163 results about "Validator" patented technology

The invention discloses a Text2SQL (Structured Query Language) implementation method and system, and relates to the field of data processing, and the method comprises the following steps: firstly, receiving a natural language query, and analyzing a query intention, field classification and a key entity through a planner; the searcher obtains domain knowledge, entity information, a database table structure and a historical query mode in a multi-path parallel mode based on the planning result; the generator constructs an SQL framework according to the retrieval result and generates an initial statement; the verifier carries out grammar, table field, authority and logic multi-dimensional verification on the SQL, and if the verification fails, iteration adjustment is carried out to generate logic; when the SQL is executed, the result is formatted and a natural language explanation containing query logic, a data source and a calculation method is generated if the SQL is executed successfully, and a diagnosis and error correction mechanism is started for correction and then rechecking is performed if the SQL is executed unsuccessfully. According to the method, through deep fusion of domain knowledge, whole-process verification error correction and interpretability enhancement, the accuracy, robustness and user interaction experience of SQL conversion in a professional scene are improved.

A quantum safe blockchain system can operate with quantum safe blockchain nodes (QSBN) and validators. A QSBN can generate a pending register certificate transaction comprising a public key, a fingerprint for a certificate, a URL for the certificate, a first registration number for a parent certificate of the certificate, and a digital signature. The QSBN can transmit the pending register certificate transaction to the quantum safe blockchain system. A validator can receive the pending transaction, validate the digital signature using the public key and include a confirmed register certificate transaction in a block. The confirmed transaction can include a second registration number for the certificate. The QSBN can receive the confirmed transaction and store in a database the fingerprint, the first registration number, and the second registration number. The QSBN can generate a pending certificate revocation transaction for the certificate, and transmit the pending transaction to the blockchain system.

Existing techniques for automated generation of test data for testing web applications need detailed requirement documents. The present disclosure receives a plurality of textual documents to extract context. Rephrasing the extracted context by implementing a plurality of rules and passing extracted context along with a first set of prompts to Large Language Model (LLM). Generating program, validator and first set of constraints for extracted context and generating test data by running the generated program. Assigning ranking to test data and selecting the test data with highest ranking. Statically refining the generated program by calling a mathematical library function on the highest ranked test data to generate structural information and modifying language of the second set of prompts passed to the LLM. Dynamically refining the generated program by passing feedback generated by executing the highest ranked test data on a web application and refining the response obtained.

Systems and methods for staking instrument cryptocurrency rewards and validator management are disclosed. The systems and methods include generating validator pods associated with cryptocurrency staking validation and generating validator keys configured to be utilized by the validator pods for validating proof of stake operations. A subsets of the validator keys may be assigned to the validator pods and a request to perform the proof of stake operations may be received. A validator pod may be provisioned for validating proof of stake operations, and the validator node may be utilized to validate proof of stake operations based on the selected validator node being a compliant validator node.

Various aspects of the subject technology relate to systems, methods, and machine-readable media for cross-chain communication in a blockchain platform. Various aspects may include accepting, at a first blockchain, a first transaction including a message and a message payload. Aspects may also include validating, at the first blockchain, the message by signing the message using signature keys of one or more validators in a first set of validators of the first blockchain. Aspects may also include generating an aggregate signature based on the signature keys of the one or more validators in a first set of validators. Aspects may also include submitting a second transaction on to a second blockchain, the second transaction including the message and the aggregate signature. Aspects may include validating, at the second blockchain, the second transaction based on a shared registry.

A method includes preparing key actions in a process design document (PDD) from historical PDD documents by tokenizing the PDD with part-of-speech (POS) grammar against key verbs and phrases, extracting automation unit pairs from the historical process documents to map the automation unit pairs with the key verbs and phrases, constructing a set of automation tasks against one or more of the key verbs and phrases, coupling different process models with domain verbs for the phrases to mark one or more of the automation unit pairs as actions against one or more of the key verbs, constructing a tree-like mapping where each key action points to different actions in a target specific automation language, and defining a vocabulary interface of a domain specific language (DSL) by using the key actions and phrases.

