173 results about "Data field" patented technology

System, apparatus, and computer program products are disclosed for protected display of a shared visual data object for an audiovisual conference session. In one aspect, a method includes receiving the shared visual data object, wherein the shared visual data object comprises visual data associated with one or more shared user interfaces; determining, using user interface generation circuitry and based on the shared visual data object, one or more protected data values associated with one or more protected data fields; generating, using the user interface generation circuitry, a masked visual data object based on each masked representation and each spatial coordinate set; and causing, using the user interface generation circuitry and / or the communications hardware, the masked visual data object to be displayed using the audiovisual conference session.

A communication device communicates a physical (PHY) frame including a preamble and a data field. The preamble includes a Legacy Short Training Field (L-STF), a Legacy Long Training Field (L-LTF), a Legacy Signal Field (L-SIG), an EHT (Extremely High Throughput) Signal Field (EHT-SIG-A), an EHT Short Training Field (EHT-STF), and an EHT Long Training Field (EHT-LTF), and the EHT-SIG-A includes at least one subfield indicating that the communication device performs communication in a frequency band more than 160 MHz.

This application discloses a data processing method, apparatus, and electronic device. Relating to the field of big data, the method includes: acquiring multiple data tables in a target database, acquiring fields located in the multiple data tables, obtaining M fields; acquiring feature information of the M fields, clustering the M fields based on the feature information, obtaining multiple target field clusters; acquiring the number of identical fields in each target field cluster, obtaining a count array for each target field cluster; determining a baseline field in each target field cluster based on the count array for each target field cluster, and updating non-baseline fields in the target database that are located in the same field cluster as the baseline field, obtaining an updated target database. This application solves the problem in related technologies where inconsistencies exist in data fields related to the same business content in the database, leading to low accuracy in processing business data.

The application discloses a sensitive data desensitization method for a bank service scene, and belongs to the technical field of data desensitization, and comprises the following steps: obtaining a query result set, and traversing fields in the query result set; identifying whether the fields belong to sensitive fields based on semantic features of the fields, wherein the sensitive fields at least include one or more types of a name field, a mobile phone number field, an ID number field, a bank card number field and an account number field; selecting corresponding desensitization rules from a preconfigured desensitization rule set according to types of the identified sensitive fields; performing field-level desensitization processing on the query result set, replacing sensitive data field by field according to the selected desensitization rules, and generating a desensitized result set. By identifying dynamically changing query result fields, desensitization omission caused by non-uniform field naming is avoided, and by using a differentiated desensitization strategy, sensitive information is protected while data service availability is retained.

Dynamic obfuscation of information displayed on a user interface. In examples, the user interface is generated by a host application, and a data field obfuscation tool is provided as an extension of the host application and configured to dynamically obfuscate one or more data fields displayed on the user interface. In examples, the data field obfuscation tool can be configured, enabled and / or disabled by a user via another user interface.

The application relates to the technical field of artificial intelligence and password security, and discloses a large language model privacy reasoning method and system capable of verifying no-log behavior and output integrity; the method first sends an encrypted question to the server by the client; the server loads a pre-compiled no-log reasoning program in a trusted execution environment, decrypts and reasons; the reasoning is completed, and zero is cleared; a joint hash value is obtained, a custom data field of a remote proof report is embedded, and a program integrity measurement value is obtained; finally, the response and the proof report are returned to the user. The user client compares the local joint hash value with the custom data in the proof, and verifies the program measurement value, so that it can be confirmed that the log function is not enabled in this reasoning, and the received model response is generated by the specified model for the question, and is not tampered with. The application first realizes double-verified guarantee of privacy and output credibility in large model services, and significantly improves the trust degree of the user for the AI system.

A system is provided for enhancing data fields within exchangeable structured data files of property listings. The system includes a server connected to a property management system via a wide area network. The server comprises a processor executing controllers, including a data extraction and parsing controller that identifies incomplete data fields and extracts contextual data. A data enhancement controller interacts with a natural language processing sub-controller and a large language model sub-controller to generate and process queries, populating the incomplete data fields. The system also includes a data pass-back controller that transmits the enhanced structured data files back to the property management system. The system automates the identification, enhancement, and updating of data fields, improving the accuracy and completeness of property listings.

