112 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.

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 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.

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.

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.

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.

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.

An identity verification-authentication system and method are used for verifying and authenticating at least a first user. The system includes a first prover for acquiring a first unique feature provided by the first user; and a verifier in communication with the first prover for generating a first proof request. The verifier sends a first variant data included in the first proof request to the first prover. The first prover generates a first to-be-compared answer data according to the first unique feature and the first variant data. The first prover or the verifier then generates a determination result according to a comparison of the first to-be-compared answer data and a first answer data included in the first proof request, and determines whether the identity verification and authentication of the first user is successfully according to the determination result.

Embodiments of this disclosure provide, but are not limited to, solutions for protecting, sharing, verifying, and / or generating data resources such as documents. In exemplary embodiments, a data resource is represented, expressed, or defined as a plurality of hashed constituent segments. Using the segment hashes as leaves, a Merkle tree is generated representing the complete original version of the data resource. An edited version of the data resource can be shared, omitting one or more segments that the data administrator wishes to keep secret or does not wish to share in its original form. To verify the authenticity and / or completeness of the edited data resource, a validator is provided with sufficient data (e.g., the hash of each of the non-shared segments) to verify the presence of non-shared segments in the tree representing the complete original version, and an authorized signature that signed the root. In some embodiments, the data resource is a code library, and the segments are parts of computer code that perform specific subtasks. Shared code segments, when executed on a processor, may be selected to combine to perform larger tasks.

The present invention discloses a Text2SQL implementation method and system, which relates to the field of data processing, including: first receiving a natural language query, analyzing the query intent, domain classification, and key entities through a planner; a retriever acquiring domain knowledge, entity information, database table structure, and historical query patterns in parallel based on the planning results; a generator constructing an SQL framework and generating an initial statement based on the retrieval results; a validator performing multi-dimensional verification of the SQL in terms of syntax, table fields, permissions, and logic, and iteratively adjusting the generation logic if failure occurs; when executing the SQL, if success occurs, formatting the result and generating a natural language explanation containing the query logic, data source, and calculation method; if failure occurs, activating a diagnosis and error correction mechanism and correcting it before re-verification. This method improves the accuracy, robustness, and user interaction experience of SQL conversion in professional scenarios through deep integration of domain knowledge, full-process verification and error correction, and enhanced interpretability.

The disclosed techniques pertain to training large language models (“LLMs”) using table data. Specifically, the disclosed techniques pertain to training LLMs for table-related tasks using two models, each model reserved for different functions. A first model is reserved for generator functions and a second model is reserved for validator functions. The first model receives table data and generates training data. The training data is fed to the second model, which identifies instances of training data meeting or exceeding at least one validity threshold. Instances of training data meeting or exceeding the at least one validity threshold are output as validated training data. The validated training data is used to iteratively fine-tune the two models by increasing or decreasing one or more numeric weight parameters in each of the models that control how the models process input data and produce outputs.

A system for computing confidence scores for large language model (LLM) based retrieval augmentation (RAG) tools includes a host device having a controller. The controller executing programmatic control logic including an algorithm for computing confidence scores for LLM based RAG tools (CLR application). The CLR application receives an input to the LLM from a user and engages an ensemble retriever. The ensemble retriever determines a similarity between the input and predetermined data in the one or more databases. The ensemble retriever generates an output and an output confidence score. The CLR application then causes a human validator to prioritize and review the output in accordance with the output confidence score, where the output is a command to one or more systems of the host device. The system progressively reduces computational resource utilization, progressively increases computational efficiency, and progressively reduces reliance on the human validator over time.

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.

Disclosed are an apparatus and method for electing a committee node in a blockchain. The apparatus for electing a committee node in a blockchain is configured to transmit a staking transaction including validator information to a blockchain system to register a validator node, generate a draw pool for electing a committee node based on the validator information, change entries of the draw pool using a predefined shuffling algorithm, select a preset number of top entries as votes of a committee member from among the entries of the shuffled draw pool, and elect, as the committee node, the validator node that has acquired votes of a committee.

This application provides a method and apparatus for training a natural language to SQL model based on reinforcement learning. The method includes: calling an external tool based on natural language instructions and the structural information of a target database to generate a response containing the tool call trajectory and SQL execution results; the external tool includes at least one of a search tool, a calculation tool, and an SQL execution tool; based on the response containing the tool call trajectory and SQL execution results, performing inference verification on the SQL execution results to obtain verification results, the inference verification including comparing the difference between the result table of the SQL generated by the model and the real table through an SQL validator; and adjusting the generation strategy of the large language model according to the verification results to generate executable SQL statements. An end-to-end natural language to SQL training framework is constructed through reinforcement learning algorithms, combining tool fusion inference and interleaved thinking mechanisms to dynamically optimize the model's decision-making capabilities throughout the entire process of data table retrieval, logical calculation, and SQL generation.

A system and method for authorization policy validation. A validator takes as input the authorization policy to be analyzed and a schema that specifies entity types and their attributes, the types of entity parents in the entity hierarchy, and which entity types can be used with which actions. The validator checks whether the policy conforms to the schema. If the check passes, the policy is guaranteed to be free of type errors and attribute access errors for any input that conforms to the schema.

System and method for validating authorization policies. A validator takes as input an authorization policy to be analyzed and a schema that defines entity types and their attributes, as well as types of parent entities in an entity hierarchy, and specifies which entity types can be used with which actions. The validator checks whether the policy conforms to the schema. If the check succeeds, the policy is guaranteed to be free of type errors and attribute access errors for all inputs that conform to the schema.

