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MULTI-TASK REAL-TIME INFERENCE SCHEDULING SYSTEM MACHINE TOOL AND METHOD THEREOF

Absstract of: EP4711869A1

The present invention relates to a multi-task real-time inference scheduling system and real-time inference scheduling method of a machine tool, wherein a central control unit is connected to each of one or more individual control units through a network, receives a use context of each machine tool through each individual control unit, generates a multi-task learning model through a neural network, infers multiple tasks required to be performed by the individual control unit of each machine tool through machine learning by using real-time use contexts collected during operation of the machine tool by a use scenario, and schedules the multiple tasks of the machine tool through machine learning.

MODEL INFERENCE METHOD AND DEVICE

Absstract of: EP4711986A1

A model inference method and apparatus are disclosed, and relates to the field of machine learning technologies. A client and a server use respective deployed models to process different parts of user data, to obtain respective output results. In addition, the client obtains the output result of the server, and obtains an inference result based on the output results of the server and the client. Compared with a case in which the server needs to obtain all the user data in an inference process, in this application, the server obtains only a part of the user data. As the server cannot obtain, based on the part of the user data, all content included in the user data, security of the user data is ensured. In addition, the client needs to send only the part of the user data to the server, so that a bandwidth resource occupied by data transmission between the client and the server and time consumed by the transmission can be reduced, and inference efficiency can be improved.

MANAGING CLINICAL TRIAL PROGRESSION USING MACHINE LEARNING-BASED DATA

Absstract of: WO2024238507A1

Systems and methods for managing progression of a clinical trial. Input data for a machine learning model is formed, based on longitudinal data for clinical trial cohort. The input data corresponds to input features and the cohort includes a plurality of subjects. A clinical outcome output is generated for each subject, using the machine learning model and a portion of the input data corresponding to each subject. Feature importance values are generated, based on the machine learning model generating the clinical outcome output for each subject. The feature importance values include, for each subject, a set of feature importance values for a set of input features. A ratio of interest is computed using the plurality of feature importance values. An output is generated using the ratio of interest in which the output indicates whether the cohort should proceed to a next phase of the clinical trial.

METHOD AND APPARATUS FOR TRANSMITTING/RECEIVING SIGNAL IN WIRELESS COMMUNICATION SYSTEM

Absstract of: EP4712540A1

A method performed by a terminal in a wireless communication system, according to at least one of embodiments disclosed in the present specification, may comprise: configuring at least one artificial intelligence/machine learning (AI/ML) model related to multiple transmission and reception points (TRPs) for positioning; acquiring input data subsets on the basis of TRP subsets of the multiple TRPs; acquiring positioning information output from the at least one AI/ML model on the basis of the input data subsets; and transmitting a positioning-related report to a network on the basis of the positioning information, wherein the positioning-related report may include information on at least one of the TRP subsets or the input data subsets.

APPARATUS AND METHOD FOR MACHINE LEARNING-BASED DOMAIN DERIVATION

Absstract of: US2025158959A1

Provided are an electronic device for deriving a domain connected to the IP address based on Open Source INTelligence (OSINT) information and for deriving the domain connected to the IP address based on an artificial intelligence (AI) model. and a method for the same.

Computer System and Method for Providing a Subject-Related Data Development Platform

Absstract of: US20260072930A1

A method, performed by a computer system connected to a network, comprises processing at least one input data object for standardizing subject-related information. The method further comprises subjecting the subject-related information contained in the processed at least one input data object to a first machine learning model for generating a uniform dataset containing the subject-related information in a uniform structured format, and storing the uniform dataset in one or more secured data repositories connected to the network. The method further comprises providing a secured virtual environment accessible to users connected to the network, the secured virtual environment enabling importation of datasets stored in the one or more secured data repositories and a use of imported datasets as part of one or more user-controlled subject-related data development operations for generating at least one workspace-developed data object.

MACHINE LEARNING ACCELERATED SEMANTIC EQUIVALENCE DETECTION

Absstract of: US20260072913A1

Examples detect equivalent subexpressions within a computational workload. Examples include converting a query plan tree associated with a first subexpression into a matrix. The first subexpression is a portion of a database query from the computational workload. Each node in the query plan tree is represented as a row of the matrix. The matrix is converted into a first vector. The first subexpression is determined to be equivalent to a second subexpression by comparing the first vector to a second vector associated with the second subexpression. The comparison includes computing a distance between the first and second vectors that is lower than a distance threshold. The computational workload is modified, based on the determining, to perform the first subexpression and exclude performance of the second subexpression as duplicative.

