1530results about "Cryptography processing" patented technology

A value chain network control tower system comprises a processor and memory configured to execute a know your model system that manages the complete lifecycle of Al models in enterprise environments. The know your model system performs model intake and registration actions including model documentation collection, registration procedures, metadata collection, input / output interface standardization, legal and licensing validation checks, and security validation. The system conducts comprehensive model evaluation and risk assessment actions by analyzing foundational properties, task performance, safety and risk management, alignment and compliance characteristics, operational metrics, and tooling transparency capabilities. The know your model system executes model deployment actions through automated environment validation, predeployment approval processes, and controlled production deployment with continuous monitoring.

The present invention discloses a system and method for secure artificial intelligence-based financial technology governance and risk management, designed to provide real-time, autonomous, and verifiable compliance assurance within digital financial ecosystems. The invention integrates a secure artificial intelligence processing unit, a governance control processor, a cryptographically anchored storage unit, a federated learning coordination processor, and a quantum-resistant communication interface enclosed within a tamper-proof hardware structure. The system performs encrypted machine learning computations on financial transaction data using homomorphic encryption and trusted execution environments to preserve confidentiality during analysis. It computes a governance risk index based on probabilistic inference and anomaly detection to identify regulatory deviations, applies adaptive compliance reasoning across multi-jurisdictional frameworks, and automatically enforces governance actions through secure decision logic.

A dynamic vision system includes a variable focus liquid lens optical assembly. The dynamic vision system includes a variable lighting assembly. The dynamic vision system includes a control system configured to adjust one or more optical parameters and data collected from the variable focus liquid lens optical assembly in real time. The dynamic vision system includes a control system configured to adjust the variable lighting assembly. The dynamic vision system includes a processing system that dynamically learns on a training set of outcomes, parameters, and data collected from the variable focus liquid lens optical assembly to train a set of machine learning models to control the variable focus liquid lens optical assembly to optimize collection of data for processing by the set of machine learning models.

An artificial intelligence driven system of systems may include a layered architecture for providing transaction support to various types of enterprises. A governance layer implements automated governance and policy enforcement through specialized governance modules utilizing generative AI technology. An enterprise layer supports enterprise functions by integrating management and control platforms with digital infrastructure. An offering layer creates and manages system offerings via content generation, personalization, and smart product modules. A transactions layer enables automated transaction orchestration through API integration, execution, and fulfillment modules. An operations layer manages AI systems through generation, training, verification and orchestration modules. A network layer provides adaptive networking capabilities through routing, protocol selection and communication modules. A data layer processes fused data from multiple sources using machine learning and AI systems. A resource layer manages computing, storage, and other resources through specialized resource modules.

Embodiments are directed toward a blockchain system including a global state and an assemblage of blocks, each block representing a collection of state transformation records, each state transformation record describing a state transformation performed on the global state, where preceding blocks referenced by any given block contain state transformation records describing state transformations performed on the global state prior to the evaluation of the given block, and where at least one state transformation record is a zero-knowledge transformation record encoding a zero-knowledge state transformation description, which zero-knowledge transformation record comprises at least the following elements: paths identifying the locations of the elements of a discrete data subset, a revised data subset, and a transition proof implemented as a non-interactive zero-knowledge proof, which transition proof proves that the transition from the discrete data subset to the revised data subset follows the established rules of the blockchain system.

A robotic fleet resource provisioning system includes a computer-readable storage system storing a fleet resources data store and resource provisioning rules. The fleet resources data store maintains a fleet resource inventory indicating fleet resources, each with features, configuration requirements, and a status. The resource provisioning rules are accessible to an intelligence layer to ensure that provisioned resources comply with the resource provisioning rules. The system receives a request for a robotic fleet to perform a job and determine a job definition data structure. The definition data structure defines a set of tasks that are to be performed in performance of the job. The system determines a robotic fleet configuration data structure corresponding to the job based on the set of tasks and the fleet resource inventory. The system determines a respective provisioning configuration for each respective fleet resource. The system deploys the robotic fleet to perform the job.

