190 results about "Data element" patented technology

Autonomous generation includes generating, with a large language model (LLM), an action list for performing an extraction task from at least one website. Generating the action list may include for each action in the action list performing operations. The operations include capturing current webpage data including a screenshot of a current webpage and a set of interactable widgets, obtaining, using an LLM processing the current webpage data, a next action attribute set, adding the next action attribute set, and performing the next action. Autonomous generation also includes traversing the action list to filter out at least one superfluous action, generating, after filtering, a set of machine executable instructions from the action list, and storing the set of machine executable instructions associated with a task identifier. Autonomous generation also includes executing the set of machine executable instructions to extract a data element from the at least one website.

Aspects of the subject disclosure may include, for example, a media consumption database that stores data elements describing conditions under which electronic media content is consumed by a user on an electronic device. A search of the media consumption database based on at least a portion of the conditions may result in at least a portion of the electronic media content to be re-presented to an electronic device of the user Other embodiments are disclosed.

A computer-implemented method of processing repeat B-scans of an imaged region of a body part, the imaged region including an anatomical feature in a first subregion and vasculature of interest in a second subregion of the imaged region, to generate cropped B-scans for use in generating OCT angiography data which provides a representation of the vasculature of interest, the method comprising processing each B-scan of the repeat B-scans by: selecting a subset of the data elements of the B-scan such that the selected data elements are distributed along a representation of the anatomical feature in the B-scan; using the selected data elements to define a respective subregion of interest in the B-scan, which includes OCT data acquired from the second subregion containing the vasculature of interest; and cropping the B-scan to leave the subregion of interest in the B-scan.

The application provides a data acquisition method, a data acquisition device and a safety monitoring system. The method comprises the following steps: acquiring a plurality of real-time monitoring messages, and converting the real-time monitoring messages into a preset format to obtain a plurality of target monitoring messages, wherein the preset format at least comprises a message name and a message structure; determining a plurality of target data element standards according to the plurality of target monitoring messages, one target monitoring message corresponds to one target data element standard, and one target data element standard corresponds to at least one target monitoring message; constructing a plurality of data elements according to the target monitoring messages and the corresponding target data element standards, one target monitoring message at least corresponds to one data element, and the data element is used for unifying the name and format of the data in the target monitoring message; and storing each data element in a corresponding database. The method solves the problem that, in the prior art, due to different manufacturers of monitoring sub-stations, the data naming and data structure are not unified, so that it is difficult to perform comprehensive analysis on multiple disasters.

Example networks and methods are provided for defining a common data model for redundant data in a data specification. One example method includes accessing a message standard, the message standard having multiple message types, each message type including a plurality of classes, each of the classes defined by an .xsd file; splitting the .xsd files into named complexTypes, where each .xsd file includes one complexType which is named for the one complexType; updating references to duplicate named complexTypes to reference an unnumbered superset complexType; and absorbing each of the unique data elements of the duplicate named complexTypes into the unnumbered superset complexType.

A data processing system includes a memory accessing agent and a memory controller coupled to the memory accessing agent. The memory controller includes an ECC check circuit for detecting errors in a data element and extracting metadata from an error correcting code, in which the detecting and extracting includes forming a plurality of error statuses based on the data element and the error correcting code for different combinations of metadata, and picking a final status and final metadata based on the plurality of error statuses.

Various embodiments are directed to apparatuses, methods, computer program products, and systems related to multi-factor digital transmission screening. In some embodiments, an outbound digital transmission originating from a monitored enterprise management system may be detected. One or more data elements associated with the outbound digital transmission may be applied to one or more anomalous transmission prediction models to generate a prediction associated with the outbound digital transmission. The one or more anomalous transmission prediction models may comprise at least a contextual analytical model configured to generate the prediction associated with the outbound digital transmission based at least in part on historical digital transmission activity data based on a plurality of past digital transmissions originating from the monitored enterprise management system. Responsive to the prediction corresponding to an anomalous transmission prediction, performance of one or more data exfiltration mitigation actions to mitigate risk of data theft may be initiated.