The application discloses a kind of belt scale data intelligent acquisition processing systems, specifically related to belt scale data field, including working condition perception module, state identification module, validity judging module, abnormality suppression module, multi-dimensional classification module, output module and multi-service interface module.A kind of belt scale data intelligent acquisition processing systems solves the core defects of prior art, such as no systematic validity judgment before collecting data into business, prone to cause measurement misuse and business process misjudgment by validity judging module;By abnormality suppression module, the action selection logic matrix is constructed with running condition semantic label as row index and differentiated validity level as column index, precise adaptation of abnormal control is realized, and the extensive processing mode of existing technology is abandoned, such as unified filtering and unified rejection;Through multi-dimensional classification module, multi-stage branch classification decision is executed, and multi-dimensional labeling classification of sampling data for different business processes is realized.

A system and a method for data privacy compliance are disclosed The system is configured to receive, from a client device, information associated with a data fiduciary, the information comprising a digital offering provided by the data fiduciary, a plurality of labels corresponding to a plurality of data fields associated with the digital offering, at least one reference data privacy policy document associated with the data fiduciary, or any combination thereof. Further, system determines one or more labels of the plurality of labels used for collecting personally identifiable information (PII) based on at least one predefined criteria, and determines one or more purposes associated with the collection of the PII and a compliance of the one or more determined labels with the one or more determined purposes. Based on the determined compliance of the one or more labels, the system performs at least one action.

The invention relates to two computer-implemented methods for storing medical data. One of the methods comprises: continuously receiving successively generated medical data packets (2), each comprising a start time (3), an end time (4), and a data packet value (5) that describes a sum of medical measurements (1) acquired between the start time (3) and the end time (4); determining a total value (6) for each received medical data packet (2) by adding the data packet value (5) according to the respective medical data packet (2) with the data packet values ​​of at least some of the received medical data packets (2) generated prior to the respective medical data packet (2); and storing medical data by storing the respective medical data packet (2) together with the respective determined total value (6) in a linked list (7) and / or in a data field (8).

There is provided a method, device and computer program for navigating an agent in a multidimensional space, the method comprising: generating a data field that spatially corresponds to a multidimensional space in which an agent resides, wherein the data field is populated with observation comparison measure data with respect to a stored observation, the stored observation including second sensor data describing an environment of a first position in the multidimensional space, wherein the generating the data field comprises: obtaining a plurality of current observations made by the agent at different respective positions in the multidimensional space, the current observations including first sensor data describing an environment of each of the different respective positions in the multidimensional space; comparing each of the current observations with the stored observation to obtain respective observation comparison measure data for the different respective positions in the multidimensional space; and populating the data field with the observation comparison measure data according to the different respective positions in the multidimensional space; the method further comprising: determining a reliability measure associated with the stored observation by performing a reliability check of the data field; and managing a digital map of the multidimensional space by maintaining, updating, or deleting information in the digital map regarding the stored observation based on the reliability measure.

Systems and methods receive, from client application(s), request(s) to access data from platforms, translate the request(s) to API calls, the translating utilizing access protocols of each of the respective platforms. The data are retrieved and aggregated from data sources of each of the platforms, the data being aggregated into a standardized format. The systems and methods ascertain which data fields of the aggregated and standardized data include information relevant for analysis, the ascertaining being based on predefined data categories. Accuracy of relevant data located in the data fields that include the information relevant for analysis is verified and the relevant data are incorporated, based on accuracy of the relevant data being verified, into data columns for display via a centralized graphical user interface dashboard. Display, via the centralized graphical user interface dashboard, of one or more graphical elements incorporating the relevant data is initiated.

