Alerta

AUTOMATED ALERT RECOMMENDER FOR REAL-TIME RISK ASSESSMENT AND ADVISORY

Absstract of: WO2026131899A1

A computer-implemented method including training an artificial intelligence/machine learning (AI/ML) algorithm to generate at least one of alerts and alert rules using a historical data set of an industrial system in an alert generation system. The historical data set includes operational data of the industrial system, at least one alert rule, a set of alerts correlated with operational data, and a set of responses to each alert in the set of alerts. The computer- implemented method receives a set of operational data from an industrial system at the alert generation system and applies the AI/ML algorithm to the set of operational data. The computer-implemented method generates an alert based on the application of the AI/ML algorithm to the set of operational data and provides the generated alert to a user.

MULTI-MODAL LARGE LANGUAGE MODELS COUPLED WITH PROBABILITY ENGINES

Absstract of: US20260178887A1

0000 In some implementations, a machine learning (ML) host may receive, from a client device, a request indicating an institution. The ML host may provide an indication of the institution to a foundational model, included in the suite of large language models, to receive a summary associated with the institution. The ML host may output the summary to the client device. The ML host may receive, from the client device, an audio stream associated with the institution and may generate a transcript of the audio stream. The ML host may provide the transcript to a rapid response model, included in the suite of large language models, to receive a conversation suggestion. The rapid response model may communicate with the probability engine to generate the conversation suggestion, and the conversation suggestion may increase a probability output by the probability engine. The ML host may output the conversation suggestion to the client device.

DEVICE AND METHOD FOR PROCESSING A QUERY FOR INFORMATION FOR A CONTROL TASK

Absstract of: WO2026135558A1

Aspects concern a method for processing a query for information for a control task, comprising receiving a query, retrieving a plurality of data elements from one or more data sources, wherein the data elements containing information related to the query, determining a ranking score for each of the plurality of data elements according to each of a plurality of ranking methods, determining a combined ranking score for each data element of the plurality of data elements by combining the ranking scores determined for the data element according to the plurality of ranking methods, selecting a subset of the plurality of data elements based on the combined ranking score, formulating a prompt with a request to respond to the query for a generative machine learning model using information from the selected subset, supplying the prompt to the generative machine learning model and generating a response to the query using an output provided by the generative machine learning model in response to the prompt.

TRANSFORMER-BASED ASSISTANT FOR IDENTIFYING, ORGANIZING, AND RESPONDING TO CUSTOMER CONCERNS

Absstract of: AU2024376764A1

Transformer-based agent assistant systems as machine learning-based customer service tools that analyze past customer-agent conversations to build a knowledge base of problem-resolution steps are disclosed. The system may include a natural language processing (NLP) model and a transfomer-based model to extract and generate customer concerns and resolutions. One embodiment also includes a head-topic and subtopic detection module for identifying trends in customer concerns. Another embodiment uses a question-answering model and a zero-shot-NLI (natural language inference) classifier for entity extraction and detection. The system is designed to be flexible, incorporating new data over time, and can retrieve company documentation or FAQs for the agent based on cosine similarity.

SYSTEM AND METHOD FOR AUTOMATED DEVELOPMENT OF MEDICAL DIAGNOSTIC SOFTWARE USING MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE

Absstract of: US20260178989A1

0000 A method for constructing Artificial Intelligence (AI) medical diagnostic tools via computer-implemented graphical user interfaces, without the need for coding, is described. The method method utilizes automated AI algorithms (autoML) to train an AI model. It implements novel methods for data preparation, training and retraining of AL/ML models. It utilizes Federated Learning technology to make medical datasets in different hospitals available to other hospitals for constructing diagnostic tools. The invention can produce multiple medical diagnostic tools suitable for clinical use. It is intended for use by medical professionals, not requiring coding in computer languages.

CHO That Considers AIML Functionality Applicability

Absstract of: US20260181514A1

0000 A wireless transmit/receive unit (WTRU), which include one or more processors, may be configured to receive a configuration to activate one or more artificial intelligence or machine learning (AI/ML) functionalities and to receive conditional handover (CHO) configurations to one or more candidate cells, and a plurality of signal level thresholds associated with the serving cell. The WTRU may be configured to compare the serving cell signal level with each of the plurality of serving cell signal level thresholds and determine applicabilities of each of the one or more AI/ML functionalities for the CHO configurations based on the comparisons between the serving cell signal level and each of the one or more serving cell signal level thresholds.

