Systems and methods for detecting tampering in software are disclosed. The system includes a preprocessor that converts source code into a minimal intermediate representation and extracts semantic and syntactic features using word embedding algorithms. The preprocessed data is then fed into two machine learning models: a classical LSTM model and a quantum LSTM model. The classical LSTM model detects basic tampering patterns, while the QLSTM model leverages quantum principles to enhance analysis and prediction of more complex tampering attempts. The system also includes a quantum cache for efficient data retrieval and manipulation, enabling real-time or near-real-time analysis. The combination of these features provides improved accuracy and effectiveness in detecting tampering, enabling timely intervention and mitigation of security threats. Remediation may be performed automatically or manually and can be based on historically determined or dynamically generated solutions.
G06F 21/57 - Certification ou préservation de plates-formes informatiques fiables, p. ex. démarrages ou arrêts sécurisés, suivis de version, contrôles de logiciel système, mises à jour sécurisées ou évaluation de vulnérabilité
2.
SYSTEM FOR INTELLIGENT TYPE-BASED IMPLEMENTATION OF RESTRICTIONS ON RESOURCE TRANSFERS
A system is also provided for intelligent type-based implementation of restrictions on resource transfers. In this regard, the system may allow for resource transfers set according to a preset resource transfer pattern to be executed while restricting net new resource transfers upon compromise of the resource transfer instrument. Subsequently, the system may use a machine learning model to analyze the attributes of incoming resource transfer requests and determine whether the resource transfer should be allowed or restricted based on the preset resource transfer pattern. In this way, the system may provide a way to fine-tune restrictions on executing resource transfers.
Quantum-based vulnerability and risk identification systems and processes are disclosed that identify vulnerabilities in configuration items (CIs) before changes are deployed. A contrastive learning algorithm compares vectors of CIs to vectors of known vulnerable components and further refines the most relevant features from those filters. Federated Learning enhances model performance, and a quantum optimization system (QOS) optimizes the results generated by the contrastive model as it has an advantage of solving optimization problems formulated with distinct variables. Proactive prediction mechanism is provided through contrastive learning. QOS provides enhanced vulnerability detection system and change management lifecycle. QOS provides deterministic results by finding optimized solutions in high-dimensional solution spaces.
G06N 10/80 - Programmation quantique, p. ex. interfaces, langages ou boîtes à outils de développement logiciel pour la création ou la manipulation de programmes capables de fonctionner sur des ordinateurs quantiquesPlate-formes pour la simulation ou l’accès aux ordinateurs quantiques, p. ex. informatique quantique en nuage
G06F 21/57 - Certification ou préservation de plates-formes informatiques fiables, p. ex. démarrages ou arrêts sécurisés, suivis de version, contrôles de logiciel système, mises à jour sécurisées ou évaluation de vulnérabilité
4.
System and method for requesting data transfers in a blockchain network
A processor receives a request to receive a transfer of a first amount of data objects into a first data file from a second data file. In response, the processor generates and transmits to the second data file a plurality of digital tokens amounting in value to the first amount of data object. The processor further transmits information associated with the first plurality of digital tokens. The processor receives the plurality of digital tokens and the first amount of data objects at the first data file from the second data file. In response, the processor determines that the requested data transfer has been completed.
H04L 9/00 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
Innovative distributed tracing system(s) and process(es) integrate automated code instrumentation, AI-driven quantum computing, particularly Photonic Quantum Computing, and real-time analysis algorithms. This system autonomously identifies and monitors network components such as gateways, service meshes, message queues, databases, and proxy servers, seamlessly capturing trace data across various communication protocols. It features dynamic scaling capabilities to handle fluctuating network loads and employs automatic tagging for precise identification of each network element and transaction. Utilizing advanced machine learning algorithms, the system efficiently analyzes trace data, identifying patterns, dependencies, and anomalies. It is equipped to intervene in operations by halting transactions under specific conditions. The incorporation of Photonic Quantum Computing allows for unparalleled anomaly detection and data analysis speed and accuracy. This system enhances trace data analysis, improves anomaly detection, and optimizes overall system performance, offering a robust, secure, and efficient approach for managing complex distributed systems.
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
6.
GEOLOCATION-BASED CONSENSUS ALGORITHM FOR USE IN MONITORING PROPOSED DATA RECORDS FOR A DISTRIBUTED LEDGER
In conventional distributed ledger systems, consensus mechanisms are required to maintain a common state of agreement among nodes while maintaining the distributed nature of the network. However, conventional consensus mechanisms require that the system access incoming data blocks during the consensus process, which can open the network to security threats. As such, a need exists to securely evaluate proposed data records prior to initiating a consensus mechanism. The system provided herein solves this problem by applying a geolocation engine to trace the origin of incoming data records and apply a digital signature to each data record. The distributed network model may then derive a location for each data record based on the digital signature. If the data record originated from a restricted location, the system may automatically block the addition of the data record without the need to access any other data contained within the data record.
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
G06Q 20/38 - Protocoles de paiementArchitectures, schémas ou protocoles de paiement leurs détails
7.
SYSTEM FOR MACHINE-LEARNING BASED IDENTIFICATION AND FILTERING OF ELECTRONIC NETWORK COMMUNICATION
A system is provided for machine-learning based identification and filtering of electronic network communication. In particular, the system may continuously monitor and pull electronic communications data from one or more networked computing systems in an enterprise environment. Based on the electronic communications data, the system may use machine learning algorithms to generate a database of associations between one or more users and one or more topics of interest. The system may then output one or more recommendations to one or more users for transmitting communications associated with the one or more topics of interest. In this way, the system may improve the efficiency of communications received and transmitted within a network.
A telephonic routing system to enable rerouting a call between a live agent and an automated agent is provided. The system may include a receiver configured to receive a telephonic call from a caller. The system may also include a router configured to route the telephonic call to a first communication pathway where the first communication pathway may connect the telephonic call to a live agent headset device. The system may also include a processor configured to retrieve a customer profile associated with a phone number of the telephonic call and display authentication data, on a graphical user interface (“GUI”) of a computing device associated with the live agent headset device. The router may be further configured to reroute the telephonic call to a second communication pathway for verifying an authenticity of the caller. The second communication pathway may establish a connection between the telephonic call and the automated agent.
H04M 3/523 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur avec répartition ou mise en file d'attente des appels
H04M 3/42 - Systèmes fournissant des fonctions ou des services particuliers aux abonnés
9.
system and method for allocating transfer of data from a data file
A processor receives a request to delegate, to a second user, one or more scheduled transfers of data objects from a first data file to a first entity. In response, the processor generates and transmits digital tokens amounting to a first amount of data objects allocated to the second user for the scheduled transfers. The processor receives a second request from the first entity for a first scheduled transfer of data objects from the first data file. In response to receiving the second request the processor transfers to the first entity a third amount of data objects equivalent to the amount of digital tokens transferred to the first entity by the second user for the first scheduled data transfer.
