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71
results.
Updated on
14/10/2025 [07:06:00]
Results 25 to 50 of 71
Absstract of: US2025308709A1
A system and a method for predicting insulin resistance and/or pancreatic β-cell function are provided, where a machine learning model is utilized to predict insulin resistance and/or pancreatic a decline of β-cell function of a subject in need thereof based on a feature set extracted from a database. Therefore, clinicians or the subject can be warned to take necessary actions on, and adjust related medical treatment or lifestyle before the subject is diagnosed with diabetes mellitus. In addition, a computer readable medium thereof is also provided.
Absstract of: WO2025207133A1
Systems and methods for accelerating plant biomass growth and plant-mediated sequestration of atmospheric carbon, in particular, for selection of microbial drivers thereof from naturally occurring fungal species and/or strains are disclosed. The systems or methods may facilitate identification and propagation of a growth-promoting fungal consortium from a natural fungal microbiome. Sampling kits to collect soil samples are provided. Sample nucleic acid material may be extracted from the soil to generate a fungal microbiome dataset comprising of nucleic acid sequences. A machine learning tool, trained on high productivity ecosystems data, may processes the microbiome dataset to identify the growth-promoting fungal consortium. Propagation may include introducing a soil sample portion into a forest bioreactor to cultivate the growth-promoting fungal consortium, followed by inoculum preparation and application onto plants at a geographic location. Monitoring plant productivity post-inoculation may be achieved using an array of sensors to assess the efficacy of the fungal consortium.
Absstract of: US2025307245A1
Examples detect equivalent subexpressions within a computational workload. Examples include converting a query plan tree associated with a first subexpression into a matrix. The first subexpression is a portion of a database query from the computational workload. Each node in the query plan tree is represented as a row of the matrix. The matrix is converted into a first vector. The first subexpression is determined to be equivalent to a second subexpression by comparing the first vector to a second vector associated with the second subexpression. The comparison includes computing a distance between the first and second vectors that is lower than a distance threshold. The computational workload is modified, based on the determining, to perform the first subexpression and exclude performance of the second subexpression as duplicative.
Absstract of: WO2025204495A1
An information processing device according to the present invention extracts, from combinations of words included in each of a plurality of sentences to which a teacher label is assigned, a combination of words in which an index value indicating a relation between the combination of words and the teacher label satisfies a predetermined condition, determines whether or not the extracted combination of words is included in the same context in each of the plurality of sentences, and generates training data associated with the teacher label by using, as a feature, presence/absence of the combination of words determined to be included in the same context, in each of the plurality of sentences.
Absstract of: EP4625199A1
Examples detect equivalent subexpressions within a computational workload. Examples include converting a query plan tree associated with a first subexpression into a matrix. The first subexpression is a portion of a database query from the computational workload. Each node in the query plan tree is represented as a row of the matrix. The matrix is converted into a first vector. The first subexpression is determined to be equivalent to a second subexpression by comparing the first vector to a second vector associated with the second subexpression. The comparison includes computing a distance between the first and second vectors that is lower than a distance threshold. The computational workload is modified, based on the determining, to perform the first subexpression and exclude performance of the second subexpression as duplicative.
Absstract of: 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).
Absstract of: EP4624960A1
A battery life estimation apparatus includes a charging/discharging unit configured to charge/discharge a battery and a controller configured to calculate a partial accumulative capacity corresponding to a partial voltage period defined as a period from a first voltage to a second voltage by charging/discharging the battery in the partial voltage period, and estimate a life corresponding to an entire voltage period of the battery by inputting the first voltage, the second voltage, and the partial accumulative capacity to an estimation model trained based on machine learning.
Absstract of: US2025299017A1
A method for obtaining a domain-informed machine learning/artificial intelligence, ML/AI, model for drive analytics includes obtaining first data indicative of a set of data points, wherein each data point is associated with a behavior of a drive apparatus and/or drive system. The method further comprises obtaining second data indicative of domain knowledge comprising physics knowledge associated with a behavior of the drive apparatus and/or drive system and/or with an environment of the drive apparatus and/or drive system. The method further comprises training a machine learning/artificial intelligence, ML/AI, model by jointly utilizing the first data and the second data to obtain the domain-informed ML/AI model for drive analytics.
Absstract of: AU2024229742A1
A computer-implemented method and computer program product for predicting a required committed capacity of an electric utility are provided. The method includes the steps of: (a) performing a stochastic optimization of raw data to produce a total committed capacity from conventional thermal units as a target data, wherein the raw data comprises grid operating conditions; (b) combining the total committed capacity from conventional thermal units with raw features and engineered features to generate training data; (c) training a machine learning model for predicting the required committed capacity of the electric utility using the generated training data; (d) predicting the required committed capacity of the electric utility using the trained machine learning model; and (e) running an augmented version of a deterministic dispatch optimization model based on the predicted required committed capacity of the electric utility. The computer program performs the aforementioned steps.
