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INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, SPAD IMAGING DEVICE, AI MODEL GENERATION METHOD, AI MODEL GENERATION DEVICE, AND INFERENCE PROCESSING DEVICE

Absstract of: EP4686214A1

An information processing device according to the present technology includes a calculation unit that calculates a lower limit value of a length of a count period for making likelihood information equal to or greater than a threshold on the basis of an inference result obtained by inputting a plurality of SPAD images obtained by an SPAD sensor to an AI model obtained by machine learning, the SPAD images having different count values by varying lengths of the count period in which photon counting is performed for each pixel, and likelihood information of the inference result.

Systems and Methods for Facial Recognition Training Dataset Adaptation with Limited User Feedback in Surveillance Systems

Absstract of: US20260023820A1

Various embodiments provide systems and methods for updating a training dataset so that the generated machine learning model can adapt to both short-term and long-term face variations including, for example, head pose, dressing, lighting conditions, and/or aging.

SYSTEM AND METHOD FOR DYNAMIC MULTI-PARTY VERIFICATION OF GENERATIVE ARITIFICIAL INTELLIGENCE SYSTEMS

Absstract of: US20260025388A1

A model verification system and associated method for employing a multi-party verification technique to verify machine learning models and generative AI systems. The models and associated systems can be deployed in an enterprise and require verification to ensure that cohorts are properly verifying the models and systems and evaluation to ensure that the models and systems operate responsibly and achieve intended outcomes. A dynamic, multi-stakeholder blinded verification process can be employed for the continuous verification and evaluation of machine learning models and the systems that use them. This helps promote unbiased, reproducible verification, evaluation and assessments by preventing potential biases from cohorts form part of the verification process.

DEVICE, SYSTEM AND METHOD FOR FEDERATED LEARNING USING RISK AUDITS

Absstract of: US20260025400A1

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.

Construction Knowledge Graph

Absstract of: US20260023889A1

An example computing platform is configured to (i) receive a data asset related to a construction project; (ii) determine, via a first machine-learning algorithm, at least one physical location within the construction project to which the received data asset is related; (iii) associate the received data asset with the determined physical location; (iv) based on the determined physical location, determine, via a second machine-learning algorithm, a respective relationship between the received data asset and one or more other data assets related to the construction project; and (v) add the received data asset to a construction knowledge graph as a node that is connected to one or more other respective nodes that represent the one or more other data assets.

EVALUATING ELECTRONIC SUBMISSIONS USING GENERATIVE ARTIFICIAL INTELLIGENCE

Absstract of: US20260023976A1

Aspects of the present disclosure relate to automated evaluation of electronic datasets. Embodiments include receiving one or more rules related to evaluation of electronic datasets. Embodiments further include generating, via an embedding model, embedding representations of the one or more rules. Embodiments further include receiving an electronic dataset. Embodiments further include identifying a rule that is applicable to the electronic dataset based on using a machine learning model configured to search the embedding representations of the one or more rules based on the electronic dataset. Embodiments further include evaluating, using the machine learning model or an additional machine learning model, the electronic dataset based on the identified rule. Embodiments further include using the machine learning model or the additional machine learning model to generate an evaluation summary for the electronic dataset based on determining that an item within the electronic dataset does not comply with the identified rule.

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.

REDUCING FALSE POSITIVES USING CUSTOMER FEEDBACK AND MACHINE LEARNING

Absstract of: US20260024101A1

A method of reducing a future amount of electronic fraud alerts includes receiving data detailing a financial transaction, inputting the data into a rules-based engine that generates an electronic fraud alert, transmitting the alert to a mobile device of a customer, and receiving from the mobile device customer feedback indicating that the alert was a false positive or otherwise erroneous. The method also includes inputting the data detailing the financial transaction into a machine learning program trained to (i) determine a reason why the false positive was generated, and (ii) then modify the rules-based engine to account for the reason why the false positive was generated, and to no longer generate electronic fraud alerts based upon (a) fact patterns similar to fact patterns of the financial transaction, or (b) data similar to the data detailing the financial transaction, to facilitate reducing an amount of future false positive fraud alerts.