The present invention relates to a computer-implemented method (100) for providing a data consistency between a first data source (20) and at least a second data source (22) in a data engineering system (200), comprising the following steps: - Providing (102) first data (10) from at least a first data source (20) and / or at least a second data source (22); - Detecting (104), by an update detector component (50), a change information (8) for the first data (10); - Providing (106), by a version area component (59), a first version information (70) of the detected change information (8); - Validating (108), the change information (8) for the first data (10), by a policy validator component (54), wherein a policy information (55) containing at least a consistency rule is executed on a common information model (52) to decide about a consistent state of the first data (10) defining whether the change information (8) can be applied to the first data (10), - Updating (112), by the version controller component (58), the first version information (70) to a second version information (72) of the validated change information (8), when the consistent state of the change information (8) is confirmed, and in case, the consistent state of the change information (8) is not confirmed, perform a step (118) of repairing the detected data inconsistency of the change information (8), before re-validating the change information (8) and updating the first version information (70); - Supplying (114) the change information (8) with the version information (70) as a production projection (80) to at least a client engineering application (82).

This invention discloses a knowledge-driven automated formal verification method and system for smart contracts, belonging to the fields of blockchain and artificial intelligence technology. First, it constructs a formal verification language knowledge graph and an expert experience knowledge graph to extract grammatical rules, vulnerability patterns, and verification logic. Then, it performs project preprocessing and static analysis on the target smart contract to extract an abstract syntax tree, call relationships, and system model. Combining the knowledge graph and large language model, it automatically generates formal verification constraints and executes the verification task through a formal verifier. For errors and counterexamples generated during the verification process, the system uses a feedback mechanism to repair rules and optimize parameters. Finally, it generates a formal verification report containing verification results, risk analysis, and remediation suggestions. This invention achieves full-process automation from verification requirement generation to verification execution and optimization, effectively improving the efficiency and accuracy of smart contract security verification.

The invention provides an incentive and secure federal learning method and system based on a block chain, and the method comprises the steps: a plurality of participants obtain training tasks from the block chain, submit corresponding bidding prices to a smart contract, the smart contract determines a winner set in current training according to the bidding prices, and randomly divides the winner set into a training set and a verification set, obtaining a corresponding trainer and a verifier; the method comprises the following steps: downloading a currently trained local model from a block chain based on winner, adding artificial Gaussian noise, calculating a noise gradient of a target function of the local model by each winner, transmitting the noise gradient to each verifier for model verification, obtaining a list of the selected local model, and performing global aggregation; all verifiers are divided into small fragments, after a verification task of a local model of the trainer is received, each verifier in the small fragments evaluates the quality of the model, and the verifiers exchange votes mutually. The fragmentation consensus algorithm based on circulation can ensure the same security as a non-fragmentation consensus protocol.

Computer-implemented systems and method train a generator and a discriminator, through machine learning, where the generator and discriminator are trained in an adversarial relationship using a simulated, multi-player game. The model parameters for the generator and the discriminator can be updated non-simultaneously. Also, the simulated, multi-player game may comprise a two-person, zero-sum game.

Disclosed is a fail-closed conformance control layer for machine-assisted decision artifacts. The system generates replay-verifiable receipts that commit to a canonical artifact digest, evidence and snapshot identifiers, assumptions, toolchain identifiers, and a policy pack identifier and policy version digest. A deterministic validator evaluates a receipt under the versioned policy pack to output PASS, FAIL, or HOLD with reason codes from a reason-code registry, including time-stamped modeled-variability codes. Downstream side effects are gated in a control path and permitted only on PASS; otherwise they are withheld or routed to remediation or human review. The system may emit portable conformance packs and revalidate receipts over time to detect drift and manage a certification lifecycle. Conformance suites with deterministic test vectors and expected validator outputs may certify validator implementations by issuing signed conformance certificates and publishing a certificate or certificate digest to a verifiable certificate registry for independent verification and procurement acceptance testing.

A system for incorporating user feedback in a large language model (LLM) based retrieval augmentation (RAG) tools includes an application for incorporating user feedback (UFA). The UFA receives an input to the LLM from a host device user, generates an LLM output as a response to the input, causes a system user to verify the LLM output and provide user feedback via a human-machine interface (HMI) of the host device, and in response to the user feedback, engages a performance tuning optimizer that modifies the LLM output by: revising domain knowledge, revising system prompts, and performing constrained regeneration of the LLM output. The performance tuning optimizer progressively reduces computational resource utilization, progressively increases computational efficiency, and progressively reduces reliance on human validators and user feedback over time, and wherein the LLM output is a command to one or more systems of the host device.

The present invention describes systems, apparatus, methods, and machine-readable media for binding verifiable claims. For example, one embodiment of the system includes: a client device; a verifier of the client device, the verifier being configured to securely store verification data, the verification data comprising one or more verifiable claims received from one or more claim providers, each verifiable claim having attributes associated with the verifiable claim; and claim / attribute processing logic, the claim / attribute processing logic being configured to generate a first verifiable claim binding for a first verifiable claim issued by a claim provider; wherein the verifier is configured to transmit a first signed assertion to a first relying party for verification to the first relying party, the first signed assertion comprising an attribute extension, the attribute extension containing data associated with the first verifiable claim binding.