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94
results.
Updated on
22/11/2025 [07:41:00]
Absstract of: AU2024267218A1
An optical coherence tomography (OCT) device includes artificial intelligence for recommending a treatment plan for a patient with a retinal or macular disease such as age-related macular degeneration (AMD). The OCT device includes a sensor configured to quantify an initial level of macular edema or retinal exudation. The OCT device receives treatment information for a series of anti-vascular endothelial growth factor (anti-VEGF) injections to the patient. The OCT device performs OCT on the patient subsequent to each anti-VEGF injection to determine subsequent levels of edema or retinal exudation. The OCT device collects a set of training data including: the initial and subsequent levels of edema or exudation, patient information, and treatment information. The OCT device applies the training data to a machine-learning model trained on training data for a plurality of patients to determine a treatment plan for the retinal or macular disease of the patient.
Absstract of: WO2025240170A1
Aspects presented herein may enable a consistency between multiple network entities in artificial intelligence (AI) or machine learning (ML) (AI/ML) related positioning training and inference. In one aspect, a first network entity transmits, to a second network entity, a request for an identifier (ID) to be used for indexing a set of datasets associated with at least one AI/ML model related to positioning. The first network entity receives, from the second network entity based on the request, the ID to be used for indexing the set of datasets associated with the at least one AI/ML model related to positioning. The first network entity stores, based on the ID, at least one of a set of positioning configurations or a set of radio statistics associated with the first network entity. The first network entity indexes the set of datasets with the ID.
Absstract of: WO2025240061A1
Various embodiments are directed to an example apparatus, computer-implemented method, and computer program product for rapid machine learning in a data-constrained environment. Such embodiments may include using a decision space generation model to generate candidate content data objects based on content generation objectives. Such embodiments may further include generating a first plurality of rated content data objects for a first target client based on a first experimental classification group and generating a second plurality of rated content data objects for a second target client based on a second experimental classification group. Such embodiments may further generate, based on a learning model, the first experimental classification group, and the second experimental classification group, a custom output content set including one or more of the first plurality of rated content data objects and one or more of the second plurality of rated content data objects.
Absstract of: WO2025239978A1
Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. In an example method, an input prompt for machine learning is received, and the input prompt is decomposed to generate a set of sub-prompts. A sequence of requests for sub-prompts of the set of sub-prompts that have sequential dependency is generated, and a parallel request for sub-prompts of the set of sub-prompts that do not have sequential dependency is generated. Based on evaluating the sequence of requests and the parallel request, an execution plan for using one or more machine learning models to generate a response to the input prompt is generated. The response to the input prompt is output according to the execution plan.
Absstract of: US2025355960A1
The present disclosure relates to systems, methods, and non-transitory computer-readable media that utilize machine learning models to generate identifier embeddings from digital content identifiers and then leverage these identifier embeddings to determine digital connections between digital content items. In particular, the disclosed systems can utilize an embedding machine-learning model that comprises a character-level embedding machine-learning model and a word-level embedding machine-learning model. For example, the disclosed systems can combine a character embedding from the character-level embedding machine-learning model and a token embedding from the word-level embedding machine-learning model. The disclosed systems can determine digital connections between the plurality of digital content items by processing these identifier embeddings for a plurality of digital content items utilizing a content management model. Based on the digital connections, the disclosed systems can surface one or more digital content suggestions to a user interface of a client device.
Absstract of: US2025355683A1
A system displays a first set of generative interfaces in a user interface. Each generative interface includes user interface elements that contain content specifying information of the generative interface. Responsive to receiving a user interaction with a user interface element, the system activates a dynamic input phase that dynamically generates responses during runtime of receiving user inputs to the user interface. The system receives a second user input and applies a machine learning model to the generative interface comprising the interacted user interface element, the content contained in the interacted user interface element and the content from the second user input. The system receives content as an output and updates the user interface to display a second set of generative interfaces. The second set of generative interfaces may include one or more runtime-determined user interface elements, and each runtime-determined user interface element include information associated with the received content.