PRESERVING PRIVACY IN GENERATING A PREDICTION MODEL FOR PREDICTING USER METADATA BASED ON NETWORK FINGERPRINTING

Absstract of: US20260074958A1

A method, an apparatus and a computer program product for machine learning based on network fingerprinting, while preserving privacy in generating a prediction model for predicting user metadata. Routing information of a device is obtained based probe packets sent by the device to a server that is connectable to the device via the Internet, such as a series of packet hops implemented to route the packets to the server or a series of Internet Protocol (IP) addresses of the series of packet hops until reaching the Internet. A fingerprint describing an architecture of connection path of the device to the Internet is created based on the routing information. The prediction model is trained using training dataset that includes pairs of fingerprints and labels using edge devices having known labels, that are indicative of a routing information of an edge device to the Internet.

User profiling using chain-of-thought knowledge graphs for querying a machine learning system

Absstract of: AU2025217419A1

Techniques are disclosed for a machine learning model, such as a large learning model (LLM), that incorporates a model of a chain of thought of a particular user when responding to a query from the user. In one example, a system generates a knowledge graph of a chain of thought of the user. The knowledge graph comprises nodes representing topics present within past queries by the user and edges representing a co-occurrence between the topics. The system determines, based on a topic present within a query from the user and the knowledge graph, a goal query comprising a goal topic. The system provides, to a machine learning model, the user to generate, by the machine learning model, a response. The machine learning model is constrained to include the goal topic of the goal query within the response. The system outputs, for display, the response to the query. Techniques are disclosed for a machine learning model, such as a large learning model (LLM), that incorporates a model of a chain of thought of a particular user when responding to a query from the user. In one example, a system generates a knowledge graph of a chain of thought of the user. The knowledge graph comprises nodes representing topics present within past queries by the user and edges representing a co-occurrence between the topics. The system determines, based on a topic present within a query from the user and the knowledge graph, a goal query comprising a goal topic. The system provides, to a machine learning m

Control Logic for Thrust Link Whiffle-Tree Hinge Positioning for Improved Clearances

Absstract of: US20260073240A1

Systems and methods for optimizing clearances within an engine include an adjustable coupling configured to couple a thrust link to the aircraft engine, an actuator coupled to the adjustable coupling, where motion produced by the actuator adjusts a hinge point of the adjustable coupling, sensors configured to capture real time flight data, and an electronic control unit. The electronic control unit receives flight data from the sensors, implements a machine learning model trained to predict clearance values within the engine based on the received flight data, predicts, with the machine learning model, the clearance values within the engine based on the received flight data, determines an actuator position based on the clearance values, and causes the actuator to adjust to the determined actuator position.

SYSTEM AND METHOD FOR USE WITH A DATA ANALYTICS ENVIRONMENT TO ENABLE USE OF AI IN PROVIDING CUSTOMER SUPPORT

Absstract of: WO2026055146A1

Embodiments described herein are generally related to data analytics environments, and are particularly directed to systems and methods for use with a data analytics environment to enable use of AI in providing customer support. Machine learning AI models are trained based on one or more previous service request lifecycles of service requests of a customer to determine latent emotions of the customer based on determined customer problem data. A customer service prioritization signal related to a current service request of the customer is generated by a predictive analytics application that includes the models. The customer service prioritization signal is indicative of a need to prioritize a current service request of the customer based on the determined latent emotions of the customer and is generated during and prior to the end of the lifecycle of the current service request whereby escalation of the current service request may be deferred or prevented.

USING MARKOV CHAINS TO EXPLAIN MACHINE LEARNING MODEL PREDICTIONS AND TO EVALUATE AUTONOMOUS COMPUTER AGENTS

Absstract of: US20260073258A1

Historical performance information of a plurality of autonomous agents configured to handle a plurality of tasks is accessed. The historical performance information indicates, for each autonomous agent, a successful outcome or a failed outcome for each of the tasks handled by the autonomous agent. A Markov chain comprising a plurality of states is constructed based on the autonomous agents. Each autonomous agent corresponds to a different state of the states. For each autonomous agent, a first score and a second score are calculated based on the Markov chain. The first score corresponds to an expected number of transitions from the autonomous agent to other autonomous agents until the successful outcome or the failed outcome is reached, The second score corresponds to a probability of the autonomous agent ultimately achieving the successful outcome. The autonomous agents are evaluated based on the first score and the second score.

