Alerta

SYSTEMS, APPARATUS AND METHODS FOR TREATMENT OF RETINAL AND MACULAR DISEASES USING ARTIFICIAL INTELLIGENCE

Resumen de: 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.

SYSTEMS AND METHODS FOR ENHANCED MACHINE LEARNING TECHNIQUES FOR KNOWLEDGE MAP GENERATION AND USER INTERFACE PRESENTATION

Resumen de: US2025356220A1

Systems and methods for extracting information from documents and constructing corresponding knowledge maps with respect to defined knowledge models. Deep-learning-based models for Natural Language Processing (NLP) are applied to tokenize words, tag, parse, and lemmatize sentences of input documents. Then an information extractor traverses the dependency tree of NLP object to recursively extract the entities of interest to the knowledge models. Finally, a knowledge map constructor traverses the dependency tree of NLP object to determine the relationships among the extracted entities and construct knowledge maps recursively following the defined knowledge models.

ADAPTIVE NAVIGATION AND CONTENT-FIRST DYNAMIC SYSTEM

Resumen de: 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.

AI/ML POSITIONING TRAINING AND INFERENCE CONSISTENCY USING DATASET INDEXING

Resumen de: 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.

INTEGRATED ONLINE TRAVEL BOOKING SYSTEM WITH ENHANCED GEOGRAPHIC COVERAGE AND SERVICE AGGREGATION

Resumen de: WO2025240668A1

A computer-implemented system for optimizing travel itineraries in real time includes a failover-capable API gateway, a search and recommendation engine, and a machine learning model trained on historical travel data. The system expands itinerary options using an Alternative Logic Service that incorporates flexible dates and nearby airports based on geographic metadata and pricing trends. Structured offer requests are transmitted to external APIs and evaluated using predictive scoring. A client-side graphical radius filter reduces request volume and latency. Updated itineraries are reranked dynamically and propagated across devices via a pub-sub notification system.

UTILIZING MACHINE-LEARNING MODELS TO GENERATE IDENTIFIER EMBEDDINGS AND DETERMINE DIGITAL CONNECTIONS BETWEEN DIGITAL CONTENT ITEMS

Resumen de: 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.

Machine-Learning Systems and Methods for Conversational Recommendations

Resumen de: US2025356223A1

Aspects of the disclosed technology include computer-implemented systems and methods for conversational recommendation systems, such as conversational chatbots that are configured to process user queries and generate responses. A recommendation system includes a conversational user interface configured to receive a user query and provide a recommendation response and a machine-learned sequence processing model that has been trained on training data including a plurality of triplets. Each triplet includes an example query, an example model reasoning plan associated with the example query, and an example response associated with the example query and the example model reasoning plan. The sequence processing model can be trained to provide conversational-based recommendations using a multi-stage recommendation process that includes a planning stage, a conversation stage, and a retrieval stage.

ADVANCED EXPLOITATION OF MULTITHREADING TO BOOST MACHINE LEARNING INFERENCE PERFORMANCE FOR DECISION-TREE BASED ENSEMBLES

Resumen de: US2025356242A1

A method for performing machine learning decision-tree based inferences includes generating multiple CPU threads on an inference function server and determining if an inference request which requires a tree traversal operation has been received. If an inference request which requires the tree traversal operation has been received, then immediately executing the tree traversal operation. If an inference request which requires the tree traversal operation has not been received, then determining if a stop thread request has been received, wherein if a stop thread request has been received, then stopping the CPU thread.

SYSTEM FOR VISUALIZING CARBON EMISSIONS OF MACHINE LEARNING

Nº publicación: KR20250161249A 17/11/2025

Solicitante:

서울시립대학교산학협력단

Resumen de: 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.