55 results about "Data enrichment" patented technology

Disclosed in some examples are methods, systems, devices, and machine-readable medium for providing a consumer collections and servicing analytics platform and application to simplify data analyst search and analysis of data from disparate data sources. The platform and application ingest data into a centralized data repository, from a plurality of disparate data sources. The application transforms the ingested data into standardized data using one or more standardized formats and applies custom processing algorithms and data enrichment techniques to the standardized data to generate enriched data. The application automates manual data collection and reporting processes using the enriched data and causes a user interface to be displayed to the user device, the user interface including self-service access to the enriched data in the centralized data repository.

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).

The invention discloses a digital modeling response method, system and device based on artificial intelligence, and a medium. The method comprises the following steps: S1, obtaining multi-level public security system business data; s2, performing preprocessing operations of data filtering, data recovery, data enrichment and data serialization on the public security system business data to form structured data; s3, inputting text information required by digital modeling by a user through a visual interface, and performing semantic recognition on the input text information by using a large language model to obtain text information features; and S4, selecting corresponding digital modeling demand information features according to the text information features, constructing a data analysis model in combination with standard structured data, and outputting a digital modeling result according to the data analysis model. According to the method, the problems that a digital modeling technology based on artificial intelligence has a high access threshold and a complex use process in the field of public security, and non-professional users are remarkably limited in the process of participating in digital modeling are solved.

Systems and methods transfer a replica of data from data provider(s), where the data provider(s) include either public-cloud resource(s) or private on-premise resource(s). The transferring includes extracting data from data provider(s) to a temporary location, transforming the extracted data into a format suitable for loading the data to a cloud operational data store, and loading the transformed data to the cloud operational data store. Further, data enrichment process(es) are performed to the replica of the data loaded to the cloud operational data store. Data access is provided, within the hybrid-cloud infrastructure environment, by routing an API call to access the data from a location selected from the group consisting of (i) the replica of the data loaded to the cloud operational data store, (ii) the on-premise resource(s), and (iii) the public-cloud resource(s).

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.

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.

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 data enrichment method, the method constituted of: receiving incident information associated with an incident in a target of interest; based at least in part on the received incident information, analyzing data associated with the target of interest to identify: an effect of the incident within the target of interest, and existing one or more mitigations associated with the incident; and outputting information regarding the incident, the output information comprising the identified effect and identified one or more mitigations, wherein the incident information comprises information regarding detected anomalous behavior in the target of interest or information regarding a detected vulnerability in the target of interest, and wherein the analyzed data is based on a plurality of sources.

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.

The present invention relates to the technical field of command line auditing, and provides a Linux host command auditing method, system and device based on ftrace, including: sending a control message to a user-state Agent deployed on a Linux host through a control module; when the control message contains an instruction to turn on command line auditing, the user-state Agent sets a plug-in in the Linux kernel and the bash binary program of the Linux host; when the user executes a command in the Linux system, the command line operation log is written into the ringbuffer of the Linux kernel according to the set plug-in; the user-state Agent continuously receives the command line operation log from the ringbuffer of the Linux kernel, enriches the command line operation log, and sends the command line operation log after data enrichment to the audit module for auditing; when the control message contains an instruction to turn off command line auditing, the user-state Agent clears the plug-in set in the Linux kernel and the bash binary program of the Linux host. The present invention can provide a highly efficient, real-time, difficult to bypass and highly customized security monitoring solution for the Linux system.

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.

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.

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.

The invention relates to the field of machining, in particular to an unknown working condition tool wear state monitoring method driven by a bilevel feature enhancement domain generalization network fusing a contrast learning module and dynamic label smoothing. According to the method, based on discriminative feature extraction of the parallel interactive fusion convolutional network, plug-and-play discriminative feature expansion without parameter introduction and multi-source domain data enrichment are adopted. According to the method, a parallel interactive fusion convolutional network structure is designed to be used for constructing the feature extractor, and compared with a classic convolutional neural network, interactive fusion convolution can learn refined local features and a dependency relationship between data in a long range, and the generalization ability of the feature extractor is enhanced; a plug-and-play and parameter-introduction-free data enhancement module is adopted to be embedded into a feature extractor, features obtained through interactive fusion convolution are directly subjected to operation to complete feature enhancement in an implicit mode, and finally a tool wear monitoring model high in generalization ability is established.