Disclosed are various approaches for facilitating verifier integrations using computer models. An exemplary system of the present disclosure comprises a computing device comprising a processor and a memory; and machine-readable instructions stored in the memory that, when executed by the processor, cause the computing device to at least: receive an unstructured claim query for a data document from an entity computing device, wherein the data document is structured in accordance with a data document schema; parse the unstructured claim query to identify attributes that are then converted to a machine-understandable representation of the unstructured claim query; prepare, using a predictive model that is trained to understand data documents that are structured in accordance with the data document schema, a query response to the machine-understandable representation of the unstructured claim query by evaluating contents of the data document; and transmit the query response to the entity computing device.

A proof of game validation system provides an efficient process to validate newly added blocks to a blockchain based on a result of a competitive entertainment activity. The proof of game validation system identifies a type of competitive online entertainment activity and determines a number of devices based on the complexity of the competitive online activity. Pairs of user devices participating in the competitive online activity and validator computing devices vying to validate a new block of the blockchain are randomly generated from a pool of user devices and validator computing devices. At a completion of the competitive online activity, the winning pair is rewarded. The proof of game validation system triggers transfer of a reward to a digital wallet of the winning user device and triggers a validation process for the blockchain to be performed by the winning validator computing device.

The invention relates to the technical field of formalized analysis and verification of a complex software system, and particularly provides a multi-modal cross-language multilayer verification formalized integration method. According to the method, a multi-dimensional analysis and verification process from codes to models is realized through a code or model self verifier layer, a code or model plug-in verifier layer, a multi-language or multi-model conversion verification layer and a code and model bidirectional cross verification layer. The multi-level verification framework covers code static analysis, model dynamic simulation and cross-language and cross-model conversion, various formalized tools are systematically integrated, verification results of different languages and models are deeply integrated while automatic and efficient verification is achieved, on one hand, system compatibility and consistency are kept, and on the other hand, the verification efficiency is improved; on the other hand, the sufficiency and reliability of system analysis and verification are remarkably improved.

Systems, devices, and methods for a blockchain network based on key performance indicator (KPI) management systems to record KPI goals, track progress towards these goals, and to trigger rewards for achieving these goals within the blockchain network including generating, by a key performance indicator (KPI) creation module (120), one or more key performance indicators (KPIs) corresponding to one or more minted tokens minted through a minting module (105); generating, by a validation rule engine module (123), one or more validation rules for the one or more KPIs; submitting, by a proof submission by employee module (124), a proof of completion of the one or more validation rules; validating, by a proof validation by validator module (125), the submitted proof of completion; and transferring, by a transfer module (106), one or more tokens based on the validated proof.

Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and / or the like for identifying one or more evidence text portions comprising one or more bases relied on by a generative machine learning model for assigning a plurality of model-assigned categorical identifiers to a plurality of text segment data objects associated with a document data object, and verifying the one or more evidence text portions with a verifier machine learning model to generate one or more classifications of the document data object and provide the one or more verified evidence text portions along with the one or more classifications.

A document handler for identifying, verifying and / or authorizing documents is disclosed herein. The document handler includes a passageway formed in a validator, and a drive unit detachably mounted in the validator. The drive unit works to convey documents along the passageway in the validator, and therefore, each part of the drive unit is gradually worn through operation cycles causing steady deterioration in its quality, performance and function. To monitor the deterioration, the drive unit has one or more drive sensors for detecting physical values related to the quality, performance and / or function of the drive unit and memory for storing the physical values detected by the drive sensors. Accordingly, reductions in physical values detected by the drive sensors can indicate the degree or level of deterioration in the drive unit. Once physical values reach a threshold value, notices can be transmitted to personnel to change out the drive unit before failure.

