simulate and detect particular feature variations leading to particular outcomes based on modeling with machine learning techniques. The disclosed technology enable improved model debugging, improved simulation efficiency and accuracy, improved model explainability, improved identification of high risk or high reward scenarios, among other improvements and combinations thereof. In some embodiments, the disclosed technology implements computerized optimization techniques applied via variation generation across a dataset of test input records to optimize for feature variation along with outcome variation. Moreover, the disclosed technology may provide and/or realize a minimized variation to input data that correspond to a point of transition from one state to another state in an outcome that results from the input data, where the transition to another state is termed a “significant” variation to the output data.
Resumen de: US2025124038A1
Computing systems, computing apparatuses, computing methods, and computer program products are disclosed for machine learning ranking. An example computing method includes receiving a search query and determining a plurality of machine learning model execution engines based on the search query and a plurality of search result types. The example computing method further includes generating a plurality of subsets of search results based on the search query and the plurality of machine learning model execution engines. The example computing method further includes generating a set of search results comprising at least one search result from each of the plurality of subsets of search results.
Resumen de: US2025124311A1
Embodiments are directed to generating and training a distributed machine learning model using data received from a plurality of third parties using a distributed ledger system, such as a blockchain. As each third party submits data suitable for model training, the data submissions are recorded onto the distributed ledger. By traversing the ledger, the learning platform identifies what data has been submitted and by which parties, and trains a model using the submitted data. Each party is also able to remove their data from the learning platform, which is also reflected in the distributed ledger. The distributed ledger thus maintains a record of which parties submitted data, and which parties removed their data from the learning platform, allowing for different third parties to contribute data for model training, while retaining control over their submitted data by being able to remove their data from the learning platform.
Resumen de: US2025124069A1
An agent-based website search interface utilizes a multimodal model to enhance enterprise operations. Data agents collect and process diverse inputs, while an orchestrator manages these agents. The system leverages machine learning models to generate insights and automate decision-making processes. It includes tools for data visualization and validation, ensuring accuracy and reliability. By integrating generative AI, the interface provides advanced search functionalities, improving user experience and operational efficiency. This facilitates seamless interaction to answer context specific questions from complex data, offering a robust solution for enterprise-level search and analysis.
Resumen de: US2025124330A1
Techniques are described for performing team member behavior identification and classification using a machine learning model and one or more rule-based models for customer communications. A computing system receives a message from a user device. The computing system uses output of a machine learning model to determine whether the message includes an indication of team member behavior including at least one behavior term and at least one team member reference. The computing system also uses output of one or more rule-based models to determine whether the message includes an indication of a type of team member behavior including a type of behavior term and a type of team member reference substantially proximate to each other within the message. Based on the message including the indication of team member behavior, the computing system sends the message to another system corresponding to the type of team member behavior included in the message.
Resumen de: US2025124353A1
Disclosed are various embodiments for implementing computational tasks in a cloud environment in one or more operating system level virtualized containers. A parameter file can specify different parameters including hardware parameters, library parameters, user code parameters, and job parameters (e.g., sets of hyperparameters). The parameter file can be converted via a mapping and implemented in a cloud-based container platform.
Resumen de: US2025124529A1
Aspects of the subject matter described in this specification are embodied in systems and methods that utilize machine-learning techniques to evaluate clinical trial data using one or more learning models trained to identify anomalies representing adverse events associated with a clinical trial investigation. In some implementations, investigation data collected at a clinical trial site is obtained. A set of models corresponding to the clinical trial site is selected. Each model included in the set of models is trained to identify, based on historical investigation data collected at the clinical trial site, a distinct set of one or more indicators that indicate a compliance risk associated with the investigation data. A score for the clinical trial site is determined based on the investigation data relative to the historical investigation data. The score represents a likelihood that the investigation data is associated with at least one indicator representing the compliance risk.
Resumen de: US2025124530A1
Embodiments of the present disclosure provide a method that may include defining an object model containing a structural representation of events and artifacts through which contracts are created, changed, and brought to an end. The method may include accessing a machine learning classifier comprising a plurality of rule sets. The method may include applying the plurality of rule sets to one or more words of each corresponding contract document. The method may include linking identified one or more core attributes and one or more words of each corresponding contract document to an applicable object of the object model, determining prevailing terms of each corresponding contract document, and evaluating contract data variables and assigning a contract data risk value to one or more of contract data values. The method may include communicating an alert via email or text message when a contract risk exceeds a threshold value.
Resumen de: US2025125013A1
Described herein in some embodiments is a method comprising: obtaining expression data previously obtained by processing a biological sample obtained from a subject; processing the expression data using a hierarchy of machine learning classifiers corresponding to a hierarchy of molecular categories to obtain machine learning classifier outputs including a first output and a second output, the hierarchy of molecular categories including a parent molecular category and first and second molecular categories that are children of the parent molecular category in the hierarchy of molecular categories, the hierarchy of machine learning classifiers comprising first and second machine learning classifiers corresponding to the first and second molecular categories; and identifying, using at least some of the machine learning classifier outputs including the first output and the second output, at least one candidate molecular category for the biological sample.
Resumen de: US2025094811A1
A relevance score for a predictor portion of a machine learning predictor is determined by performing a reverse propagation of an initial relevance score, which is attributed to a first predetermined predictor portion, along propagation paths of the machine learning predictor, and by filtering the reverse propagation with respect to a second predetermined predictor portion. Furthermore, respective affiliation scores for a set of data structures with respect to a predictor portion of a machine learning predictor are determined by performing reverse propagations of an initial relevance score from a first predetermined predictor portion to the predictor portion.
Resumen de: US2025118439A1
Systems, methods, and computer program products are provided for diagnosing, prognosing, or monitoring cancer in a subject, particularly the assessment of minimal residual disease (MRD).
