1519 results about "Data element" patented technology

In some examples, computer-implemented systems and processes deploy securely de-risked elements of confidential data within a distributed computing environment. For example, an apparatus may obtain configuration data associated with a source data table. The configuration data may specify an identifier of a column of the source data table that includes elements of confidential data, and based on the configuration data, the apparatus perform operations that anonymize the elements of confidential data within the column of the source data table and generate an anonymized column within the source data table. The apparatus may also perform operations that provision an anonymized data table that includes the anonymized column to at least one computing system, which may process the anonymized data table and generate an output data table that includes the anonymized column and maintains a referential integrity between the source data table and the output data table.

The disclosed embodiments include computer-implemented systems and processes that generate and provision, in real time, directed digital content based on decomposed structured messaging data. For example, an apparatus may receive a plurality of messages that characterize first data exchanges initiated between a first counterparty and second counterparties during a first temporal interval. Each of the messages includes elements of message data associated with a real-time payment requested from the first counterparty by a corresponding one of the second counterparties. Based on the elements of message data, that apparatus may predict an occurrence of a second exchange of data that involves the first counterparty during a second temporal interval, and may transmit notification data that includes product data characterizing an available product associated with the predicted occurrence of the second data exchange to a device operable by the first counterparty for presentation within a digital interface.

Systems, methods, and devices that relate to routing requests to large language models (LLMs) are disclosed. In one example aspect, the system receives session-specific data elements in response to a request to generate an output using LLMs. The system determines a hierarchy of operational constraints including privacy protocols and performance requirements. Weights for a multi-variable optimization are dynamically updated using the session-specific data elements. The system executes the multi-variable optimization across candidate LLMs that satisfy privacy constraints and optimize performance constraints. Based on the optimization, at least one candidate LLM is selected and the request is routed to it. In response to performance feedback, the system automatically selects a different LLM to improve one constraint, resulting in degradation of another constraint.

Various embodiments of the present disclosure provide automated encryption and data de-identification techniques for improving computer security. The techniques apply machine learning and encryption techniques to transform input data objects to tagged data objects that may be locally decrypted using encrypted element representation stored within the tagged data objects. The techniques may include determining a protected data element from an input data object based on privacy criteria and generating the tagged data object from the input data object by replacing the protected data element with an anonymized privacy tag that identifies a privacy type of the protected data element. The techniques may further include generating an encrypted element representation of the protected data element and inserting the encrypted element representation to a portion of the tagged data object to enable decryption of the tagged data object by authorized entities.

The invention provides a prefabricated dish production batch traceability analysis method and system, and relates to the technical field of data processing, and the method comprises the steps: carrying out the field arrangement optimization of an original data set based on a genetic algorithm, determining the optimal field arrangement and combination through dynamic iterative calculation, carrying out the splicing processing of all data elements according to the optimized field sequence, and obtaining the traceability of the original data set; generating a long character string with dynamic optimization characteristics; hash digest operation is carried out on the generated long character string, a digital fingerprint with a fixed length is generated through a preset one-way encryption algorithm, and a unique Hash identification code of the production batch is formed; and writing the generated Hash identification code into a distributed node of a block chain database, constructing a storage time sequence record based on a timestamp chain structure, and establishing an index mapping relationship between the Hash identification code and the original data set. According to the invention, the data preprocessing fineness can be improved.

Provided is a method, system, and computer program product for adaptatively training a large language model based on predicted changes in computing resources. A processor may identify data elements of computing resources for training a large language model. The identified data elements include a configured model topology. The processor may generate a forecast vector capturing a predicted change in the configured model topology over a time period. The processor may execute, using an optimization algorithm and the forecast vector, a series of optimization experiments using each training type of a plurality of training types to determine an optimal computing resource supply and demand over the time period. The processor may determine, based on the series of optimization experiments, an accuracy penalty value associated with each training type. The processor may train the large language model using a first training type that has a lowest accuracy penalty.

