44 results about "Data validation" patented technology

A server computer may receive an authentication data packet including authentication data from a relying party computer in communication with an authenticator associated with a user device. The server computer may verify the authentication data in the authentication data packet. The server computer may store the authentication data packet in a database. The server computer may transmit to an authorizing entity computer, a data packet including data relating to the verification of the authentication data.

Systems and methods are provided for a prompt and content generation service to validate generated references and training data sets of large language models (LLMs). The prompt and content generation service may determine if a reference generated by an LLM is valid by searching a training dataset for tokens using a prompt, generated answer, and generated reference for content and references in the training data set. If the prompt and content generation service finds content or a valid reference with a certain confidence level, the prompt and content generation service may indicate that the generated content and reference is valid. The prompt and content generation service may additionally validate a saved checkpoint during training of an LLM by executing use case scenarios against the LLM checkpoint and determining if expected answers match generated answers.

The process validation platform disclosed herein enables automated validation of data transformation processes based on simulation-based data. For example, the process validation platform can generate metadata associated with a data transformation environment, evaluate the metadata, and generate test data based on the metadata. The process validation platform can generate test cases (e.g., test records) and associated code samples based on the test data and enables updates and review of the test cases. The process validation platform can transmit the code samples to the data transformation pipeline for execution of the associated data validation tasks.

The invention relates to the technical field of artificial intelligence and education, and discloses a composition AI review improving system and method based on scene deduction, and the method comprises the steps: obtaining the basic information of a learner and the text data of a composition, and carrying out the standardization processing; performing text statistical analysis on the standardized learner basic information set; carrying out scene type identification and task difficulty assessment; establishing a matching relationship between the writing ability of the learner and the task difficulty evaluation result, and obtaining personalized review strategy configuration parameters according to the matching result; processing the personalized review strategy configuration parameters and the to-be-reviewed composition text; verifying the accuracy of the fractal-dimension scoring result and the comprehensive evaluation report in combination with feedback data, and adjusting personalized review strategy configuration parameters according to the verification result; according to the invention, by constructing a scene deduction mechanism and a personalized review strategy, the evaluation standard can be automatically adjusted according to different teaching scenes, learner characteristics and composition types.

The invention relates to the field of artificial intelligence, in particular to a method and device for generating a programming language according to a clinical DVP table, a medium, electronic equipment and a computer program product. According to the method, the clinical DVP table is automatically read and the corresponding program code is generated, so that the manual programming time and cost are greatly reduced, the data verification efficiency is improved, and the data quality problem caused by human errors is also reduced. In addition, the clinical test data can be comprehensively and accurately verified, the quality and reliability of the data are improved through the comprehensive verification mechanism, and a powerful guarantee is provided for smooth proceeding of clinical research.

A system for providing an interactive inspection map of an inspection surface inspected by an inspection robot includes an inspection circuit structured to interpret inspection data of the inspection surface from the inspection robot and a user interaction circuit structured to interpret a user focus value from a user device. The user focus value includes an activation state value. The system further includes an inspection visualization circuit structured to provide the interactive inspection map to the user device in response to the user focus value. The interactive inspection map is based on the inspection data. Also, the system includes an inspection data validation circuit that determines an inspection data validity description of the inspection data. The inspection data validity description is provided as a display layer on the interactive inspection map to indicate whether the inspection data is valid.

A computer system for performing cloud-based enhanced rules conflict checking is provided. The computer system is programmed to store a plurality of rules for transmitting to a plurality of destination systems and receive a data message for transmission to the plurality of destination systems from a first requesting system. The computer system is programmed to compare the data message to a first set of rules to validate the data message and if the data message is validated for the first set of rules, instruct the first requesting system to transmit the data message to the plurality of destination systems. The computer system is further programmed to receive the data message for transmission to one or more remaining destination systems from a second requesting system and compare the data message to a second set of rules for validating the data message.

Embodiments of the present disclosure are directed to a system for monitoring operating conditions of a saltwater disposal pump system, which includes a processing device that causes a controller to receive an alarm from the at least one machine that exceeds a predetermined alarm level indicative of an abnormal machine operation, receive a plurality of data from a plurality of sensors that are indicative of a current operating conditions of a saltwater disposal pump assembly, derive an expected value utilizing a statistical regression analysis, determine whether the expected value and a history of the plurality of data correlate at a point where the plurality of data exceeds the predetermined alarm level, determine whether a deviation from a predefined operating profile exceeds a predetermined machine data validation threshold value, validate the alarm, output an alert of the deviation and inhibit further operation of the saltwater disposal pump assembly.