A method for wireless communication, a transmitter, and a receiver are provided. The method includes the following. A transmitter transmits a first ultra-high reliability (UHR) enhanced long range (ELR) physical layer protocol data unit (PPDU). The first UHR ELR PPDU at least includes a legacy short training field (L-STF), a legacy long training field (L-LTF), a legacy signal (L-SIG) field, a repeated L-SIG (RL-SIG) field, a universal signal (U-SIG) field, a UHR-STF, a UHR-LTF, a data field, and a packet extension (PE) field, where the data field is transmitted at a granularity of a first frequency-domain resource unit with N times duplication in a bandwidth of the first UHR ELR PPDU, and N equals to 4.

A method and apparatus for placing data in a hierarchical memory are disclosed. A computing device comprising a hierarchical memory composed of a first memory and a second memory, wherein the first memory has a faster access speed than the second memory but has a limited capacity, and the second memory has a slower access speed than the first memory but provides a larger capacity, and a method for placing a packet received through a network into the hierarchical memory comprises: a step of extracting layout information from the payload of the packet; a step of storing the layout information in a queue corresponding to the socket in which the packet was received; a step of determining a destination memory among the first memory or the second memory based on the size of a data field included in the layout information stored in the queue; a step of allocating a space corresponding to the size of the data field to the determined destination memory using a memory allocator; and a step of reading data from the socket in which the packet was received and storing it in the allocated space.

The application provides a new energy grid connection data intelligent auditing method and system, relating to the technical field of new energy grid connection approval. The method comprises the following steps: performing semantic metadata encapsulation on key data fields in the data to obtain a first semantic data package; attaching loss information to the first semantic data package to obtain a second semantic data package; performing informed data conversion on the key data fields in the second semantic data package based on a preset semantic mapping rule to generate view data conforming to the next department's business requirements; storing the first semantic data package and performing cross-department semantic consistency auditing based on the semantic metadata in the first semantic data package and the view data. The method aims to solve the problem of cross-department semantic inconsistency of new energy grid connection data, effectively ensures the consistency of cross-department data semantics, reduces errors caused by interpretation bias and interface loss, and improves the auditing efficiency and intelligent level.

Embodiments of the present application disclose an interactive data construction method, a training method of an interactive prediction model, and related devices. The interactive data construction method comprises: obtaining a first interface sequence collected by a sample application when the sample application executes a sample interactive task; performing field masking based on the correlation between each data field in the interface structure data of the sample interactive interface and the sample interactive interface to obtain the masked structure data of the sample interactive interface; selecting a first interactive interface to perform a deduplication operation based on the first similarity between the data feature representations extracted from the masked structure data of each sample interactive interface, to obtain a new first interface sequence; dividing based on the new first interface sequence to obtain a second interface sequence, and obtaining first sample interactive data of the interactive prediction model. The above scheme can improve the construction quality of the interactive data and help improve the training effect of the interactive prediction model.

In a field bus system, the sub-users (3) each have a sub-connection (33) and a functional connection (34) connected to the sub-connection (33), wherein in each sub-user status information is acquired which indicates whether the functional connection is active or inactive. A telegram output by a master user in the field bus system has at least one data report, wherein the status information of the sub-users (3) addressed in the data report is acquired together with the telegram. Furthermore, a counter field of the data report is changed by the sub-connections (33) of the sub-users (3) which have processed the useful data field in a manner predefined for the data transmission operation performed in each case. The data report is assigned an expected value of the counter field which arises when the useful data field has been processed by the sub-users (3) addressed in accordance with the data transmission operation predetermined in the command field. The master user (2) then determines the expected value of the counter field of the data report on the basis of the status information acquired with the telegram and compares it with the value of the counter field in the data report processed by the sub-users (3) addressed in order to determine a processing error in the data report processed by the sub-users (3) addressed in the event of a deviation.

