245 results about "Entity identifier" patented technology

Guiding query creation for a generative artificial intelligence (AI)-enabled assistant, including: receiving, via a natural language interface for a security framework monitoring a cloud deployment, a natural language input comprising one or more entity identifiers; gathering information based on the one or more entity identifiers; providing, to a generative artificial intelligence (AI) model, a prompt based on the natural language input and comprising the gathered information; and receiving, from the generative AI model, a response to the prompt.

The invention discloses a knowledge graph-driven water conservancy intelligent question and answer interaction platform and method, and relates to the related field of data processing, and the platform comprises an entity disambiguation module which is used for constructing a water conservancy knowledge graph to carry out standardized modeling, generating structured knowledge nodes to carry out entity disambiguation on water conservancy objects, and generating entity identifiers; the full-text retrieval module is used for performing semantic analysis on a natural language query text of the user according to the entity identifier, identifying a query intention type, performing full-text retrieval and generating a multi-modal answer; and the iterative interaction module is used for performing dynamic questioning based on the multi-modal answers, generating user semantic feedback information, optimizing a query path according to the user semantic feedback information, and realizing iterative interaction of water conservancy knowledge questions and answers. The technical problem that existing water conservancy intelligent question and answer interaction is insufficient in question and answer accuracy and richness is solved, and the technical effect of improving the question and answer accuracy and richness is achieved.

A computer-implemented method of training a machine learning model to identify biological entities for drug discovery is disclosed. The method comprises providing a training data set comprising a plurality of entity-linked text sequences, each text sequence including a mention of a biological entity, where the biological entity is linked to a corresponding biological entity identifier from a set of possible biological entity identifiers; masking the mention of the biological entity within each text sequence; encoding each masked text sequence into an input representation for a machine learning model; and training a machine learning model to predict the unique entity identifier of the masked biological entity based on the input representation. The described method is able to utilise the full breadth of the rich contextual information available in the biomedical text corpus to predict new biological targets for drug discovery and avoids the restrictions intrinsic to relationship prediction using knowledge graphs. The ability to identify more promising, biologically relevant targets in an automated manner, significantly reduces the requirement of human input and reduces the failure rate in targets that are progressed in the drug delivery pipeline.

Systems, methods, and devices to detect unauthorized third-party connections within a network infrastructure, such as by analyzing network traffic data using fuzzy matching and machine learning techniques. One aspect includes receiving network traffic data comprising records of communication events involving network identifiers, determining communication relationships between network entities identified by the network identifiers, accessing entity identifiers associated with known third-party systems, and determining associations between the network identifiers and the entity identifiers using a fuzzy matching process. Other aspects include identifying communication relationships involving the third-party systems based on the associations and detecting unregistered or unknown third-party connections within the network infrastructure. Further aspects include normalizing identifiers, computing string similarity metrics, assigning confidence scores, incorporating external data sources, building network association patterns, comparing current patterns to baseline patterns to detect anomalies, and updating security policies or firewall rules in response to detected anomalies. Additional aspects are provided.

The invention discloses a false information multi-source association reasoning system based on a knowledge graph, and the system comprises a data collection and standardization module which is used for carrying out the multi-source collection and duplicate removal of a text, and generating a structured input data set; the semantic extraction and normalization module is used for executing alias merging and disambiguation and outputting a semantic extraction result; the entity alignment module is used for cross-platform entity matching and confidence evaluation, entity identification unification and attribute and alias merging; the graph construction and anchor point module is used for constructing a fact graph and a traceability graph; the candidate constraint generation module is used for forming a candidate constraint set based on the multi-source evidence statistics support degree; the space-time constraint and weight reduction module is used for executing path consistency and joint constraint according to Allen and RCC8 to obtain an updated evidence weight result; and the evidence chain and pushing module is used for enumerating and scoring the evidence chain and pushing the evidence chain through an external interface. According to the invention, false information multi-source association reasoning is realized.