A system for extracting a number of data elements from one or more unstructured data sources. The system may separate the text from the tables in a document, such that only the table data may be sent to the large language model (LLM), when the LLM only needs to review the table data. The system generates chunks from the document. The system associates unique identifiers with each chunk to provide traceability. The system identifies relevant chunks from the documents and includes the relevant chunks with a request to extract the data elements in a prompt to the LLM. The system also includes a request for the LLM to report the chunks used during extraction of the data elements. The reported chunks are stored with the extracted data for verification, auditing, and error control.

The invention provides a multi-source data management method based on machine learning and a related device, and the method comprises the steps: obtaining a multi-source data set in an emergency command rescue scene, carrying out the data preprocessing of the multi-source data set, obtaining a standardized data set with a unified data form, extracting a data element information set from the standardized data set, and carrying out the processing of the data element information set. And calling a pre-trained blood relationship modeling model to perform association analysis processing on the data element information set, generating a blood relationship description set among the data entries in the standardized data set, and generating a data blood relationship tracking result based on the blood relationship description set. The data consanguinity tracking result comprises a historical record of the full life cycle of the data and cross-data entry dependency graph information. According to the invention, the method can provide a governance support with a data source capable of being checked, a processing process capable of being traced and a clear dependency relationship for emergency command rescue, enables a rescue decision to be developed based on reliable data consanguinity information, and effectively improves the support capability of data governance for the rescue decision.

The invention is suitable for the technical field of data asset management, and provides a data asset management system based on a block chain and big data analysis, and the system comprises a first terminal which carries out the storage of the ownership information of data assets through a block chain network, and generates a storage data package; generating an evaluation result based on a preset technical index analysis model; writing the hash value of the evaluation result into transaction data of the main chain of the block chain, and generating an evidence storage voucher matched with the block chain transaction; an interaction data sequence is packaged for identity signature, encryption and compression to generate a target code stream, and the target code stream is sent to the second terminal; the second terminal decrypts the encrypted data in the target code stream by using a private key, decompresses the data by using a compression algorithm matched with the first terminal, and restores the data into an interactive data sequence; verifying the access authority and the transaction condition of the evidence storage data packet; checking the anchoring state of the evidence storage voucher on the main chain of the block chain; and the data transaction is completed, the data asset state report is generated, and the data element marketization configuration efficiency is improved.

The invention relates to the technical field of artificial intelligence, and discloses a multi-agent collaborative data question-answering system and method based on a large model, and the system comprises an agent cluster module which is composed of five special agents, namely a data question-answering agent, a data extraction agent, a data analysis agent, a visualization agent and a quality examination agent, and achieves the task decomposition and collaborative execution through dynamic scheduling; the knowledge management module comprises a business knowledge base, a data element knowledge base and a user feedback base, and adopts a hierarchical knowledge fusion technology to provide domain knowledge support for the intelligent agent; and the supporting function module covers a front-end dialogue component and a verification and execution engine and is responsible for interactive interface rendering and result reliability verification. According to the method, the fine tuning requirement on the large model is remarkably reduced, the illusion of the large model is effectively intercepted through a dual verification mechanism, and the accuracy and reliability of question and answer results are improved.

Various embodiments described herein relate to systems, methods, and non-transitory computer-readable media structured to perform server-to-device secure data exchange using a device access token. In an embodiment, a smart device receives, from a requestor entity provided to the smart device, an account data provisioning request for an account. Based on the account data provisioning request, an account identifier for the account is determined. In some arrangements, the account identifier comprises or is associated with a device access token. Based on the device access token, a data element associated with the account is determined. In some embodiments, the data element is accessible to the requestor entity only if it is not access-restricted based on the device access token. Based on the data element, an executable graphic rendering instruction is generated. The executable graphic rendering instruction is executed, which includes generating and displaying, on a user interface of the smart device, a dynamic account status indicator relating to the account.

An apparatus for generating obfuscated data within a computing environment, comprising a processor and a memory containing instructions configuring the processor to access a database containing a plurality of private data elements belonging to at least a private record, generate a set of obfuscated data elements, representative of the at least a private record, as a function of the plurality of private data elements using an generative model, determine a first distance measure between at least an obfuscated data element within the set of obfuscated data elements and at least a private data element of the plurality of private data elements within the database, and verify the first distance measure is within a distance range, wherein a minimum threshold of the distance range is determined as a function of a deidentification parameter and a maximum threshold of the distance range is determined as a function of an obfuscation parameter.