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
G06F 21/62 - Protection de l’accès à des données via une plate-forme, p. ex. par clés ou règles de contrôle de l’accès
H04L 9/00 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité
10.
ELECTRONIC SYSTEM FOR AUTOMATICALLY GENERATING RESOURCE DISTRIBUTIONS BASED ON SMS-BASED INSTRUCTIONS USING MACHINE LEARNING
Systems, computer program products, and methods are described herein for automatically generating resource distributions. The present invention may be configured to receive a text-based instruction and parse, using a machine learning model, the text-based instruction to generate a structured resource distribution including predicted distribution elements. The present invention may be configured to generate, based on the structured resource distribution, a resource distribution. In some embodiments, the text-based instruction may include an email message, an SMS message, recorded speech converted to text, text input to a chat function, text recognized in an image, and/or the like.
The present disclosure pertains to systems, computer program products, and methods for an automated mesh service-based deployment intelligence system. This system is designed to streamline the deployment of software by integrating a processing device and a non-transitory storage device. The storage device contains instructions that, when executed by the processing device, enable the ingestion of data from various sources such as monitoring systems, databases, and application performance management tools. Once ingested, the data is stored and processed to discern deployment patterns and detect any anomalies. Utilizing a machine learning model, the system anticipates potential deployment issues by analyzing this data. It then orchestrates the deployment of software artifacts accordingly, taking into account the insights gained from the machine learning model. Furthermore, the system is capable of real-time optimization of the deployment process to preemptively resolve any predicted issues, thereby enhancing the efficiency and reliability of software deployment operations.
H04L 67/12 - Protocoles spécialement adaptés aux environnements propriétaires ou de mise en réseau pour un usage spécial, p. ex. les réseaux médicaux, les réseaux de capteurs, les réseaux dans les véhicules ou les réseaux de mesure à distance
H04L 67/51 - Découverte ou gestion de ceux-ci, p. ex. protocole de localisation de service [SLP] ou services du Web
12.
CONTACTLESS TRANSACTION PROTECTION USING GENERATIVE ADVERSARIAL NETWORK ("GAN") DEEP LEARNING TECHNOLOGY
Methods, apparatus, and systems for identifying and suspending attempted illegitimate transactions. The methods may include attempting a transaction between a contactless transmitter included in a transaction device and a contactless receiver not included in the transaction device. In response to attempting the transaction, the methods may include generating a real-time synthetic data model at a generator module. The methods may include transmitting the real-time synthetic data model to a discriminator module. The methods may include comparing the real-time synthetic data model to identifiers of authorized contactless receivers stored in a knowledge graph. The methods may include executing a prediction whether the attempted transaction will be a legitimate transaction based on the comparison. The methods may include executing the transaction in response to a prediction that the transaction will be a legitimate transaction and terminating the transaction, in response to a prediction that the transaction will be an illegitimate transaction.
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
G06Q 20/34 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des cartes, p. ex. cartes à puces ou cartes magnétiques
13.
SYSTEM FOR AUGMENTED HASH-BASED PORTIONING OF NON-FUNGIBLE ELECTRONIC RESOURCES FOR DETECTION OF UNAUTHORIZED DUPLICATES
Systems, computer program products, and methods are described herein for augmented hash-based portioning of non-fungible electronic resources for detection of unauthorized duplicates. The present invention is configured to receive an unauthorized duplication detection request for a first NFT from a first user; segment the first digital resource into a first set of resource portions; generate a first set of hash values corresponding to the first set of resource portions; retrieve a second digital resource, wherein the second digital resource is associated with a second NFT; segment the second digital resource into a second set of resource portions; generate a second set of hash values corresponding to the second set of resource portions; determine that the second digital resource is similar to the first digital resource; determine that the second NFT is an unauthorized duplication of the first NFT; and trigger one or more responsive actions.
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
G06F 21/10 - Protection de programmes ou contenus distribués, p. ex. vente ou concession de licence de matériel soumis à droit de reproduction
14.
PERMISSION BASED INFORMATION TRANSFER BASED ON INTERNET OF THINGS (IOT) INSIGHTS
A computing platform may receive, from a plurality of IoT information sources, historical information associated with a user. The computing platform may generate, based on the historical information, a user specific policy, defining information distribution rules for the user. The computing platform may receive, from an information collection system, a request for information. The computing platform may identify whether or not the request for information requests personal information of the user. Based on identifying that the request for information requests the personal information of the user, the computing platform may identify whether or not the requested personal information violates the user specific policy. Based on identifying that the requested personal information does not violate the user specific policy, the computing platform may obscure the requested personal information, and send, to the information collection system, the obscured personal information.
A system is provided for identification of security vulnerabilities using artificial intelligence-based analysis of computing environment logs. The system may analyze data logs associated with each of the endpoint devices associated with a software packet or service. Using an artificial intelligence engine, the system may identify the failure points of the software packet or service, which may result from outdated or nonfunctional code, incorrect parameter configuration, lack of computing resources, and/or the like. The system may further detect security issues associated with the software packet or service, such as a failure to mask or obfuscate sensitive data. Based the identified failure points, the system may generate recommended remediation processes to remediate the failure points. In this way, the system provides a way to remediate failure points in software using intelligent analysis of data logs.
G06F 21/57 - Certification ou préservation de plates-formes informatiques fiables, p. ex. démarrages ou arrêts sécurisés, suivis de version, contrôles de logiciel système, mises à jour sécurisées ou évaluation de vulnérabilité
G06F 21/56 - Détection ou gestion de programmes malveillants, p. ex. dispositions anti-virus
Various aspects of the disclosure relate to identification and analysis associated with real-time resource allocation for code execution. A real-time resource allocation framework may estimate computing resource utilization for any given codebase using various models in real-time. The framework captures metadata corresponding to each codebase to be supported by the real-time resource allocation framework using a crawler that performs an initial full scan of all codebases and a later incremental scan for any changes in codebases onboarded onto framework to identify atomic code blocks in each of the codebases, to categorize those code blocks with respect to various computing resource utilization parameters and to predict an expected value for each code block for any given parameter. Blockchain and smart contract technology enables operation of each atomic code block to provide services via an enterprise network and feedback of actual values to improve prediction capabilities.
A system is provided for artificial intelligence-based detection and remediation of issues during software onboarding and deployment. In particular, the system may comprise an artificial intelligence (“AI”) engine that may be configured to identify gaps and/or potential issues in an onboarding and/or integration process for a computing software solution. In this regard, the AI engine may identify the variables and/or parameters of the target computing environment and the variable and/or parameters of the incoming software. Based on the variables and/or parameters, the AI engine may determine the potential issues that may arise during onboarding, how such issues may affect the target computing environment, and/or the remediation processes that may be required to resolve the issues. In this way, the system may provide an efficient way to identify and remediate potential issues during the software onboarding process.
