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SYSTEMS AND METHODS FOR AUTOMATING EMAIL TO ORDER USING GENERATIVE ARTIFICIAL INTELLIGENCE (AI)

Resumen de: EP4756641A1

0001 Systems and methods provide for automating the conversion of email orders into structured order entries using generative AI, leveraging an integrated architecture comprising a Real-Time Data Mesh (RTDM), Advanced Analytic and Machine Learning (AAML) Module, and Single Pane of Glass (SPoG) User Interface. The system includes an Email Parser that extracts order information from emails, an Order Generation Engine that converts this information into structured entries, and an Integration Gateway that synchronizes the entries with external systems. The RTDM manages data flow and transformation, while the AAML provides predictive analytics and process automation. The SPoG UI performs real-time data visualization and user interaction. The system enhances order processing efficiency, accuracy, and scalability, enabling businesses to process email orders with minimal manual effort and greater precision.

MACHINE LEARNING TECHNIQUES FOR DISCOVERING KEYS IN RELATIONAL DATASETS

Resumen de: WO2025029579A1

Techniques for discovering primary, unique, and/or foreign keys for relational datasets are described. The techniques include profiling the relational datasets to obtain respective data profiles; identifying one or more primary key candidates for a first relational dataset using a first data profile of the first relational dataset and a first trained machine learning model; identifying one or more foreign key proposals for a second relational dataset using the one or more primary key candidates by performing a subset analysis of the second relational dataset with respect to the first relational dataset; identifying one or more foreign key candidates for the second relational dataset using the first data profile, a second data profile of the second relational dataset, and a second trained machine learning model different from the first trained machine learning model; and outputting the at primary key candidate(s) and the foreign key candidate(s).

BINARY FILE MALWARE DETECTION WITH STRUCTURE AWARE MACHINE LEARNING

Resumen de: CN121569294A

Machine learning integrally receives input data from static analysis and dynamic analysis of the binary file to output a maliciousness/goodness determination of the binary file. The machine learning entirety includes structure-aware dynamic compressors ("compressors"). A compressor receives, as input, tree data structures generated based on application programming interface calls of binary files in various sandbox environments. A compressor performs various compression, tokenization, embedding, and shaping operations on the tree data structure to generate a compression tensor that holds a structure context from the tree data structure. Machine learning wholly uses compression tensors to generate malicious/good decisions for binary files.

KNOWLEDGE-DRIVEN FEDERATED BIG DATA QUERY AND ANALYTICS PLATFORM

Resumen de: EP4756640A2

0001 A system for querying a federated data store includes a metadata knowledge graph describing the contents and relationships among one or more underlying data stores, an interactive user interface receiving requests from a data consumer, a predefined constrainable query ('nodegroup') store containing predefined constrainable queries that define data subsets of interest across one or more of the underlying data repositories, a knowledge-driven querying layer generating and executing queries against the federated data store and merging responsive results, a scalable analytic execution layer receiving the search results from the federated data store and applying machine learning/artificial intelligence techniques to analyze the results, and a user interface presenting visualizations of raw or analyzed results to the consumer. A method and a non-transitory computer-readable medium are also disclosed.

METHODS FOR FEDERATED LEARNING

Resumen de: EP4756676A1

0001 A method, apparatus, and computer program are described comprising: receiving an input; and determining a classification of the input using a machine learning model, the machine learning model comprising a local part and a collaboratively learned part, the determining comprising: determining extracted features of the input using a feature extractor of the collaboratively learned part, the feature extractor being caused to extract features of the input; determining a set of similarity scores using a prototype layer of the local part of the model, the prototype layer being caused to determine similarities between extracted features of the input and a set of trained prototypes of the prototype layer; and determining a classification of the input using a prototypical classifier of the local part of the model, the prototypical classifier being caused determine the classification based on the similarity scores.

PLATFORMS, SYSTEMS, AND METHODS FOR AUDIT AND VALIDATION IN ARTIFICIAL INTELLIGENCE MODELS

Resumen de: US20260154179A1

0000 A system may include one or more processors and memory storing instructions that, when executed by the one or more processors, cause a platform to: identify an appropriate analytic method based on an assessment of a data characteristic, implement a data preparation procedure specific to a particular application, apply a machine learning model to analyze data and generate a prediction, perform a model validation procedure to ensure analytical reliability, create an audit trail documenting an analytic procedure and result; and generate technical documentation and visualization of an analytic finding.

