1049 results about "Data element" patented technology

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 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.

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.

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.

A system for digital authentication may include an authentication server. The system may be configured to receive a login request from a user device associated with a user. The login request may include a user identity data element and a login request data element. The system may be configured to retrieve, based on the login request, a supplemental data element associated with the user from a recording server and determine whether the login request data element matches the supplemental data element and in response to a determination that the login request data element matches the supplemental data element: perform an authentication on the user device; configure the user device as authenticated in response to the authentication; and in response to configure the user device as authenticated: configure a first login mode as an authenticated status; and configure a second login mode as the authenticated status.

A system for extracting a number of data elements from one or more data sources. Image-based documents are indexed using optical character recognition and a text embedding model to convert the document text to vector embeddings. Relevant portions of the document are identified by comparing the vector embedding of the documents to a vector embedding of a prompt or a request to extract information. The relevant text is mapped to a corresponding page of the documents. The page may be provided to a multi-modal language model for information extraction. The multi-modal language model can process contextual information included in the layout, figures, markings, etc. of the document to extract the information. The system populates an ontological data store based on the response from the language model. Extraction accuracy is improved without significant increases in computations performed by the system.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a temporal sequence of data elements conditioned on an input. One of the methods includes obtaining the input, wherein the input comprises a noise input comprising a plurality of latent representations for the output temporal sequence; updating each latent representation using a latent denoising neural network; and generating the output temporal sequence of data elements by processing the updated latent representations using a decoder neural network.

In various examples, systems and methods are disclosed relating to accurately extracting requested portions of video data by concatenating video data with selective transcoding. The systems can receive a request indicating a start position and an end position and select a video data element including the start position. The systems can decode a portion of the video data element including the start position and encode a subset of a plurality of first frames of the video data element to provide a first video output. The systems can combine the first video output with a second video output that includes one or more second frames of the video data up until the end position.

A memory device includes a memory array including compressed zones including a first compressed zone including slots of a first slot size and a second compressed zone including slots of a second slot size; and a controller configured to compress a first data element received from a processing block and store the compressed first data element in a first slot of the slots of the first compressed zone based on a data size of the compressed first data element.

The invention belongs to the technical field of medical information, and provides a standardized electronic medical record shared document automatic generation method and system, and the method comprises the steps: obtaining related specifications, constructing a document directory structure, and generating directory data; the method comprises the following steps: loading a pre-constructed standard data element dictionary and a local business system database structure, respectively analyzing the pre-constructed standard data element dictionary and the local business system database structure to obtain standard data elements and local fields, and establishing a mapping relationship between the standard data elements and the local fields through an intelligent matching algorithm to obtain a field mapping table; acquiring configuration information including node names, data types, whether the user must be filled or not and value ranges, and binding the configuration information with local fields to generate complete node configuration data; and generating a document template according to the node configuration data, generating a shared document file based on the basic information of the patient, the doctor-seeing record and the selected document template, and performing verification. According to the method, the full-process automation of the shared document from directory definition to content generation is realized, and the method has high efficiency, standard performance and intelligence.

Various systems and methods are disclosed relating to protecting cross-system exchanges. A data processing system includes one or more processing circuits configured to identify a plurality of data elements and generate a first plurality of cryptographic objects. The one or more processing circuits are further configured to record the first plurality of cryptographic objects on a distributed ledger and generate metadata corresponding to at least one of the plurality of data elements, the metadata including temporal data. The one or more processing circuits are further configured to generate a bi-temporal record including a plurality of links to the plurality of data elements and storing the corresponding metadata. The one or more processing circuits are further configured to provide an interface including a query element corresponding to the bi-temporal record.

The invention relates to the technical field of medical big data analysis, and discloses a hospital medical record data anomaly analysis method based on big data. According to the method, multi-source medical record data including diagnosis and treatment event records, nursing operation sequences and patient sign monitoring streams in a hospital information system are collected, and structured reforming is carried out. And constructing a medical record element interaction graph based on the reformed data, and detecting an abnormal state conduction path between the data elements through the graph model. And performing hierarchical traceability calculation according to the detected conduction path, positioning an abnormal source and generating a positioning report, and finally outputting a calibration instruction set according to the report. According to the method, the abnormal conduction chain is systematically identified from the correlation perspective, and the root source of the abnormality can be automatically and accurately traced, so that the logicality and the positioning accuracy of the medical record data abnormality analysis are improved.

The invention discloses a data processing device, a data processing method, a chip and electronic equipment, and relates to the technical field of data processing. The data processing apparatus includes: a storage module configured to store data; the calculation module is coupled to the storage module and is configured to access an original data vector stored in the storage module, and the original data vector comprises m data elements; splitting the original data vector based on the target k value to obtain a plurality of first data vectors; performing odd-even merging sorting on the data elements in the first data vectors to obtain a plurality of first ordered data vectors; and merging and sorting the data elements in the plurality of first ordered data vectors to obtain top-k data elements in the m data elements. According to the data processing device, the data processing method, the chip and the electronic equipment provided by the invention, the top-k data elements can be obtained without a special hardware sorting network.