A computing system is disclosed for generating fulfillment-ready coordination sessions based on tokenized item data and user-submitted participation parameters. The system ingests structured, semi-structured, or unstructured item data from merchant sources and applies schema-aligned transformation logic to normalize the data into structured item representations. The normalized records are tokenized into machine-readable item tokens that encode fulfillment constraints and canonical attributes. The system receives user item selections and associated participation parameters, encodes the item tokens and participation data into structured vector embeddings, and applies compatibility scoring logic using vector comparison and rule-based threshold evaluation. Compatibility scores are evaluated against session eligibility constraints derived from the item tokens. When eligibility conditions are met, the system generates a coordination session payload comprising match outcomes, proportional pricing, and fulfillment metadata, and issues orchestration instructions to external fulfillment systems for group-based delivery or preparation execution.

An AI-based energy edge platform is provided herein with a wide range of features, components and capabilities for management and improvement of legacy infrastructure and coordination with distributed systems to support important use cases for a range of enterprises. The platform may incorporate emerging technologies to enable ecosystem and individual energy edge node efficiencies, agility, engagement, and profitability. Embodiments may forecast, plan for, and manage the demand and utilization of energy in greater distributed environments. Embodiments may use AI, IoT, and technologies that filter, process, and move data more effectively across communication networks. Embodiments of the platform may leverage energy market connection, communication, and transaction enablement platforms. Embodiments may employ intelligent provisioning, data aggregation, and analytics.

An information technology system for a distributed manufacturing network includes an additive manufacturing platform configured to manage workflows for a set of distributed manufacturing network entities associated with the distributed manufacturing network. The information technology system includes a set of digital twins generated by the additive manufacturing platform. The information technology system includes an artificial intelligence system configured to be executed by a data processing system in communication with the additive manufacturing platform. The artificial intelligence system is trained to generate process parameters for the workflows managed by the additive manufacturing platform using data collected from the set of distributed manufacturing network entities. The information technology system includes a control system configured to adjust the process parameters during an additive manufacturing process performed by at least one of the set of distributed manufacturing network entities.

Systems and methods for computer-implemented commodity transactions using blockchain-based smart contracts. A server with memory executes application to authenticate buyer and seller, receive listing with commodity identifier, quantity, quality, delivery terms, price; generate smart contract specifying participant identifiers, wallet or banking info, order and contract IDs, milestone events, settlement terms; commit contract to blockchain; escrow funds associated with contract; obtain shipment, inspection, and delivery status via oracle; verify milestone satisfaction; release escrowed funds accordingly; record settlement on chain; and update database. Modules may include compliance to compute taxes and fees, identity to perform KYC / AML, and dispute resolver to execute predefined remedies. Optional multisignature escrow and fiat or crypto settlement are supported. Executions store hashes and ledger indices to provide tamper evident audit trails.

A system for an automated generation of legal documents based on legal case-related data, including a processor of a legal assistant server (LAS) node configured to host a machine learning (ML) module coupled to a chatbot module and connected to at least one user-entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire a user request comprising legal case-related data from the at least one user-entity node; parse the legal case-related data to extract a plurality of key classifying features; acquire legal consultation with the user data from the chatbot module; query a local database to retrieve local historical legal cases′-related data based on the plurality of key classifying features and the legal consultation data; generate at least one classifier vector based on the plurality of the key classifying features, the legal consultation with the user data and the local historical legal cases′-related data; and provide the at least one classifier vector to the ML module configured to generate a legal jurisdiction-based predictive model for producing a set of legal case evaluation parameters for a document generation module configured to generate at least one legal document for the legal case comprising an electronic pleading paper.

An example operation may include one or more of detecting a first amount of energy that is received by an energy data storage system during a predetermined period of time, detecting at least one source of the first amount of energy, detecting a second amount of energy that is consumed from the energy data storage system during the predetermined period of time, generating a digital token that includes a value based on the first amount of energy, the second amount of energy, and the at least one source of the first amount of energy, and storing the digital token in a digital wallet.