A model-based functional hazard assessment (FHA) is disclosed. A disclosed example apparatus for generating a model-based FHA associated with a product includes an associator to associate a function of the product to a failure condition to define a first association, and associate a hazard assessment with the failure condition to define a second association, and associate a safety requirement with the hazard assessment to define a third association. The apparatus also includes an organizer to generate the FHA based on the first, second, and third associations. Disclosed examples include automated FHA data validation capabilities.

A posthumous memory-sharing system including a memory storage system storing user-selected digital content and instructions for communicating with a computing device of a recipient. The memory storage system authenticates death of the user based on receiving biometric data and / or third-party data validation, then transmits verification to the computing device that the death of the user has been authenticated. The memory storage system also receives a unique, recipient-specific encryption key indicating that the recipient wishes to receive at least some of the user-selected digital content or at least one physical item associated with the recipient. In response to receiving the encryption key, the memory storage system sends the user-selected digital content or initiates delivery of the physical item at user-specified or algorithmically determined times. The memory storage system also dynamically adjusts delivery schedules of these items based on recipient availability or real-time interaction feedback.

Systems and methods for using data reference links to maintain data validation are disclosed. The method may include: initiating, via a trial server, a trial session for a user; determining, via the trial server, a validated instrument to be accessed for the trial session; transmitting, to an instrument library and via the trial session, a request for an instance of the validated instrument; receiving, from the instrument library, a data reference object that is operable as a data reference link to the validated instrument stored in the instrument library, such that the data reference object is operable to execute the validated instrument without generating a copy of the validated instrument; and causing a Graphical User Interface (GUI) of the trial session to operate the data reference object, such that a user device executes the validated instrument by reference.

This application relates to the field of financial document management technology, and discloses a method and system for automatic identification, generation, and decision-making of financial documents. The method first receives and preprocesses the original document image, then uses computer vision technology to perform layout analysis and classification on the enhanced image, distinguishing between known standard layouts and unknown layouts. For known layouts, a template-based OCR module is used to accurately extract key text blocks; for unknown layouts, a general document understanding model is used to obtain preliminary key-value pairs of key information. Subsequently, the extracted information is transformed into structured data through a key information structure extraction module, and verified by a data validation and business rule engine. If the validation passes, the structured document information is output; otherwise, it is sent to a manual intervention queue for further review. This method effectively improves the efficiency and accuracy of automated financial document processing, while enhancing the system's flexibility in adapting to different document layouts.

1. Name of the product in this design: Guided operation graphical user interface for electronic devices. 2. Intended use of this design: for use in an electronic device. 3. The key design feature of this product is its graphical user interface. 4. The image or photograph that best illustrates the design's key points: the front view. 5. Uses of graphical user interface: Used in international logistics, warehouses and other scenarios to guide personnel operations and facilitate human-computer interaction. 6. Description of the changing states of the graphical user interface: Based on the main view, click any icon in the main view to jump to the changing state diagram 1; enter data in the input box in the changing state diagram 1 to jump to the changing state diagram 2; the changing state diagram 2 is the data validation process; if the validation is successful, jump to the changing state diagram 3; click the button in the lower right corner of the changing state diagram 3 to jump to the changing state diagram 4. The front view reference diagram is the reference diagram for the front view, and the change state reference diagram 1-4 is the reference diagram for the change state diagram 1-4. The “XX” in each view represents text and / or numbers.

There is provided an information processing apparatus capable of making the accuracy and behavior of data observable when constructing and updating a database by aggregating data from multiple data sources so as to improve data accuracy and availability. The apparatus includes a data set extraction unit configured to extract a data set from a plurality of databases belonging to a plurality of platforms, respectively, a quality analysis unit configured to analyze quality of the data set per data set by applying a first metric to the data set; a data validation unit configured to validate a data value per data item in the data set by applying a second metric to the data set; and a metric construction unit configured to dynamically construct at least a part of the first and second metrics to be applied to the data set based on a processing history of the data set.

