Alerta

SYSTEMS AND METHODS FOR AUTOMATING GENERATION OF INFORMATION TECHNOLOGY PROJECT ESTIMATES USING ARTIFICIAL INTELLIGENCE/MACHINE LEARNING TECHNIQUES

Resumen de: US20260094122A1

0000 Aspects of the subject disclosure may include, for example, receiving a user query describing a new information technology (IT) project and a request for a desired action including obtaining an estimate of the new IT project, maintaining a knowledge repository that stores a data set relevant to IT projects, training an artificial intelligence/machine learning (AI/ML) model with a training data set which includes at least a subset of the data set relevant to IT projects, generating, using the trained AI/ML model, the estimate of the new IT project by using the knowledge repository, and returning the generated estimate of the new IT project as a response to the user query. 0000 Other embodiments are disclosed.

PREDICTIVE MACHINE LEARNING MODELS BASED ON DATA COLLECTED FROM A VARIETY OF SOURCES

Resumen de: WO2026072162A1

The computer-based methods and systems presented in this disclosure provide prediction occurrence of an event for an individual on a user device of the individual. The system receives, from a plurality of remote devices, pieces of input data about the individual. The system pre-processes the pieces of input data to make them ready to be processed by respective input modules of a machine learning model running on the system. Each input module is associated with a respective marker and processes the pre-processed data for that marker. Outputs of the input modules are further processed by the model. The model provides an output indicating respective probabilities that particular events happen. The system can generate one or more alerts based on the output of the model, and can send the alerts to contacts of the individual.

GENERATION METHOD AND INFORMATION PROCESSING APPARATUS

Resumen de: US20260094020A1

0000 A processing unit acquires a combination of a first entity included in a first knowledge graph and a second entity included in a second knowledge graph. The processing unit inputs the first entity and the second entity to a machine learning model and instructs the machine learning model to decrease the similarity between the first entity and the second entity if the relationship between the first entity and the second entity is a predetermined relationship. The processing unit acquires the similarity between the first entity and the second entity output by the machine learning model. The processing unit generates a third knowledge graph by merging the first knowledge graph and the second knowledge graph, treating the first entity and the second entity as identical entities if their similarity is greater than a threshold.

HIERARCHICAL MONITORING OF ARTIFICIAL INTELLIGENCE OR MACHINE LEARNING MODELS FOR AIR INTERFACE

Resumen de: WO2026072207A1

Certain aspects of the present disclosure provide techniques for performing wireless communication. In some aspects, the techniques include obtaining a first metric associated with a model that is associated with wireless communication; and obtaining, in response to a trigger condition associated with the first metric being satisfied, a second metric associated with the model, the second metric providing a different measure of the model than the first metric.

DATA PRIVACY PROTECTION AND REMOVAL FOR ARTIFICIAL INTELLIGENCE MODEL TRAINING AND DEPLOYMENT

Resumen de: WO2026072419A1

There are provided systems and methods for data privacy protection and removal for artificial intelligence model training and deployment. An online transaction processor or other service provider may provide computing services and platforms to entities, which may include use of machine learning (ML) models including large language models (LLMs). To comply with data privacy protections and copyright enforcement, a system may provide unlearning of content from ML models. The system may receive a request to unlearn a content and, after verifying the request is valid, identify the content used for during training of or inferencing by an ML model. The system may then map the content to concepts and correlate those concepts with ML model outputs using projections in a vector space. Based on the mapped concepts and outputs, neuron activation of the ML model may be analyzed to identify a negation vector and perform selective parameter dampening.

SYSTEMS AND METHODS FOR FACILITATING CONVERSATION BASED ON TEXT DATA AND SPEECH DATA

Resumen de: AU2024407921A1

A method includes receiving a user input and generating a set of user input tokens based on the user input. The method also includes generating a set of enhanced input tokens by providing the set of user input tokens as input to a first machine learning model. A state is determined based on a previous state and at least one of the set of user input tokens or the set of enhanced input tokens. Predetermined data is retrieved from a database based on the state and at least one of the set of user input tokens or the set of enhanced input tokens. The method also includes generating a set of response tokens by providing the set of user input tokens and the predetermined data as input to a second machine learning model. Based on the set of response tokens, a response is sent to a user device.

ENHANCED SOCIAL MEDIA PLATFORMS BASED ON MACHINE LEARNING BASED AGGREGATION OF TRENDING EVENTS

Resumen de: AU2024354389A1

An example method for automatic generation of content based on an aggregation of trending events is provided. The method includes determining, by a computing device, a topic of interest. The method also includes determining additional information related to the topic of interest. The determining of the additional information includes, generating a prompt based on the topic of interest, submitting the prompt to an information search and retrieval system, and retrieving the additional information as an output of the information search and retrieval system. The method also includes generating, by a generative artificial intelligence model, a piece of annotated content associated with the topic of interest. The piece of annotated content comprises media content annotated with at least one selectable graphical object that links to the additional information. The method also includes providing, by the computing device, the piece of annotated content.