Systems, computer program products, and methods are described herein for dynamically encrypting network-based interactions based on evaluated infrastructure security. The present disclosure is configured to evaluate a set of infrastructure security within a network-based interaction. The system includes evaluating a set of infrastructure security within a network-based interaction. The set of infrastructure security for the network-based interaction may comprise at least one node and a layer of security protocols. The system may further include assessing the set of infrastructure security for the network-based interaction, wherein the set of infrastructure security is assessed on a set of predetermined criteria. The system may include encrypting the network-based interaction based on assessment of the infrastructure security for the network-based interaction, with encryption complexity correlating to assessment of the set of infrastructure security for the network-based interaction.
H04L 41/16 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p. ex. des réseaux de commutation de paquets en utilisant l'apprentissage automatique ou l'intelligence artificielle
19.
METHOD AND APPARATUS TO AUTO-HEAL MICROSERVICES INFORMATION FLOW BREAKDOWN ACROSS CONTAINERS IN A CLOUD-BASED APPLICATION
A system and method for auto-healing a microservices information flow across containers in a cloud. A cloud service request may be received in connection with a previous electronic transaction request that was not successfully performed by a cloud-based application. The unsuccessful performance may be related to parameters that were used for the transaction. A generative AI engine may be used to auto-heal the microservices information flow. The auto-healing may include updating one or more parameters associated with the electronic cloud service request. The generative AI engine may update the parameters based on one or more previously used solutions and may perform the updates on the fly. The updated parameters may be application program interface (API) parameters that have been added, deleted, or changed. The requested transaction may be a banking transaction. The transaction may be reprocessed based on the updated parameters.
G06Q 20/38 - Protocoles de paiementArchitectures, schémas ou protocoles de paiement leurs détails
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
20.
SYSTEM AND METHOD FOR SECURING ELECTRONIC IDENTITY DATA USING ELECTRONIC DATA OBFUSCATION AND MASKING
A system is provided for securing electronic identity data using electronic data obfuscation and masking. In particular, the system may comprise a networked environment comprising a database of unique characteristic identifiers, where each unique characteristic identifier may be associated with an entity or individual. In addition, the database may comprise a plurality of decoy identifiers that may be generated by a scrambling engine through one or more modifying operations on one or more unique characteristic identifiers. Furthermore, the unique characteristic identifiers within the database may be encrypted or converted from the “raw” unique characteristic data that may be stored offline. In this way, the system may provide a way to obfuscate legitimate unique characteristic data within the database to increase the security of the data stored therein.
Systems, computer program products, and methods are described herein for generating alternative software configurations using advanced computational models for data analysis and automated processing. The present disclosure is configured to analyze a software configuration to determine a non-compliant software portion, wherein the non-compliant software portion comprises a mismatch between at least a portion of the software configuration and a compliance database. Further, the present disclosure is configured to receive a compliant software portion, wherein the compliant software portion is associated with the compliance database. Further, the present disclosure is configured to reconfigure, using an artificial intelligence model, the software configuration to include the compliant software portion, wherein reconfiguring the software configuration comprises modifying the software configuration to comply with the compliance database. Further, the present disclosure is configured to deploy an updated software configuration, wherein the updated software configuration comprises the compliant software portion.
Systems, computer program products, and methods are described herein for AI-based adaptive security parameter calculation in license key generation with quantum-resistant protections. The present disclosure is configured to utilize advanced cryptographic techniques, including elliptic curve cryptography and SHA-3 hashing, to produce secure digital signatures for license keys. An AI-driven engine analyzes historical security data to adaptively adjust key generation parameters, ensuring enhanced protection against current and emerging threats. This adaptive approach allows for continuous learning and improvement of security measures, making the system robust against both classical and quantum cryptographic attacks. The disclosed methods integrate the generation, assessment, and validation of keys into a streamlined process that promotes high security while maintaining efficiency.
H04L 9/06 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité l'appareil de chiffrement utilisant des registres à décalage ou des mémoires pour le codage par blocs, p. ex. système DES
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
Centralized management of web application firewalls (WAFs) is disclosed. Network-security devices in data centers perform server load balancing and implement WAFs for applications. Vendor-specific bridges map application and system parameters for use by a management process. Policies for policy-name/device pairs are provided and grouped into policy groups, which can be included with global parent policy groups. Portions of policy metadata can be retrieved without degrading system performance to detect changes, which can then be synchronized across other applicable policies, groups, devices, and WAFs.
Systems and methods are disclosed for executing large quantum programs efficiently and securely. It breaks down programs into small, logical components, leveraging runtime analysis and AI-driven labeling. These components are then deployed across distributed quantum hardware nodes based on factors like noise levels, capabilities, and geolocation, optimized by a deep learning engine. Ownership and deployment paths are tracked using non-fungible tokens (NFTs) on a blockchain network, ensuring security and transparency. Outputs from each node are validated and aggregated based on their NFT linkage, resulting in accurate and reliable program results. This distributed quantum DevOps framework promotes scalability, performance optimization, and secure management, accelerating the development and application of quantum computing across diverse fields.
G06N 10/80 - Programmation quantique, p. ex. interfaces, langages ou boîtes à outils de développement logiciel pour la création ou la manipulation de programmes capables de fonctionner sur des ordinateurs quantiquesPlate-formes pour la simulation ou l’accès aux ordinateurs quantiques, p. ex. informatique quantique en nuage
Systems, computer program products, and methods are described herein for determining module interdependency using advanced computational models for data analysis and automated processing. The present disclosure is configured to receive a module characteristic from a system environment, wherein the system environment comprises a module; map the module characteristic, wherein mapping the module characteristic comprises comparing the module characteristic associated with the module with the module characteristic associated with the system environment; determine a weight for the module, wherein determining the weight comprises evaluating a significance level of the information produced by the module, analyzing a number of active modules contributing to a process, computing a weight value, and prioritizing the modules based on the weight value; determine, using an artificial intelligence module, a module update for the module characteristic, wherein the module update comprises updating the module characteristic; and implement the module update in the system environment.
A method includes receiving a data population and a plurality of constraints. The method includes representing a plurality of units corresponding to the data population by a first set of vectors, determining a holistic feature, and representing the holistic feature by a second vector. The method further includes defining a target volume based on a subset of the plurality of constraints, and for each unit, subtracting a vector representing a respective unit from a vector representing a current holistic feature to determine a third vector. The method thus includes instantiating a first data bucket and a second data bucket based on the plurality of units and the units corresponding to the current data population, and for each third vector, transferring units from the first data bucket to the second data bucket so as to cause the holistic feature to converge to the current holistic feature defined by the target volume.