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.

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.

SAFETY NET ENGINE FOR MACHINE LEARNING-BASED NETWORK AUTOMATION

Absstract of: US20260025327A1

In one embodiment, a device obtains data regarding routing decisions made by a machine learning-based predictive routing engine for a network. The device determines, based on the data regarding the routing decisions, a behavior of the machine learning-based predictive routing engine. The device compares the behavior of the machine learning-based predictive routing engine to a behavioral policy for the machine learning-based predictive routing engine. The device adjusts operation of the machine learning-based predictive routing engine, when the behavior of the machine learning-based predictive routing engine violates the behavioral policy.

METHOD FOR GENERATING A SET OF MANUFACTURING DATA MAKING IT POSSIBLE TO MANUFACTURE A COSMETIC COMPOSITION AND ASSOCIATED DEVICES

Absstract of: WO2026017666A1

The present invention relates to a method for generating a set of manufacturing data of a cosmetic composition capable of complying with at least one acceptability criterion, said method comprising a step of: - obtaining an set to be completed and optionally at least one constraint parameter to be complied with by the chemical composition, and - applying a technique to the set to be completed and optionally the at least one stress parameter to obtain a completed set, the technique including using at least one machine learning model, the at least one machine learning model being capable of completing a set to be completed.

SYSTEM AND METHOD FOR DYNAMIC MULTI-PARTY VERIFICATION OF GENERATIVE ARITIFICIAL INTELLIGENCE SYSTEMS

Absstract of: WO2026019909A1

A model verification system and associated method for employing a multi-party verification technique to verify machine learning models and generative Al systems. The models and associated systems can be deployed in an enterprise and require verification to ensure that cohorts are properly verifying the models and systems and evaluation to ensure that the models and systems operate responsibly and achieve intended outcomes. A dynamic, multi-stakeholder blinded verification process can be employed for the continuous verification and evaluation of machine learning models and the systems that use them. This helps promote unbiased, reproducible verification, evaluation and assessments by preventing potential biases from cohorts form part of the verification process.

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

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.

DETERMINING LABELS OF INHERITANCE DATASETS USING SIMULATED DATA INSTANCES

Absstract of: US20260017284A1

Disclosed is a method for determining inheritance labels of users based on inheritance datasets of the users. The method includes generating a plurality of reference panels for a plurality of data-inheritance origins, each reference panel corresponding to a data-inheritance origin and comprising reference-panel datasets representative of the data-inheritance origin. The method constructs a plurality of simulated data trees that are built using the reference-panel datasets that are selected from the plurality of reference panels. The method generates a plurality of simulated inheritance datasets representing a plurality of simulated named entities, each representing a descendant named entity in one of the simulated data trees. The method trains a machine learning model to determine inheritance labels of an inheritance dataset.

DYNAMIC RESOURCE ALLOCATION FOR A DISTRIBUTED NODE NETWORK

Absstract of: US20260017115A1

Methods and systems for dynamically allocating resources for a distributed node network. A system may receive a workflow comprising computer program code configured to perform one or more processes of the workflow when executed. The system may generate a set of data inputs, each data input being representative of a (a) resource allocation for allocating compute resources to the one or more nodes during performance of the one or more processes and (b) sample data on which to perform the process(es). The system may determine a performance metric value for each data input by executing at least a portion of the workflow to perform the process(es) on the sample data using the specified resource allocation. Using the generated set of data inputs, a machine learning model may be trained to identify a required resource allocation for a given set of data inputs for meeting the target performance value.

INTELLIGENT AND REAL-TIME TASK GUIDANCE SYSTEM FOR SURGICAL OPERATING ROOMS

Absstract of: WO2026015586A1

Systems and methods are described for determining and assigning tasks for performing medical procedures. The system may be configured to receive a plurality of data streams related to a medical procedure, wherein the plurality of data streams includes one or more of system data, medical environment data, and indications of personnel performing the medical procedure; analyze, using a task generation machine learning model, the plurality of data streams to generate natural language output relating to one or more tasks to be performed in furtherance of the medical procedure, wherein one or more inputs into the task generation machine learning model includes inputting embeddings of the plurality of data streams; analyze, via a task assignment machine learning model, the one or more tasks to assign the tasks to respective personnel; and provide indications to the respective personnel for performing the respective tasks assigned to the respective personnel.

