32 results about "Data enrichment" patented technology

The invention relates to the technical field of network operation and maintenance, in particular to an automatic operation and maintenance method and system based on artificial intelligence, and the method comprises the steps: collecting detailed log information containing a security event in real time through a security information and event management system configured at a target endpoint; performing cleaning, labeling and structured processing on the log data by utilizing a proxy artificial intelligence system to generate standardized metadata, and performing MITRE ATTamp with the standardized metadata; mapping the CK knowledge base, identifying technical features and behavior patterns of attacks, comparing enriched log data with an external threat intelligence source, analyzing TTP of a known attack group, generating a threat intelligence association report, generating a targeted response plan by using a large language model, generating an executable command sequence according to the response plan, and generating a threat intelligence association report; the proxy executor is connected with the server through a WebSocket protocol, executes a command in a POSIX shell environment, captures and returns an execution result, verifies the execution result, carries out necessary command optimization, records an optimized response to a vector database, and automatically matches a historical event and triggers a predefined response process through a vector retrieval mechanism. And continuous threat monitoring and adaptive response are realized. According to the method, the problem that a closed loop aiming at a terminal executor, data enrichment and historical event recall cannot be formed by security operation and maintenance in the prior art can be solved.

The present invention concerns a computer-implemented method (300) for enriching ambiguous, incomplete or sparse health data, comprising: obtaining an input dataset (302) comprising a plurality of electronic health records (EHRs) associated with a patient; extracting (308) health information which is explicitly recited in the EHRs from the input dataset (302), including at least one diagnosis indicated by a name of a disease (504) or a medical classification code (502) which denotes a disease (504); generating (310) supplementary health information which is not explicitly documented in the EHRs based, at least in part, on the extracted health information, the supplementary health information including at least one or more symptoms (506) inferred from diseases (504) directly or indirectly documented in the input dataset (302); and validity-scoring (312) at least part of the extracted health information and the supplementary health information to produce an output dataset (314).

A method and system are disclosed for improving the accuracy, efficiency, and scalability of data interpretation and extraction of structured data from unstructured documents utilizing Large Language Models (LLMs). Applicable in finance, healthcare, legal, and government contexts, the disclosed invention addresses limitations of conventional Optical Character Recognition (OCR), machine learning, and LLM-based methods. In particular, the system and method incorporate feedback loops for continuous learning and leverage pre-processing, contextual tagging, customized prompt engineering, and post-processing to achieve robust data extraction. By integrating data enrichment techniques, the invention manages the inherent complexities of multilingual documents and evolving content standards.

Methods, systems, and apparatus, including computer-readable media, for data preparation and modeling using artificial intelligence. In some implementations, a system receives user input that indicates one or more data sets. In response, the system generates a set of recommendations for data modeling, data preparation, or data enrichment for the one or more data sets, where at least one recommendation in the set of recommendations is generated using one or more artificial intelligence and / or machine learning (AI / ML) models. The system provides the set of recommendations for display in the user interface in association with one or more interactive controls to accept or dismiss the recommendations. In response to receiving user input the system updates the data model or the one or more data sets to apply an update corresponding to an accepted recommendation. The system provides the updated data set or updated data model to a chatbot or other application.

A first application generates a first user interface to display a pricing quote to a user. The first application is configured to detect a request by the user for an enhanced view of the pricing quote. In response to detecting such a request, the first application retrieves first data from a second application and second data from a pricing and configuration service. Also, the first application populates a temporary database with the first data and the second data. Additionally, the first application performs data enrichment of the first data and the second data to create third data by translating technical information of the first data and the second data into a user-friendly notation. Finally, the first application generates a second user interface to display, to the user, the third data associated with the pricing quote.