Arrangements for leveraging LiDAR for user validation are provided. A computing platform may receive, from a spatial computing device, first data of a first user environment at a first time captured via LiDAR. A first spatial computing map of the first user environment at the first time may be generated and scored to indicate a likelihood that a user of the spatial computing device is valid. The computing platform may receive a request to process an event. In response, the computing platform may cause the spatial computing device to capture, via LiDAR, second data of a current environment of the user at a current time. A second spatial computing map based on the current environment of the user at the current time may be generated and scored. The score for the second spatial computing map may be compared to a threshold to determine whether to process the requested event.
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
Systems, computer program products, and methods are described herein for preventing synthetic misappropriation events over an electronic network. The present disclosure is configured to identify a user communication associated with a communication channel; identify a user account associated with the user communication; collect user account data associated with the user account; receive at least one user communication data via the communication channel; transmit the at least one user communication data to at least one internal communication component; receive at least one internal communication data from the internal communication component; generate a virtual avatar based on at least one of user account data, the at least one user communication data, or the at least one internal communication data; and transmit a virtual avatar interface component based on the virtual avatar to a user device associated with the communication channel, wherein the virtual avatar comprises the at least one internal communication data.
Systems, methods, and apparatus are provided for tracking user interactions in a quantum system. An authentication persona may be routed to a quantum processor. The quantum processor may generate a virtual quantum channel. The virtual quantum channel may execute multiple attribute-based authentication chains concurrently. The quantum processor may dynamically change the attributes in the authentication chains for the duration of a user session. The quantum processor may substantially continuously validate the attributes in each chain for the duration of the user session. The quantum processor may map the virtual authentication channel to a potential state of quantum information. In some embodiments, the system may automatically scale the quantum processor during an authentication task by adding additional quantum circuits to each quantum thread when the authentication task has a duration that is longer than a threshold duration and/or a volume that is larger than a threshold volume.
G06N 10/40 - Réalisations ou architectures physiques de processeurs ou de composants quantiques pour la manipulation de qubits, p. ex. couplage ou commande de qubit
A method for detecting document forgery in real-time at a smart device is provided. The method may include capturing, via a smart device, an image of a document. The method may include extracting, via the smart device, data from the image. Based on the data, the method may include creating a dataset that may include a document type for the document and document details included in the document. The method may include confirming, on the smart device, that user account data retrieved from a remote server correlates to user account identifying data included in the document details. Following the confirming, the method may include determining a validity of the document using a fraud document detection engine. When one or more discrepancies are identified between the dataset and the ML data, the method may include transmitting an electronic fraud alert notification from the smart device to a network of smart devices.
Provided herein is a spatial computing system. The system may include a spatial computing apparatus configured to receive an anchoring instruction and consequently anchor a virtual object to a physical object; an onboarding module configured to interface with an existing spatial computing software platform; a spatial telemetry extraction module configured to extract positioning metadata associated with the physical object; a spatial analyzer module configured to detect an anomaly in the anchoring instruction, wherein the anomaly may indicate a suspected attempt to glean personal information from an image of the physical object; an anchor validation module configured to monitor changes in anchoring instructions; a session management module configured to terminate a spatial computing session, upon receiving an indication of an anomaly; a spatial security rule orchestration module configured to block implementation of the anchoring instruction; and a deep learning module.
G06F 21/52 - Contrôle des utilisateurs, des programmes ou des dispositifs de préservation de l’intégrité des plates-formes, p. ex. des processeurs, des micrologiciels ou des systèmes d’exploitation au stade de l’exécution du programme, p. ex. intégrité de la pile, débordement de tampon ou prévention d'effacement involontaire de données
G06T 19/00 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie
Systems, methods, and apparatus are provided for adaptive network routing protocols. A mobile device application may receive a request to pair a first payment instrument with a second payment instrument. The application may generate a preauthorized routing packet encoding information associated with the first payment instrument. The routing packet may be QR code encrypted using a private key associated with the network and functioning as a digital wallet. The routing packet may be deployed to a processing network associated with the second payment instrument. The processing network may receive transaction information from a point of sale, including a charge initiated using the second payment instrument. The processing network may access the routing packet and determine whether transaction routing is active. When transaction routing is active, the charge may be routed to the first payment instrument. When transaction routing is not active, the second payment instrument may be charged.
Arrangements for remotely developing an application are provided. A computing platform may receive a request to build an application. The computing platform may receive the request through an API gateway. The computing platform may use a smart contract to identify a first node as a parent node and a plurality of nodes as child nodes. The computing platform may direct the child nodes to transmit virtual copies of dependency packages to the parent node. The parent node may build the application after receiving the dependency packages. The computing platform may detect that the application has been successfully built and may automatically publish the application at an enterprise server.
Solutions for integration and ongoing management of enterprise applications operating in a networked environment are disclosed. An AI transformer extracts, transforms, and manages integration point information from various sources, including documents, UMLs, and other system diagrams. Instead of relying on traditional documentation, the invention utilizes blockchain-based NFTs (non-fungible tokens) to store and manage integration point information. NFTs are created using smart contracts, which define the rules and conditions for the NFT. A metadata engine prepares metadata for generating the Integration Point NFTs. An NFT manager creates the integration point NFTs. A change analyzer determines the impact of changes to integration points on different applications and functionalities. A release compliance component is integrated with DevOps and is responsible for ensuring that integration point changes are incorporated into all impacted applications according to the release defined in the NFT.
User authentication/login occurs prior to a user accessing a network, application, web site/service or coming in contact with a computing device. Pre-authenticating/logging-in of users in advance of the user visiting or accessing the computing technology. A single device passcode that unlocks the device and a passcode vault storing the user's passcodes. Unlocking the passcode vault provides for the passcodes stored therein to be automatically applied to the corresponding entities requiring authentication to grant users access without requiring any user input/actions. Machine-Learning (ML) models that are trained to learn user login patterns are implemented to identify login pattern(s) and subsequently passcodes are retrieved for the entities making up the login pattern(s) from the passcode vault. Prior to the user accessing the entities that make up the login pattern, the present invention accesses the entities and applies the retrieved passcodes to pre-authenticate the user.
Automated updating/changing of passcodes based on network, application and/or website specific passcode rules. Passcode rules for passcodes stored in a passcode vault are identified, including passcode structure rules and passcode expiration rules. Subsequently the expiration dates trigger automatic update/change of the passcodes in accordance with the passcode structure rules. Additionally, networks, applications and/or web sites/services associated with the passcodes may be monitored for security threats, which, when detected, trigger automatic update/change of the passcodes in accordance with the passcode structure rules. Such updating/changing of the passcodes may be transparent to the users or the users may receive notification alerts. In further embodiments of the networks, applications and/or web sites services are monitored for changes to their respective passcode rules, such that future automated passcode updates/changes may occur in accordance with the updated passcode rules.
