Alerta

WEBSITE DEPLOYMENT ARTIFACT GENERATION USING TASK-SPECIFIC MACHINE LEARNING PROMPTING

Resumen de: US20260170076A1

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for dynamically performing website creation. In some implementations, a server receives request data specifying a natural language description of a webpage modification. The server determines web development tasks corresponding to the webpage modification. The server determines web development tools configured to execute the web development tasks. The server generates prompt data for trained machine learning models. The prompt data includes instructions for generating a code update segment for the webpage modification. The server obtains from the trained ML models output data for the code update segment. The code update segment causes the web development tools to execute the tasks. The server generates a deployment artifact by executing the code update segment. The server provides an instruction that causes the computing device to display a representation of a modified webpage based on the deployment artifact.

HEALTH TRACKING APPLICATIONS FOR SMART GLASSES

Resumen de: US20260166225A1

0000 Systems, computer programs, devices, and methods that enable coordination across multiple devices of the mobile ecosystem. In one embodiment, smart glasses detect when a user is about to eat food or take a drink and capture the consumable and portion. The data is recorded in a “morsel track” for health activity analysis. Low-fidelity captures provide preliminary recognition, while higher-fidelity captures are selectively invoked for definitive classification. Machine-learning logic generates predicted metabolic responses, such as real-time glucose trends, based on the recorded events. Predicted responses may dynamically adjust the operation of continuous glucose monitors, heart-rate sensors, or other biomedical devices. In some embodiments, the system triggers a pharmaceutical dispenser, such as an insulin pump, inhaler, or transdermal patch, to provide closed-loop therapeutic intervention in real time.

CHATBOT FOR DEFINING A MACHINE LEARNING (ML) SOLUTION

Resumen de: US20260170363A1

0000 The present disclosure relates to systems and methods for an intelligent assistant (e.g., a chatbot) that can be used to enable a user to generate a machine learning system. Techniques can be used to automatically generate a machine learning system to assist a user. In some cases, the user may not be a software developer and may have little or no experience in either machine learning techniques or software programming. In some embodiments, a user can interact with an intelligent assistant. The interaction can be aural, textual, or through a graphical user interface. The chatbot can translate natural language inputs into a structural representation of a machine learning solution using an ontology. In this way, a user can work with artificial intelligence without being a data scientist to develop, train, refine, and compile machine learning models as stand-alone executable code.

SYSTEMS AND METHODS FOR AUTOMATING EMAIL TO ORDER USING GENERATIVE ARTIFICIAL INTELLIGENCE (AI)

Resumen de: AU2025230786A1

Systems and methods provide for automating the conversion of email orders into structured order entries using generative AI, leveraging an integrated architecture comprising a Real-Time Data Mesh (RTDM), Advanced Analytic and Machine Learning (AAML) Module, and Single Pane of Glass (SPoG) User Interface. The system includes an Email Parser that extracts order information from emails, an Order Generation Engine that converts this information into structured entries, and an Integration Gateway that synchronizes the entries with external systems. The RTDM manages data flow and transformation, while the AAML provides predictive analytics and process automation. The SPoG UI performs real-time data visualization and user interaction. The system enhances order processing efficiency, accuracy, and scalability, enabling businesses to process email orders with minimal manual effort and greater precision. Systems and methods provide for automating the conversion of email orders into structured order entries using generative AI, leveraging an integrated architecture comprising a Real-Time Data Mesh (RTDM), Advanced Analytic and Machine Learning (AAML) Module, and Single Pane of Glass (SPoG) User Interface. The system includes an Email Parser that extracts order information from emails, an Order Generation Engine that converts this information into structured entries, and an Integration Gateway that synchronizes the entries with external systems. The RTDM manages data flow and transformat

AUTOMATED TRAVEL PLANNING DATA PROCESSING SYSTEM, AUTOMATED TOUR GUIDE AND METHOD UTILIZING ADVANCED ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING ALGORITHMS

Resumen de: WO2026128044A1

The present invention relates to an automated travel planning data processing system and method that leverages advanced artificial intelligence (Al) and machine learning (ML) algorithms to generate personalized travel itineraries in real-time. The system comprises a central server with one or more processors, memory, and a machine learning module, as well as a travel database that stores aggregated data from multiple sources. Users interact with the system through a natural language processing-based user interface, which receives inputs comprising travel dates, destinations, and preferences. An Al-powered itinerary generation engine processes user inputs and aggregated data to create personalized travel plans, utilizing a multithreading module for simultaneous data retrieval and processing. The machine learning module continuously optimizes the itinerary generation process by analyzing user preferences and travel data patterns. The invention also provides a method for automated travel itinerary planning using the data processing system.