An Al-driven system and method for healthcare lead nurturing are disclosed. A system includes a data enrichment module configured to integrate internal and external data sources to enhance customer profiles, and a stigma prediction engine employing machine learning algorithms to analyze behavioral patterns and predict stigma levels. The system further includes a multi-channel communication module for delivering personalized content via email, SMS, chat, and voice calls. The system further includes an Al audiologist virtual assistant for real-time interactions, configured to adapt dynamically based on user input.

The invention relates to the technical field of physical training and data processing, and discloses a physical training multi-dimensional data analysis system fused with radio and television technology, comprising a main time code reference module used for generating a global time code signal; the video acquisition and synchronization module is used for outputting a synchronous video stream carrying a timestamp; the context-aware network module is used for providing identity and position contexts of athletes; the athlete data unit is used for collecting athlete sensor data; and the central processing and analysis module adopts a multi-path merging and sorting algorithm, fuses all data streams into a single unified training event stream which is absolutely ordered according to time, and completes data enrichment. According to the method, through hardware-level synchronization and a unified event stream architecture, submillisecond-level accurate synchronization of multi-source data, automatic association of context information and simplification of a data processing architecture are realized.

The present invention, BCCBuddy, is an automated workflow management system that dynamically triggers multi-step workflows using email recipient fields (To, CC, and BCC). Unlike traditional automation tools relying on static triggers or basic email parsing, BCCBuddy executes multiple workflows simultaneously, each mapped to distinct recipient fields. The system incorporates advanced email parsing, a configurable storage system for workflow mapping, and optional data enrichment through natural language processing (NLP). By integrating seamlessly with third-party platforms such as CRM, task management, and scheduling tools, the invention supports high-volume email processing and complex automation scenarios. Its intuitive design ensures accessibility for technical and non-technical users alike, eliminating the need to modify email content or user behavior. BCCBuddy redefines email-driven automation by streamlining workflows, reducing manual effort, and enhancing productivity across industries and applications.

Various embodiments relate generally to data science and data analysis, computer software and systems, and control systems to provide a platform to facilitate implementation of an interface as a computerized tool, among other things, and, more specifically, to a computing and data platform that implements logic to facilitate moderation of electronic messages, postings, content, etc., via implementation of an application configured to, for example, perform one or more actions automatically implementing one or more data models (or a combination of data models), such as large language models (“LLMs”) or any other machine-learning algorithms.

Described are systems, methods, and computer-readable storage mediums for processing an event that is detected on a client device. The event is processed at a first data stream corresponding to a first branch of an event processing timeline. After initial processing and data enrichment, the event is processed at a second data stream corresponding to a second branch of the event processing timeline in parallel with the processing of the event at the first data stream. The processing of the event at the second data stream includes performing additional initial data processing and additional data enrichment of the data associated with the event. The additional initial data processing and the additional data enrichment is specific to a destination associated with the event. After the additional initial data processing and the additional data enrichment, the event is conditionally forwarded to the destination associated with the second branch.

Systems and procedures are provided for extracting relations from textual data, comprising the collection (502) of labeled textual data from diverse sources, including digital archives and online repositories, each source comprising sentences annotated with detailed grammatical structures. Initial relational data are generated from the grammatical structures by applying advanced parsing and machine learning techniques using a sophisticated rule-based algorithm (506). Training sets are generated (508) to improve the diversity and complexity of a relational dataset by applying data enrichment techniques to the initial relational data. A neural network model is trained (510) using an array of semantically equivalent but syntactically varied prompt templates designed to test and refine the linguistic capabilities of a model.A final relation extraction output is determined (512) by implementing a voting-based decision system that integrates statistical analysis and uses a weighted voting mechanism to optimize extraction accuracy and reliability.