The invention discloses a geographic space data element circulation method and system based on a smart contract. The geospatial data element circulation system comprises a perception and execution layer, an element and right layer, a calculation and discovery layer and an immunization and compliance layer. The perception and execution layer is used for determining a trust benchmark of the whole system through the distributed trusted bookkeeping network; the essential factor and right layer is used for endowing the data with rich connotation and unique identity through deep identity; the calculation and discovery layer is used for understanding the structure and evolution of data ecology through a dynamic pedigree map engine; the immunization and compliance layer is used for automatically identifying and clearing inferior and malicious data in the system through a data immunization system; and providing a special interface with analysis capability and a data visualization tool through a compliance audit and supervision interface. Trusted circulation, efficient calculation and value multiplication of geographic space data elements in the whole life cycle can be achieved, and the method can dynamically adapt to evolutionary data ecology and complex application requirements.

The present disclosure discloses a method and system for constructing a knowledge graph of a standard data element of a biomedical dataset, comprising collecting relevant standard texts of data elements of different types of biomedical datasets and data of a relevant standard of the biomedical dataset; analyzing and summarizing the relevant standard texts of the data elements of the different types of biomedical datasets and the data of the relevant standard of the biomedical dataset; constructing a knowledge model of the knowledge graph of the standard data element of the biomedical dataset; extracting entity type data and attribute data from structured data and an unstructured text in the structured data; and obtaining the knowledge graph of the standard data element of the biomedical dataset by performing knowledge fusion on a plurality of types of data based on an a plurality of types of semantic associative relationships between one or more entity types.

Systems and methods are provided for completing a knowledge graph associated with an artificial intelligence pipeline. For example, the system may determine a missing data element in a knowledge graph, initiate an extraction process from a manuscript file associated with identifying the missing data element in the knowledge graph, initiate an intent identification process of the manuscript file that generates a label for a cluster of terms of the manuscript file, provide the cluster of terms from the manuscript file and the label as input to a large language model (LLM), and iteratively update the knowledge graph with output from the LLM.

The invention discloses a data element income automatic distribution system based on an intelligent contract. The system is constructed based on an alliance chain, the contribution degree of each party is quantified through an intelligent contract, the distribution proportion is dynamically calculated, and finally income settlement and on-chain evidence storage are automatically completed. According to the invention, high efficiency, transparency and fairness of data transaction income distribution are realized, rights and interests of all parties are effectively guaranteed, and credible circulation of a data element market is promoted.

The invention discloses an operation-oriented activated carbon oil gas recovery device digital twinborn construction method, relates to the technical field of industrial equipment digital twinborn and operation control, and solves the problems that the operation and the maintenance of a traditional activated carbon oil gas recovery device depend on regular inspection tour and maintenance after failure, resources are wasted due to excessive maintenance, shutdown is caused by sudden failure and the like. The method comprises the following steps: determining operation and maintenance requirements of the activated carbon oil gas recovery device and a digital twinborn architecture, and determining core elements and an interaction mechanism of a digital twinborn; establishing an operation and maintenance mechanism model, and establishing an operation and maintenance mechanism model library of the oil and gas recovery device; information is extracted from the activated carbon oil-gas recovery device design file, and an activated carbon oil-gas recovery device operation and maintenance geometric model is established; carding required data elements, and establishing an operation and maintenance data model of the activated carbon oil gas recovery device; and combining the established operation and maintenance mechanism model, the operation and maintenance geometric model and the operation and maintenance data model to complete the construction of the operation and maintenance-oriented digital twin of the activated carbon oil gas recovery device.

The embodiment of the invention provides a retrieval method and device based on a knowledge graph, and the method comprises the steps: carrying out the semantic recognition and question extraction of a question text in a retrieval process based on the knowledge graph, and obtaining question features, according to the problem characteristics, map hierarchy retrieval is carried out in a resource knowledge map obtained through knowledge map construction and map hierarchy division based on resource data to obtain a retrieval result, and a retrieval sub-map is constructed based on a corresponding map relation of data elements contained in the retrieval result in the resource knowledge map; and performing filling processing on the retrieval sub-graph to obtain a target sub-graph, and performing text conversion on the target sub-graph to obtain a retrieval answer, thereby realizing retrieval processing on the question text through the resource knowledge graph and the retrieval sub-graph.

A causal inference stack implements a targeted maximum likelihood scheme to conduct causal analysis of the observational data. At a data-handling layer, the causal inference stack obtains one or more memory locations for a dataset and establishes analysis nodes to setup localized data handling for the memory locations. At a data classification layer, the causal inference stack characterizes the missingness of the dataset. At a pipeline layer, the causal inference stack obtains a data element dependency query from a user and sets up an end-to-end solution path to determine the presence of a causal relationship between data elements identified in the data element dependency query.