Absstract of: US2025299070A1
An embodiment for managing machine learning models to generate and utilize perforations within machine learning models to improve their ability to consider and learn from exception decisions. The embodiment may detect an exception decision in a base model. The embodiment may automatically determine a feature associated with the base model in making the exception decision. The embodiment may automatically identify a remaining additional feature in making the exception decision, and generating a perforation corresponding to the remaining additional feature. The embodiment may, in response to detecting a subsequent decision including a shared additional feature to the generated perforation, automatically validate a feature boundary within the generated perforation. The embodiment may automatically outputting a decision recommendation for the subsequent decision using both the base model and the generated perforation.
Absstract of: US2025299088A1
An apparatus for resource allocation, may include at least a processor; and a memory communicatively connected to the at least processor, wherein the memory contains instructions configuring the at least processor to receive a resource datum and a periodic activity pattern datum; identify input metadata as a function of the resource datum and the periodic activity pattern datum; select a resource allocation machine learning model as a function of the input metadata; and generate a resource allocation datum using the resource allocation machine learning model.
Absstract of: US2025299231A1
Systems and apparatuses for generating object dimension outputs and predicted object outputs are provided. The system may collect an image from a mobile device. The system may analyze the image to determine whether it contains one or more standardized reference objects. Based on analysis of the image and the one or more standardized reference objects, the system may determine an object dimension output. The system may also determine a predicted object output that includes additional objects predicted to be in a room corresponding to the image. Using object dimension outputs and the predicted object output, the system may determine an estimated repair cost.
Absstract of: US2025299803A1
A computer-implemented method for processing digital pathology images, the method including receiving a plurality of digital pathology images of at least one pathology specimen, the pathology specimen being associated with a patient. The method may further include determining receiving metadata corresponding to the plurality of digital pathology images, the metadata comprising data regarding previous medical treatment of the patient. Next, the method may include providing the medical images and metadata as input to a machine learning system, the machine learning system having been trained by receiving as input historical treatment information and digital images labeled with a predicted treatment regimen. Lastly, the method may include outputting, by the machine learning system, a treatment effectiveness assessment.
Absstract of: US2025299780A1
Techniques for predicting performance of biological sequences. The technique may include using a statistical model configured to generate output indicating predictions for an attribute of biological sequences, the biological sequences generated using a machine learning model trained on training data. The statistical model is configured to allow for at least some of the predictions to occur outside a distribution of labels in the training data.
Absstract of: US2025299802A1
A computer-implemented method for processing digital pathology images, the method including receiving a plurality of digital pathology images of at least one pathology specimen, the pathology specimen being associated with a patient. The method may further include determining receiving metadata corresponding to the plurality of digital pathology images, the metadata comprising data regarding previous medical treatment of the patient. Next, the method may include providing the medical images and metadata as input to a machine learning system, the machine learning system having been trained by receiving as input historical treatment information and digital images labeled with a predicted treatment regimen. Lastly, the method may include outputting, by the machine learning system, a treatment effectiveness assessment.
Absstract of: US2025301187A1
A method may include determining a combination of values of attributes represented by reference data associated with computing devices by training a machine learning model based on an association between (i) respective values of the attributes and (ii) the computing devices entering a device state. The combination may be correlated with entry into the device state. The method may also include selecting a subset of the computing devices that is associated with the combination of values. The method may additionally include determining a first rate at which computing devices of the subset have entered the device state during a first time period and a second rate at which one or more computing devices associated with the combination have entered the device state during a second time period, and generating an indication that the two rates differ.
Absstract of: AU2024234871A1
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.
Absstract of: US2025298920A1
A secured virtual container is enabled to securely store personal data corresponding to a user, where such data is inaccessible to processes running outside the secured virtual container. The secured virtual container may also include an execution environment for a machine learning model where the model is securely stored and inaccessible. Personal data may be feature engineered and provided to the machine learning model for training purposes and/or to generate inference values corresponding to the user data. Inference values may thereafter be relayed by a broker application from the secured virtual container to applications external to the container. Applications may perform hyper-personalization operations based at least in part on received inference values. The broker application may enable external applications to subscribe to notifications regarding availability of inference values. The broker may also provide inference values in response to a query.