METHOD AND SYSTEM FOR DETERMINING BEHAVIORAL PATTERNS OF USER GROUPS BASED ON INTERACTIONS WITH OTHER USER GROUPS USING MACHINE LEARNING

Resumen de: US20260154579A1

0000 The disclosure describes a method of generating a target profile including the target's sequence of events (SOE) for a task. Such target profile sequence of events is derived from several source group's transactions, where any source group's transactions cannot be shared with other source groups but the derived target group's profile is the only information that is shared. Source-side information is periodically extracted for a plurality of sources that each interact with a plurality of targets. The information includes source stages, resources, and stage transition events for a task with a target. Source information is used to generate a set of normalized stages, and a set of normalized events for transitioning between the stages of the set of normalized stages. An artificial intelligence (AI) model is trained using the source information. The AI model can generate a target profile with target process information inferred using the trained model. The target process information can include the target's identifiers for each stage, an estimated duration of the stage, deliverables for the stage, and one or more stage transition events for the stage.

MULTI-FACETED SITE EVALUATOR INTEGRATING USER DEFINED EVALUATION ENGINES

Resumen de: US20260154488A1

A website building system (WBS) includes at least one hardware processor and a site evaluator running on the at least one hardware processor to evaluate at least one application area of a website according to at least one user category of the WBS. The site evaluator includes at least one evaluation engine to evaluate the at least one application area according to rules and at least one of: scripts and machine learning (ML) models, a site modifier to implement at least one of automatic and manual modifications to the website according to recommendations from the at least one evaluation engine and an evaluation engine handler to enable user creation and editing of the at least one evaluation engine.

MACHINE LEARNING BASED VIDEO ANALYSIS, DETECTION AND PREDICTION

Resumen de: US20260154962A1

A method comprises obtaining video data from one or more data sources, and processing the obtained video data in a machine learning system comprising an inference stage and an anticipation stage. The inference stage is configured to assign one or more labels to at least one of a group activity and an individual activity detected in the obtained video data. The anticipation stage is configured to predict one or more future actions relating to at least one of the group activity and the individual activity based at least in part on the one or more labels assigned in the inference stage. The method further comprises generating at least one control signal based at least in part on the predicted one or more future actions. The method is illustratively configured to implement role inference and action anticipation in team sports, although it is applicable to a wide variety of other contexts.

AUTOMATING BIAS EVALUATION FOR MACHINE LEARNING PROJECTS

Resumen de: US20260154622A1

0000 A method includes obtaining descriptive information for a first machine learning project, identifying, based on the descriptive information, a plurality of past machine learning projects which are similar to the first machine learning project, retrieving digital documents that describe the bias evaluation pipelines that were used to evaluate the plurality of past machine learning projects, detecting a common bias evaluation pipeline step among at least a subset of the digital documents, extracting, from the subset, a snippet of machine-executable code that corresponds to the common bias evaluation pipeline step, modifying the snippet of machine-executable code with use case data that is specific to the first machine learning project to generate modified machine-executable code, and generating a proposed bias evaluation pipeline for evaluating the first machine learning project, wherein the proposed bias evaluation pipeline includes the modified machine-executable code.

INTELLIGENT DATA INGESTION

Resumen de: US20260154624A1

Intelligent data ingestion is provided. A determined column header name of a selected column in an imported data file is mapped to a predicted corresponding column header name of a particular column in a database corresponding to a human capital management application using a plurality of machine learning models. It is determined whether the predicted corresponding column header name output by each respective machine learning model of the plurality of machine learning models matches. In response to determining that the predicted corresponding column header name output by each respective machine learning model of the plurality of machine learning models does match, the predicted corresponding column header name of the particular column in the database is utilized as a target column name for the determined column header name of the selected column in the imported data file.

TERMINAL POWER SUPPLY DEGRADATION DETECTION

Resumen de: US20260154575A1

Event streams of terminals for a given interval of time are preprocessed to label event types and label predefined time-based or sequence-based patterns associated with terminal power supply unit (PSU) failures. The labeled event streams are provided as input to a trained machine-learning model (MLM), which outputs a score for each terminal representing a likelihood that the corresponding terminal is or is not going to experience a PSU failure. In an embodiment, each score is compared against one or more threshold values and each terminal is classified as low risk, medium risk, or high risk of a PSU failure. In an embodiment, the scores and/or the classifications for the terminals are reported to an enterprise associated with the terminals at predefined intervals of time.