NEURAL NETWORK REPRESENTATION OF QUANTUM CIRCUITS

Absstract of: US20260178958A1

A method may include obtaining a configuration of a quantum circuit comprising n qubits and k quantum gates. The k quantum gates include at least a single-qubit gate or a two-qubit control gate. The method may include constructing a neural network representing the quantum circuit, wherein the neural network includes k+1 layers that include k pairs of adjacent layers, with each pair of the adjacent layers corresponding to one of the k quantum gates. The method may include connecting one or more nodes in each pair of the adjacent layers based on a representation of a corresponding quantum gate of the k quantum gates. The method may include training the neural network using machine learning techniques to obtain an output. The method may include applying the output to the quantum circuit.

HYBRID MACHINE LEARNING SYSTEM FOR EFFICIENT OPERATION REVIEW WITH ANOMALY DETECTION, ADAPTIVE CONFIDENCE SCORING, AND MULTI-LAYERED CONTEXTUAL RISK SCORING

Absstract of: US20260178934A1

0000 Systems and methods may validate operation request data associated with operations requested or triggered by maker-user inputs. A computing device can execute software routines and/or one or more machine-learning architectures to obtain one or more operation records for an operation from one or more data sources; extract a feature vector for the operation based upon a plurality of operation features extracted using the one or more operation records for the operation; determine an operation type for the operation by applying a classifier of a machine-learning architecture on the feature vector for the operation; generate a risk score for the operation by applying a risk model of the machine-learning architecture on the operation feature vector and the operation type; determine one or more authorization thresholds for the operation based upon the risk score; and transmit the operation record to one or more checker client devices corresponding to the authorization thresholds.

GAME ENGINE AND ARTIFICIAL INTELLIGENCE ENGINE ON A CHIP

Absstract of: EP4765027A2

An electronic chip, a chip assembly, a computing device, and a method are described. The electronic chip comprises a plurality of processing cores and at least one hardware interface coupled to at least one of the one or more processing cores. At least one processing core implements a game engine and/or a simulation engine and at least one or more processing cores implements an artificial intelligence engine, whereby implementations are on-chip implementations in hardware by dedicated electronic circuitry. The at least one or more game and/or simulation engines performs tasks on sensory, generating data sets that are processed through machine learning algorithms by the hardwired artificial intelligence engine. The data sets processed by the hardwired artificial intelligence engine include at least contextual data and target data, wherein combining both data and processing by dedicated hardware results in enhanced machine learning processing.

APPARATUS AND METHOD FOR DESIGNING MULTILAYER FILM

Absstract of: EP4764452A1

0001 An apparatus and method for designing a multilayer film is disclosed. An apparatus for designing a multilayer film may perform: modeling a lamination structure of a multilayer film to be designed, collecting stress-strain data with respect to the single-layer film forming the lamination structure, reading a value pre-stored in a storage space accessible by an apparatus for designing a multilayer film, and obtaining a feature setting mode for designating different feature setting manners depending on the read value, calculating a strain energy from a plurality of physical indicators selected from the stress-strain data, depending on the feature setting mode, and setting the strain energy as the feature, selecting at least one among a plurality of supervised learning models capable of a regression analysis as a machine learning model, predicting the dart impact strength of the multilayer film by using the machine learning model learned by taking the feature as an independent variable, and a dart impact strength of the multilayer film as a target variable, and generating design data for the multilayer film, by combining predicted values for other properties and a predicted value of the dart impact strength, so as to satisfy the design requirements of the multilayer film.

MACHINE LEARNING BASED SYSTEM AND METHOD FOR AUTOMATICALLY EXTRACTING AND CORRECTING FINANCIAL INFORMATION FROM DOCUMENTS

Absstract of: US12664136B1

A machine learning based (ML-based) method and system for automatically extracting and correcting financial information from documents, is disclosed. Initially, the documents are obtained from data sources and pre-processed to generate the pre-processed data associated with contents within the document. The contents are classified as potential key-value pairs corresponding to the financial information based on the system prompts and extracted using the ML model. The potential key-value pairs are corrected to obtain the corrected key-value pairs based on custom prompts, using the ML model. The corrected key-value pairs corresponding to the financial information are provided as the output to the end users on user interfaces associated with an electronic device. This technique extracts financial information regardless of structure or alignment by learning to recognize any added or removed prefixes or suffixes, enabling the prefixes or suffixes to make corrections and generate accurate key-value pairs.

SYSTEMS, METHODS AND DEVICES FOR INDOOR TRACKING AND NAVIGATION

Absstract of: CA3293754A1

Provided are methods, systems, and devices for indoor tracking and navigation. The method includes receiving a plurality of sensor signals and a navigational repository; integrating, by a fusion algorithm, the plurality of sensor signals and the navigational repository to generate a tempospatial guidance dataset; training a machine learning module on the tempospatial guidance dataset, wherein the machine learning model is configured to learn spatial patterns by analyzing relationships between the plurality of data signals and the navigational repository; and determining, by the machine learning module, a navigation signal for guiding a user, wherein the navigation signal is generated by analyzing a user location and predicted movement patterns.