The invention provides an intelligent object attribute cascade condition verification method and system, and relates to the technical field of communication data processing, and the method comprises the following steps: configuring a basic type verifier annotation, configuring a dependency relationship by using a cascade condition verification mechanism, and detecting pre-acquired to-be-checked data based on a Java reflection mechanism, obtaining a detection result; based on the detection result, constructing a verification rule engine, analyzing the verification annotation through the verification rule engine, and calling corresponding verification logic to perform data verification to obtain a verification result; and according to the result type of the verification result, dynamic feedback is executed according to a preset feedback mode. Through the combined action of the dynamically-adaptive annotation system and the intelligent verification rule engine, the readability and maintainability of codes are remarkably improved, addition of self-defined verification rules is supported, and the requirements of future business development are met.

Lightweight mechanisms provide a way to assert provenance when live streaming media content and establish provenance upon playback. For example, a provenance claim generator generates a key pair including a live-stream private key and live-stream public key. The claim generator signs, with a long-term private key reliably associated with a sender, manifest metadata including the live-stream public key, thereby producing a manifest signature. During live streaming, the claim generator signs respective portions of media content with the live-stream private key, producing portion signatures for the respective portions. A provenance claim validator receives the manifest signature and manifest metadata. The claim validator verifies the manifest metadata using a long-term public key (reliably associated with the sender) and the manifest signature. During live streaming, the claim validator verifies the respective portions of the media content using the live-stream public key and the portion signatures for the respective portions.

Systems for authenticating a file are disclosed. A system may include one or more physical devices. The one or more physical devices may select, based on an identifier, a subset of data segments of a computer file for generating a first digest with a cryptographic function. The one or more physical devices may also execute the cryptographic function on the selected subset of data segments of the computer file to generate the first digest. Further, the one or more physical devices may generate an authenticator based on the first digest and a private key. The one or more physical devices may further send the computer file, the identifier, and the authenticator to a secure node. Associated methods and non-transitory machine-readable medium are also disclosed.

Lightweight mechanisms provide a way to assert provenance when live streaming media content and establish provenance upon playback. For example, a provenance claim generator generates a key pair including a live-stream private key and live-stream public key. The claim generator signs, with a long-term private key reliably associated with a sender, manifest metadata including the live-stream public key, thereby producing a manifest signature. During live streaming, the claim generator signs respective portions of media content with the live-stream private key, producing portion signatures for the respective portions. A provenance claim validator receives the manifest signature and manifest metadata. The claim validator verifies the manifest metadata using a long-term public key (reliably associated with the sender) and the manifest signature. During live streaming, the claim validator verifies the respective portions of the media content using the live-stream public key and the portion signatures for the respective portions.

Techniques for fast smart contract processing and validation. A C3N smart contract may be written in a high-level programming language such as Go rather than a domain-specific language (DSL) for smart contracts that is difficult to learn and utilize correctly. The smart contract may support a predefined list of C3N libraries, including APIs for accessing components within a C3N containerized environment. The smart contract may natively support access to oracles and data external to the C3N blockchain. The C3N smart contact may be deployed as source code or executable code for one or more target architectures. Such executable code may be run directly on the target architectures without additional compilation or interpretation. Validator nodes can verify correct execution of C3N smart contracts through unit tests.

An embodiment provisions a Validation Area within a consistency group of a primary site and a Validation Area within a consistency group of a secondary site. The embodiment provisions a Validation Writer in the primary site and provisions a Validation Reader in the secondary site; the Validation Writer writes a validation metric into the Validation Area in the primary site at an interval determined by a recovery point objective. The embodiment also reads the validation metric by the Validation Reader in the secondary site, computes a validation result based on the validation metric, the recovery point objective and a recovery point time delta where the validation result comprises an outcome of a replication of the primary site to the secondary site where the Validation Area of the primary site, the Validation Area of the secondary site, the Validation Writer and the Validation Reader are storage and application agnostic.

