Alerta

METHOD AND APPARATUS FOR INTELLIGENT CLUSTERING OF CELLS AND NETWORK ENTITIES USING NETWORK INTELLIGENCE-AS-A-SERVICE

Absstract of: WO2026020169A1

Described are systems, apparatuses and methods for a machine learning k- means clustering in an Operations, Administration and Maintenance (OAM) module of a Radio Access Network to generate clusters of strongly-interfering cells together, while splitting apart weekly-interfering cells across different clusters.

VEHICLE TRAJECTORY TREE STRUCTURE INCLUDING LEARNED TRAJECTORIES

Absstract of: US20260021828A1

Techniques for generating a tree structure based on multiple machine-learned trajectories are described herein. A planning component (“ML system”) within a vehicle may receive and encode various types of sensor and/or vehicle data. The ML system can provide the encoded data as input to multiple machine-learning models (“ML models”), each of which may be trained to output a unique candidate trajectory for the vehicle follow. In some examples, each ML model may be trained to output a unique type of learned trajectory that causes the vehicle to perform a certain type of action. Using the learned candidate trajectories, the ML system may generate a tree structure that includes some or all of the candidate trajectories. The vehicle may determine a control trajectory based on the generation and traversal of the tree structure using a tree search algorithm, and may follow the control trajectory within the environment.

SYSTEM, METHOD, AND COMPUTER PROGRAM PRODUCT FOR INTEGRATED PROCESSING OF GENERATIVE AND INSTRUCTIVE PROMPTS IN MACHINE LEARNING MODELS

Absstract of: WO2026019423A1

Systems, methods, and computer program products are provided for integrated processing of generative and instructive prompts in machine learning models. An example system includes a processor configured to receive reference data, store a representation of the reference data, and receive a prompt. The processor is also configured to determine a first portion of the prompt associated with a generative prompt and a second portion of the prompt associated with an executable action. The processor is further configured to retrieve a subset of the representation and determine a generative output from a machine learning model based on the subset and the first portion of the prompt. The processor is further configured to generate content based on the generative output, determine an encoding of a plurality of action steps, and execute the executable action using a sequence-to-sequence decoder model and based on the content and the encoding.

PREDICTION SELECTION FOR ITEM IDENTIFIERS USING EFFICIENT SELECTION ALGORITHM

Absstract of: WO2026019632A1

A smart system, such as a smart shopping cart system, uses an efficient selection algorithm to select an item identifier prediction for an item. The smart cart system uses a set of machine-learning models to generate identifier predictions based on images. To select an item identifier, the smart system applies an efficient selection algorithm to the predictions from the machine-learning models. An efficient selection algorithm is an algorithm that requires minimal computational resources to perform. For example, the efficient selection algorithm may be a simple majority algorithm that selects the identifier prediction generated by a majority of the models or a weighted voting algorithm where each model's vote is weighted by some metric. The smart system applies the efficient selection algorithm to select an item identifier prediction from the ones generated by the models. The smart system may display content related to the item associated with the item identifier prediction.

SYSTEMS AND METHODS FOR AUTONOMOUS TELEMETRY ORCHESTRATION

Absstract of: US20260024068A1

Systems, computer program products, and methods are described herein for autonomous telemetry orchestration. The present disclosure is configured to initiate and attempt transactions using IoT devices, generate unique session tokens, and verify session details against an orchestration engine by analyzing various parameters such as IP address, device ID, location, operating system, and mobile number. The system conducts a calculated score assessment and compares the score against a predefined threshold to determine transaction legitimacy. Transactions proceed if the score is below the threshold, otherwise, they are halted and alerts are issued. The system dynamically adjusts assessment models using machine learning algorithms based on historical data, employs blockchain technology for unique session tokens, and generates alerts via messaging services for suspicious activities.

DEVICE, SYSTEM AND METHOD FOR FEDERATED LEARNING USING RISK AUDITS

Absstract of: EP4682769A1

A computing device, that is configured to configure a global machine learning model, performs respective electronic risk audits of client devices configured to train respective local machine learning models that correspond to a global machine learning model. Based on respective electronic risk scores of one or more of the client devices, determined via the respective electronic risk audits, the computing device implements one or more parameter privacy adjustment methods on respective parameters received from the client devices prior to using the respective parameters to configure the global machine learning model, wherein respective client devices determined to have higher electronic risk scores have more of the parameter privacy adjustment methods applied than other respective client devices determined to have lower electronic risk scores. The computing device provides, to the client devices, the global machine learning model configured according to the respective parameters as adjusted.

Selection of random access preamble

Nº publicación: GB2642672A 21/01/2026

Applicant:

NOKIA TECHNOLOGIES OY [FI]
Nokia Technologies Oy

Absstract of: GB2642672A

Determination and implementation of a random access channel (RACH) preamble selection policy (PSP). An apparatus such as a distributed unit (DU) 420 of a first radio access technology (RAT) determines a RACH PSP based upon first information. The DU receives from another DU of a second RAT, second information at step (6) and updates the RACH PSP at step (7) based upon the first and second information. At step (8) the RACH PSP is transmitted to a user equipment (UE), 410. The UE selects a RACH preamble based upon the selection policy and transmits the preamble to the DU. The RACH PSP may comprise a probability distribution parameter which may include a type of distribution function, e.g. normal, Gaussian or exponential distribution, a parameter associated with a distribution function or allocation information of RACH preambles. The information may comprise: a mode or state of operation of the apparatus, an arrival rate of random access requests for the apparatus, a number of RACH preamble collisions at the apparatus or load information of the apparatus. A trained machine learning model or algorithm may be used to determine the RACH PSP based on the information to reduce potential RACH preamble collisions.