Embodiment herein discloses methods and devices for waste management by using an artificial intelligence based waste object categorizing engine. The method includes receiving an image while detecting a waste disposal activity (WDA) on a first waste bin and a second waste bin. Further, the method includes generating an entity identifier (EID) during a material disposal event (MDE) and associating the entity identifier with the material disposal event generated during the waste disposal activity. The method also includes identifying and displaying a brand, a product, a material, a usage of the material and a service information from the received image using a data driven assisted vision-based component based on the entity identifier. The method also includes identifying a waste stream type in the first waste bin and the second waste bin.

Systems, computer program products, and methods are described herein for systems and methods for identifying electronic accounts based on near real time machine learning in an electronic network. The present disclosure is configured to: identify at least one data storage component associated with an at least one application; determine at least one current requirement of the data; receive at least one data segment from the data storage component; collect at least one current element from at least one data segment; reformat and aggregate the at least one current element and link the at least one current element to a unique entity identifier; apply a trained machine learning model to the at least one current element; generate a new data segment; and generate a propensity score based on at least one of unique entity identifier, at least one current element, or at least one new data segment.

The invention provides a report loading processing method, system and device in an industrial scene and a medium, and belongs to the technical field of report loading. Data source information associated with a service entity identifier is obtained; establishing a dynamic binding relationship between business entity attributes and data source fields, and generating an index metadata template; acquiring data source information associated with the service entity identifier through a data registration component; establishing a calculation mapping relation between the index field and the data source field; generating a standardized configuration file; persisting the generated standardized configuration file to a database; a three-party incidence relation index among an index instance identifier, a service entity identifier and a data source identifier is established, in actual industrial data processing, even if network faults and other problems occur, it can be guaranteed that configuration files are accurately updated, when service requirements change, calculation rules of corresponding levels can be rapidly positioned and adjusted, and the service requirements are met. And the flexibility and accuracy of data processing are improved.

The invention discloses a knowledge base retrieval method fused with a natural language large model, and relates to the technical field of artificial intelligence. Comprising the following steps that 1, a knowledge representation structure is constructed, and the knowledge representation structure comprises a knowledge base hypergraph based on knowledge fragments, entity identifiers and theme tags and a dense vector index recording dense vectors of the knowledge fragments; 2, analyzing an original natural language query input by a user, and performing matching in the knowledge base hypergraph based on the original natural language query to obtain an initial node set; 3, on the knowledge base hypergraph, determining a candidate knowledge fragment set and a corresponding multi-view local semantic abstract; and 4, calling the natural language large model, and generating a retrieval answer based on the target knowledge fragment set. According to the method, the accuracy, coverage and interpretability of knowledge retrieval can be remarkably improved.

An entity-level privacy system receives a query directed towards a shared dataset, the shared dataset comprising one or more data entries associated with one or more distinct entities, each entity of the one or more distinct entities being identifiable by one or more unique entity identifiers. The entity-level privacy system implements an entity-level privacy constraint, the entity-level privacy constraint comprising a dynamic aggregation constraint based on the one or more unique entity identifiers. The entity-level privacy system determines that the one or more unique entity identifiers satisfy a threshold condition comprising a minimum number of entities. The entity-level privacy system enforces the entity-level privacy constraint on the query and generates an output to the query based on the entity-level privacy constraint and the dynamic aggregation constraint while maintaining entity-level privacy associated with the one or more distinct entities.

Systems, methods, and other embodiments associated with a framework for multi-entity detection, data sourcing, and processing are described. In one embodiment, a multi-entity processing method includes parsing an electronic document being processed in a processing flow for a first entity to detect that the document also concerns a second entity based on entity identifiers in the document. In response to the detection, the method determines an inter-entity agreement that governs processing between the first entity and second entity by matching attributes of the document against stored agreement data structures. The method automatically orchestrates processing of an inter-entity transaction between the first entity and the second entity by generating execution steps according to sequential and parallel dependencies specified by the inter-entity agreement. And, the method generates electronic records in a system of the first entity and a system of the second entity that reflect execution of the inter-entity transaction.