G06F 21/46 - Structures ou outils d’administration de l’authentification par la création de mots de passe ou la vérification de la solidité des mots de passe
Arrangements for simulation generation and abnormality detection are provided. In some examples, a computing platform may identify a plurality of applications for analysis. The computing platform may execute a plurality of simulated scenarios for each application and, based on execution of the simulated scenarios, the computing platform may capture abnormality results for each application. The abnormality results, as well as application information and/or simulated scenarios may be stored. In some examples, the computing platform may compare deployed versions of each application to the captured abnormality data to identify an abnormality in a deployed version of an application. Based on the identified abnormality, the computing platform may evaluate the identified abnormality to determine whether it can be resolved automatically. If so, the computing platform may execute one or more commands modifying the deployed application to resolve the abnormality.
Provided herein is an automated data sanitization system for sanitizing and overlaying data to a non-production environment. The system may include a data sanitization rule engine, which may be configured to receive a recommended set of rules for sanitizing a data source and/or define rules for the dataset. The system may include a feature comparison component, which may be configured to generate suggested sanitization rules for a new dataset, based on comparison with previous datasets. The system may include a data sanitization component, which may be configured to implement sanitization of a plurality of datasets. The system may include a gatekeeper, which may be configured to restrict export of a non-sanitized data source and/or to restrict overlaying of a non-sanitized data source onto a non-production environment.
A method, apparatus, and system for minting blockchain transactions, leveraging generative artificial intelligence (“AI”), based on information stored in a deoxyribonucleic acid (“DNA”) storage apparatus is provided. The method, apparatus, and system may include receiving a request to execute a transaction from a generative AI user prompt interface. The method, apparatus, and system may include, in response to receipt of the request, analyzing the request to identify user requirements associated with the request, generating a smart contract for each of the user requirements, and converting each of the user requirements to a binary code. The method, apparatus, and system may include synthesizing a DNA sequence including nucleotides, each group of the nucleotides corresponding to one of the binary codes. The method, apparatus, and system may include, after the synthesizing of the DNA sequence, producing a blockchain storing data relating to the transaction using data stored in the DNA sequence.
G06N 10/80 - Programmation quantique, p. ex. interfaces, langages ou boîtes à outils de développement logiciel pour la création ou la manipulation de programmes capables de fonctionner sur des ordinateurs quantiquesPlate-formes pour la simulation ou l’accès aux ordinateurs quantiques, p. ex. informatique quantique en nuage
40.
Monitor and Control Toxic Configuration in Container Deployment
Systems and processes are disclosed to monitor and control toxic configuration in software container deployment. AI-based monitor in each node provides end-to-end fault management system that can detect, diagnose, classify, and suggest remediation actions for non-virtualized cloud-based misconfiguration vulnerabilities. Anomalies are diagnosed using pre-computed fault signatures and automated remediation is integrated with a cloud management stack. Multiple monitoring layers within nodes secure the container-based virtualization environment. Container-based “CSTC security framework” virtualization technology provides security and identifies potential toxic configuration threats. CSTC security locates container-based systems at severe risk for DDOS attacks to kernel vulnerability/container breakout and ensures appropriate privilege configuration for user processes. Resource access limited based on policies and CSTC security profiles as well as provide AI-based monitoring of container runtime behavior to provide additional security layer that ensures safety and protection in container-based virtualization.
G06F 21/57 - Certification ou préservation de plates-formes informatiques fiables, p. ex. démarrages ou arrêts sécurisés, suivis de version, contrôles de logiciel système, mises à jour sécurisées ou évaluation de vulnérabilité
G06F 21/55 - Détection d’intrusion locale ou mise en œuvre de contre-mesures
41.
SINGLE DEVICE-LEVEL PASSCODE THAT PROVIDES AUTOMATED ACCESS TO MULTIPLE COMPUTING DEVICES AND PASSCODE-PROTECTED APPLICATIONS AND WEB SITES/SERVICES
A single device-level passcode serves to unlock a passcode vault, such that, automated login/access to other computing devices, applications, networks and/or websites/web services, which have the user's passcodes stored in the vault, occurs. Specifically, in the case of other computing devices, short-range wireless communication between the initial logged-in computing device and another computing device triggers retrieval of the other computing devices passcode from the passcode vault and automated user log-in at other computing device. The passcode may be mapped to previously-captured user characteristics, such that multi-factored authentication including user characteristic verification may occur at the time the passcode vault is unlocked (i.e., when the single passcode is entered), continuously throughout the device session and/or when the passcode vault is accessed to retrieve a passcode for another device, network, application or web site/service.
H04W 4/80 - Services utilisant la communication de courte portée, p. ex. la communication en champ proche, l'identification par radiofréquence ou la communication à faible consommation d’énergie
42.
Orchestrate events in Distributed DevOps Apparatus Leveraging Generative AI
Systems and methods for orchestrating events in distributed DevOps apparatus leveraging generative AI are disclosed to automate and streamline software development and deployment in distributed DevOps environments using Generative Adversarial Networks (GANs) and other AI techniques. The method involves interpreting UML diagrams, design documents, or the like with generative AI and computer vision to create DevOps tasks, integrating with various DevOps tools for task management, and deploying generated rules for automated event execution. Metadata is generated and processed in order to facilitate AI analysis. The systems and methods reduce manual intervention, increase efficiency, and improve accuracy, scalability, security, and compliance in DevOps workflows.
Aspects related to providing explainable artificial intelligence (XAI) using distributed ledger technology are provided. An XAI platform may train a first iteration of a deep learning model. The platform may store a record of the model to a distributed ledger. The platform may automatically perform an iterative recording process to generate additional iterations of the model and store records of the additional iterations of the model. During the process, the platform may receive outputs of the model. Based on the outputs, the platform may identify an anomalous output. The platform may identify one or more source iterations of the model that correspond to the anomalous output. The platform may initiate one or more response actions based on identifying the one or more source iterations of the model.
H04L 9/00 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité
H04L 41/16 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p. ex. des réseaux de commutation de paquets en utilisant l'apprentissage automatique ou l'intelligence artificielle
44.
SYSTEM FOR FEATURELESS IMAGE CATEGORIZATION AND RECOGNITION USING UNSUPERVISED LEARNING
Systems and methods are described for image categorization and recognition using unsupervised learning. The disclosure is configured to receive an unseen image; implement a first filter on the unseen image to generate a first version of the unseen image; implement a second filter on the first version of the unseen image to generate a second version of the unseen image; determine a subset of the second version of the recorded images that matches the second version of the unseen image; retrieve a subset of first version of recorded images associated with the subset of second version of recorded images; and determine a first recorded image from the subset of the first version of the recorded images that matches the first version of the unseen image. Thereafter, retrieve a first set of responsive actions associated with the first recorded image and provide the first set of responsive actions to the device.