The disclosed computer-implemented method may include generating an interface code configured to connect to a payment server and generating, in response to loading a merchant website hosted on a merchant server that integrates the interface code, a payment interface with the interface code. The interface code may bypass the merchant server to connect to the payment server. The method may also include completing a payment using the payment interface that bypasses the merchant server to connect to the payment server. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods leverage a multi-stage artificial-intelligence pipeline to convert raw point-of-sale data into bank-grade digital receipts. A convolutional-OCR front end extracts line-item text, which a bidirectional-LSTM classifier normalises and categorises in real time, learning continuously from user feedback. A graph-based anomaly detector flags suspicious spend patterns, while a recommender sub-engine delivers personalised rewards and sustainability insights by fusing purchase context with external carbon-intensity data. The enriched receipt is cryptographically hashed, streamed through an encrypted gateway, and auto-matched to the corresponding payment entry inside the banking core. By driving extraction, classification, enrichment and integrity checks entirely through AI, the system eliminates manual mapping and enables immediate, tamper-evident reconciliation across heterogeneous merchants and payment rails.

Various systems and methods are disclosed relating to protecting cross-system exchanges. A data processing system includes one or more processing circuits configured to identify a plurality of data elements and generate a first plurality of cryptographic objects. The one or more processing circuits are further configured to record the first plurality of cryptographic objects on a distributed ledger and generate metadata corresponding to at least one of the plurality of data elements, the metadata including temporal data. The one or more processing circuits are further configured to generate a bi-temporal record including a plurality of links to the plurality of data elements and storing the corresponding metadata. The one or more processing circuits are further configured to provide an interface including a query element corresponding to the bi-temporal record.

An information technology system generally includes a cloud-based management platform with a micro-services architecture deploying a set of adaptive intelligence facilities that can be configured to automate a set of capabilities of the platform related to at least one of the value chain network entities and the features of the platform and a set of data storage facilities that can be configured to store data collected and handled by the platform. The data can be related to at least one of the value chain network entities and the features of the platform. A set of monitoring facilities can be configured to monitor the value chain network entities. The platform can be configured to host a set of applications for directing an enterprise to manage the value chain network entities from a point of origin of a product of the enterprise to a point of customer use.

A system includes a fleet resources data store that maintains a fleet resource inventory indicating fleet resources that can be assigned to perform tasks. For each fleet resource, the inventory indicates features of each fleet resource and a respective status. A set of task definitions is accessible to an intelligence layer to facilitate improving task definition based on feedback from task-specific outcomes. The system receives a job request for a robotic fleet to perform a job and determines a job definition data structure indicating a set of tasks to be performed for the job. The system applies an outcome of performing a task by a resource assigned to perform the task to a machine learning system of the intelligence layer that facilitates improving, based on the outcome, the set of task definitions. The system updates the set of task definitions based on a result of applying the machine learning system.

An emerging service in the private and non-profit sectors is to electronically facilitate engagement of employees and volunteers for various causes. The instant systems and methods provide a software as a service platform that provides user engagement via machine learning and artificial intelligence.

A VCN process may receive information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of the value chain network and at least one member of the set of AI-based learning models is trained on the training data set to determine, upon receiving the classification of the at least one of: the operating state, the fault condition, the operating flow, or the behavior, a task to be completed for the value chain network. A VCN process may configure a robotic process automation system to execute the task to facilitate an improvement in the value chain network.

The present invention discloses methods and system for receiving at least one input from one or more participants, measure and analyse the inputs with stored data to suggest models to automatically transform and close permanently any gaps found upon such comparison, analysis and implementation of suggested models. Further, an expert knowledge base and capability map (106) supports contextual understanding, and an inference and recommendation engine (107) generates adaptive insights aligned with the user's current state and long-term goals. The system (100) delivers personalized transformation pathways to enable the user development across diverse life domains. The invention further discloses a method for implementation of targeted and ecological permanent transformations of the user.

An AI-based platform for enabling intelligent orchestration and management of at least one operating process is provided herein. The AI-based platform includes an artificial intelligence system that is configured to generate a prediction of an energy pattern associated with the at least one operating process. The AI-based platform is also configured to manage the at least one operating process based on the prediction of the energy pattern.