Absstract of: WO2025237802A1
Disclosed is a method for training a machine learning model for generating synthetic data, the method comprising: Providing an encrypted data set, resulting from encryption of an original data set, to a server, the encrypted data set comprising a set of entries, wherein each entry of the encrypted data set comprises values for a set of attributes; application of a homomorphic machine learning algorithm for data synthesis comprising a set of homomorphic group operations on the encrypted data set, such a homomorphic algorithm as understood herein encompasses any algorithm the execution of which causes the decrypted results to coincide with the results of applying the same homomorphic group operations on the original data set.
Absstract of: WO2025237631A1
Improved systems and methods for constructing a machine-learning model associated with lithography are disclosed. The method may include accessing a first set of data comprising metrology data, training a machine-learning model iteratively based on the first set of data, the machine-learning model associated with a lithography process, obtaining information generated by the machine- learning model from each of multiple iterations during the training, and identifying outlier data from the first set of data based on the obtained information.
Absstract of: US2025358769A1
Aspects presented herein may enable a consistency between multiple network entities in artificial intelligence (AI) or machine learning (ML) (AI/ML) related positioning training and inference. In one aspect, a first network entity transmits, to a second network entity, a request for an identifier (ID) to be used for indexing a set of datasets associated with at least one AI/ML model related to positioning. The first network entity receives, from the second network entity based on the request, the ID to be used for indexing the set of datasets associated with the at least one AI/ML model related to positioning. The first network entity stores, based on the ID, at least one of a set of positioning configurations or a set of radio statistics associated with the first network entity. The first network entity indexes the set of datasets with the ID.
Absstract of: WO2025238443A1
The present disclosure relates to systems, methods, and program applications for identifying separation-related problems in a pet. The methods, for example, can include identifying the presence or absence of multiple behavioral signs exhibited by a pet where each of the multiple behavioral signs are given a sign score based on binary annotations representing either the presence or the absence of each of the behavioral signs, and grouping subsets of the multiple behavioral signs into one of multiple principal component behavioral groupings using the binary annotations to generate principal component scores for each of the multiple principal component behavioral groupings. Methods can also include using one or more machine-learning algorithms under the control of at least one processor for accessing and correlating the principal component scores for each of the multiple principal component behavioral groupings with a population cluster associated with a type of separation-related problem.
Absstract of: US2025348879A1
A computing system for automated fraud risk reduction for travel-related transactions, the computing system including at least one processing circuit including at least one processor and at least one memory, the at least one memory storing instructions therein that, when executed by the at least one processor, cause the at least one processor to: receive data corresponding to a first travel-related transaction, process, using a first machine learning model, the data to automatically generate an output data set comprising a plurality of characteristics relating to the first travel-related transaction, the first machine learning model configured to generate the output data set by identifying the plurality of characteristics to include responsive to determining the plurality of characteristics are potentially relevant to a determination of whether the first travel-related transaction is fraudulent, and provide the generated output data set for use in analyzing whether the first travel-related transaction is fraudulent.
Absstract of: WO2025235501A1
A computing system for automated fraud risk reduction for travel-related transactions, the computing system including at least one processing circuit including at least one processor and at least one memory, the at least one memory storing instructions therein that, when executed by the at least one processor, cause the at least one processor to: receive data corresponding to a first travel -related transaction, process, using a first machine learning model, the data to automatically generate an output data set comprising a plurality of characteristics relating to the first travel-related transaction, the first machine learning model configured to generate the output data set by identifying the plurality of characteristics to include responsive to determining the plurality of characteristics are potentially relevant to a determination of whether the first travel -related transaction is fraudulent, and provide the generated output data set for use in analyzing whether the first travel -related transaction is fraudulent.
Absstract of: US2025348062A1
A digital twin of a facility defines relationships between different components of the facility and a system of record for the facility. Information from different monitoring systems for the facility are related to events by the digital twin of the facility. Historical operation information for the facility is used to train a machine learning model. The trained machine learning model facilitates operations at the facility by providing descriptive information, predictive information, and/or prescriptive information on the operations at the facility.