G06V 10/75 - Organisation de procédés de l’appariement, p. ex. comparaisons simultanées ou séquentielles des caractéristiques d’images ou de vidéosApproches-approximative-fine, p. ex. approches multi-échellesAppariement de motifs d’image ou de vidéoMesures de proximité dans les espaces de caractéristiques utilisant l’analyse de contexteSélection des dictionnaires
G06V 10/36 - Utilisation d’un opérateur local, c.-à-d. des moyens pour opérer sur des points d’image situés dans la proximité d’un point donnéOpérations de filtrage locales non linéaires, p. ex. filtrage médian
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
45.
Transforming mainframe processes and routing based on system status
A mainframe processing system comprises a memory operable to store data transformation instructions and one or more processors, at least one of the one or more processors operably coupled to the memory. The one or more processors are configured to receive batch processing data from one or more data sources, identify a plurality of mainframe processes associated with corresponding batch processes, and dispatch each mainframe process to a respective mainframe site.
G06F 11/20 - Détection ou correction d'erreur dans une donnée par redondance dans le matériel en utilisant un masquage actif du défaut, p. ex. en déconnectant les éléments défaillants ou en insérant des éléments de rechange
G06F 9/46 - Dispositions pour la multiprogrammation
G06F 9/48 - Lancement de programmes Commutation de programmes, p. ex. par interruption
G06F 9/50 - Allocation de ressources, p. ex. de l'unité centrale de traitement [UCT]
G06F 11/14 - Détection ou correction d'erreur dans les données par redondance dans les opérations, p. ex. en utilisant différentes séquences d'opérations aboutissant au même résultat
Aspects of the disclosure relate to a network traffic monitoring platform. The platform may train an attack pattern analysis model to output a behavior profile and a cumulative attack score. The platform may identify a password failure rate spike. The platform may extract a password hash from the network traffic. The platform may generate the behavior profile based on the password hash. The platform may generate the cumulative attack score based on the behavior profile. The platform may compare the cumulative attack score to a threshold. Based on identifying that the cumulative attack score is below the threshold, the platform may identify the password hash as a secure hash. Based on identifying that the cumulative attack score meets or exceeds the threshold, the platform may and generate password complexity rules. The platform may refine the attack pattern analysis model based on the attacked hash and the cumulative attack score.
Systems, computer program products, and methods are described herein for data block analysis prioritization and routing via quantum machine learning. The present disclosure includes retrieving distributed ledger transactions, retrieving a stream of telemetry data of computer hardware, clustering, based on the transaction metadata, the distributed ledger transactions using a clustering engine, generating, using a machine learning model, a predetermined number of transaction placement schemas of the computer hardware, determining, from a probability output by parallel simulation testing via a quantum computer, a prime schema and a configuration of the prime computer hardware, and routing, based on the prime schema, a transaction cluster to the prime computer hardware.
G06F 18/2321 - Techniques non hiérarchiques en utilisant les statistiques ou l'optimisation des fonctions, p. ex. modélisation des fonctions de densité de probabilité
G06N 10/60 - Algorithmes quantiques, p. ex. fondés sur l'optimisation quantique ou les transformées quantiques de Fourier ou de Hadamard
48.
Generative artificial intelligence-based automated teller machine process generation
Arrangements for using generative artificial intelligence models for ATM process generation are provided. In some examples, a computing platform may receive, from at least one image or measurement capture device, dimension data associated with an ATM. The ATM may have a plurality of components arranged on a face of the ATM. The dimension data may be input to a generative artificial intelligence model and the model may be executed to output, based on the dimension data, a position or location of each ATM component relative to a reference point on the ATM. In some examples, the model may further output at least one audio script describing the location of each component. The model may output one or more translations of the at least one audio script. The computing platform may transmit or send the at least one audio script to the ATM for presentation during user interaction with the ATM.
G07F 19/00 - Systèmes bancaires completsDispositions à déclenchement par carte codée adaptées pour délivrer ou recevoir des espèces ou analogues et adresser de telles transactions à des comptes existants, p. ex. guichets automatiques
Aspects of the disclosure relate to NFT exchanges. A computing platform may generate a plurality of F-NFTs including F-NFT identifiers that configure the F-NFTs for end-to-end tracking. The computing platform may extract, from a voice communication, information of a request to transfer an F-NFT. The computing platform may generate, based on the information of the request, a text file. The computing platform may input, into a GPT-4 algorithm, the text file to produce a F-NFT contract identifying the F-NFT, the transferor of the F-NFT, and a transferee of the F-NFT. The computing platform may send, to an NFT exchange platform, the F-NFT contract, which may cause the NFT exchange platform to transfer custody of the F-NFT from an account of the transferor at a first institution to an account of the transferee at a second institution.
Tracking digital resources used in resource exchange events conducted in virtual reality computing environments. Digital resources used in resource exchange events conducted in virtual reality computing environments are tagged with data that at least identifies the location of the resource exchange event in terms of virtual reality computing environment and/or sub-environments. In response, machine-learning algorithm(s) are implemented that analyze the tags to determine resource exchange event movement patterns for specific digital resources. The resource exchange event movement patterns may include digital resource movement across multiple different virtual computing environments.
A computing platform may train, using historical information classification information, an information classification model, which may configure the information classification model to classify information and identify, based on the classification, a storage location. The computing platform may receive, from a user device, a request to store information. The computing platform may identify, using the information classification model, a cloud based storage location for the information. The computing platform may generate an NFT representative of the first cloud based storage location. The computing platform may direct a cloud based storage system to store the information at the cloud based storage location. The computing platform may send, to the user device, the NFT. The computing platform may receive, from the user device, the NFT and a request to access the information. Based on validating the NFT, the computing platform may grant the user device access to the information.
G06F 21/64 - Protection de l’intégrité des données, p. ex. par sommes de contrôle, certificats ou signatures
H04L 67/1097 - Protocoles dans lesquels une application est distribuée parmi les nœuds du réseau pour le stockage distribué de données dans des réseaux, p. ex. dispositions de transport pour le système de fichiers réseau [NFS], réseaux de stockage [SAN] ou stockage en réseau [NAS]
54.