Described are systems, methods, and computer-readable storage mediums for processing an event that is detected on a client device. The event is processed at a first data stream corresponding to a first branch of an event processing timeline. The processing of the event at the first data stream includes performing initial processing and initial data enrichment of data associated with the event. After the initial processing and data enrichment, the event is processed at a second data stream corresponding to a second branch of the event processing timeline in parallel with the processing of the event at the first data stream. The processing of the event at the second data stream includes performing additional initial data processing and additional data enrichment of the data associated with the event. The additional initial data processing and the additional data enrichment is specific to a destination associated with the event. After the additional initial data processing and the additional data enrichment, the event is conditionally forwarded to the destination associated with the second branch.

Methods, systems, and apparatus, including computer-readable media, for data preparation and modeling using artificial intelligence. In some implementations, a system receives user input that indicates one or more data sets. In response, the system generates a set of recommendations for data modeling, data preparation, or data enrichment for the one or more data sets, where at least one recommendation in the set of recommendations is generated using one or more artificial intelligence and / or machine learning (AI / ML) models. The system provides the set of recommendations for display in the user interface in association with one or more interactive controls to accept or dismiss the recommendations. In response to receiving user input the system updates the data model or the one or more data sets to apply an update corresponding to an accepted recommendation. The system provides the updated data set or updated data model to a chatbot or other application.

This disclosure provides a mechanism for the enrichment of sparse datasets using language models. By training language models on the specific distribution of known values in a dataset, missing values can be predicted, and the predicted values added, thereby resulting in a more complete dataset. This method also facilitates the enhancement and augmentation of datasets by predicting values for new properties that were not previously available. The approach proves particularly effective at scale, transforming large sparse datasets into more complete and enhanced datasets. Masking language modeling may be employed to train language models capable of generating representations of technical data. Training data includes corpuses of technical data that may be represented as text strings. These pretrained models are fine-tuned to predict various properties. The resulting models can predict missing values in large technical datasets, providing valuable data for guiding scientific research.

The present disclosure provides an approach of generating a request to obtain information corresponding to a data sample. The approach produces, by a processing device, sample metadata using an artificial intelligence (AI) model trained to analyze the data sample and generate the sample metadata. In turn, the approach enriches the data sample based on the sample metadata to produce an enriched data sample.

There are provided systems and methods for a data integration framework that provides an institutional or organizational user data enrichment capability locally. Specifically, instead of relying on the fraud detection platform to constantly updating and / or building new data connectors to intake data from updated or a new data provider, an institutional user, such as a financial institution, may receive a software development kit (SDK) from the fraud detection platform, using which the institutional user may build its own data connector deployed at the institutional user.

This disclosure provides a mechanism for the enrichment of sparse datasets using language models. By training language models on the specific distribution of known values in a dataset, missing values can be predicted, and the predicted values added, thereby resulting in a more complete dataset. This method also facilitates the enhancement and augmentation of datasets by predicting values for new properties that were not previously available. The approach proves particularly effective at scale, transforming large sparse datasets into more complete and enhanced datasets. Masking language modeling may be employed to train language models capable of generating representations of technical data. Training data includes corpuses of technical data that may be represented as text strings. These pretrained models are fine-tuned to predict various properties. The resulting models can predict missing values in large technical datasets, providing valuable data for guiding scientific research.

The invention discloses a pandas-based lightweight offline data analysis method and system, and the method comprises the steps: receiving original data from a distributed message system in real time, carrying out the data type conversion of the original data, and storing the converted data according to the date; according to the time range, acquiring the converted data as to-be-processed data, and performing data enrichment on the to-be-processed data to obtain enriched data; carrying out data filtering on the enriched data by adopting a pandas vectorization operation to obtain filtered data; slicing the filtered data by adopting a pandas grouping operation to obtain a plurality of groups of data, and calculating an aggregation result of the plurality of groups of data to obtain daily process data; and summarizing the daily process data, and calculating according to a baseline algorithm to obtain a daily baseline. Data mapping, data enrichment, data filtering, process data calculation and baseline calculation are creatively realized based on pandas, so that a lightweight data analysis scheme is realized.