SYSTEMS AND METHODS FOR ANALYZING AND MITIGATING COMMUNITY-ASSOCIATED RISKS

Resumen de: US20260154769A1

0000 A computer system for analyzing and mitigating risks associated with a building is provided. The computer system is configured to: (i) receive environment data from the at least one sensor; (ii) receive building data from the at least one database; (iii) utilize a trained machine learning model to determine at least one potential risk associated with the building based upon the environment data and the building data; (iv) generate a building risk profile that includes the at least one potential risk associated with the building; and/or (v) generate a risk mitigation output based upon at least one of the building risk profile and the at least one potential risk, wherein the risk mitigation output includes at least one of a risk alert, a risk mitigation recommendation, and risk mitigation instructions. Computer systems for analyzing and mitigation risks associated with a city, a user, and an event are also provided.

INFORMATION PROCESSING DEVICE, VEHICLE, AND PROGRAM

Resumen de: US20260152192A1

0000 An information processing device of the disclosure includes an information acquisition unit capable of acquiring plural pieces of information related to a vehicle, an inference unit that uses deep learning to infer plural index values from the plural pieces of information acquired by the information acquisition unit, and a driving control unit that executes driving control of the vehicle on the basis of the plural index values.

CYBERSECURITY THREAT INTELLIGENCE GRAPH CONSTRUCTION

Resumen de: US20260154316A1

A computer-implemented method includes storing threat intelligence documents and a graph data store. The graph data store includes entity nodes and a plurality of edges between the nodes extracted from the plurality of threat intelligence documents. Data is also stored linking the entity nodes and edges to the threat intelligence documents from which they were extracted. A generative machine learning model is employed to generate a summary text of threat intelligence for a first entity node, based on the first entity node, second entity nodes connected to the first entity node and the threat intelligence documents from which they were extracted. The summary text is inserted as a summary node into the graph comprising the generated summary text.

METHOD FOR USING A CLOSED LAYER MODEL AS TRAINING DATA FOR MACHINE LEARNING

Resumen de: WO2026117526A1

A method for generating a closed layer model of a subsurface includes receiving input data including a 3D seismic volume having a plurality of horizons. The method also includes identifying labeled areas of the plurality of horizons based to produce identified labeled areas for the plurality of horizons. The method further includes determining lateral extents of the identified labeled areas based on the input data, and sorting the plurality of horizons based on the lateral extents to produce a plurality of sorted horizons. The method also includes identifying boundary points of the plurality of horizons based on the lateral extents, and creating truncation maps for the plurality of horizons based on the boundary points of the plurality of sorted horizons. The method also includes generating the closed layer model based on the plurality of horizons and the truncation maps thereof.

SYSTEMS AND METHODS FOR ARTIFICIAL INTELLIGENCE ANALYSIS OF SECURITY ACCESS DESCRIPTIONS

Resumen de: US20260156160A1

Some implementations described herein relate to a system for artificial intelligence analysis of security access descriptions. The system identifies a security access description. The system determines metadata information associated with the security access description. The system determines, by processing the security access description using a first set of one or more machine learning models, a descriptive quality label associated with the security access description. The system determines, by processing the security access description using a second set of one or more machine learning models, one or more descriptive components associated with the security access description and one or more descriptive component labels that correspond to the one or more descriptive components. The system provides the metadata information, the descriptive quality label, the one or more descriptive components, and/or the one or more descriptive component labels.

REAL-TIME HALLUCINATION DETECTION AND CORRECTION FOR LARGE LANGUAGE MODEL(S)

Resumen de: US20260154578A1

Implementations are described herein for automatically identifying and correcting potentially false information in generative model output by performing entailment evaluation of generative model output. In various implementations, data indicative of a query may be processed to generate generative model output. Textual fragments may be extracted from the generative model output, and a subset of the textual fragments may be classified as being suitable for textual entailment analysis. Textual entailment analysis may be performed on each textual fragment of the subset, including formulating a search query based on the textual fragment, retrieving document(s) responsive to the search query, and processing the textual fragment and the document(s) using entailment machine learning model(s) to generate prediction(s) of whether the at least one document corroborates or contradicts the textual fragment. Based on the textual entailment analysis, at least a portion of the generative model output which is to be rewritten is determined.