Absstract of: WO2025235630A1
Machine learning predicted outcomes for scenarios of a procedure performed with a robotic medical system is provided. A system can identify state information related to a medical procedure to be performed by a robotic medical system. The system can generate, using the state information and with one or more models trained by machine learning, scenarios of operation of the robotic medical system to perform the medical procedure. The system can determine, using the one or more models, performance metrics for the scenarios. The system can select, based on a comparison of the performance metrics for the scenarios, a scenario of the scenarios for operation. The system can provide an indication of the selected scenario to cause the robotic medical system to perform at least a portion of the medical procedure in accordance with the selected scenario.
Absstract of: WO2025233314A1
A computer-implemented method of creating a digital representation of a toy construction model, the method comprising: receiving a user prompt to create a digital representation of a toy construction model, the user prompt including one or more desired attributes of the toy construction model; using a generative machine-learning model to create, based on the user prompt, a structured representation of a toy construction model, the toy construction model being constructed from a set of mutually interconnected toy construction elements, the structured representation being indicative of the toy construction elements of said set and of the mutual interconnections between respective ones of said toy construction elements; translating the created structured representation into a digital 2D or 3D representation of a visual appearance of the toy construction model and/or into a set of building instructions for creating the toy construction model.
Absstract of: US2025350634A1
Techniques for performing cyber-security alert analysis and prioritization according to machine learning employing a predictive model to implement a self-learning feedback loop. The system implements a method generating the predictive model associated with alert classifications and/or actions which automatically generated, or manually selected by cyber-security analysts. The predictive model is used to determine a priority for display to the cyber-security analyst and to obtain the input of the cyber-security analyst to improve the predictive model. Thereby the method implements a self-learning feedback loop to receive cyber-security alerts and mitigate the cyberthreats represented in the cybersecurity alerts.
Absstract of: US2025350815A1
The present disclosure generally relates to generating a video corresponding to a memory (e.g., an event or context) from media assets on a device. In some embodiments, the device receives user inputs requesting a video based on a natural language description of a memory. The device sends information of the natural language description to a first machine-learning (ML) model, and receives query tokens, which are used to find media items on the device that match the query tokens. The device sends information representing the found media items to another ML model that determines traits from the media items. These traits are sent to a third ML model to generate a story outline, and the video is generated by comparing the descriptions of shots in the story outline to visual embeddings of the found media assets to curate and arrange them into the video consistent with the story outline.
Absstract of: WO2025233668A1
A computer-implemented method includes receiving input text documents in a majority language and training a plurality of models using the input text documents. Training the plurality of models using the input text documents includes conjunctively training a text to knowledge graph model in the majority language and a text to knowledge graph model in the minority language and conjunctively training a text translation model and a knowledge graph translation model, wherein one or more weights from the text to text translation model is shared with the knowledge graph translation model. The method has applications including, but not limited to, use cases in machine learning and medicine / healthcare, e.g., performing uniform analysis of multilingual records of a patient via knowledge graph translation and transfer learning, and improving text to knowledge graph extraction and knowledge graph translation for medical records in multiple languages, which can help optimize decision making.
Absstract of: US2025349438A1
A system for optimizing supplement decisions is disclosed. The system includes a computing device configured to receive a longevity inquiry from a remote device. The system retrieves a biological extraction pertaining to a user and identifies a longevity element associated with a user. The system selects an ADME model utilizing a biological extraction. The system generates a machine-learning algorithm utilizing the selected ADME model to input a longevity element associated with a user as an input and output an ADME factor. The system identifies a second longevity element compatible with the ADME factor as a function of the first longevity element. The system selects the second longevity element as a tolerant longevity element. A method for optimizing supplement decisions is also disclosed.
Absstract of: US2025348967A1
A computer system for analyzing and mitigating risks associated with a building is provided. The computer system is configured to: (i) receive environment data from the at least one sensor; (ii) receive building data from the at least one database; (iii) utilize a trained machine learning model to determine at least one potential risk associated with the building based upon the environment data and the building data; (iv) generate a building risk profile that includes the at least one potential risk associated with the building; and/or (v) generate a risk mitigation output based upon at least one of the building risk profile and the at least one potential risk, wherein the risk mitigation output includes at least one of a risk alert, a risk mitigation recommendation, and risk mitigation instructions. Computer systems for analyzing and mitigation risks associated with a city, a user, and an event are also provided.