PREDICTIVE INVENTORY AVAILABILITY

Resumen de: US20260170453A1

A method for predicting inventory availability, involving receiving a delivery order including a plurality of items and a delivery location, and identifying a warehouse for picking the plurality of items. The method retrieves a machine-learned model that predicts a probability that an item is available at the warehouse. The machine-learned model is trained, using machine learning, based in part on a plurality of datasets. The plurality of datasets include data describing items included in previous delivery orders, whether each item in each previous delivery order was picked, a warehouse associated with each previous delivery order, and a plurality of characteristics associated with each of the items. The method predicts the probability that one of the plurality of items in the delivery order is available at the warehouse, and generates an instruction to a picker based on the probability. An instruction is transmitted to a mobile device of the picker.

EARLY WARNING AND COLLISION AVOIDANCE

Resumen de: US20260170959A1

Among other things, equipment is located at an intersection of a transportation network. The equipment includes an input to receive data from a sensor oriented to monitor ground transportation entities at or near the intersection. A wireless communication device sends to a device of one of the ground transportation entities, a warning about a dangerous situation at or near the intersection, there is a processor and a storage for instructions executable by the processor to perform actions including the following. A machine learning model is stored that can predict behavior of ground transportation entities at or near the intersection at a current time. The machine learning model is based on training data about previous motion and related behavior of ground transportation entities at or near the intersection. Current motion data received from the sensor about ground transportation entities at or near the intersection is applied to the machine learning model to predict imminent behaviors of the ground transportation entities. An imminent dangerous situation for one or more of the ground transportation entities at or near the intersection is inferred from the predicted imminent behaviors. The wireless communication device sends the warning about the dangerous situation to the device of one of the ground transportation entities.

METHOD AND DEVICE FOR SIGNAL TRANSMISSION AND RECEPTION IN WIRELESS COMMUNICATION SYSTEM

Resumen de: EP4761205A1

A method performed by a first apparatus in a wireless communication system according to at least one of the embodiments disclosed in the present specification may comprise the steps of: transmitting, to a second apparatus, a first signal including information about a first artificial intelligence/machine learning (AI/ML) model configured in the first apparatus; and receiving a second signal, including information about a second AI/ML model configured in the second apparatus, from the second apparatus in response to the first signal, wherein the first AI/ML model configured in the first apparatus and the second AI/ML model configured in the second apparatus are linked to each other, and the second signal includes at least one of information about a reference AI/ML model used in the second apparatus in order to evaluate the performance of the first AML model or information about conditions under which the second AI/ML model is applied.

MACHINE-LEARNING TECHNIQUES FOR PREDICTING UNOBSERVABLE OUTPUTS

Resumen de: WO2025034318A1

In some aspects, a computing system can generate and optimize a machine learning model to estimate an unobservable capacity of a target system or entity. The computing system can access training vectors which include training predictor variables, training performance indicators, and task quantities. A training performance indicator indicating performance outcome corresponding to the predictor variables and a task quantity associated with a task assigned to the target entity that leads to the training performance indicator. The machine learning model can be trained by performing adjustments of parameters of the machine learning model to minimize a loss function defined based on the training vectors. The trained machine learning model can be used to estimate the capacity of the target system or entity for handling tasks and be used in assigning tasks to the target entity according to the determined capacity.

SYSTEMS AND METHODS FOR IDENTIFYING COMMERCIAL DOMICILES

Nº publicación: EP4760672A1 17/06/2026

Solicitante:

GEOTAB INC [CA]
GEOTAB Inc.

Resumen de: EP4760672A1

The present disclosure relates to systems and methods for identifying commercial domiciles. An example of one such method includes operating at least one processor to: receive telematics data originating from a plurality of telematics devices installed in a plurality of vehicles; identify, using the telematics data, a vehicle stop zone, each vehicle stop zone comprising a vehicle stop cluster; and identify the vehicle stop zone as a commercial domicile by applying to the vehicle stop cluster of the vehicle stop zone at least one machine learning model trained to classify vehicle stop zones based on one or more vehicle stop features thereof.