The scheme discloses a government affair big model-based Internet of Things data sharing and data quality inspection method, system, equipment and storage medium, and a data department inputs a data application composed of natural language through a data sharing module, the data application includes data sharing requirements and data quality inspection requirements, a data sharing model extracts the data sharing requirements in the data application, intelligently generates required data tables, data fields and other contents, and a data quality inspection model intelligently identifies and extracts specific quality inspection requirements of a user, so that quality inspection of government affair data is completed, and the fine operation precision of data quality inspection is improved. The method only needs the data department to input data sharing and quality inspection requirements through a front-end interaction module, so that standard data applications and data quality inspection rules are automatically generated, so that the threshold and cost of data use of the data department are reduced, the rules of data quality inspection are increased according to specific data use requirements of the data department, so that the fine operation precision of data quality inspection is improved.

The present invention relates to a method 100 and a system configured to generate several target videos from an input video using different sets of target features. The invention involves obtaining 110 the input video, selecting 120 the sets of target features, comparing 130 with the input feature set, and generating 140 sets of encoding parameters to modify the data in the data fields to be modified. Then, encoders are initialized 160 with these sets of encoding parameters, and a single decoding 170 is performed on the input video. The extracted video, audio, and data frames are then encoded 180 in parallel using the initial N encoders. Finally, each elementary stream is multiplexed 190 to obtain 191 the target videos. Fig. 1

A machine-learning based (ML-based) system and method for automatically extracting one or more data fields from one or more documents, are disclosed. The ML-based system includes a document obtaining subsystem to obtain documents, a document pre-processing subsystem to generate pre-processed data, a field identifying subsystem to identify data fields using a trained ML model, and a field extracting subsystem to extract financial information. The ML-based system also comprises an output subsystem to deliver the extracted data to end users via user interfaces. The ML model is trained using historical documents, labelled data fields, and features such as distance-based features, direction-based features, dimension-based features, positional features, and value-based features. The M-based system employs hyperparameter optimization, noise removal, and accuracy assessment mechanisms to enhance performance. This ML-based system provides a scalable, accurate, and automated solution for financial information extraction, ensuring efficiency, adaptability, and seamless integration with enterprise systems.

This application discloses a method and system for constructing a dynamic graph structure based on offline semantic constraint mapping, relating to the fields of semantic information processing and graph structure modeling. The method first constructs a predefined semantic node system to define semantic node identifiers, semantic core descriptions, semantic boundary constraints, and inter-node relationships. In the offline phase, semantic matching is performed between data field descriptions and semantic core descriptions of semantic nodes to generate and solidify an offline semantic mapping table. In the runtime phase, data fields are mapped to corresponding semantic nodes according to the offline semantic mapping table, and standardization and aggregation calculations are performed to obtain node state values. Subsequently, the graph structure is initialized based on node relationships, and edge weights are dynamically calculated and edge activation states are determined based on node states, thereby constructing a dynamic graph structure that responds to the current batch of data. This application achieves consistency and repeatability of semantic mapping by solidifying semantic mapping relationships offline.

The application discloses a kind of equipment health degree evaluation methods, the data field of object equipment is established in this method, the structure tree of object equipment is determined according to different levels;Download the historical data of all data items;According to the fault number determined by the historical alarm number or maintenance record statistics of different data, the initial weight is determined in proportion according to the same parent node under the child node;Build benchmark data feature matrix, calculate score scale, download the data of each data item, calculate the KPI score of data item, calculate KPI dynamic weight, respectively calculate the KPI score of part, equipment, system and the corresponding dynamic weight.This method takes equipment structure as model input, and is associated with data item, quantifies the initial weight, calculates dynamic weight, uses unlabeled data modeling, effectively improves the reliability of equipment health degree evaluation.

Methods and systems for retrieving data from at least one database based on a query input related to a clinical trial. A system processes a received query input related to a clinical trial using a pre-trained language model neural network. The neural network generates a structured representation of the query input. The system maps a first data field of the structured representation to a first column name and maps a second data field of the structured representation to a second column name. The system generates a database query based on (i) a database schema, (ii) the first column name, (iii) the second column name, (iv) data values associated with the first data field, and (v) data values associated with the second data field. The database query specifies an operation for joining data associated with the first column name with data associated with the second column name.