Data enrichment and identity translation techniques using probabilistic data structures are described. In one or more examples, a plurality of datasets are received from a plurality of entities. Each dataset has a plurality of dataset records describing a respective audience. A plurality of sets of sketches are generated as probabilistic data structures, respectively, based on the plurality of datasets. A result is formed by processing a query. The result includes at least one sketch having a probabilistic data structure generated based on one or more of the plurality of sets of sketches. An entity is identified from the plurality of entities corresponding to the at least one sketch and entity is exposed for display in a user interface.

Embodiments of the present application disclose a data enrichment method and device for an engine and an electronic device. After obtaining historical test data of a target engine, the historical test data is input into a pre-trained data enrichment model to obtain target test data output by the data enrichment model. The target test data includes at least one target parameter of the target engine that is not measured in historical tests. The data of the target engine that is not measured in the historical tests under corresponding historical working conditions can be quickly obtained without repeating the historical tests of the target engine, and the method is applicable to engines of any type.

The invention discloses a component basic information data cleaning method, and belongs to the technical field of data management. The method comprises the following steps: formulating basic information data standard specifications of components; constructing a data dictionary database; performing standardized preprocessing on the multi-source heterogeneous component data; performing basic attribute cleaning of uniqueness verification based on a dictionary rule mapping table; performing multi-condition comparison cleaning on the basic attributes of the non-dictionary class; merging, de-duplication and endowing unified codes; and data enrichment and system online are realized. According to the invention, the problems of multi-source isomerism, non-uniform standard and low cleaning efficiency of component data in the prior art are solved, efficient and accurate component basic information data cleaning is realized, and high-quality data support is provided for a digital supply chain system.

A request for enrichment data for enriching user data for a plurality of users may be received from a user device within a first data environment. The request may include, for each user, a respective encryption key used to encrypt the respective user data, respective encrypted user data, and an indication of a respective hashing rule used to generate the respective encryption key. The encrypted user data for the users may be decrypted in a second data environment using the encryption keys. The hashing rule may be used to generate pseudonymized representations of the decrypted user data. The pseudonymized representations of the decrypted user data may be mapped to pseudonymized representations of the enrichment data that correspond to the user data. The pseudonymized representations of the decrypted user data mapped to the pseudonymized representations of the enrichment data may be sent to the user device within the first data environment.

Described are systems, methods, and computer-readable storage mediums for processing an event that is detected on a client device. The event is processed at a first data stream corresponding to a first branch of an event processing timeline. The processing of the event at the first data stream includes performing initial processing and initial data enrichment of data associated with the event. After the initial processing and data enrichment, the event is processed at a second data stream corresponding to a second branch of the event processing timeline in parallel with the processing of the event at the first data stream. The processing of the event at the second data stream includes performing additional initial data processing and additional data enrichment of the data associated with the event. The additional initial data processing and the additional data enrichment is specific to a destination associated with the event. After the additional initial data processing and the additional data enrichment, the event is conditionally forwarded to the destination associated with the second branch.

Techniques for performing sequential data enrichment are presented herein. In some examples, a data enrichment system includes an input pipeline and a set of enrichment routines. In some examples, a datagram is retrieved from the input pipeline and an enrichment type associated with the retrieved datagram is determined. In some examples, after determining the enrichment type that is associated with the retrieved datagram, the datagram is processed by the enrichment routine that corresponds to the enrichment type to determine an enriched datagram. In some examples, the enriched datagram is stored in the input pipeline, which can enable determining a new enrichment type associated with the enriched datagram or storing the enriched datagram in an output pipeline if it is determined that the enriched datagram is not associated with any enrichment types.

A data enrichment method includes: receiving event information associated with an event in a target of interest; based at least in part on the received event information, analyzing data associated with the target of interest to identify: an impact of the event within the target of interest, and existing one or more mitigation measures associated with the event; and outputting information about an event, the output information including the identified impact and the identified one or more mitigation measures, where the event information includes information about an abnormal behavior detected in the target of interest or information about a vulnerability detected in the target of interest, and wherein the analyzed data is based on a plurality of sources.