Various aspects described herein relate to systems and methods for merging records from multiple database systems. A method may include comparing, by one or more processors, two or more records of a plurality of records. The plurality of records may be from a plurality of database systems. Each of the plurality of records may be a patient record or an employee record. The method may also include determining, by the one or more processors, that a threshold match exists between the two or more records based upon a set of threshold matching criteria. The method may also include merging, by the one or more processors, the two or more records, based upon determining that the threshold match exists, into a person database entity. A unique entity identifier may be associated with the person database entity.

According to an aspect of an embodiment of the present disclosure, a method may include receiving a quick response (QR) code from a computing device, the QR code including a first entity identifier, and analyzing the QR code to determine a request to initiate a data transfer between a first entity associated with the first entity identifier and a second entity, and to determine one or more characteristics associated with the data transfer. The method may also include, based on the one or more characteristics, identifying a first QR code processing flow from multiple QR code processing flows to follow to process the data transfer, and verifying the first entity in the transaction based on the first entity identifier of the QR code and based on the first QR code processing flow. The method may also include processing the data transfer between the second entity and the first entity based on the first QR code processing flow.

According to some embodiments, systems and methods are provided including a memory, a processing unit and program code to: create an event, the event including an item value for a given quantity of an item provided by each of a plurality of entities; receive an event target; extract an entity identifier from the event target for each entity from the event; generate one or more simulations based on a plurality of characteristic parameter constraints and the event target; generate a characteristic parameter value for each of the plurality of characteristic parameters via execution of a machine learning model trained with prior values of characteristic parameters for the plurality of entities; scale the generated characteristic parameter values to a common unit of measure; generate an output, including a quantity distribution of the given quantity to at least one of the plurality of entities. Numerous other aspects are provided.

Embodiments of the present disclosure provide for providing performance optimization predictions related to an entity dataset. Such embodiments may include selecting a candidate machine learning model from a plurality of candidate machine learning models based at least in part on (i) a data profile for an entity dataset associated with an entity identifier and (ii) a predefined model profile associated with the candidate machine learning model. Such embodiments may additionally or alternatively include generating a candidate feature set by modifying a predefined feature set for the candidate machine learning model based at least in part on the data profile associated with the entity identifier. Such embodiments may additionally or alternatively include generating one or more performance optimization data objects by applying the candidate machine learning model to the candidate feature set based at least in part on an objective rules set for the entity identifier.

The invention relates to the technical field of data analysis, in particular to a multi-mode heterogeneous data query method which comprises the following steps: receiving a query statement sent by a user side, performing lexical analysis on the query statement through an analyzer, and performing semantic check on the analyzed query statement to obtain an initial query statement; sending the initial query statement to a query optimizer to generate a corresponding query syntax tree; after the query syntax tree is sent to a Data-speed engine for prompt word engineering processing, indexing is carried out in combination with an index vector library to obtain an index result, and an execution plan is intelligently predicted based on the index result; inputting the intelligent prediction execution plan into a large language model system, outputting an execution code corresponding to a current execution task and operators of an available instruction set corresponding to different execution tasks in real time, and introducing an entity identifier into an attention mechanism of the large language model system for supervision and fine adjustment; and calling an operator corresponding to the execution code through a Data-speed JIT generator, and generating an execution result based on the operator.

Systems, computer program products, and methods are described herein for systems and methods for identifying electronic accounts based on near real time machine learning in an electronic network. The present disclosure is configured to: identify at least one data storage component associated with an at least one application; determine at least one current requirement of the data; receive at least one data segment from the data storage component; collect at least one current element from at least one data segment; reformat and aggregate the at least one current element and link the at least one current element to a unique entity identifier; apply a trained machine learning model to the at least one current element; generate a new data segment; and generate a propensity score based on at least one of unique entity identifier, at least one current element, or at least one new data segment.

Various embodiments of the present disclosure provide a user interface and a natural language interface for predictive models. The techniques may include receiving a user interface application programming interface (API) request that indicates an entity feature dataset associated with the entity identifier and / or an event progression model, receiving a model API request via a conversational user interface comprising a natural language query for interacting with the event progression model, receiving a simulated event risk data object for the entity identifier that is generated using the entity feature dataset, the event progression model, and the natural language query, and initiating a rendering of an event progression graphical visualization via the conversational user interface that is based on the simulated event risk data object.