The invention relates to an inter-thread data accumulation method and device, computer equipment and a computer readable storage medium. The method comprises the steps that an accumulation calculation instruction corresponding to a target thread bundle is received, the target thread bundle comprises N threads, M data elements are stored in a register of any thread, the data elements comprise accumulation dimensions and non-accumulation dimensions, and the accumulation dimensions of the M data elements stored in the same thread are the same; the global shared memory is used as a transfer, data elements stored in the N threads are rearranged into the K threads, and the non-accumulative dimensions of the data elements stored in different threads after rearrangement are different; and in each thread, performing accumulation operation on the data elements stored in the register corresponding to each thread according to the accumulation dimension, and storing an accumulation result in the local register of each thread. By adopting the method, the resource utilization rate of the register can be improved.

Disclosed are some techniques for implementing a data discovery agent in a network associated with a customer organization of a data privacy management system. Some implementations relate to one or more connections with one or more data sources storing private data of the customer organization. The one or more data sources can be scanned to obtain scanned data from the private data. The scanned data can be processed, including anonymizing the scanned data, to obtain preprocessed data for use by one or more classification operations. The preprocessed data can be shared with the data privacy management system. One or more classification promotion operations can be performed on classified data elements.

This invention relates to the field of data element management technology, and particularly to a method and system for managing the circulation of data elements. The method includes: identifying data elements to be circulated; acquiring the circulation process of the data elements; setting usage permissions for each user; configuring initial values; tracing back the source of the data elements; dividing the circulation process into several nodes; and collecting circulation data from each node. The circulation data includes at least: the user, usage permissions, and time. By determining a verification token, this invention can determine whether data elements have been tampered with during circulation, ensuring the integrity of data elements, greatly improving data element security, and reducing the risk of data element loss or errors. By constructing a circulation management chain and management reports, it can achieve full traceability of data elements, enhance data element quality control, and significantly improve the decision-making efficiency and accuracy of data element management.

The invention discloses an industrial internet resource autonomous scheduling method based on data element micro-transaction. The method comprises the following steps: acquiring industrial internet resource energy consumption; constructing a comprehensive energy consumption model; associating the model with a real-time power grid carbon factor; the method comprises the following steps: performing parallel transaction on a double-layer market of function resources and carbon quotas, quoting by a resource party based on a comprehensive energy consumption model, performing transaction according to a carbon quota difference by a task party, combining quoted price and carbon transaction cost, solving a multi-objective optimization problem, selecting a comprehensive cost optimal resource, establishing an elastic scheduling contract between the task and the resource, and executing dynamic re-negotiation. According to the actual consumption and carbon emission closed-loop settlement recorded by the contract, the carbon net balance is subjected to token excitation or purchase compensation; according to the method, the environment cost is internalized into a market economic variable, industrial internet resources meet performance requirements, meanwhile, autonomous evolution towards a low-carbon target is achieved, the resource utilization rate is increased, and economic and environmental benefits of a scheduling system are improved.

A computer-implemented method for supporting bias mitigation in an artificial intelligence (AI) system includes determining a set of sensitive attributes and providing a dataset including a number of data elements. Each data element is labelled with sensitive attributes. The AI system runs on the dataset and determines whether a prediction for an element is correct. Upon checking whether a bias with regard to a sensitive attribute is present, for each sensitive attribute that exhibits a bias, a model is trained for an attribute-based global explanation for each class of correct and incorrect predictions. For each incorrectly predicted data element based on the trained model for the at least one attribute-based global explanation, a counterfactual data element is generated that leads to a correct classification. The method has applications including, but not limited to, use cases in facial recognition and medical / healthcare for optimizing machine learning and supporting decision making.

The invention discloses a data element type access control method and system, and relates to the field of data access processing, and the method comprises the steps: creating a data element model; wherein the data element model comprises a data piece and an embedded policy execution engine, and the data piece comprises encrypted data and access control policy information; transmitting the data element model to a target environment; when a user initiates a data access request, the embedded policy execution engine performs access control policy verification according to the data access request; and if the access control strategy is successfully verified, the embedded strategy execution engine decrypts the encrypted data in the data piece, executes a corresponding data processing operation according to the data access request, and returns a processing result. According to the method, the data and the control strategy are bound into a whole in an inseparable manner, and a simplified control mechanism based on task starting time is adopted, so that controllable circulation of the data is realized.

An opportunistic approach is described to accelerate certain memory copy operations to be performed by a computing engine by executing instructions. An instruction for a memory copy operation can be identified that has a first data type with a smaller number of bits per data element than supported by the computing engine to perform the copy operation. The instruction can be replaced with another instruction that has a second data type with a higher number of bits per data element based on the alignment of the memory addresses for the copy operation and the total number of data elements to be copied. The second data type may not only accelerate the copy operation but also provide better utilization of the underlying hardware of the computing engine.