STRUCTURED PROMPT FRAMEWORK FOR MACHINE LEARNING MODEL OUPUT GENERATION

Absstract of: CA3290647A1

Aspects of the present disclosure relate to structuring prompt frameworks in machine learning models. Embodiments include instructing a machine learning model via a prompt to generate an output according to a series of steps that reference one or more sections of the prompt. Embodiments include providing the machine learning model, via the prompt, with the one or more sections delineated with corresponding tags, each section of the one or more sections being referenced in the prompt via a corresponding tag. Embodiments include providing the machine learning model, via the prompt, with an output template indicating a target structure for the output and instructing the machine learning model to score the generated output according to a set of scoring criteria. Embodiments include instructing the machine learning model via the prompt to provide the output only when a calculated score, based on the scoring of the generated output, exceeds a threshold value.

USING MACHINE-LEARNING MODEL FOR PERSONALIZED REARRANGEMENT OF ITEMS FROM A PHYSICAL DOCUMENT IN A USER INTERFACE

Absstract of: US20260169992A1

An online system uses a trained machine-learning model for personalized rearrangement of items from a physical document in a user interface. The online system obtains an electronic version of the physical document including metadata for each item in the physical document. The online system applies the machine-learning model to the metadata, information about each item, and information about a user to generate an item conversion score for each item that is indicative of the likelihood of the user converting on each item. The online system ranks, using the item conversion score for each item, items from the physical document. Based on the ranking, the online system generates a user interface signal including information about a rearrangement of each item. The user interface signal causes a device associated with the user to display a user interface with each item placed at the user interface in accordance with the rearrangement.

SYSTEM AND METHOD FOR SEMANTIC MACHINE LEARNING FEATURE SEARCH AND REUSABILITY IN FEATURE STORES USING ARTIFICIAL INTELLIGENCE EMBEDDING MODELS

Absstract of: US20260170019A1

A system for automatic generation of machine learning feature definitions includes a feature schema, a user interface, a large language model (LLM), and a feature database. The feature schema defines a structured representation of a machine learning feature, including event fields, filter fields, and aggregation or categorical calculation fields. The user interface receives a natural language description of a desired feature from a user. The large language model generates, based on the natural language description and the feature schema, a candidate feature definition that conforms to the structured representation. The feature database then stores the candidate feature definition as a feature object for subsequent use in training or serving a machine learning model.

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

Absstract of: US20260172849A1

A method performed by a terminal in a wireless communication system according to at least one of the embodiments disclosed herein may include configuring at least one artificial intelligence/machine learning (AI/ML) model related to multiple transmissions and receptions (TRPs), monitoring the at least one AI/ML model, and performing, based on a performance of the monitored at least one AI/ML model, AI/ML model management to maintain or at least partially change the at least one AI/ML model, wherein the performance of the at least one AI/ML model may be determined based on a first multi-TRP data set related to training of the at least one AI/ML model and a second multi-TRP data set related to monitoring of the at least one AI/ML model.

Leveraging Machine-Learning Models for Determining a Collection Sequence

Absstract of: US20260170452A1

Embodiments are described for leveraging machine-learning models to determine a collection sequence of items of an order. Sequences for collecting a plurality of items of an order are determined, and each of the sequences has a different arrangement of the plurality of items. Total appeasement values are determined for each of the sequences based in part on an appeasement model. Collection times are predicted for each of the sets of sequences. The sequences of the set are scored based in part on the total appeasement values and the collection times. A sequence is selected from the set based in part on the scoring. The selected sequence is provided to a picker client device. A picker associated with the picker client device may fulfill the order and collect the plurality of items in accordance with the selected sequence.

METHOD OF GENERATING AN OPTIMAL CELL ARCHITECTURE MACHINE LEARNING MODEL

Absstract of: US20260170399A1

A machine learning (ML) model architect identifies a problem space for a ML model. The ML model architect then selects a cell architecture skeleton. The ML model architecture defines a set of operations for the cell architecture skeleton. An overparameterized model may be built based at least in part on the cell architecture skeleton and the set of operations for the problem space. The overparameterized model may be reduced to generate a reduced model. The reduced model may be trained using a training dataset to produce a trained, reduced model. Suboptimal operations may be pruned from the trained reduced model to produce a pruned reduced model. Reverse reduction processing may then be performed on the pruned reduced model to generate an optimal cell architecture model for the identified problem space.