Systems and methods for securely managing digital assets and generating immutable transaction records linked to decentralized identifiers (DIDs). A processing system operating within a distributed ledger technology (DLT) network receives digital asset transaction requests including entity identifiers associated with unique DIDs. The processing system establishes cryptographically secured connections with digital wallets corresponding to these entity identifiers, retrieves digital asset balances from a distributed ledger—including non-transferable cryptographic tokens linked to the DIDs—and executes smart contracts to cryptographically modify digital asset balances. Immutable transaction records comprising timestamps, cryptographic hashes linked to DIDs, and event reference identifiers are generated and stored within the distributed ledger, preserving cryptographic integrity. Authorized access to these immutable records is governed by access permissions encoded in the executed smart contracts, enhancing security, transparency, and compliance in decentralized digital asset management.

The invention provides an electronic archive processing method and device based on multi-modal data. The method comprises the following steps: acquiring normalized electronic archive data in a current period; performing layout analysis on the electronic archive data, and identifying text information of the electronic archive data; utilizing a natural language processing model to extract entities and corresponding business elements in the text information; matching the entity with an existing entity in a stored electronic archive knowledge graph, and determining a unique entity identifier; based on the semantic features of the electronic archive data, the business elements and the text information, determining semantic relationships among different electronic archives; updating the stored electronic archive knowledge graph by taking the unique entity identifier as a node and the semantic relationship as an edge to obtain a new electronic archive knowledge graph; and based on the business elements and the new electronic archive knowledge graph, classifying the electronic archive data to obtain a classification result and corresponding cataloguing information. According to the method, intelligent extraction, semantic association and automatic cataloguing of the electronic archive elements are realized.

The invention belongs to the field of big data, and relates to a directory path analysis method, which comprises the following steps: acquiring directory information of a file system, and creating a metadata cache in a metadata client for storing the directory information; receiving a directory path analysis request, and judging whether a directory accessed by the directory path analysis request exists in the metadata cache or not based on the directory information; if the directory accessed by the directory path analysis request exists in the metadata cache, acquiring an entity identifier corresponding to the directory from the metadata cache; sending the entity identifier to a metadata server, so as to query result data corresponding to the directory from a key value database through the metadata server; and returning result data corresponding to the directory path analysis request. The invention further provides a device, equipment and a medium. In addition, the invention also relates to a block chain technology, and directory information and result data can be stored in a block chain. According to the method, the directory path analysis time delay can be reduced, and the problem of near root hotspots is solved.

The invention relates to the technical field of automatic driving, particularly provides a data query method, intelligent equipment and a storage medium, and aims to solve the technical problem that an existing ElasticSearch-based tag query method cannot meet actual requirements. In order to achieve the purpose, the data query method comprises the steps that label data is obtained, and the label data at least comprises a label name and an entity identifier; acquiring a first mapping relationship between the entity identifier and the tag name based on the tag data; obtaining a second mapping relationship between the tag name and the bitmap based on a first mapping relationship between the entity identifier and the tag name; and performing data query based on a second mapping relationship between the tag name and the bitmap. Therefore, the storage space of the data is effectively reduced, the query speed is effectively improved, and the data query performance of the equipment is improved.

In some implementations, a device may receive, from one or more data sources, information indicating a plurality of data sets, where the plurality of data sets indicate information associated with respective physical locations or online locations. The device may identify a data set, from the plurality of data sets, that indicates information associated with an online location, where the information includes at least one of an entity name, an address, a phone number, a uniform resource locator, an entity identifier, or metadata. The device may parse the data set to identify information for a set of features. The device may analyze the information for the set of features to determine a brand associated with the online location. The device may pair the online location with the brand in the database such that the online location is linked with a first physical location of the brand in the database.

A computer implemented method for determining entity attributes. The method comprises determining one or more entity identifiers, determining an entity server address of the entity based on the one or more entity identifiers, wherein the entity server address points to an entity server; verifying the entity server address transmitting a message for request for information to the entity server address, receiving entity information from the entity server; and providing, to a machine learning model, the received entity information. The machine learning model is trained to generate a numerical representations of entities based on the entity information.