SYSTEM AND METHOD FOR LEVERAGING DISTRIBUTED REGISTER TECHNOLOGY TO MONITOR, TRACK, AND RECOMMEND UTILIZATION OF RESOURCES
Embodiments of the present invention provide a system for leveraging distributed register technology to securely monitoring, tracking, and recommending utilization of resources. The system is configured for gathering one or more input parameters from one or more entity systems, collecting activity data from one or more third party systems, analyzing the activity data collected from the one or third party systems, generating one or more recommendations based on the one or more input parameters and analyzing the activity data, wherein the one or more recommendations are associated with one or more activities, estimating resource usage for the one or more recommendations, and allocating resources to the one or more recommendations.
Systems, and methods are described herein for integrated analysis of foreground and background communication data. The present disclosure is configured to collect foreground communication data from one or more data sources; collect background communication data from one or more data sources; pre-process the collected data to clean, normalize, and transform it into a uniform format; integrate the pre-processed data into a cohesive dataset via data alignment based on common identifiers such as timestamps or session IDs; analyze the integrated dataset using an artificial intelligence (AI) engine employing machine learning algorithms to identify patterns and generate responses based on the cohesive dataset; generate an initial AI response based on the analysis; present the AI-generated response and alternative solutions to a user through an interactive user interface; capture user feedback on the presented responses and alternative solutions; and retrain the AI engine based on the captured user feedback to improve future decision-making.
G06F 16/00 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet
G06F 16/215 - Amélioration de la qualité des donnéesNettoyage des données, p. ex. déduplication, suppression des entrées non valides ou correction des erreurs typographiques
G06F 16/26 - Exploration de données visuellesNavigation dans des données structurées
56.
ELECTRONIC ARTIFICIAL INTELLIGENCE SYSTEM FOR AUTHENTICATING SOFTWARE
An artificial intelligence (AI) and machine learning (ML) (collectively “AI/ML”) system that provides dynamic detection of potential of resource updates, authentication of the resources updates, and tracking of the links between resources through the use of resource signatures. The resource signatures may provide an indication of the application information, the resources that are accessed by the application, and the resources that access the application. As such, the AI/ML system can monitor and track the applications and updated resources that interact with the applications in order to identify any potential security issues, as well as to optimize and standardize the use of resources by the users when developing applications.
H04L 41/16 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p. ex. des réseaux de commutation de paquets en utilisant l'apprentissage automatique ou l'intelligence artificielle
57.
TechWiki - Computer Diagnostics using Quantum Annealing and Blockchain Framework
A quantum computing system for determining diagnostic solutions for detected faults in computing devices using at least quantum annealing is described. The quantum computing system takes advantage of superposition and entanglement properties of qubits. A plurality of qubits is initialized into states representing historical data associated with historical faults and historical diagnostic solutions. Couplers entangle the plurality of qubits together based on the detected fault, generating a set of states in uniform superposition. Biases influence the energy levels of the set of states based on the detected fault. The quantum computing system measures a state with the lowest energy level within the set of states to determine diagnostic solutions to the detected fault. The system may further leverage a blockchain ledger for securely storing and accessing historical data associated with historical faults and historical diagnostic solutions.
Embodiments of the invention are directed to a system, method, or computer program product for convenient, secure digital data archiving and utilization across multiple communication channels. The system and methods may also include an array of indications regarding what documents may be required to be uploaded by a user, how many documents to upload, and a confirmation of documents uploaded being the correct types following a user upload action. As such, the system may utilize JSON templates indicating a number of required documents, a correct confirmation message following document upload, and a mechanism for confirming that the documents being uploaded are the correct types of documents via an analysis of input fields of the uploaded documents.
A method for managing metaverse instances includes registering a physical control level for a user. The physical control level includes information about physical locations and data items that the user is allowed to access within an entity. A digital identity is generated for the user. The digital identity includes a virtual control level for an avatar of the user and allows the avatar to access a metaverse instance of a metaverse of the entity. The virtual control level is synchronized with the physical control level. Computing resources to be allocated to the metaverse instance are determined. A first instruction is sent to a metaverse server to open the metaverse instance. A second instruction is sent to the metaverse server to allocate the computing resources to the metaverse instance. A third instruction is sent to the metaverse server to generate the avatar. The avatar is allowed access to the metaverse instance.
Various aspects of the disclosure relate to enforcing dynamically updated network security policies in real-time (or upon an identified update) from multiple organizations and anonymously analyze computing system configuration information uploaded from third-party computing systems. An analysis engine monitors compliance information and compare the compliance information against the security rules and/or requirements for one or more enterprise networks. A visualization providing a network map with a visual representation of each product system service system may include communication links between internal applications and/or computing systems and drill-down capability to identify issues as they are occurring or are predicted to occur. The security escrow system may include a mechanism to automatically enable/disable access between third party networks and one or more enterprise computing systems in real-time based on identified compliance information.
Enterprise organizations often have thousands or tens of thousands of employees accessing various websites while conducting business. Users may inadvertently reach compromised websites, unsecure websites, and/or websites that include broken links, which may be used by bad actors to redirect users to websites that include malware threats. Accordingly, arrangements described herein provide for real-time detection and notification of non-functioning websites to enable users to avoid potential malicious acts associated with these non-functioning websites.
A method includes analyzing a first log of a first log type generated by a first data store and determining if the first log has a first format of the first log type. In response to determining that the first log does not have the first format of the first log type, the first data store and the first log type are identified as degraded. A first impact score is determined for the first log type. First dependency information is analyzed for the first data store. In response to determining that no data store receives data items from the first data store, a report is generated. The report includes an identification that the first data store is degraded, an identification that the first log type is degraded, and the first impact score associated with the first log type.
G06F 16/20 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet de données structurées, p. ex. de données relationnelles
G06F 16/21 - Conception, administration ou maintenance des bases de données
A computing platform may train a machine learning model to detect and analyze threat actor activities. The computing platform may generate dynamic honeypotted files and deploy the generated dynamic honeypotted files as adaptive defenses to threat actors in a computing environment. The computing platform may adapt to threat actor activities based on analyzed behavior of the threat actor and any identified tools used by the threat actor to gain access to the computing system. Threat actor activities may be written to a blockchain to publicly record all transactions related to a threat actor's activities for analysis and generation of adaptive defenses to threat actor attacks. The computing platform may cause redirection of the threat actor into a specific computing environment through generation and deployment of dynamic honeypotted files.
Various aspects of the disclosure relate to enforcing dynamically updated network security policies in real-time (or upon an identified update) from multiple organizations and anonymously analyze computing system configuration information uploaded from third-party computing systems. An analysis engine monitors compliance information and compare the compliance information against the security rules and/or requirements for one or more enterprise networks. A visualization providing a network map with a visual representation of each product system service system may include communication links between internal applications and/or computing systems and drill-down capability to identify issues as they are occurring or are predicted to occur. The security escrow system may include a mechanism to automatically enable/disable access between third party networks and one or more enterprise computing systems in real-time based on identified compliance information.