ARTIFICIAL-INTELLIGENCE-ENHANCED BIAS RESPONSE PROTOCOLS

Absstract of: US20260073248A1

Bias response methods, systems, and computer program products for detecting and responding to behavioral biases in user plans. A method may include receiving a plan on behalf of a user, calculating an estimated net consequence (ENC) of the plan using machine learning models trained on historical data, and comparing the plan against bias patterns to determine if the plan has recognizable biases. The method may also include generating notifications or tracking user responses to refine response protocols or establish new bias patterns. A system may implement AI enhancement protocols to improve bias detection, analysis, or response capabilities. The system may refine logical bases for plans through user interactions, monitor actual outcomes over time, adjust estimation protocols based on discrepancies between estimated and actual consequences, or improve a bias filter with more or better bias pattern definition.

KNOWLEDGE GRAPH CREATION UTILIZING EMBEDDING AND LARGE LANGUAGE MODELS

Absstract of: US20260073247A1

Certain aspects of the disclosure provide techniques for creating a knowledge graph. A method generally includes for each respective item, of a plurality of items, associated with a respective industry: adding an item node in the knowledge graph for the respective item; adding an industry node in the knowledge graph for the respective industry if no industry node for the respective industry exists in the knowledge graph; generating semantically similar items to the respective item; prompting one or more machine learning models to determine that the respective item and at least one semantically similar item of the set of semantically similar items are associated; and generating an edge between the respective item and the at least one semantically similar item in the knowledge graph based on the association determination.

GENERATIVE AI-DRIVEN SYSTEM FOR AGILE EDUCATIONAL CONTENT CREATION AND MANAGEMENT IN RAPIDLY CHANGING AND HIGH-STAKES FIELDS

Absstract of: US20260073246A1

A method for creating and managing knowledge-based content using generative artificial intelligence (AI) includes: storing one or more profiles including a user identifier and a user knowledge-based history for one or more knowledge-based topics; storing one or more knowledge maps for an knowledge-based topic including at least links between concepts of an knowledge-based topic and knowledge-based material items; receiving a content request from a computing device, the content request including a user identifier and an knowledge-based topic; identifying a user profile of the one or more user profiles including the user identifier of the content request; identifying a knowledge map of the one or more knowledge maps matching the knowledge-based topic of the content request; identifying one or more user knowledge gaps; generating one or more new knowledge-based material items for addressing each of the identified one or more user knowledge gaps using a generative machine learning model.

SYSTEM AND METHOD FOR CONTROLLING RESOURCE MANAGEMENT USING MACHINE LEARNING

Absstract of: AU2024327251A1

In some aspects, a computing system can use a machine learning model for resource management. For example, the system can receive a request for a set of steps associated with a target model output of a machine learning model. The request can include a starting input feature set and a number of steps. For each of the number of steps, the system can calculate a change to one or more features from the starting input feature set to arrive at the target model output based on a current position in feature space of the machine learning model. The system can update a feature vector by applying the change to the features of the starting input feature set and transmitting the set of steps. The system can then cause a resource of the external computing system to transition toward a position defined by the target model output.

AUTOMATED SOURCE ROCK CHARACTERISTICS AND CLASS PREDICTION

Absstract of: WO2026054765A1

Disclosed is a method comprising: determining a computing platform for modeling source rocks, the computing platform including a database system, a data processing system, and a machine learning engine; generating, using the database system, analyzed graph data; filtering, using the data processing system, the analyzed graph data based on vitrinite reflectance data and thereby generate trainable data; resolving, using the data processing system, data discrepancies within the trainable data and thereby generate resolved data; holistically enhancing, using the data processing system, the resolved data to be compatible with a plurality of subterranean structures and thereby generate training data; applying, using the machine learning engine, the training data to train a subterranean model and thereby generate a trained subterranean model; and testing, using the machine learning engine, the trained subterranean model and thereby generate a prediction report indicating rock characteristics and classification of a source rocks.

ANOMALY DETECTION BASED ON ENSEMBLE MACHINE LEARNING MODEL

Absstract of: US20260073310A1

A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.

COMPUTER SYSTEM AND METHOD FOR PROVIDING A SUBJECT-RELATED DATA DEVELOPMENT PLATFORM

Absstract of: US20260074078A1

A method comprises receiving at least one input data object containing subject-related information according to at least one of information types encoded in at least one of data formats; and processing the at least one input data object for standardizing the subject-related information. The method further includes subjecting the subject-related information to a first machine learning model for generating a uniform dataset containing the subject-related information in a uniform structured format; storing the uniform dataset in one or more secured data repositories connected to a network; and providing a secured virtual environment accessible to users connected to the network, the secured virtual environment enabling importation of datasets stored in the one or more secured data repositories and a use of imported datasets as part of one or more user-controlled subject-related data development operations for generating at least one workspace-developed data object.