Techniques for concept shift detection and correction using probabilistic models and learned feature representations are described. A gaussian process model is trained using representations generated by a primary machine learning (ML) model for existing training data elements in a training memory. For a new batch of data elements, representations again generated by the primary ML model can be used as input for the gaussian process model to generate predictive distributions. When the true targets for the new data elements are not sufficiently likely according to the corresponding predictive distributions, concept shift is likely and the training memory can be purged of the existing data elements before further retraining of the primary ML model.

The invention provides a method for establishing an ecological resource intelligent credible data element model. The method comprises the following steps: acquiring natural ecological basic data; obtaining natural ecology derivative data, wherein the natural ecology derivative data comprises ecological tourism description metadata and leisure health care description metadata; performing controlled feature mining on the natural ecology basic data and the natural ecology derivative data to generate ecological basic data elements and ecological derivative data elements; performing key feature extraction on the ecological basic data elements and the ecological derivative data elements to obtain basic ecological key features and derivative ecological key features; and obtaining green financial data elements, and establishing association relationships between the green financial data elements and the basic ecological key features and between the green financial data elements and the derivative ecological key features to generate an ecological resource credible data element model. According to the technical scheme, the problem that effective association between green financial data elements and ecological resource data is lacked is at least solved.

The present disclosure is directed to methods and systems for data-driven de-identification tool that can detect personally identifiable information (PII) elements within a given dataset and apply selective mapping transformations rules to de-identify the dataset. The disclosed data de-identification tool identifies direct identifiers, quasi-identifiers and unique values within a structured dataset in an example embodiment. The data de-identification tool then transforms these potentially personal or sensitive data elements into de-identified data elements and replaces the identified direct identifiers, quasi-identifiers and unique values within the structured dataset with the de-identified data elements. The data de-identification tool calculates a risk of re-identification and based on the risk level, repeat the de-identification process iteratively until the risk levels are within an acceptable range.

A system for extracting a number of data elements from one or more data sources. The system may separate the text from the tables in a document, such that only the table data may be sent to the large language model (LLM), when the LLM only needs to review the table data. The system may include converting a PDF to text, and separating the tables form the document text using markdown language from converting the PDF. The system may form table chunks and text chunks, index the chunks using a vector embedding and store a chunk identifier, document identifier, and or a page identifier with the chunk to provide result traceability. The system may, in response to a prompt, retrieve and send the targeted table chunks or text chunks to the LLM to extract the data elements. The system populates an ontological data store based on the LLM response.

Systems and methods are provided to perform multiply-accumulate operations of reduced precision numbers in a systolic array. Each row of the systolic array can receive reduced inputs from a respective reducer. The reducer can receive a particular input and generate multiple reduced inputs from the input. The reduced inputs can include reduced input data elements and / or a reduced weights. The systolic array may lack support for inputs with a first bit-length and the reducers may reduce the bit-length of a given input from the first bit-length to a second shorter bit-length and provide multiple reduced inputs with second shorter bit-length to the array. The systolic array may perform multiply-accumulate operations on each unique combination of the multiple reduced input data elements and the reduced weights to generate multiple partial outputs. The systolic array may sum the partial outputs to generate the output.

The invention relates to an optimization method of a data type conversion operator, computer equipment, a readable storage medium and a computer program product. The method comprises the following steps: allocating memory resources for target data; establishing a read mapping relationship between each logic index in the logic structure of the source data and the memory address of the source data, and establishing a write mapping relationship between each logic index in the logic structure and the memory address of the target data; traversing the logic structure, aiming at any traversed logic index, determining a read address corresponding to the logic index based on the read mapping relationship, performing data reading from the memory resource corresponding to the source data based on the read address, converting the read data element from the first data type to the second data type, and storing the converted data element in the memory resource. And writing the data elements subjected to data type conversion into memory resources corresponding to the target data according to the write mapping relationship. By adopting the method, the calculation capability of the data type conversion operator can be improved.