METHODS OF PREDICTING PROPERTIES OF A CHEMICAL SYSTEM USING SURROGATE MODELS

Resumen de: US20260094677A1

Methods of predicting physicochemical properties of a chemical system using a family of surrogate or reduced order models, trained on first principle simulation results. The models are created using machine learning techniques. The chemical system can be a complex multicomponent and multiphase system such as produced water.

Multi-Stage Federated Learning in Wireless Networks

Resumen de: US20260094032A1

first group of AI agents to train and report, per each AI agent of the first group, a respective first partial AI or machine learning (ML) (AI/ML) model to the AI manager, receive the first partial model from each AI agent of the first group, generate a first version of a global model from the first partial models, if the first version of the global model is determined to be trustworthy, select a second group of AI agents to train and report, per each AI agent of the second group, a respective second partial AI/ML model to the AI manager, receive the second partial models and aggregate the second partial models and the first version of the global model into a second version of the global model.

Granular Taxonomy for Customer Support Augmented with AI

Resumen de: US20260094166A1

A computer-implemented method for augmenting customer support is disclosed in which a granular taxonomy is formed to classify tickets based on customer issue topic. A dashboard and user interface of performance metrics may be generated for the topics in the taxonomy.Recommendations may also be generated to aid servicing customer support issues for topics in the taxonomy. This may include generating information to aid in determining topics for generating automated responses or generating recommended answers for particular topics. In some implementations, an archive of historic tickets is used to generate training data for a machine learning model to classify tickets.

SYSTEMS AND METHODS FOR GENERATING AND DEPLOYING MACHINE LEARNING APPLICATIONS

Resumen de: US20260094427A1

A method comprising receiving data associated with a business, the data comprising first values for first attributes; processing the data, in accordance with a common data attribute schema that indicates second attributes, to generate second values for at least some of the second attributes including a group of attributes, the second values including a group of attribute values for the group of attributes; identifying, using the common data attribute schema and from among pre-existing software codes, software code implementing an ML data processing pipeline configured to generate a group of feature values; processing the group of attribute values with the software code to obtain the group of feature values; and either providing the group of feature values as inputs to a machine learning (ML) model for generating corresponding ML model outputs, or using the group of feature values to train the ML model.

CENTRALIZED PLATFORM FOR ENHANCED AUTOMATED MACHINE LEARNING USING DISPARATE DATASETS

Resumen de: US20260094009A1

Systems and techniques are disclosed for a centralized platform for enhanced automated machine learning using disparate datasets. An example method includes receiving user specification of one or more data sources to be integrated with the system, the data sources storing datasets to be utilized to train one or more machine learning models by the system, and the datasets reflecting user interaction data. A dataset is imported from the data source, and machine learning models are automatically trained based a particular machine learning model recipe of a plurality of machine learning model recipes. A first trained machine learning model is implemented, with the system being configured to respond to queries based on the implemented machine learning model, and with the responses including personalized recommendations.

A METHOD FOR DETERMINING A HARDWARE ARCHITECTURE FOR A MACHINE LEARNING MODEL

Resumen de: EP4718234A1

The invention relates to a method (100) for determining an hardware architecture (3) for a machine learning model (50), comprising:- Providing (101) an initial hardware architecture (1), the initial hardware architecture (1) describing hardware components (2) and computing characteristics of said hardware components (2),- Providing (102) the machine learning model (50),- Converting (103) the machine learning model (50) to an intermediate representation, the intermediate representation depicting a topology and/or a temporal structure of the machine learning model (50) as a graph structure,- Analysing (104) the intermediate representation to determine a memory footprint of the machine learning model (50),- Determining (105) the hardware architecture (3) for the machine learning model (50) based on the initial hardware architecture (1) and a result of the analysing (104).Furthermore, the invention relates to a computer program, an apparatus, and a storage medium for this purpose.

SYSTEMS AND METHODS FOR COLLISION DETECTION

Resumen de: EP4718405A1

A method for detecting vehicle collisions using multi-stage data analysis is described. Telematics data from a vehicle-installed computing device is received and processed through a heuristic filter to identify potential collisions. A feature vector is generated from the filtered data and input into a trained predictive model, which classifies the vector as representing a collision or not. The method then retrieves associated dashcam footage and uses it, along with the predictive model's output, to confirm the occurrence of a collision. Upon confirmation, a notification is transmitted to a remote computing device. This approach combines telematics data analysis, machine learning prediction, and video verification to achieve accurate collision detection and notification.

AUTOMATED MACHINE LEARNING BASED WORKFLOW FOR TIMESERIES FORECASTING

Nº publicación: EP4718195A1 01/04/2026

Solicitante:

FUJITSU LTD [JP]
ARCHIMEDES CONTROLS CORP [US]
FUJITSU LIMITED,
Archimedes Controls Corporation

Resumen de: EP4718195A1

In an embodiment, workflow for timeseries forecasting may be performed based on automated machine learning. Sensor data for measurement parameter is received from plurality of sensors installed in built environment and the received sensor data is stored in table of relational database. Cut-off record associated with previous training checkpoint is determined of the forecasting model for the measurement parameter. Records including new records are determined for which respective timestamps occur after the measurement timestamp of cut-off record. Size of the determined records are compared with threshold size and training dataset is prepared. The forecasting model is trained on the training dataset based on the comparison.