This invention discloses a privacy-preserving energy-data collaborative optimization method and system for Virtual Power Plants (VPPs), relating to the field of virtual power plant operation optimization technology. The method includes establishing an outer master-slave game model to obtain strategic parameters output by the model; combining these strategic parameters with an inner multi-timescale operation optimization model to obtain operational data; constructing a bidirectional coupling feedback mechanism and a Stackelberg equilibrium mechanism using the strategic parameters and operational data; and employing a distributed collaborative solution method based on the bidirectional coupling feedback mechanism and Stackelberg equilibrium mechanism to obtain the VPP privacy-preserving energy-data collaborative optimization results. This invention constructs a bidirectional closed-loop coupling framework, achieving deep integration and collaborative value-added of data element value and energy physical value.

The application discloses a kind of software evaluation method, device, equipment and storage medium, and specifically discloses: receiving the software requirement document uploaded by user, and the software requirement document is parsed, and target analysis data is extracted;Target analysis data includes function item description, data element type and file reference type;Based on function item description and interface image associated with function item description, a multi-modal fusion analysis technique is used to classify the function items, resulting in a classification result;The function items are checked for duplicates, resulting in a first inspection result, and the data element types and file reference types are checked for form, resulting in a second inspection result;Form checking includes illegal character checking and character duplication checking;According to the classification result, the first inspection result and the second inspection result, a function point analysis table is generated, and a software evaluation report is generated based on the function point analysis table.

The embodiment of the application provides a data element sharing transaction system based on a low-code platform, and relates to the technical field of data element sharing transaction technology.The method comprises a modeling module, a compiling engine and an execution sandbox cluster.Through the application, the problems of low system security and real-time performance are solved, and the effect of improving security and real-time performance is achieved.

The invention relates to a method for training a machine learning model for an autonomous driving function, in particular for behavior prediction and / or behavior planning and / or for tracking one or more vehicles, the method comprising the steps of: providing (S1) a training data set with training data elements, each comprising scenario data (204) as training input data and associated ground truth data; determining (S2) latent feature vectors for the scenario data (204) using a feature embedding unit; clustering (S3) the training data elements based on the latent feature vectors into scenario clusters (202) using a clustering algorithm;Determine (S4) scenario cluster weights by applying the scenario data of the training data elements to the machine learning model to be trained and comparing the output data of the machine learning model thus generated with the ground truth data of the respective training data elements; determine a performance measure and / or a loss function based on the comparisons between the output data and the ground truth data and determine or adjust the scenario cluster weights based on the performance measure and / or the loss function; train (S5) the machine learning model with the training data set sampled taking into account the scenario cluster weights; and deploy (S6) the trained machine learning model.

A read-only memory (ROM) device comprises an active region extending along a first direction and a plurality of gates extending above and across the active region along a second direction perpendicular to the first. The plurality of gates, together with the active region, constitute a plurality of transistors. Each of the plurality of transistors is configured to store a data element. The active region extends continuously beneath and across more than four conductive gates of the plurality of gates.

The disclosed embodiments include computer-implemented apparatuses and processes that provision, in real-time, targeted recommendations based on decomposed structured messaging data. For example, an apparatus receives a message that includes elements of message data disposed within corresponding message fields. The message data may characterizes an exchange of data requested by a first counterparty from a second counterparty and associated with a first product provisioned to the second counterparty by the first counterparty. Based on elements of message data, the apparatus determines a value of a parameter that characterizes at least one of the second counterparty or a relationship between the second counterparty and the first counterparty, and transmits digital content associated with the parameter value to a device operable by the second counterparty, which presents a portion of the digital content within a digital interface.

A system and method for determining a geographical location of one or more computing devices may include coupling a first computing device with one or more second computing devices, obtaining two or more position data elements representing geographical locations of the computing devices, and calculating a refined position data element of the first computing device based on the received position data elements. The system may include a non-transitory memory device, where modules of instruction code are stored, and at least one processor associated with the memory device, configured to execute the modules of instruction code. The method may include associating the at least one processor with a vehicle module of a vehicle and sending the refined position data element to the vehicle module, where the vehicle module may be configured to, for example, control a steering system of the vehicle.

