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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.

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.

MACHINE LEARNING BASED APPROACH FOR AUTOMATICALLY GENERATING A COMPLIANCE GRAPH FOR COMPLETING A WORKFLOW

Resumen de: US20260094010A1

0000 A method for automatically generating a compliance graph for a workflow is provided. The method includes providing a set of forms as an input to a language processing machine learning model. The set of forms are related to the workflow and include a plurality of fields for receiving user-input. The method includes generating, using the language processing machine learning model, a plurality of nodes based on the plurality of fields, with at least one node of the plurality of nodes is represented as a quadruple. The method includes generating, using the language processing machine learning model, the compliance graph for the workflow based on the plurality of nodes, with the compliance graph providing a visual representation of a logic flow associated with completing the workflow in a compliant manner.

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.

COMPUTER SYSTEMS AND METHODS FOR FEATURE SELECTION FOR MACHINE-LEARNING MODELS

Resumen de: US20260094053A1

0000 A computing platform may be configured to: (i) identify an initial set of features; (ii) obtain an input dataset comprising a set of data records that each includes respective values for the initial set of features; (iii) build a feature graph based on the input dataset; (iv) determine a reduced set of features from the initial set of features by selecting features for inclusion in the reduced set of features based on a balancing between (a) diffusion size of features within the feature graph and (b) diffusion overlap of features within the feature graph relative to any features that were selected for inclusion in the reduced set of features; and (v) utilize the reduced set of features in a machine-learning process for training a machine-learning model.

APPARATUS AND METHOD FOR DYNAMIC MICROARCHITECTURE ADAPTION USING MACHINE LEARNING TO IMPROVE CORE PERFORMANCE

Resumen de: US20260093501A1

An apparatus and method for dynamic microarchitecture adaptation based on machine learning implementations. For example, one embodiment of a method comprises: configuring a trained reinforcement learning model on a machine learning circuitry integral to a first processor of a first processor type, the trained reinforcement learning model having been trained with microarchitectural performance data and workload data corresponding to the first processor type; determining, by the machine learning circuitry using the trained reinforcement learning model, microarchitectural configuration updates based on first telemetry data and characteristics of workloads to be executed; and applying the microarchitectural configuration updates on the first processor.

PRIVACY PRESERVING IDENTITY RESOLUTION USING DEEP LEARNING

Resumen de: WO2026072270A1

A system and method for privacy-preserving identity resolution using deep learning enables accurate matching of personally identifiable information (PH) while maintaining data security. The system employs a deep learning model trained with transformer architecture and contrastive learning on third-party identity graph data. Custom tokenizers process data by leveraging hierarchical structures and domain-specific characteristics. The trained model generates vector embeddings that enable fuzzy matching, accounting for variations in spellings, typographical errors, and data inconsistencies. A vector database stores embeddings for nearest neighbor searches to identify potential identity matches. The system enables identity resolution without requiring Pll data movement from first-party environments. The invention facilitates building accurate first-party identity graphs and enables secure collaboration between parties without exposing underlying Pll data.

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

Nº publicación: US20260094122A1 02/04/2026

Solicitante:

AT & T IP I LP [US]

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.