An integrated document portfolio management and governance system and method are disclosed. The system includes a computing unit having an application interface adapted to present and / or formulate at least one input query. The system further includes aa central controller having a backend server communicably connected to the application interface of the computing unit. The backend server includes a data receiving component adapted to receive document dataset, each comprising a plurality of data elements, from a plurality of data sources in one or more formats. The backend server further includes a data ingestion module adapted to detect, normalize, and aggregate the plurality of data elements of the document dataset and subsequently store them within a central data repository. Furthermore, the backend server includes an ontology generator module adapted to create and maintain a dynamic ontology for the ingested datasets in real-time, wherein the plurality of data elements is categorized and contextualized in accordance with the dynamic ontology. Additionally, the backend server includes a governance module adapted to enforce & monitor data compliance policies and a data analysis module adapted to analyze the ingested data and generate actionable insights, wherein the actionable insights include one or more predictive analysis, data accuracy status, governance status, operational inefficiency, risk indicators, compliance gaps, and risk lineage and integrity. In operation, a user formulates an input query towards the central controller which in response is configured to automatically manage, govern & monitor the received data and subsequently visualize one or more actionable insights and / or compliance gaps onto the application interface of the computing unit.

A system for extracting a number of data elements from one or more data sources. A text representing tables using a markdown language is extracted from spreadsheets and or other grid-based documents. The text is provided to a language model with a prompt. The prompt may be a chain-of-thoughts prompt. The prompt includes several requests and / or steps that cause the language model to extract one or more tables from the spreadsheet and output the tables using the markdown language or a different markdown language. The tables extracted from the spreadsheet are converted into table chunks and indexed for retrieval by a retrieval augmented architecture. When a prompt to extract particular information from the spreadsheet is provided, one or more relevant table chunks are identified and provided to a language model for extraction. Using the language model to separate tables improves information extraction accuracy while maintaining downstream instructions.

Methods and systems for identifying poisoned training data used for training artificial intelligence (AI) models are disclosed. To identify poisoned training data in a proposed training dataset, a causal model may be obtained. The causal model may include relationships relating data elements. The proposed training dataset may be identified as poisoned when data elements within the proposed training dataset do not satisfy the relationships set forth by the causal model. When the identification of poisoned training data is made, the AI model may not be updated using the proposed training dataset and the proposed training dataset may be discarded. If poisoned training data is not identified prior to training an AI model, methods and systems are disclosed for the remediation of the poisoned training dataset and subsequent tainted AI models. By doing so, the effect of poisoned training data may be prevented and / or efficiently computationally mitigated.

System for observing medical conditions and methods used therein include a processor and a memory connected to the processor, wherein the memory contains instructions configuring the processor to receive, from a data repository, a plurality of reference electronic health records and a plurality of reference cardiac data elements, generate medical training data, train at least an observation machine learning model using the generated medical training data, receive a query pertaining to a subject, wherein the query includes at least a query cardiac data element and at least a query electronic health record, and output at least an observation outcome as a function of the query using the at least an observation machine learning model.

The disclosed exemplary embodiments include computer-implemented systems, devices, apparatuses, and processes that dynamically implement and manage consent and permissioning protocols using container-based applications. By way of example, a device may receive a request for an element of data that includes a first digital token associated with an executed application program. The device may load a second digital token from a portion of the memory that is inaccessible to the executed application program, and when the first digital token is consistent with the second digital token, the device may present, within a digital interface, an interface element that confirms a verification of a digital signature associated with the data element.

A computer-implemented method comprising receiving, via one or more processors, an input query associated with a patient; obtaining, via one or more processors, a healthcare record associated with the patient; analyzing, via the one or more processors and using a trained machine learning model, the healthcare record associated with the patient to extract data elements relevant to the input query; and generating, via the one or more processors and using the trained machine learning model, a first mapping of the extracted data elements relevant to the query.