This application provides a method, apparatus, equipment, and storage medium for efficient on-site quality control of node data. The method includes: selecting target node synthetic seismic data for a target work area; performing maximum amplitude limitation processing on the target node synthetic seismic data to obtain node synthetic seismic data to be processed, wherein the recording length of the target node synthetic seismic data is within a preset time period and the number of traces is within a preset range; for each shot's node synthetic seismic data to be processed, determining whether the current shot's synthetic seismic data is an empty file based on the amplitude of the sampling points in each trace of the current shot's synthetic seismic data, and removing the empty files; for the synthetic seismic data after removing the empty files, statistically analyzing the synthetic quality and the working status of nodes in multiple domains based on the non-working trace head attributes and the multi-domain overlay profile trace head attributes, and performing high-quality node data synthesis checks and node status quality control for massive 3D node data.

This invention discloses a method for implementing a multi-source heterogeneous data transformation pipeline for time-series databases, comprising: parsing, transforming, and modeling the original data according to configuration rules to obtain a streaming transformation pipeline including a parsing stage, a transformation stage, and a modeling stage connected in sequence; in the parsing stage, parsing various data formats of the original data and converting the fields extracted after parsing into data in a unified columnar storage format; in the transformation stage, filtering, mapping, calculating, and type conversion operations are performed on the data processed in the parsing stage; in the modeling stage, the data processed in the transformation stage is mapped to the super table and sub-table structure of the time-series database according to the configuration rules; the modeling process includes: dynamic table name generation based on template expressions, extraction of label fields and data fields, and batch data assembly; the configuration rules specifically include loading, validating, and hot-updating the configuration file of the streaming transformation pipeline.

Systems and methods for generation, categorization and rendering of custom dynamic view layouts of secure customer data of a data storage, including systems and methods for generation and management of multiple view layouts of secure customer data enabled using a view layout profile. Custom dynamic view layouts are established, wherein each custom dynamic view layout corresponds to a data object type and comprises at least one page including one or more sections used to organize a subset of a data object of the data object type from the data storage. Custom dynamic view layouts are assigned to view layout profiles. The view layout profile is referenced to identify a matching object type to the accessed data object record from the data storage. The matching custom view layout is used to render and display the subset of data fields defined in the identified approved custom dynamic view layout.

This application relates to an intelligent visualization processing method, apparatus, device, and medium for geological data. The method includes: acquiring raw interactive behavior data, an information entropy map, and a competition hypothesis map; performing structured processing on the raw interactive behavior data to obtain a high-dimensional interactive behavior sequence; inputting the high-dimensional interactive behavior sequence, the information entropy map, and the competition hypothesis map into a pre-trained anchoring bias quantification model to obtain a dynamic anchoring index heatmap; based on the dynamic anchoring index heatmap and the competition hypothesis map, using a real-time shader in a 3D graphics rendering pipeline to convert risk probability values ​​and likelihood quantification values ​​into dynamic visual parameters superimposed on a 3D geological data field, and generating a 3D geological data field that enhances the 3D visualization scene; based on the dynamic anchoring index heatmap, combined with specific interactive events in the high-dimensional interactive behavior sequence, adjusting interaction damping parameters and generating a comparative data analysis view. This method can improve the comprehensiveness of the geological interpretation process.

In an approach, a processor performs document layout analysis on a document generating a plurality of textual regions; extracts characteristics from each of the plurality of textual regions and associates the respective characteristics to the respective textual region as metadata; classifies each of the plurality of textual regions as an optical character recognition (OCR) region, non-OCR valuable region, or non-OCR non-valuable region using a classifier; performs OCR on each OCR region generating an OCR output; identifies associated constant OCR data from a constant OCR data repository for each non-OCR valuable region; merges the associated constant OCR data with the OCR output generating a complete OCR data for the received document; performs data extraction on the complete OCR data to identify data fields and key-value pairs generating extracted data; and determines whether the extracted data is valid based on a set of rules.