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.

The present disclosure provides a system (200) and a method for data enrichment. The system (200) may enrich the data by performing arithmetical and string operations on two or more fields and may create the other fields, such that it may index that field in the database (220) or provide it to an external entity for further analytics. Dynamic rules and policies may be applied in real-time to incoming data that is then normalized and not enriched. The system (200) includes an AI / ML engine that may receive the enrichment and normalization information. Based on the received enrichment and normalization information, the AI / ML engine may begin to suggest new or different fields, or further enrichment and normalization. The suggestion may further be supplemented by a generative AI that may be configured within the AI / ML engine. The generative AI may be configured to learn and be trained on new data sets.

A method for auto discovery of sensitive data may include: (1) receiving, at data enrichment computer program in a metadata processing pipeline, raw metadata from a plurality of different data sources; (2) enriching, by the data enrichment computer program, the raw metadata; (3) converting, by the data enrichment computer program, the raw metadata and the enhanced raw metadata into a sentence structure; (4) predicting, by a category prediction computer program in the metadata processing pipeline, a predicted category for the sentence structure; (5) identifying, by a sensitive data mapping computer program, a sensitive data category that is mapped to the predicted category based on a policy mapping rule; (6) determining, by the sensitive data mapping computer program, a risk classification rating for the predicted category; and (7) tagging, by the sensitive data mapping computer program, the data source associated with the metadata based on the risk classification rating.

Techniques for identity resolution and data enrichment include instantiating, at an account of a data consumer, an identity resolution application using an installer stored procedure. A data object is configured, at the account of the data consumer, to share log data generated by the identity resolution application with an account of a data provider. The identity resolution application is enabled, at the account of the data consumer, for an identity resolution process using source data based on an instruction originating from the account of the data provider. The instruction is based on the configuring of the data object.

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.

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.

To find enriching contextual information for an abbreviated domain name, a data enrichment engine can comb through web content source code corresponding to the abbreviated domain name. From textual content in the web content source code, the data enrichment engine can identify words with initial characters that match characters of the abbreviated domain name to thereby establish a relationship there-between. This relationship can facilitate more accurate and efficient domain name classification. The data enrichment engine can query a WHOIS server to find out if candidate domains having initial characters that match the characters of the abbreviated domain name are registered to the same entity. If so, keywords can be extracted from the candidate domains and used to find more relevant domains for domain risk analysis and detection. Candidate domains determined by the data enrichment engine can be provided to a downstream computing facility such as a domain filter.

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.

An automated method of predictive model development first cleans up raw supervised and unsupervised training data with a step that uses an algorithm to make every field of every record consistent, cohesive, and productive. Then the resulting flat data is given texture in a next step by a data enrichment algorithm that culls fields that do not contribute to predictive model building and that adds new fields computed from data combinations that are tested to add value to later steps that build different types of predictive models. Another late step for building smart-agents and their entity profiles uses another algorithm that benefits greatly from the cleaned and highly enriched training data. The predictive models and smart-agents and their entity profiles are then rendered as deliverable predictive model markup language documents in a final step executed by a specialized algorithm.

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.

Aspects and examples disclosed herein are directed to data enrichment on insulated appliances. An appliance for performing knowledge mining in a disconnected state is operative to: ingest data of a first type from a first data source coupled to the appliance; enrich at least a first portion of the ingested data, when in the disconnected state, with at least one of the cognitive functions of the plurality of containerized cognitive functions; identify at least a second portion of the ingested data for enrichment by a cognitive function that is not within the plurality of containerized cognitive functions; store the enriched data in an index; triage the ingested data and the enriched data in the index for uploading; upload the triaged data when reconnected to a network; and import an updated plurality of containerized cognitive functions when reconnected.