Absstract of: US2025349433A1
A sequencing module configured to provide metatranscriptomic reads from an oral sample from a subject; metatranscriptomic reads from the sequencing of oral sample and identify and cluster microbes identified in the oral sample into taxon clusters (TCs) using the metatranscriptomic reads mapped to a metagenomic library; generate TC-specific orthogroups for each of the TCs via protein clustering; determine KEGG orthology for each of the TC-specific orthogroups, or genes directly; generate phylogenomic functional categories (PGFCs) from grouping of gene expression counts by the KEGG modules for each of the TCs; retain the PGFCs having an MCR above an MCR threshold to obtain input data; and identify or predict, using a classifier model including variables selected by a feature selection machine learning algorithm, dental caries in said subject based on the input data.
Absstract of: US2025348921A1
An online concierge identifies orders to shoppers, allowing shoppers to select orders for fulfillment. The online concierge system may generate batches that include multiple orders, allowing a shopper to select a batch to fulfill multiple orders. As orders are continuously being received, delaying identification of orders to shoppers may allow greater batching of orders. To allow greater opportunities for batching, the online concierge system estimates a benefit for delaying identification of an order by different time intervals and predicts an amount of time to fulfill the order. The online concierge system then delays assigning orders for which there is a threshold benefit for delaying and selects a time interval for delaying identification of the order that does not result in greater than a threshold likelihood of a late fulfillment of the order.
Absstract of: US2025348819A1
In a threat management platform, a number of endpoints log events in an event data recorder. A local agent filters this data and feeds a filtered data stream to a central threat management facility. The central threat management facility can locally or globally tune filtering by local agents based on the current data stream, and can query local event data recorders for additional information where necessary or helpful in threat detection or forensic analysis. The central threat management facility also stores and deploys a number of security tools such as a web-based user interface supported by machine learning models to identify potential threats requiring human intervention and other models to provide human-readable context for evaluating potential threats.
Absstract of: WO2025234546A1
The present invention discloses a system for visualizing carbon emissions. More specifically, the present invention relates to a system for calculating and visualizing carbon emissions generated by applying a machine learning model to various technical fields. According to an embodiment of the present invention, a developer accesses a system online to obtain information on carbon emissions for various ML models, selects a model to be reviewed among a plurality of registered ML models, and uploads prepared query data, and the system converts and inputs the query data into the ML model, and provides, in a graph form, carbon emissions for each inference process generated by performing analysis, so that the developer can easily check the carbon emissions of the ML model being developed thereby.
Nº publicación: US2025348617A1 13/11/2025
Applicant:
LEDGERDOMAIN INC [US]
LedgerDomain Inc
Absstract of: US2025348617A1
Multi-layer ensembles of neural subnetworks are disclosed. Implementations can classify inputs indicating various anomalous sensed conditions into probabilistic anomalies using an anomaly subnetwork. Determined probabilistic anomalies are classified into remedial application triggers invoked to recommend or take actions to remediate, and/or report the anomaly. Implementations can select a report type to submit, or a report recipient, based upon the situation state, e.g., FDA: Field Alert Report (FAR), Biological Product Deviation Report (BPDR), Medwatch, voluntary reporting by healthcare professionals, consumers, and patients (Forms 3500, 3500A, 3500B, Reportable Food Registry, Vaccine Adverse Event Reporting System (VAERS), Investigative Drug/Gene Research Study Adverse Event Reports, Potential Tobacco Product Violations Reporting (Form 3779), USDA: APHIS Center for Veterinary Biologics Reports, Animal and Plant Health Inspection Service: Adverse Event Reporting, FSIS Electronic Consumer Complaints, DEA Tips, Animal Drug Safety Reporting, Consumer Product Safety Commission Reports, State/local reports: Health Department, Board of Pharmacy.
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