The invention belongs to the technical field of data verification, and particularly relates to a data verification rule configuration method and system and a data distribution system. The configuration method comprises the following steps: S1, a programmer uses a unified code writing logic in advance to build a running environment of a configuration module; s2, business personnel convert the business demand into logical operation among a plurality of basic demands; and S3, the business personnel in the configuration module reserves the logical operation among the basic demands, converts each basic demand into the logical operation among the plurality of basic verification units, and completes verification rule configuration for the current business demand in the configuration module. According to the data verification method and device, business personnel can conveniently and independently complete configuration and maintenance of the verification rules, the data verification efficiency and verification effect are improved, and meanwhile the stability of data verification is guaranteed.

Examples described herein include implementations for big-data validation. One aspect includes generating a configuration file including dynamic matching data describing a first plurality of data entries and a second plurality of data entries, and generating a data action file. A plurality of data queries are generated based on the dynamic matching data indicated in the configuration file. The plurality of data queries are dynamically executed in parallel, including execution of a plurality of simultaneous data queries to the data source system. Fields of the first plurality of data entries and the second plurality of data entries are matched using the key type and the value structure, corresponding fields of the first data fields and the second data fields having a data mismatch are identified, and a mismatch database entry for the corresponding fields having the data mismatch is automatically generated.

A method and a system for performing schema-based data validation are disclosed. The method includes receiving a first dataset that comprises a plurality of entries, the first dataset is received from at least one data source. Next, the method includes receiving a second dataset that comprises a predefined set of instructions corresponding to a set of conformity standards for validation of the first dataset. Next, the method includes applying the predefined set of instructions to the received first dataset. Next, the method includes validating each of the plurality of entries of the first dataset based on the applying of the predefined set of instructions. Thereafter, the method includes generating a consolidated assessment report based on the validation, and displaying, using a display unit, the generated consolidated assessment report.

An example operation may include one or more of storing a model of training data of an artificial intelligence (AI) model in a storage of a software application, receiving a request to execute an AI pipeline including the AI model on input data via the software application, determining that the input data is not valid data based on a comparison of the input data to the model of the training data, retrieving additional input data from the storage of the software application, determining that the additional input data is valid data based on a comparison of the additional input data to the model of the training data, and executing the AI pipeline including the AI model on the additional input data to generate a predictive output.

An automated, multi-layered QA system for cloud-native machine learning pipelines, including: a data validation module configured to receive input data from one or more data sources and evaluate the data for quality, completeness, consistency, and schema compliance; a preprocessing validation module that is functionally coupled with the data validation module and configured to check the correctness of data transformations, feature engineering, and normalization processes; a pipeline orchestration interface configured to coordinate the execution of pipeline components within containerized environments; a model validation module that is functionally coupled with the preprocessing validation module and configured to evaluate the performance, accuracy, robustness, and reproducibility of machine learning models during training and inference; a deployment validation module configured to validate the correctness, compatibility, and stability of model deployment in cloud-native environments; a monitoring and anomaly detection module configured to continuously track pipeline behavior and detect anomalies including data drift, model drift, and performance degradation; and a feedback and adaptive learning module configured to dynamically update validation rules and test strategies based on historical data and detected anomalies, the system is configured to provide continuous, end-to-end, and multi-layered quality assurance across all phases of the machine learning pipeline within cloud-native infrastructures.

Embodiments described herein are generally directed to client / server software application development frameworks, and are particularly directed to systems and methods for supporting portable and extensible dynamic user interfaces and data validation. An application development framework is provided, wherein a rules engine component and rules are provided at both the client-side and the server-side, for use with a particular application. The rules engine and rules can be different implementations at each end. The client and server can communicate to exchange the latest versions of the rules; however the rules themselves are agnostic as to which (client-side or server-side) engine they are run on. The system allows users to configure policies associated with a client-side user interface and a backend server, that operate together to provide application portability and extensibility, for example to control the presentation, logic, defaulting, and operation of a data set associated with a dynamic user interface.

A method includes receiving an indication that a record has been updated and determining a confidence score. The method includes, in response to a determination that the confidence score has not met a threshold, filtering a set of communications. The method includes determining whether the record change has met one or more of a set of validity criteria. The method includes, in response to a determination that the user record change has not met the set of validity criteria, based on a first outcome of a set of reporting criteria, automatically generating a prompt with a first suggested revision of the first change. The method includes, in response to a determination that the record change has not met the set of validity criteria, based on a second outcome of the set of reporting criteria, automatically revising the first change with the first suggested revision.