A diversified validation test suite application (DVTSA) for a generative AI powered tool or large language model (LLM) tool includes at least first, second, and third control logics. The first control logic receives a seed test input or user input, analyzes the seed test input or user input, and extracts key elements for variations. The second control logic performs a coverage measurement of outputs of the first control logic. The third control logic causes a human validator to evaluate outputs of the second control logic relative to predefined coverage metrics and selectively and continuously iterate to cause outputs of the second control logic to increase LLM tool input robustness and output robustness from a first level to a second level greater than the first level, in both production and pre-production LLM tool processes.

A system, method, and computer-readable medium enforce compliance, policy governance, and jurisdiction-aware control of digital artifacts generated by AI models. A deterministic digest of each artifact is stored in a registry, and a compliance token is generated encoding policy predicates, consent scope, jurisdictional lineage, monetization parameters, and a zero-knowledge proof component including a proof statement ID, cryptographic commitment, and verification key. A gateway verifier receives the artifact and token, verifies the signature, performs zero-knowledge proof validation without revealing regulated data, evaluates encoded policies, and renders a permit-or-deny decision. Verification results and compliance decisions are recorded via a dual-ledger atomic commit engine that updates both compliance and financial ledgers. A settlement engine calculates royalty values based on validated performance metrics and appends the results to the commit entry. Upon jurisdictional transitions, a transition engine detects boundary crossings and issues a forked compliance token with updated predicates. The system ensures verifiable, policy-aware, and jurisdictionally adaptive governance.

The invention relates to a large language model unified verification method, which comprises the following steps of synthesizing an assembly line based on evolutionary verification data, and outputting to obtain synthesized training data; in combination with an improved GRPO algorithm, the current large language model is trained by using the synthetic training data, and a verifier model is constructed; and evaluating the output of the current large language model by using the verifier model to obtain an evaluation result. Compared with the prior art, the method has the advantages that comprehensive and universal verification capability is injected into a compact and efficient model, and objective fact verification and subjective quality judgment tasks can be processed in a single model at the same time by constructing a unified, universal and customizable verification model; fine-grained verification instructions and output format requirements which are issued by a user in a natural language and are in any form can be understood and followed, and detailed and rational explanation can be provided for verification results.

Reinstating access to a system of an admin whose certificate is invalid or expired is disclosed using a secret that is remapped. When the admin's certificate is expired, the admin may send a request for reinstatement to tenant admins. The voting operation is based on shares of a secret that have been distributed to validators. When the shares are successfully reconstructed to obtain the secret, the voting operation is affirmed or allowed to proceed. The secret is remapped after a period of time. Remapping the secret includes creating a new secret and distributing new shares of the new secret to a different set of validators.

The invention discloses a website data analysis method and device based on a large model technology, and belongs to the field of fusion of web crawlers and artificial intelligence. According to the method, a local knowledge base drives a large model to generate a precise XPath rule, and the precise XPath rule is packaged into a decoupling plug-in and integrated into an existing crawler framework; a dynamic feedback optimization mechanism is innovatively designed, rule validity is monitored in real time by using a composite verifier, and XPath reconstruction and knowledge base updating are automatically triggered. According to the method, the high concurrency performance of a traditional crawler is reserved, the rule generation efficiency and the system robustness are remarkably improved, the manual maintenance cost is reduced, and the method is suitable for a website data collection scene with a complex structure or frequent updating.

The present disclosure envisages a system (100) and a method (600) for validating quick response (QR) codes The system (100) comprises an input module (102) to receive at least one input string from a user for adding to a quick response (QR) code via a user interface (102a), an encoder module (104) to receive the input string and generate a pre-quick response (QR) code in accordance with a plurality of randomizers and further to encode the pre-QR code to generate a signed QR code by adding a signature on the pre-QR code by means of a set of encoder rules, a decoder module (106) to receive the signed QR code by scanning the signed QR code by means of a scanner device (114) and decode the signed QR code by means of a set of decoder rules, and a validator module (108) to receive the signed-QR code and validate the signed-QR code by means of a set of validator rules to identify the genuine signed-QR code.