SYSTEM AND METHOD FOR WORDLINE GROUP-BASED MACHINE LEARNING MODELS

Resumen de: US20260155193A1

A data storage system includes a memory device (e.g., a solid state drive) including a wordline (WL), a memory (e.g., a random access memory) including instructions stored thereon, and at least one processor. The memory includes instructions stored thereon that, when executed by the at least one processor, cause the at least one processor to: select a machine learning (ML) model based at least in part on error values for the WL; determine, by the ML model, a read voltage threshold; and read data from the WL using the read voltage threshold.

METHOD AND DEVICE FOR SIGNAL TRANSMISSION AND RECEPTION IN WIRELESS COMMUNICATION SYSTEM

Resumen de: EP4753225A1

: A method performed by a terminal in a wireless communication system according to at least one of the embodiments disclosed in the present specification may comprise the steps of: acquiring configurations for artificial intelligence/machine learning (AI/ML) models on the basis of higher layer signaling; monitoring one or more of the AI/ML models; and, on the basis of the monitoring of the one or more AI/ML models, performing AI/ML model management for switching or updating. The monitoring of the one or more AI/ML models includes at least one of the monitoring of a first AI/ML model in an inactive state or the monitoring of a second AI/ML model in an active state. The monitoring of the first AI/ML model in the inactive state may occupy fewer terminal processing units or occupy the terminal processing units for a shorter period of time than the monitoring of the second AI/ML model in the active state.

DETECTION PROGRAM, DETECTION METHOD, AND DETECTION DEVICE

Resumen de: EP4752787A1

A detection program causes a computer to execute a process including converting input tabular data into image data, generating a first machine learning model by machine learning using the tabular data as training data, and generating a second machine learning model by machine learning using the image data as training data, and detecting presence of an adversarial attack on input data based on a first prediction result for the input data obtained using the first machine learning model and a second prediction result for the input data obtained using the second machine learning model.

ACCOUNT MANAGER VIRTUAL ASSISTANT USING MACHINE LEARNING TECHNIQUES

Resumen de: US20260148271A1

0000 A computing system may normalize messages by parsing, extracting attachments, masking data, and storing queued records. A non-transitory computer-readable medium may store instructions that normalize messages by parsing, masking, and queueing. A computer-implemented method may normalize messages by parsing content, extracting records, rewriting links, and masking data.

METHOD AND DEVICE FOR GENERATING A LOGIC-BASED EVALUATION RESULT

Resumen de: US20260148108A1

A method for generating a logic-based evaluation result for an automated subsumption of a life situation, in particular under applicable legal norms. The steps may include: providing data documenting the facts of the case, the data comprising at least textual, pictorial, natural and/or other evidence; processing the provided data by a machine learning model to extract relevant information from the data and to present it in a structured form; matching the structured information with legal norms and/or requirements by a logic network that draws logical conclusions based on the extracted information and the legal requirements; assessment of the life situation by subsuming the extracted information under the applicable legal norms based on the results of the logic network; and output of an assessment result that includes an assignment of the life situation to the legal norms.

DETERMINING FILTRATION PERFORMANCE BASED ON SENSOR DATA ANALYSIS

Resumen de: WO2026112470A1

The systems and processes described herein can be used to determine pressures and flowrates in a filter system that includes one or more filters. The systems and processes described herein can be used to determine a performance of the one or more filters. In one or more examples, machine learning computational models can be generated that determine when maintenance is to be performed with respect to the one or more filters. Additionally, machine learning computational models can simulate the operation of the filter system based on input obtained via one or more user interfaces.

CONDITIONAL ATTRIBUTES

Nº publicación: WO2026112414A1 28/05/2026

Solicitante:

RELTIO INC [US]
RELTIO, INC.

Resumen de: WO2026112414A1

Ingesting data from one or more data sources, wherein the data is associated with a tenant of a multi-tenant platform. Generating a machine learning model input based on the ingested data. Providing the generated machine learning model input to a machine learning model. Inferring, using the machine learning model, an inferred dynamic data structure, wherein the inferred dynamic data structure includes a subset of entity attributes inferred by the machine learning model from a set of the entity attributes, wherein at least a portion of the entity attributes include conditional entity attributes, wherein the conditional entity attributes depend on the values of one or more of the other entity attributes of the set of the entity attributes. Presenting, via a graphical user interface (GUI), a visual representation of the inferred dynamic data structure. Tracking user interactions received through the GUI associated with the visual representation of the inferred dynamic data structure. Dynamically adjusting, using one or more other machine learning models, the inferred dynamic data structure based on the tracked user interactions. Presenting, via the GUI, the dynamically adjusted inferred dynamic data structure.