A computer system for organizational maturity and competency development comprising: a processor; memory; computer-readable instructions that cause the system to: receive a dataset concerning organizational goals; generate a maturity acceleration plan (MAP) with maturity metrics for entities; generate performance acceleration models (PAMs) based on the MAP, each PAM can include measurement criteria representing knowledge, competence, and proficiency; store a hierarchical data structure with MAP at first level, PAMs at second level, and accolades at third level; monitor user interactions detecting accolade completion by tracking training course completion, platform feature usage, data element creation, data enrichment, and specified actions; display a visual dashboard showing organizational maturity progress, deployed MAPs, and completed PAMs. The system can provide an AI algorithm with pre-defined parameters and organization rules to suggest additional aspects of the maturity acceleration plan and utilizes the AI algorithm to provide a new or modified maturity acceleration plan.

Systems and methods are disclosed for implementing a low-latency data stream monitoring system. The data stream monitoring system may obtain raw data from a data source as soon after the data is generated, and may classify the data according to different topics. The topics may be published in a publish-subscribe messaging model, and data enrichment systems may subscribe to the topics to receive data for enrichment. The data enrichment systems may supplement or replace the raw data with additional information, and may further classify or reclassify the enriched data into different topics. The enriched data may then be published to an alert generation system, which may apply various criteria to the enriched data to determine that alerts should be generated, generate the alerts, and publish or transmit the alerts to client devices. Individual data streams, topics, enrichments, criteria, and alarms may be added, removed, or modified as required.

The application relates to the technical field of network operation and maintenance, in particular to an automatic operation and maintenance method and system based on artificial intelligence. The method collects detailed log information containing security events in real time through a security information and event management system configured at a target endpoint, then uses an agent artificial intelligence system to clean, label and structure process the log data, generates standardized metadata, maps the metadata with an MITRE ATT&CK knowledge base, identifies the technical features and behavior patterns of attacks, compares the enriched log data with external threat intelligence sources, analyzes the TTP of known attack groups, generates a threat intelligence correlation report, uses a large language model to generate a targeted response plan, generates an executable command sequence according to the response plan, connects the agent executor with the server through a WebSocket protocol, executes the command in a POSIX shell environment, captures and returns the execution result, verifies the execution result, performs necessary command optimization, records the optimized response to a vector database, automatically matches historical events through a vector retrieval mechanism, triggers a predefined response process, and realizes continuous threat monitoring and adaptive response. The application can solve the problem that the existing security operation and maintenance cannot form a closed loop of terminal executor, data enrichment and historical event recall.

A request for enrichment data for enriching user data for a plurality of users may be received from a user device within a first data environment. The request may include, for each user, a respective encryption key used to encrypt the respective user data, respective encrypted user data, and an indication of a respective hashing rule used to generate the respective encryption key. The encrypted user data for the users may be decrypted in a second data environment using the encryption keys. The hashing rule may be used to generate pseudonymized representations of the decrypted user data. The pseudonymized representations of the decrypted user data may be mapped to pseudonymized representations of the enrichment data that correspond to the user data. The pseudonymized representations of the decrypted user data mapped to the pseudonymized representations of the enrichment data may be sent to the user device within the first data environment.

Aspects of the present disclosure enable a storage manager to provide access to storage systems of varying types, such as database storage systems or indexed data storage systems, to a data source and allow for use of reserved storage capacity of the storage systems. The storage manager may provide access to storage systems under the control of different cloud storage providers. The storage manager may provide for temporary data storage in a reserved portion of a storage system when processing capacity of a data processing system is exceeded. The storage manager may provide for temporary storage of query results retrieved from a database system in a reserved portion of a data store. The storage manager may provide for temporary storage of data used for data enrichment in a reserved portion of a data store.