This application relates to a hardware-implemented sorting method, apparatus, computer device, readable storage medium, and program product. The method includes: acquiring a first data sequence to be sorted, where each data element in the first data sequence contains a key value and its position index within the sequence; concatenating the key value and position index of each data element according to a preset sorting direction to obtain the first data elements to be sorted; and performing hardware parallel sorting on the first data elements using a bitonic merge sort network to obtain a sorted second data sequence, wherein the sorting order of the data elements in the second data sequence retains the relative positional relationships in the first data sequence. This allows the primary sorting order to be determined based on the key value, and the relative order of data with the same key value to be determined based on the positional information. While maintaining sorting stability, it inherits the hardware parallel advantages of bitonic merge sort, resulting in fast sorting speed suitable for real-time video decoding requirements.

A processing system and method for executing arithmetic and conversion operations involving 16-bit brain floating-point (BF16)-formatted data are described. An instruction specifying either an arithmetic or conversion operation and a first data element in BF16 data format are received. For arithmetic operations, the exponents of the data elements are aligned, and a result is generated using the aligned exponents. For conversion operations, the mantissa of the BF16 data element is scaled based on its exponent, and the element is converted to a second data format, such as FP32 or a reduced-precision format, using precision-aware scaling and rounding. The result is stored in operations registers, such as for additional processing.

Techniques are disclosed relating to a graphical user interface (GUI) that is operable to depict data lineage information in levels. In some embodiments, data lineage information may specify a directed graph that is indicative of a data lineage associated with a plurality of data elements. For example, in the data lineage information, the plurality of data elements may be represented as a corresponding plurality of nodes and, in the directed graph, the plurality of nodes may be connected by edges in a manner that is indicative of the data lineage relationships between the plurality of data elements. In various embodiments, the disclosed techniques may generate a data lineage GUI that, for a selected data element of the plurality of elements, is usable to navigate different levels of the data lineage in an upstream and downstream direction relative to a particular level of the selected data element.

Training an artificial intelligence model to categorize data by sensitivity and for applying the model to selectively protect sensitive portions of multimodal datasets. Sensitive training data can be obfuscated with synthetic noise or randomized errors to preserve confidentiality while enabling the model to learn patterns correlated with sensitivity. The trained model is validated on labeled data and can be refined as classification standards evolve. In operation, the classifier assigns sensitivity levels to data elements and directs tiered protection. Elements assigned to a higher relative sensitivity classification level are protected using post-quantum key establishment, for example a key encapsulation mechanism, combined with symmetric authenticated encryption of payloads, and associated metadata is authenticated using a post-quantum digital signature scheme. Less sensitive elements can be protected using conventional symmetric encryption for efficiency. This approach automates sensitivity classification, optimizes cryptographic resource allocation, and improves confidentiality and integrity for simulation and mission data.

An automatic billing enrollment (ABE) computing device and methods for automatically enrolling a merchant in a bill paying application are provided. The ABE computing device receives a payment transaction associated with a cardholder account, identifies a merchant associated with the payment transaction, and determines whether the merchant is enrolled with a bill pay application associated with the cardholder account by performing a database lookup of enrolled merchants. The ABE computing device then transmits a message to a user device associated with the cardholder account indicating that the identified merchant is not enrolled by the cardholder with the bill pay application and includes an option for the cardholder to enroll the merchant. To enroll the merchant, the ABE computing device determines merchant-associated data elements required by the bill pay application to enroll the merchant, retrieves the data elements, and inputs the data elements into the bill pay application.

This application discloses a data processing apparatus, a data processing method, a chip, and an electronic device, relating to the field of data processing technology. The data processing apparatus includes: a storage module configured to store x sets of original data vectors, each original data vector containing m data elements, where x and m are positive integers; and a computation module including a single instruction multiple data (SIMD) execution unit and a vector register group, the SIMD execution unit being coupled to the vector register group, and the vector register group being coupled to the storage module. The computation module is configured to: determine whether x is an integer multiple of the SIMD processing capability, split x, and obtain the top-k data elements in the original data vectors based on different data strategies. The algorithm of this application is flexible, requires no dedicated hardware sorting network, avoids dependence on dedicated hardware, and improves programming flexibility and versatility, facilitating the expansion and migration of large models across different hardware platforms.