The application relates to a flow upgrade execution and rollback method based on verification lag confirmation, which comprises the following steps: S1, upgrade flow initialization; S2, upgrade data receiving and segment division; S3, upgrade data prewriting; S4, background verification processing; S5, upgrade data validation control; S6, upgrade exception processing and rollback. The device comprises an upgrade management module, an upgrade execution module, a verification module, a validation control module and a rollback control module. The flow upgrade execution and rollback method and device based on verification lag confirmation have the advantages that the prewritten upgrade data segments do not need to wait for verification completion, background independent verification processing is executed, verification results are used for accurately controlling data validation states and rollback operations, the upgrade efficiency is improved, the upgrade time is reduced, the accurate rollback mechanism is realized, and therefore the system reliability and stability are improved.

The invention provides a page configurable generation system and method based on a dynamic rendering engine, and the system comprises a dynamic canvas rendering engine which completes the dynamic rendering of a page based on JSON configuration, and supports the free dragging and position adjustment of a component in a canvas; the financial industry-level component library is used for providing configurable financial business components for the canvas; the permission sandbox module is used for carrying out parameter verification, interface fault isolation and permission control of the movable component on component configuration; and the configuration management system is used for dynamically loading a corresponding attribute configuration panel according to the component selected on the canvas, and supporting real-time preview, configuration synchronization and data persistent storage. Through a multi-level permission verification and data verification mechanism, the compliance and safety of financial marketing activities are ensured, the operation risk and the data leakage risk are effectively prevented, and good safety and reliability are achieved.

The invention provides a universal method, medium and equipment for automatic analysis and storage of multi-source data, and belongs to the technical field of big data. According to the method, a big data calculation framework is introduced, more types of data source data can be quickly read, analyzed and processed, and an efficient calculation engine and an optimization technology are provided, so that quick and accurate data processing operation is realized. And declaring a data source, realizing a connect method, and completing data connection integration. And field relation mapping is configured, so that the capability of automatically identifying data types for analysis is achieved, and a unified data structure is formed. And setting a filtering rule and a verification rule for the field, and realizing the verification capability of the basic data according to the loaded unified data structure. Finally, summarizing to form an RDD / DataFrame result set, judging a result storage type and result source information, and performing result set conversion according to the storage result type; and outputting the automatically analyzed data to a corresponding data source according to output control.

Embodiments described herein are generally directed to client / server software application development frameworks, and are particularly directed to systems and methods for supporting portable and extensible dynamic user interfaces and data validation. An application development framework is provided, wherein a rules engine component and rules are provided at both the client-side and the server-side, for use with a particular application. The rules engine and rules can be different implementations at each end. The client and server can communicate to exchange the latest versions of the rules; however the rules themselves are agnostic as to which (client-side or server-side) engine they are run on. The system allows users to configure policies associated with a client-side user interface and a backend server, that operate together to provide application portability and extensibility, for example to control the presentation, logic, defaulting, and operation of a data set associated with a dynamic user interface.