A method performed by an NFT platform includes generating NFT secondary file information relating to a digital file and the NFT, wherein the NFT secondary file information comprises a content ID of the digital file stored in a data storage, wherein the data storage is a permanent and immutable data storage. The method may also include the NFT platform generating an NFT metadata file comprising the content ID of the digital file stored in the data storage, storing the NFT metadata file and the NFT secondary file information in the data storage, generating a smart contract of the NFT comprising a link to a content ID of the NFT metadata file stored in the data storage, and deploying the smart contact to a distributed ledger system.

AI data and datasets that are represented as NFTs and carry all applicable data for a dataset's provenance, authenticity, and ownership. NFTs are used to validate datasets useful in AI training and can also be used to identify datasets that include faulty, biased, or otherwise erroneous data to improve predictive usefulness and reliability in decision making from the AI models.

A machine comprising a transceiver, one or more processors, and memory performs communications operations via one or more user devices. The communications operations include establishing via the transceiver a connection with a first user device, and transmitting first information to the first user device. Upon not receiving an acknowledgement that the first information was received by a server, the machine maintains the first information in the memory and establishes, via the transceiver, a connection with a second user device, appends the first information to second information, and transmits the first and second information to the second user device. Upon receiving acknowledgement that the first and second information were received by a server, the offline retail machine deletes the first and second information.

Merchants provide incentives for customer transactions on accounts issued to them by issuers. Incentives include a merchant making a donation to entities with whom the merchants and / or the consumers have an affinity such as residence in the community. Each merchant can define the donation to be percentage of the transaction amount. Cryptographically secure chains are provided for uniquely labeling each such transaction and each such donation by way of incorporating role-based digital wallets and multiple synchronized transactional blockchains. Where the donations are used to purchase an Internet-of-Things (IOT) enabled system, real and / or near-real time usage information can be received and transmitted to the logical addresses of the customers and or the merchants so as to confirm efficacy of the donations.

Techniques are described, as implemented by computing devices, to control access to transactions through use of cosigning based on tokenized reputation scores. This is performed by leveraging a blockchain such that tokenized reputation scores are generated based on amounts of cryptographic reputation tokens associated with blockchain account addresses associated with applicant and co-signer service provider accounts. Transactional functionality is made available to an applicant service provider account having an insufficient tokenized reputation score by using a co-signer service provider account having a sufficient tokenized reputation score to at least partially back an obligation of the applicant service provider for a transaction.

A robot fleet management platform includes one or more processors configured to execute instructions. The instructions include receiving a job request comprising information descriptive of job deliverable and request-specific constraints for delivering the job deliverable. The instructions include applying content and structural filters to content received in association with a job request to identify portions thereof suitable for robot automation. The instructions include establishing a set of robot tasks, each defining at least a type of robot and a task objective, based on the portions of the job request that are suitable for robot automation and meet a first fleet objective. The instructions include applying fleet configuration services to the job content and the set of robot tasks to produce a fleet resource configuration data structure for the job request that associates at least one robot operating unit with each task in the set of tasks and robot adaptation instructions.

Systems and methods for enabling secure and efficient interoperability between multiple blockchains, particularly for Central Bank Digital Currency (CBDC) issuance and transfer are described. This architecture allows independent blockchains, each managed by different financial institutions or authorities, to exchange digital assets using a unified cross-chain protocol. Key features include smart contracts for locking, minting, unlocking, and burning digital tokens, relayer subroutines for message transfer, and observer nodes for real-time monitoring and reconciliation. Multi-signature wallets ensure that critical actions require approval from multiple parties, enhancing security and compliance. The system supports both reversible and irreversible cross-chain transfers, dynamic load management, and upgradable smart contracts.

Computer-implemented methods for locking a blockchain transaction based on undetermined data are described. The invention is implemented using a blockchain network. This may, for example, be the Bitcoin blockchain. A locking node may include a locking script in a blockchain transaction to lock a digital asset. The locking script includes a public key for a determined data source and instructions to cause a validating node executing the locking script to verify the source of data provided in an unlocking script by: a) generating a modified public key based on the public key for the determined data source and based on data defined in the unlocking script; and b) evaluating a cryptographic signature in the unlocking script based on the modified public key. The blockchain transaction containing the locking script is sent by the locking node to the blockchain network. The lock may be removed using a cryptographic signature generated from a private key modified based on the data.