Absstract of: EP4621643A1
A method for obtaining a domain-informed machine learning/artificial intelligence, ML/AI, model for drive analytics. The method comprises obtaining first data indicative of a set of data points, wherein each data point is associated with a behavior of a drive apparatus and/or drive system. The method further comprises obtaining second data indicative of domain knowledge comprising physics knowledge associated with a behavior of the drive apparatus and/or drive system and/or with an environment of the drive apparatus and/or drive system. The method further comprises training a machine learning/artificial intelligence, ML/AI, model by jointly utilizing the first data and the second data to obtain the domain-informed ML/AI model for drive analytics.
Absstract of: WO2024104614A1
The present invention describes a self-adaptive system capable of extracting correlations between multiple faults from network topologies, with the innovative component being the data pre-processing phase generating causality matrices to provide as an input to ML models. The proposed fault correlation system is responsible for, without any configuration, identi fying the hierarchical relationships between the multiple alarms, allowing for a better understanding of the causality and impact of each mal function, hence assisting the implementation of RCA rules. This allows, not only for a huge dimensionality reduction of alarms needed to be processed by a TO ' s, but also signi ficantly increases the knowledge about the topology, thus reducing downtime and increasing the quality of service of the network and services.
Absstract of: US2025291864A1
A system for determining data requirements to generate machine-learning models. The system may include one or more processors and one or more storage devices storing instructions. When executed, the instructions may configure the one or more processors to perform operations including: receiving a sample dataset, generating a plurality of data categories based on the sample dataset; generating a plurality of primary models of different model types using data from the corresponding one of the data categories as training data; generating a sequence of secondary models by training the corresponding one of the primary models with progressively less training data; identifying minimum viable models in the sequences of secondary models; determining a number of samples required for the minimum viable models; and generating entries in the database associating: model types; corresponding data categories; and corresponding numbers of samples in the training data used for the minimum viable models.
Absstract of: US2025292211A1
A method comprises obtaining a hypernym tree for a word selected from each skill name of a plurality of skill names, the obtained hypernym tree including a node for each meaning of the word, each node of an obtained hypernym tree for a meaning of a word including one or more synonyms corresponding to the meaning and a summary description of the meaning, at least one hypernym tree including nodes for meanings of different words; creating an embedding for each skill name and for each synonym and the summary description included in a node of each obtained hypernym tree computing, a distance between the embedding for a skill name and the embedding for each synonym and summary description included in a node of an obtained hypernym tree; assigning to the skill name a meaning of a word associated with a node of any obtained hypernym tree that leads to a lowest distance.
Absstract of: US2025292148A1
Systems and methods are disclosed for analyzing real-time data utilizing machine learning for determining the condition of an entity. The method includes receiving a control dataset and a system dataset or a non-system dataset for a first entity; determining, via input of a first subset of the control dataset into a first machine learning model, classification of the first entity; determining, via input of the system dataset into a second machine learning model or the non-system dataset into a third machine learning model, system score or non-system score, respectively; determining, via input of the system score, the non-system score, or a second subset of the control dataset into a fourth machine learning model, composite score; determining lateral score or longitudinal score based on the classification of the first entity or the composite score; and comparing the lateral score or the longitudinal score with a pre-determined threshold for initiating mitigation action(s).
Absstract of: US2025292096A1
An approach is provided for partnership optimization. Using a supervised machine learning model, a categorized profile of partners is generated based on feedback from clients and past performances of the partners. The categorized profile indicates strengths of the partners. A network graph is generated based on data about interactions between the partners and clients and the categorized profile. The network graph has nodes representing the partners and the clients and edges representing connections between the partners and the clients. Using the categorized profile and the network graph, a three-dimensional model is generated and represented by a three-dimensional matrix of cells. A given cell represents a part of a project and includes constraint(s) of the project. Using a machine learning algorithm based on Thistlethwaite's algorithm, the model is solved to optimally match the constraint(s) with strength(s) of partner(s) and strength(s) of connection(s) between the partner(s) and client(s).
Nº publicación: US2025294033A1 18/09/2025
Applicant:
SECURITYSCORECARD INC [US]
SecurityScorecard, Inc
Absstract of: US2025294033A1
The present disclosure presents methods and systems for determining cybersecurity risk exposure for entities. In one aspect, a method is provided that includes providing first text data to a trained LLM to identify data associated with a first candidate cybersecurity event for an entity, comparing the entity's identifier to domain information to verify the entity's identifier, determining if the first candidate cybersecurity event represents a new cybersecurity event based on com with previous data, and updating a cybersecurity risk score for the entity based on this determination. Further enhancements include training the LLM with cybersecurity event data, outputting documentation of the event source, and various methods for evaluating the novelty and severity of the cybersecurity event, including similarity measures and manual review triggers. The techniques leverage LLMs, machine learning models, and automated actions to provide a comprehensive approach to cybersecurity risk assessment and response. Other aspects are also provided.
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