AUTOMATED FEATURE SELECTION FOR SPLIT NEURAL NETWORKS

Absstract of: US20260017517A1

A computer-implemented method and apparatus for feature selection using a distributed machine learning (ML) model in a network comprising a plurality of local computing devices and a central computing device is provided. The method includes training, at each local computing device, the ML model during one or more initial training rounds using a group of input features representing a input features layer of the ML model. The method further includes generating, at each local computing device, based on the one or more initial training rounds, feature group values. The method further includes transmitting, from each local computing device, to the central computing device, the generated feature group values. The method further includes receiving, at each local computing device, from the central computing device, central computing device gradients. The method further includes computing, at each local computing device, local computing device gradients, using the received central computing device gradients. The method further includes generating, at each local computing device, a gradient trajectory for each input feature in the group of input features based on the computed local computing device gradients. The method further includes identifying, at each local computing device, based on the generated gradient trajectory, whether each input feature in the group of input features is non-contributing. The method further includes removing, at each local computing device, from the group

AUTOMATED TASK DETECTION USING MACHINE LEARNING AND LARGE LANGUAGE MODEL

Absstract of: US20260017573A1

The invention relates to a method for improving task detection through a combination of machine learning and natural language processing. The method involves preparing data by preprocessing and cleaning to ensure suitability for machine learning algorithms, followed by training a LightGBM model using the prepared data. Task detection results are generated using the trained LightGBM model. The method further includes analyzing feature importance and generating new features using a large language model (LLM). These new features are used to expand the dataset, and the LightGBM model is retrained to enhance task detection performance. This approach automates feature extraction, improves performance, increases adaptability, and enhances the generalizability of task detection methods.

MACHINE LEARNING MODEL CONTINUOUS TRAINING SYSTEM

Absstract of: US20260019655A1

Described is a system for performing a set of machine learning model training operations that include: accessing media content items associated with interaction functions initiated by users of an interaction system, generating training data including labels for the media content items, extracting features from a media content item of the media content items, identifying additional media content items to include in the training data based on the extracted features from the media content item, processing the training data using a machine learning model to generate a media content item output; and updating one or more parameters of the machine learning model based on the media content item output. The system checks whether retraining criteria has been met, and repeats the set of machine learning model training operations to retrain the machine learning model.

Response Prediction for Electronic Communications

Absstract of: US20260017544A1

Systems, methods, and apparatuses are described herein for performing sentiment analysis on electronic communications relating to one or more image-based communications methods, such as emoji. Message data may be received. The message data may correspond to a message that is intended to be sent but has not yet been sent to an application. Using a first machine learning model, one or more subsets of the plurality of emoji may be determined. The one or more subsets of the plurality of emoji may comprise one or more different types and quantities of emoji, and may each correspond to the same or a different sentiment. Using a second machine learning model, one or more emojis may be selected from the one or more subsets. The one or more emojis selected may correspond to responses to the message.

APPLICATION OF ARTIFICIAL INTELLIGENCE MACHINE LEARNING (AIML) MODELS ASSOCIATED WITH SAME FUNCTIONALITY

Nº publicación: WO2026015208A1 15/01/2026

Applicant:

QUALCOMM INCORPORATED [US]
QUALCOMM INCORPORATED

Absstract of: WO2026015208A1

Disclosed are techniques for wireless communication. In an aspect, a processing device may receive, from a server device, a request for an output based on application of a plurality of artificial intelligence machine learning (AIML) models associated with a same functionality. The processing device may apply the plurality of AIML models to obtain a plurality of respective candidate outputs, the plurality of candidate outputs being associated with the functionality. The processing device may transmit the output to the server device in response to the request, the output indicating at least one of the plurality of candidate outputs.