SYSTEMS AND METHODS FOR TACTICAL ENCOUNTERS

Absstract of: WO2026128021A2

Provided herein are systems and methods that support preferred outcomes of tactical encounters. More particularly, the invention relates to artificial intelligence and machine learning systems and methods to select preferred ballistics solutions and tactical courses of action.

HALLUCINATION MITIGATION TECHNIQUES FOR TEXT-TO-CODE CONVERSIONS

Absstract of: US20260169714A1

Various embodiments of the present disclosure provide hallucination mitigation techniques for text-to-code conversions that improves the functionality of a computer in various aspects. The techniques comprise receiving a text-based file that defines a set of standards for a prediction domain; generating, using a machine learning model, a decision tree based on (i) the text-based file and (ii) a decisioning prompt for the text-based file; generating, using the machine learning model, computer programmable code for the set of standards based on the decision tree and a code conversion prompt for the decision tree; and providing the computer programmable code to implement an automated task for the prediction domain.

DIGITAL LIFESTYLE INTERVENTION SYSTEM USING MACHINE LEARNING AND REMOTE MONITORING DEVICES

Absstract of: US20260171246A1

0000 Systems and methods for a digital lifestyle intervention system using machine learning and remote monitoring devices is described herein. The disclosed systems can include a processor configured to train, based on historical user data, a personal machine learning model to generate an output indicative of a blood pressure prediction and lifestyle feature impact on blood pressure prediction. The trained personal machine learning model can be further configured to receive user data including blood pressure data for the user and generate output including lifestyle feature impact and a blood pressure prediction by applying the trained personal machine learning model to the received user data. At least one lifestyle recommendation can be generated based on the output of the trained personal machine learning model and/or output from a population model applied to the received user data. The at least one lifestyle recommendation can be provided to a user via a user interface.

CONNECTED MODEL FRAMEWORK FOR FORECASTING CAUSAL PREDICTIONS

Absstract of: US20260170293A1

Various embodiments of the present disclosure provide a technique for forecasting causal predictions that improves the functionality of a computer in various aspects. The techniques comprise receiving a historical sequence for a time-based prediction, generating, by a connected model framework, a first time-based output for a first time position in a prediction sequence for the time-based prediction, generating, using a directed acyclic graph, an output modification for a second time position in the prediction sequence; generating, using a machine learning model, a second time-based output for the second time position in the prediction sequence based on the output modification and the first time-based output, and initiating performance of a prediction-based action based on the prediction sequence.

WEIGHTED MODEL FUSION FOR MITIGATING CATASTROPHIC FORGETTING IN CUSTOMIZED GENERATIVE ARTIFICIAL INTELLIGENCE MODELS

Absstract of: US20260170346A1

A method for mitigating catastrophic forgetting is provided. The method includes providing an input prompt to a classifier machine learning model configured to classify the input prompt as a general knowledge query for a base generative artificial intelligence model, a specific knowledge query for a customized generative artificial intelligence model, or a mixed knowledge query for a weighted fusion model. The method includes computing, based on an output of the classifier machine learning model, a weighted mean of weights of the base generative artificial intelligence model and corresponding weights of the customized generative artificial intelligence model. The method includes generating the weighted fusion model based on the weighted mean of the weights of base generative artificial intelligence model and the corresponding weights of the customized generative artificial intelligence model. The method includes generating, based on the output, a response to the input prompt using the weighted fusion model.

SEQUENTIAL MODEL PIPELINES FOR SIMULATING DEPENDENT PREDICTION CHAINS

Absstract of: US20260170298A1

Various examples of the present disclosure utilize a sequential model pipeline to predict time-based effect output that improves the functionality of a computer in various aspects. The techniques comprise receiving a labeled output comprising a historical ground truth cause label and a historical ground truth effect label, generating a time-based cause output using a first machine learning model of the sequential model pipeline, generating a time-based effect output using a second machine learning model of the sequential model pipeline, and initiating a performance of a prediction-based action based on the time-based effect output.

CLICK ENGAGEMENT SIGNALS

Nº publicación: US20260170513A1 18/06/2026

Applicant:

WALMART APOLLO LLC [US]
Walmart Apollo, LLC

Absstract of: US20260170513A1

A computer implemented method including determining an expected click-through-rate (CTR) of a query-item pair with a first machine learning model by using content-based features. The computer implemented method can also include, determining a click engagement (CE) feature by determining a Bayesian inference based on the expected CTR and a historical CTR for the query-item pair. The computer implemented method can further include, determining a rerank score of the query-item pair with a second machine learning model by using the content-based features and the CE feature. The computer-implemented method can additionally include reranking the query-item pair based in part on the rerank score and the expected CTR. Other embodiments are described.