The invention discloses an artificial intelligence auxiliary authentication method and system for data element right confirmation, and relates to the technical field of data element management and credible circulation. Comprising the following steps: data acquisition: acquiring a data product, an ownership statement and contract terms, and calculating an initial digital fingerprint of the data product; and ownership identification: extracting core value elements and features of the data product based on a multi-modal intelligent analysis model, quantifying value contribution weights formed by all links to the data product, and automatically generating a data element circulation graph. According to the method, the multi-modal intelligent analysis model is constructed, and the data product is deeply scanned and analyzed automatically, so that the dependence on predefined rules is reduced, and the intellectualization and automation of ownership identification are realized; by constructing an intelligent compliance verification model, data operation behaviors are monitored and compared in real time, substantive compliance control over the whole data use process is achieved, and a credible use closed loop is established.

An aquatic cleaning system includes a pool cleaner having a housing, a drive system for moving the housing, and a debris basket associated with the housing. The debris basket is designed to receive debris from the aquatic environment. The system includes a data capture system including an imaging device operably coupled to the cleaner. The system also includes a detection system having a storage medium storing a trained model, and a processor designed to receive data elements from the data capture system and input the data elements into the trained model to identify a detected object within the aquatic environment as being debris or non-debris. The system also includes a control system in communication with the detection system and the drive system to move the pool cleaner toward the detected object identified by the detection system when the detected object is identified by the detection system as being debris.

The invention provides a data quality rule automatic matching method, system and device for public security service data, and belongs to the technical field of computer information processing. The method comprises the following steps: structurally analyzing a public security data element standard document; constructing a basic rule and an association rule according to an analysis result; constructing a quality rule knowledge graph, creating four types of entity nodes including data element nodes, rule nodes, standard nodes and synonymous name nodes, and establishing three types of relation edges including a dependency relation, a synonymous name relation and a reference relation; extracting multi-modal features of the to-be-retrieved field, wherein the multi-modal features comprise semantic features and structured features; a rule is generated through a hierarchical matching engine, and matching processes of an accurate matching layer, a semantic matching layer and a statistical matching layer are executed in sequence; and executing online optimization. According to the method, the bottleneck problems of insufficient rule coverage, semantic understanding deficiency, poor dynamic adaptability and the like of a traditional method are solved, and the method has remarkable technical progress and practical value.

Data structures and applications are defined with an application development environment which employs the use of a natural language artificial intelligence interface and recorded metadata descriptions, which are converted at runtime to generate underlying software code (e.g., C#, SQL, JavaScript, etc.) to implement the application. The data structures include application data elements described in a data serialization format. Each application data element includes fields that each represent an individual value. Each field is defined with a label, a data type, and attributes. The data structures also describe relationships or links between application data elements, as well as data-driven or time-based rules that drive further data manipulation and interactions within and external to the programming development environment through a variety of interfaces (e.g., HTTP, SMTP, SMS).

Methods and systems for synergistic use of a large language model (“LLM”) and Graph-of-Thoughts (“GoT”) for unified data access across GraphQL application protocol interfaces (“APIs”) via a single API. Methods and systems may include requesting to identify and retrieve data concerning GraphQL APIs from a GraphQL API schema hub. Methods and systems may include identifying key relationships between data elements in the GraphQL APIs using relationship semantic analysis and transporting a text prompt via a cache to an LLM-GOT synergistic processor. Methods and systems may include modeling information generated by the LLM-GoT synergistic processor as an arbitrary graph, generating a unified GraphQL API based on synergistic outcomes, and storing the unified GraphQL API in the GraphQL API schema hub.

The invention discloses a city data element platform construction method and system. The method comprises the steps of multi-source data integration and three-dimensional geographic mapping, industrial chain and enterprise dynamic matching and data visualization and decision support. And conversion from addresses to longitudes and latitudes and spatial association are realized through geocoding API and GIS technologies. And the industrial chain and enterprise dynamic matching part constructs an industrial chain atlas, and adopts an artificial intelligence model to judge enterprise nodes and optimize a matching result. And the data visualization part is used for generating industry aggregation distribution and an enterprise distribution diagram in a three-dimensional scene according to screening conditions such as project names and industrial chains, counting the number of immigration and emigration of enterprises and dynamically updating the industry aggregation distribution. According to the method, multi-source data are integrated, accurate spatial association and industrial chain atlas construction are realized, support is provided for industrial chain analysis and optimization, enterprise spatial distribution and industrial chain association conditions are visually displayed, and the method has important application value and practical significance.