Systems for using bifurcated non-fungible tokens for transaction approval are provided. Systems may include a blockchain, a memory and a processor. The processor may, in a pre-approval stage: mint an identity block for each approver included in a plurality of approvers, upload each identity block to the blockchain and assign a non-fungible token to each approver. The processor may, in an approval stage: receive a file for approval; label the file with a file identifier; identify a subset of approvers for file approval; mint an approval block including the file identifier and an entry slot for each non-fungible token associated with each approver included include in the subset; initiate an approval process by the subset of approvers; receive the non-fungible token that identifies the approver; place each received token within an entry slot of the approval block; upload the approval block to a blockchain; validate the approval block and close the approval block.

This invention provides a method for generating complex functions on a low-code platform based on large model code interpretation and generation. By capturing and standardizing user input, a structured intent graph containing business entities, operational relationships, parameter constraints, and control flow topology is constructed as a unified semantic anchor. Combined with a pre-defined domain rule base, a semantic alignment validator is designed to achieve multi-dimensional semantic comparison and conflict detection between code and requirements. Code snippets are generated in parallel by multiple models and verified in real time, and semantic conflicts are resolved through local regeneration. By fusing the results of multiple models to construct a joint abstract syntax tree, lightweight symbolic execution is used to simulate the correctness of cross-model code, locate and correct core logical conflicts. This invention significantly improves the consistency, accuracy, and automatic correction capabilities of low-code platform code generation.

A diversified validation test suite application (DVTSA) for a generative AI powered tool or large language model (LLM) tool includes at least first, second, and third control logics. The first control logic receives a seed test input or user input, analyzes the seed test input or user input, and extracts key elements for variations. The second control logic performs a coverage measurement of outputs of the first control logic. The third control logic causes a human validator to evaluate outputs of the second control logic relative to predefined coverage metrics and selectively and continuously iterate to cause outputs of the second control logic to increase LLM tool input robustness and output robustness from a first level to a second level greater than the first level, in both production and pre-production LLM tool processes.

The invention relates to a semantic abstract generation and security verification method and system for multi-modal data, and belongs to the technical field of data security and information process.The method comprises the steps that feature extraction is carried out, specifically, a client processes a multi-modal plaintext data stream, and key features of the multi-modal plaintext data stream are extracted; vector mapping: the client performs vector space transformation on the extracted features to generate a unique corresponding data abstract; local persistence: the system stores the generated data abstract back to a user local database for subsequent use; abstract collection, wherein the detector obtains generated data abstract information from the data stream; and integrity verification: carrying out consistency comparison on the data stream with the data abstract by the detection and verification device, and judging whether the data stream is complete and reliable or not. The data abstract generation system comprises a client module, a server module and a middleware module. According to the method, efficient and reliable data stream abstract generation and credible management are realized on the premise of fully guaranteeing user data privacy.

A cache validation system receives Content Delivery Network (CDN) caching rules and a request log, such as a log of HTTP requests received by a CDN. The cache validation system generates test requests from the CDN caching rules and the request log. In a testing environment, the cache validation system determines one or more cache validation indicators, such as, but not limited to, a hit-to-miss value, from the test requests and the CDN caching rules before application of a proposed change to a CDN caching rule. The cache validation system applies the proposed change to the CDN caching rules in the testing environment. The cache validation system determines one or more updated cache validation indicators from the test requests for the CDN caching rules after application of the proposed change. If the initial and updated cache validation indicator(s) satisfy a threshold, then the cache validation system can provide a notification.

A system is provided for electronic data artifact testing using a hybrid centralized-decentralized computing platform. In particular, the system may comprise an artifact testing platform that may be accessed by users and computing devices within a network. Users may upload a data artifact to the artifact testing platform to be validated by the system. The system may then use a number of different validators (e.g., artificial intelligence-based modules) that may read the data artifact and / or the associated metadata and generate a confidence level based on the characteristics of the data artifact. The confidence levels from each validator may be aggregated to generate a final trust score for the data artifact. In this way, the system may provide a secure way to perform validation of data artifacts within a network environment.