Resumen de: WO2025073138A1
An AI-based system that uses data sets of existing banner ads, advertising materials, and design and marketing parameters, based on image algorithms and technology, to generate, evaluate, and predict performance of banner ads and present them to specific customers or specific types of customers. The graph-based model is used to generate original banner ads, which are then evaluated by a machine learning model, which assigns them scores. The highest-scoring banner ads are then presented to customers. A genetic algorithm in combination with iterative evaluation and generation are used to diversify design and choose the highest ranked banner ads.
Resumen de: US2025117662A1
The invention relates to a computer-implemented method for determining similarity relations between various tables by means of machine-learning computing modules.
Resumen de: WO2025074193A1
A method of detecting sample anomalies within a laboratory information management system includes obtaining a first result for a sample, processing the first result via a univariate machine learning model, processing a plurality of results for the sample via a multivariate machine learning model in response to the univariate machine learning model generating a normal output for the first result, and flagging, within the laboratory information management system, the sample for rejection processing in response to the multivariate machine learning model generating an abnormal output for the plurality of samples. The first result represents a first type of result, the univariate machine learning model is trained using unsupervised machine learning, the plurality of results includes the first result, each of the plurality of results represents a different type of result for the sample, and the multivariate machine learning model trained using unsupervised machine learning.
Resumen de: US2025119360A1
A method for improving communication network performance comprises identifying a favorability status of individual predictions and/or decisions of a plurality of decisions of a machine-learning algorithm acting on the communication network. The favorability statuses are stored with corresponding values of network parameters used as features in the algorithm. A counterfactual algorithm is generated, e.g., by generating a tree-based classification algorithm, based on the stored favorability statuses and network parameter values, to derive rules for producing a favorable status based on one or more of the network parameters. A proposed recourse action comprising a change in at least one of the network parameters is identified, based on the rules, and a decision network, such as a Bayesian inference network, is generated for determining a confidence level estimating a reliability of achieving a favorable status by changing the network parameter(s). Whether to implement the proposed recourse action is determined, based on the confidence level.
Resumen de: US2025114710A1
A game modification engine modifies configuration settings affecting game play and the user experience in computer games after initial publication of the game, based on device level and game play data associated with a user or cohort of users and on machine-learned relationships between input data and a use metric for the game. The modification is selected to improve performance of the game as measured by the use metric. The modification may be tailored for a user cohort. The game modification engine may define the cohort automatically based on correlations discovered in the input data relative to a defined use metric.
Resumen de: US2025118057A1
An unlabelled or partially labelled target dataset is modelled with a machine learning model for classification (or regression). The target dataset is processed by the machine learning model; a subgroup of the target dataset is prepared for presentation to a user for labelling or label verification; label verification or user re-labelling or user labelling of the subgroup is received; and the updated target dataset is re-processed by the machine learning model. User labelling or label verification combined with modelling an unclassified or partially classified target dataset with a machine learning model aims to provide efficient labelling of an unlabelled component of the target dataset.
Resumen de: US2025116678A1
A method of detecting sample anomalies within a laboratory information management system includes obtaining a first result for a sample, processing the first result via a univariate machine learning model, processing a plurality of results for the sample via a multivariate machine learning model in response to the univariate machine learning model generating a normal output for the first result, and flagging, within the laboratory information management system, the sample for rejection processing in response to the multivariate machine learning model generating an abnormal output for the plurality of samples. The first result represents a first type of result, the univariate machine learning model is trained using unsupervised machine learning, the plurality of results includes the first result, each of the plurality of results represents a different type of result for the sample, and the multivariate machine learning model trained using unsupervised machine learning.
Resumen de: US2025119448A1
To analyze cybersecurity threats, an analysis module of a processor may receive log data from at least one network node. The analysis module may identify at least one statistical outlier within the log data. The analysis module may determine that the at least one statistical outlier represents a cybersecurity threat by applying at least one machine learning algorithm to the at least one statistical outlier.
Resumen de: US2025117537A1
A method for interactive explanations in industrial artificial intelligence systems includes providing a machine learning model and a set of test data, a set of training data and a set of historical data simulating a piping and process equipment; predicting a result for the piping and process equipment based on the machine learning model using the set of test data and the set of training data, wherein the set of historical data is used by the machine learning model to predict at least one parameter of the piping and process equipment; and presenting the predicted at least one parameter on a piping and instrumentation diagram of the piping and process equipment.
Resumen de: US2025119451A1
Embodiments disclosed include methods and apparatus for visualization of data and models (e.g., machine learning models) used to monitor and/or detect malware to ensure data integrity and/or to prevent or detect potential attacks. Embodiments disclosed include receiving information associated with artifacts scored by one or more sources of classification (e.g., models, databases, repositories). The method includes receiving inputs indicating threshold values or criteria associated with a classification of maliciousness of an artifact and for selecting sample artifacts. The method further includes classifying and selecting the artifacts, based on the criteria, to define a sample set, and based on the sample set, generating a ground truth indication of classification of maliciousness for each sample artifact in the sample set. The method further includes using the ground truth indications to evaluate and display, via an interface, a representation of a performance of sources of classification and/or quality of data.
Nº publicación: WO2025076103A1 10/04/2025
Solicitante:
FISHER ROSEMOUNT SYSTEMS INC [US]
FISHER-ROSEMOUNT SYSTEMS, INC
Resumen de: WO2025076103A1
Methods and apparatus for artificial intelligence control of process control systems are described. An example non-transitory machine readable storage medium comprising instructions to cause programmable circuitry to at least: collect a measurement of an operation of a process; utilize machine learning based on a state of the process and a goal function that references one or more measurement(s); and modify operation of a controller based on the machine learning.
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