1935 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.

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.

The invention provides a data asset multi-dimensional assessment method and system based on a block chain and machine learning, an assessment index system covering dimensions such as data quality, value, risk and security is constructed, a block chain technology is adopted to carry out assessment process and result evidence storage, a machine learning model is introduced to dynamically optimize index weights, and the assessment efficiency is improved. And a data asset marketization pricing mechanism is explored and formed. According to the method, the problems that an existing data asset evaluation method is single in dimension, high in subjectivity and lack of credibility are solved, scientific evaluation, marketization pricing and credible publication of the data asset value are achieved, important reference is provided for data element circulation, and the method has great significance in promoting prosperous development of the data element market.

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.

The invention relates to a matrix double linked list compression storage computing device which comprises a definition module used for defining a plurality of register variables to represent double linked list compression storage of a matrix; the matrix data element processing module is used for deleting, inserting and / or covering matrix data elements in the Jacobian matrix; the matrix operation module is used for executing multiplication calculation of the Jacobian matrix and the column vector when the Jacobian matrix is a sparse matrix; and the matrix block resource allocation calculation module is used for calculating a product result component based on the matrix blocks of the Jacobian matrix when the Jacobian matrix is a non-sparse matrix, and the product result component is a result obtained by dividing a product result of the Jacobian matrix and the column vector according to row blocks. According to the method, the matrix operation speed during load flow calculation can be increased, consumption of storage resources on a chip is reduced, and the matrix calculation efficiency and the resource utilization rate are improved.

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.

Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and / or the like for generating a plurality of training embeddings based on a pre-training dataset, wherein the plurality of training embeddings comprises one or more of descriptive embeddings, sequential ordering embeddings, age / time embeddings, locale embeddings, or encounter number embeddings; generating one or more initialized weights associated with respective one or more layers of a machine learning model based on the plurality of training embeddings; generating one or more fine-tuned weights for the machine learning model by updating at least a portion of the one or more initialized weights using a fine-tuning dataset associated with a target classification; and generating, using the machine learning model, one or more prediction scores for one or more prediction encounter data elements associated with the target classification, based on one or more input temporal sequence of encounters data records.

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.

Disclosed embodiments relate to systems and methods for classifying network session activities. Techniques include identifying a session established using a native client between a network identity and a network resource; monitoring messages conveyed during the session to identify a first data element associated with the session; analyzing the first data element to identify a characteristic of the native client; monitoring messages conveyed during the session to identify a second data element associated with an action requested by the network identity; and analyzing the action and the characteristic of the native client to determine whether the action is a human-initiated action.

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.

The invention discloses a data processing method for compliance evaluation of data element processing operation, and relates to the technical field of data processing and compliance management, and the method comprises the steps: collecting data elements and regulatory rules, carrying out the preprocessing, building a knowledge graph of the association of the data elements and the regulatory rules based on the preprocessed data elements and regulatory rules, and carrying out the compliance evaluation of the data elements and the regulatory rules. The method comprises the following steps: monitoring data element processing operation in real time, extracting operation meta-information, performing rule matching in combination with a knowledge graph, fusing a rule matching result, the knowledge graph and the operation meta-information to obtain data processing features, constructing a compliance evaluation model by using a machine learning algorithm, inputting the data processing features into the compliance evaluation model, and analyzing potential risks. According to the method, high-risk operation is pre-warned and limited, the compliance evaluation model is optimized according to user feedback, a dynamic compliance evaluation method is formed, and the method has important significance in the field of data processing and compliance management.

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 discloses a multi-source heterogeneous agricultural data element fusion system and method, and relates to the technical field of data fusion processing, and the method comprises the steps: carrying out the data screening of cleaned data; the screened data is preprocessed by the constructed agricultural field knowledge base, the logic consistency of the data is checked through a rule engine, and a corresponding coping strategy is adopted according to a check result; identifying abnormal points in the multi-source data by adopting a Gaussian mixture model and a constructed logic rule combination, and correcting or complementing the detected abnormal points in combination with a specific scene; key features are selected through information gain, multi-source fusion is carried out on the key features and original multi-source data, an abnormal mode library is established after abnormal data modes are classified, and source categories of the abnormal data are matched from a fault matching rule library according to abnormal features of the abnormal data; and the fault matching rule base is established, so that the source category of the abnormal data can be accurately identified, and a targeted solution is provided for processing the abnormal data.

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.

A computer system that detects a change and automatically performs a remedial action associated with an application programming interface (API) for an application is described. During operation, the computer system may receive data from a second computer system that implements a data-query engine for a database and the data may be associated with the database. Then, the computer system may detect the change in the data based at least in part on one or more of: a schedule of when to check for the change; a location in the database associated with the data; expected content of the data; or a relationship between the data and additional data. When the change is detected, the computer system may perform the remedial action, where the remedial action includes providing a notification with information specifying a data element associated with the API that is affected by the change.

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.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for collaboratively training an interaction prediction machine learning model using a plurality of user devices in a manner that respects user privacy. In one aspect, the machine learning model is configured to process an input comprising: (i) a search query, and (ii) a data element, to generate an output which characterizes a likelihood that a given user would interact with the data element if the data element were presented to the given user on a webpage identified by a search result responsive to the search query.

Non-transitory computer readable storage mediums have instructions executed by processors to convert a first list at a first server into a first encrypted, permuted list with first dummy data elements and form a first encrypted linked list of pointers to locations in the first encrypted, permuted list. A second list at a second server is converted into a second encrypted, permuted list with second dummy data elements and a second encrypted linked list is formed of pointers to locations in the second encrypted, permuted list. Elements of the first encrypted, permuted list are combined with elements of the second encrypted, permuted list into a merged, sorted list of secret sharings, where the merged, sorted list of secret sharings is formed solely utilizing local computations at the first server, local computations at the second server and communications solely between the first server and the second server.

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.

An apparatus is described having processing circuitry to perform vector processing operations, a set of vector registers, and an instruction decoder to decode vector instructions to control the processing circuitry to perform the required operations. The instruction decoder is responsive to a given vector memory access instruction specifying a plurality of memory access operations, where each memory access operation is to be performed to access an associated data element, to determine, from a data vector indication field of the given vector memory access instruction, at least one vector register in the set of vector registers associated with a plurality of data elements, and to determine, from at least one capability vector indication field of the given vector memory access instruction, a plurality of vector registers in the set of vector registers containing a plurality of capabilities. Each capability is associated with one of the data elements in the plurality of data elements and provides an address indication and constraining information constraining use of that address indication when accessing memory. The number of vector registers determined from the at least one capability vector indication field is greater than the number of vector registers determined from the data vector indication field. The instruction decoder controls the processing circuitry: to determine, for each given data element in the plurality of data elements, a memory address based on the address indication provided by the associated capability, and to determine whether the memory access operation to be used to access the given data element is allowed in respect of that determined memory address having regard to the constraining information of the associated capability; and to enable performance of the memory access operation for each data element for which the memory access operation is allowed.

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.