The invention discloses a file generation method and device based on a knowledge base, equipment and a storage medium, and the method comprises the steps: in response to a file generation instruction, carrying out semantic feature extraction processing on a target file description text carried in the file generation instruction, and obtaining a target file semantic feature; matching a target entity identifier corresponding to the target file semantic feature from a preset knowledge semantic feature library; searching a target knowledge node corresponding to the target entity identifier from a preset knowledge graph database, and obtaining target entity attribute data stored in the target knowledge node; according to the file generation instruction and the target entity attribute data, generating a target file corresponding to the file generation instruction; according to the method, code files such as software codes or software documents can be quickly and accurately generated, and the software development efficiency of developers is effectively improved.

The embodiment of the invention provides a data processing method and device, equipment and a storage medium. The method described herein includes: determining whether a target data entry includes a first entity identifier corresponding to a first entity type; in response to a first entity identifier included in the target data entry, determining a first region corresponding to the first entity identifier based on first mapping information, the first mapping information indicating a first mapping relationship between a first group of entity identifiers and a group of preset regions, and the first group of entity identifiers corresponding to a first entity type; and associating the target data entry to the first region. On the basis of the mode, the region corresponding to the data entry can be automatically identified, so that region-based data management can be supported.

The invention discloses a financial sharing center business and financial integrated process automatic processing system, which belongs to the technical field of financial processing, and specifically comprises: a business event receiving module receives a business and financial event flow containing a business operation code, a transaction entity identifier and a transaction track description; the business and financial trajectory analysis module matches the reference value flow model, segments the reference value flow model based on the business nodes, and generates a trajectory model; a voucher reconstruction triggering module calls a historical transaction feature set during mapping conflict; the voucher template fusion module screens historical templates, removes conflict fragments, calibrates the curvature radius and outputs a reconstruction template; the voucher generation module analyzes the initial coordinate, converts the subject mark group into a voucher entry, and records value fluctuation as a verification factor; the traceability output module fuses the coordinate sequence, the operation record and the environment snapshot to generate a traceability identifier, and outputs the traceability identifier to a financial sharing center; according to the invention, the accuracy, adaptability and traceability of the business and financial integrated process processing of the financial sharing center are improved.

Techniques are provided for processing rules expressed in a domain-specific language. A computing system may receive a rule including an entity identifier, a property of the entity, and one or more logical operators delimited by syntactic tokens of the language. The rule is parsed into an abstract syntax tree (AST) representation that structurally represents entities, properties, and conditional logic, and the AST is transformed into an executable function configured to evaluate input data objects defined according to a machine-readable data model. Execution of the function on input data produces a logical outcome along with intermediate results corresponding to evaluations of individual conditions and logical operators. The computing system generates an execution trace comprising the logical outcome and the intermediate results, the trace being stored in a structured log retrievable to provide evidence of how outcomes were determined.

Zero trust network security is provided without modifying the underlying network infrastructure. A first entity at a first node in a network environment obtains an entity identifier and host certificate from a second entity installed on a second node. A determination is made as to whether the host certificate is valid based on a firewall policy and an intermediate certificate that was issued to the first entity. A determination is also made as to whether the entity identifier is valid based on a known infrastructure of the network environment. If the host certificate and entity identifier are valid, communications between the first and second entities can be allowed, while communications are blocked if at least one of the host certificate and the entity identifier is not valid.

Systems and methods are disclosed for centralized management and application of distributed parameters through a data generation platform. The platform obtains an aggregate rule set including zone-specific parameter sets and zone classifiers for multiple zones, where the rule set determines allocation metric values associated with transaction values of resources. Upon receiving input data including entity identifiers, resource identifiers, and transaction data, the platform maps the data to zones by comparing zone classifiers, filters the aggregate rule set to generate a zone-specific rule subset, and applies the subset to generate allocation metric values. The platform can process requests synchronously through online message queues and / or asynchronously for bulk processing. Machine learning models can use distribution patterns across multiple resource sets to generate overall allocation metrics and modification suggestions. The platform can provide a graphical user interface for displaying allocation metrics, resource information, and / or supplemental explanations.