TECHNIQUES FOR INTUITIVE MACHINE LEARNING DEVELOPMENT AND OPTIMIZATION

Absstract of: US20260073308A1

Various embodiments are generally directed to techniques for intuitive machine learning (ML) development and optimization, such as for application in a content services platform (CSP), for instance. Many embodiments include a ML model developer and a ML model evaluator to provide a graphical user interface that guides ML layman in developing, evaluating, implementing, managing, and/or optimizing ML models. Some embodiments are particularly directed to a common interface that provides a step-by-step user experience to develop and implement ML techniques. For example, embodiments may include computing a health score for various aspects of developing and/or optimizing ML models, and using the health score, and the factors contributing thereto, to guide production of a valuable ML model. These and other embodiments are described and claimed.

SYSTEM FOR TIME BASED MONITORING AND IMPROVED INTEGRITY OF MACHINE LEARNING MODEL INPUT DATA

Absstract of: US20260073309A1

Embodiments of the invention are directed to systems, methods, and computer program products for providing intelligent system and methods for identifying and weighting volatile data in machine learning data sets. The system is adaptive, in that it can be adjusted based on the needs or goals of the user utilizing it, or may intelligently and proactively adapt based on the data set or machine learning model being employed. The system may be seamlessly embedded within existing applications or programs that the user may already use to interact with one or more entities, particularly those which aid in the managing of user resources.

GENERATION OF DIGITAL STANDARDS USING MACHINE-LEARNING MODEL

Absstract of: US20260073251A1

One embodiment provides a method for generating a digital standard, the method including: receiving an underlying standard; extracting conceptual units from the underlying standard; classifying at least a portion of the extracted conceptual units into one of a plurality of classification groups, wherein the classifying includes classifying conceptual units from the underlying standard based upon sections of a schema corresponding to a digital standard; storing the classified extracted conceptual units into a data repository, wherein the storing is performed as defined by the schema; displaying, within a user interface, a digital standard in a format based upon the schema, wherein the displaying includes accessing conceptual units from the data repository corresponding to the digital standard and displaying the conceptual units in a format in accordance with the schema; and providing, within the user interface, search and filter functions allowing for finding information related to the digital standard.

MULTI-SCALE SPEAKER DIARIZATION FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS

Absstract of: US20260073937A1

Disclosed are apparatuses, systems, and techniques that may use machine learning for implementing speaker diarization. The techniques include obtaining a speaker embedding for various reference times of a speech and for various differently-sized time intervals, identifying a plurality of clusters, each cluster associated with a different speaker of the speech. The techniques further include computing, using the speaker embeddings, a set of embedding weights for various differently-sized time intervals, and identifying, using the computed set of the embedding weights, one or more speakers speaking at a respective reference time.

AUTOMATIC RADIATION THERAPY TREATMENT PLANNING WITH DEEP REINFORCEMENT LEARNING GUIDED BY DOSE DISTRIBUTION-BASED REWARD FUNCTION

Absstract of: WO2026055619A1

In some embodiments, there is provided a method of automating radiation therapy treatment planning. The method may include receiving at least an objective for a control structure associated with a plurality of organs-at-risk; optimizing, using a first machine learning model configured as an optimization engine, a treatment plan including the control structure and the objective; selecting, based on one or more scores, at least a first organ‐at‐risk to enable objective adjustment; adjusting, using a second machine learning model using a continuous action space, one or more objective parameters for the selected first organ‐at‐risk; and re-optimizing, using the first machine learning model configured as the optimization engine, the treatment plan including the adjusted one or more objective parameters for the selected first organ‐at‐risk; and outputting the re-optimized treatment plan including one or more treatment parameters. Related systems, methods, and articles of manufacture are also disclosed.

DIGITAL PATHOLOGY MACHINE LEARNING INFRASTRUCTURE

Nº publicación: WO2026055331A1 12/03/2026

Applicant:

PROSCIA INC [US]
PROSCIA INC

Absstract of: WO2026055331A1

Techniques for using a digital pathology machine learning model implementation without requiring transmission of digital pathology images to a location of the digital pathology machine learning model implementation are presented. The techniques may include: providing, on a server computer, a digital pathology image embeddings API; receiving, from a client computer, an embeddings job request including an identification of at least one digital pathology image, an image resolution instruction, and an identification of an embeddings network; passing, to an embeddings server, metadata characterizing the digital pathology image(s) resolved according to the resolution instruction; obtaining, from the embedding server, at least one embeddings vector after the embeddings server transforms a resolved set of digital pathology image(s) into at least one embeddings vector; and transmitting, by the server computer, the embeddings vector(s) to a storage location, without the client computer transmitting or receiving the digital pathology image(s).