This disclosure relates to a neural symbolic action transformer for video question answering. A mechanism for performing AI-based video question answering is provided. A video parser parses an input video data sequence to generate situational data structures, each including data elements corresponding to entities and a first relation between the entities, said entities and the first relation being identified by the video parser as existing in images of the input video data sequence. A first machine learning computer model operates on the situational data structures to predict a second relation between the situational data structures. A second machine learning computer model executes on a received input question to predict an executable program to be executed to answer the received question. This program executes on the situational data structures and the predicted second relation. An answer to the question is output based on the result of executing the program.

A machine learning-driven system for pattern deviation detection and autonomous correction of data integrity errors in digital networks, comprising: (a) a data acquisition interface configured to receive digital data elements from a variety of network-connected sources and to associate the digital data elements with source metadata; (b) a preprocessing and feature extraction engine configured to transform the digital data elements into feature representations that include at least structural features, temporal features, protocol compliance features, and origin features; (c) a pattern modeling engine configured to maintain a reference behavior representation derived from baseline-consistent feature representations;(d) an anomaly scoring engine configured to calculate a composite anomaly score for an incoming feature representation based on at least one reconstruction deviation and one consistency deviation relative to the reference behavior representation; (e) a fault classification engine configured to assign an integrity fault category and an affected area, which may include a field, record, packet, block, or stream segment; (f) a corrective orchestration engine configured to generate and evaluate a variety of corrective candidates and select a corrective action according to a confidence score derived from the agreement of the fault category, the origin state, the source trustworthiness, and the expected validation success;(g) a validation engine configured to check a corrected data element against integrity constraints; (h) a rollback control configured to restore a saved previous state if the check fails; and (i) an audit log generator configured to record artifacts relating to anomalies, corrections, validations, and rollbacks.

A system for extracting a number of data elements from one or more data sources. The system increases the size of training examples that can be used to test, score, and generate the extraction procedure by generating additional training examples. The additional training examples are generated by automatically labeling unlabeled examples and augmented the labeled training examples with the unlabeled examples for which a ground truth value has been estimated. The system queries a number of language models to extract the information from the unlabeled examples and uses an algorithm to estimate the ground truth value from the values estimated by the ensemble of language models. A flag is also generated indicating those unlabeled examples of particular difficulty which may have high uncertainty and require supplemental validation of the estimated ground truth value. The system can populate an ontological data store using the extraction procedure developed using the additional training examples.

When evaluating a trained model, regions that should be detected as cellular regions and regions that may or may not be detected as cellular regions are distinguished and evaluated accordingly. [Solution] A dataset generation method for generating a dataset used to generate or evaluate a trained model for detecting cell regions in a target image comprises the steps of preparing multiple target images (step S11) and generating a dataset by acquiring multiple annotation images containing cell region information from the multiple target images (steps S12 to S15). The dataset is a collection of multiple data elements, each data element containing a combination of a target image and an annotation image. In the dataset generation step, for each target image, a first designated region is specified, which is a region that should be detected as a cell region, and a second designated region is specified, which is a region that may or may not be detected as a cell region.

The invention provides methods, systems and computer program products for securely provisioning an internet-of-things (IoT) device for implementing an electronic payment transaction. The invention comprises (i) retrieving a first credential element from the client device, and a second credential element from a router, (ii) generating payment credential data by applying split secret cryptography based reconstruction to the first credential element and the second credential element, (iii) retrieving a unique identifier associated with the IoT device, (iv) generating a combined data element comprising the payment credential data and the unique identifier associated with the IoT device, (v) applying split secret cryptography based splitting to the combined data element to generate a first verifiable secure element and a second verifiable secure element, (vi) storing the first verifiable secure element and the retrieved unique identifier within the router, and (vii) storing the second verifiable secure element within the IoT device.

Disclosed are methods, systems, and computer programs for generating petrophysical analysis data. The methods include: accessing first porosity data and first well log data associated with an NMR well; randomly selecting data elements comprised in the first porosity data and the first well log data; developing, based on the randomly selected first porosity data and first well log data, a learning data structure; generating, based on the learning data structure, a machine learning model; receiving second well log data using sensor(s) deployed about a non-NMR well; adapting, based on the second well log data, variable input parameters of the machine learning model to generate second porosity data; and formatting the second porosity data to generate petrophysical analysis data. The petrophysical analysis data indicates one or more of: a petrophysical characterization of a rock type; flow dynamic data of a carbonate reservoir; well test or perforation zone data; and pore data.