A privacy-compliant modular AI gateway system (100) implemented as a hardware gateway device or as a server-based processing unit within an enterprise network and configured to connect to one or more enterprise resource systems (110), comprising: an input interface (120) including a data ingestion parser (122) configured to receive and parse enterprise data from one or more enterprise resource systems (110), including legacy enterprise resource planning (ERP) platforms (112), enterprise databases (114), or enterprise applications (116); a processing gateway module (130) functionally connected to the input interface (120) and configured to act as an intermediary communication layer between the enterprise resource systems (110) and an external, cloud-based artificial intelligence platform (150);a module (132) for masking personally identifiable information (PII), which forms part of the processing gateway module (130) and comprises: a detection engine (132a) configured to identify sensitive data fields within the received enterprise data, wherein the detection engine (132a) comprises a pattern recognition unit (132a-1), a rule engine (132a-2) and a named entity recognition model (132a-3); a classification unit (132b) configured to categorize identified sensitive data according to sensitivity levels and data categories; and a masking engine (132c) configured to apply safeguards to the classified fields containing sensitive data, comprising a tokenization unit (132c-1), an anonymization unit (132c-2), an encryption unit (132c-3) and a redaction unit (132c-4);a data preprocessing and filtering module (134) designed to clean, structure, and filter enterprise data, comprising a schema normalization unit (134a), a data validation unit (134b), and a content filtering unit (134c); a policy validation module (136) configured to apply predefined regulatory and policy-related restrictions to the processed data; an access control module (160) comprising an authentication unit (162) and an authorization unit (164) configured to authenticate and authorize incoming data requests prior to processing by the PII masking module (132);and a secure communication interface (140) comprising an encryption module (142) and an API gateway (144) configured to transmit protected corporate data to the cloud-based artificial intelligence platform (150) via secure network protocols;wherein the processing gateway module (130) is configured to receive parsed enterprise data from the input interface (120), to authenticate and authorize incoming data requests via the access control module (160), to identify and classify sensitive data fields via the PII masking module (132), to apply tokenization, anonymization, encryption or redaction to the sensitive data fields, to normalize and validate the cleaned data via the data preprocessing and filtering module (134), to enforce predefined policy restrictions via the policy validation module (136), and to output protected enterprise data to the cloud-based artificial intelligence platform (150) via the secure communication interface (140).

A data processing system implements obtaining a string representing a JSON object at a JSON validator; obtaining a JSON schema for validating the JSON object using the JSON validator, the JSON schema specifying one or more first constraints on the data of the JSON object must be satisfied in order for the JSON object to be valid against the JSON schema; determining, using the JSON validator, that the JSON schema references one or more JSON subschemas, the one or more JSON subschemas specifying one or more second constraints on the data of the JSON object must be satisfied in order for the JSON object to be valid; validating the JSON object using the JSON schema and the one or more JSON subschemas; and performing one or more actions using the JSON object in response to validating the JSON object.

The invention discloses a physical constraint enhanced and multi-factor driven satellite-borne GNSS-R sea surface ocean current velocity estimation combination model construction method. The method comprises the following steps: acquiring satellite-borne GNSS-R observation data and multi-source ocean environment parameter variable data; data quality control and variable parameter extraction and processing; the invention discloses a normalized bistatic radar scattering cross section physical empirical model construction method for spaceborne GNSS-R sea surface ocean current velocity estimation. A developed model simultaneously considers the comprehensive influence of multiple factors such as wind speed, ocean current, surge, rainfall, sea surface temperature and salinity on a normalized bistatic radar scattering cross section. Constructing a satellite-borne GNSS-R sea surface ocean current velocity estimation combination model, and selecting a Transform modulation diffusion algorithm to develop the ocean current velocity estimation combination model; and the performance of the combined model is verified by adopting OSCAR data. The problem of high-precision estimation of the multi-factor-driven satellite-borne GNSS-R sea surface ocean current speed is solved.

A hybrid cloud bridge system (100) for synchronizing legacy data between on-premises IBM i systems and AWS cloud instances, the system comprising: an on-premises legacy interface module configured to connect to one or more IBM i (AS / 400) systems via native data access protocols, including Distributed Relational Database Architecture and Distributed Data Management, the module further comprising a journal read component for capturing change data in real time from IBM i journal subsystems; a data transformation engine operatively coupled with the on-premises legacy interface module and configured to convert data between the EBCDIC encoding native to IBM i systems and the UTF-8 encoding required by cloud-native applications, the engine comprising an EBCDIC-to-UTF-8 converter, a schema mapping processor, a data validation unit, and a format serializer;a secure communication gateway that establishes encrypted communication channels between the on-premises network and the AWS cloud environment using mutual Transport Layer Security authentication with a pure outbound connection model; a cloud integration module deployed within the AWS cloud environment that includes service adapters for connecting to AWS services; a synchronization controller configured to coordinate bidirectional data synchronization operations via data change detection mechanisms; and a conflict resolution module that implements vector-clock-based versioning to detect and resolve conflicts during concurrent changes;characterized by the fact that the system acts as an intermediary bridge, performing bidirectional real-time synchronization between IBM i legacy systems and AWS cloud instances through integrated data format transformation, secure tunneled communication, and vector clock-based conflict resolution, without requiring any changes to the legacy application code or database schemas.