H04L 41/16 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p. ex. des réseaux de commutation de paquets en utilisant l'apprentissage automatique ou l'intelligence artificielle
Various aspects of the disclosure relate to enforcing dynamically updated network security policies in real-time (or upon an identified update) from multiple organizations and anonymously analyze computing system configuration information uploaded from third-party computing systems. An analysis engine monitors compliance information and compare the compliance information against the security rules and/or requirements for one or more enterprise networks. A visualization providing a network map with a visual representation of each product system service system may include communication links between internal applications and/or computing systems and drill-down capability to identify issues as they are occurring or are predicted to occur. The security escrow system may include a mechanism to automatically enable/disable access between third party networks and one or more enterprise computing systems in real-time based on identified compliance information.
A computing platform may train a machine learning model to detect and analyze threat actor activities. The computing platform may generate dynamic honeypots and deploy the generated dynamic honeypots as adaptive defenses to threat actors in a computing environment. The computing platform may adapt to threat actor activities based on analyzed behavior of the threat actor and any identified tools used to by the threat actor to gain access to the computing system. The computing platform may cause redirection of the threat actor into a specific computing environment through generation and deployment of dynamic honeypots.
A method analyzes dependency information for a first data store. Upon determining that a data pipeline associates a first log type generated by the first data store with a second log type generated by a second data store, a first number of logs of the first log type that are generated at a first time, a first baseline number, and a first threshold value are determined. Upon determining that the first number of logs differs from the first baseline number by less than the first threshold value, a second number of logs of the second log type that are generated at a second time, a second baseline number, and a second threshold value are determined. Upon determining that the second number of logs differs from the second baseline number by more than the second threshold value, the data pipeline is identified as degraded.
A computing platform may generate, using a test case generation model, a plurality of large language model (LLM) test cases. The computing platform may input, into an LLM, the plurality of LLM test cases, which may produce a plurality of unverified LLM test results. The computing platform may input, into a validation model, the plurality of LLM test cases, which may produce a plurality of validated LLM test results. The computing platform may compare, using a falsified output evaluation model, the plurality of unverified LLM test results with the corresponding plurality of validated LLM test results, which may produce an LLM compliance score for the LLM. The computing platform may compare the LLM compliance score to a compliance threshold. Based on identifying that the LLM compliance score meets or exceeds the compliance threshold, the computing platform may automatically deploy the LLM for use in an enterprise environment.
A computing platform may configure a dependency knowledge graph indicating file dependencies for mainframe applications, and an error knowledge graph indicating errors and corresponding solutions for the mainframe applications. The computing platform may receive mainframe source code. The computing platform may analyze, using the knowledge graphs, the mainframe source code to identify potential errors and corresponding solutions. Based on identifying an error in the mainframe source code, the computing platform may cause the mainframe source code to be updated according to the corresponding solution. The computing platform may analyze, using the dependency knowledge graph and the error knowledge graph, the updated mainframe source code to identify remaining errors. Based on identifying an absence of the remaining errors, the computing platform may send, to a mainframe build and deployment engine, the updated mainframe source code, which may cause the mainframe build and deployment engine to automatically execute a build process.
Access control within a virtual reality computing system(s) is provided. Once a user requests entry into a virtual reality computing environment and their identity is verified (i.e., authenticating), access privileges are determined/assigned for the user that restrict or allow user access to virtual locations and/or virtual objects/avatars present in the virtual reality computing environment. In specific instances, the access privileges may be dynamically altered during the user's virtual reality computing session based on the user's interactions or other behaviors exhibited during the user's virtual reality computing session. As such the present invention provides necessary control over what areas of a virtual reality computing environment a user can access and/or which virtual objects/avatars a user can interact with.
Promoting various banking and financial services by means of discounts, advertisements, and incentives generated in connection with customers' use of deposit accounts, checking accounts, certificates of deposit, individual retirement accounts, brokerage accounts and general investment accounts
Promoting various banking and financial services by means of discounts, advertisements, and incentives generated in connection with customers' use of deposit accounts, checking accounts, certificates of deposit, individual retirement accounts, brokerage accounts and general investment accounts
Promoting various banking and financial services by means of discounts, advertisements, and incentives generated in connection with customers' use of deposit accounts, checking accounts, certificates of deposit, individual retirement accounts, brokerage accounts and general investment accounts
74.
INTELLIGENT RESOURCE EVALUATOR SYSTEM FOR ROBOTIC PROCESS AUTOMATIONS
Aspects of the disclosure relate to an intelligent resource evaluation engine. A computing platform may monitor the plurality of RPA machines to detect parameter information. The computing platform may store the parameter information along with corresponding RPA machines as a key value pairs in a database. The computing platform may identify first current parameter information for a first RPA machine using the key value pairs. The computing platform may input the first current parameter information into an intelligent resource evaluation model, which may output first machine selection information for the first RPA machine. Based on identifying that the first RPA machine is sufficient to execute the first robotic automation process, the computing platform may send direct the first RPA machine to execute the first robotic automation process.
G05B 13/02 - Systèmes de commande adaptatifs, c.-à-d. systèmes se réglant eux-mêmes automatiquement pour obtenir un rendement optimal suivant un critère prédéterminé électriques
G06F 9/50 - Allocation de ressources, p. ex. de l'unité centrale de traitement [UCT]
Polymorphic non-attributable processes and architectures to monitor threat domains (e.g., pharming or phishing websites) are disclosed. Obfuscated requests may be generated by control servers to be blended in with normal traffic sent over cloud networks with randomized exit nodes or with normal traffic sent through an anonymization network. Requests may be sent at randomized intervals or time periods determined algorithmically. The requests are obfuscated in order to mask the origination information and location so that the threat actor does not detect that the website is being monitored. User agents may be spoofed and requests may present as if they originated from residential IP addresses. Automatic real-time monitoring can be provided to determine when sites resolve and are addressable. Fingerprint information, screenshots, security certificate, and other threat domain data can be captured. Request responses can be scanned for threat indicia.
Aspects of the disclosure relate to computing hardware and software for dynamic control limit configuration. A computing platform may receive and format historical data. The computing platform may input the formatted data into a control limit prediction model, which may output predicted control limits by weighting, using an ensemble model, outputs from a plurality of other models to produce predicted control limits. The computing platform may adjust existing control limits based on predicted control limits to create actual control limits. The computing platform may receive real time data, and may identify, using the actual control limits and the real time data, a deviation score for the real time data. The computing platform may compare the deviation score to the actual control limits. Based on detecting that the deviation score breaches the actual control limits, the computing platform may send an indication of the breach.
A method for facilitating electronic transactions between a first mobile device and a second mobile device is provided. At a central server, when internet connection is established, the method may include receiving transaction data and prior to processing an electronic transaction based on the transaction data, verifying the electronic transaction. The verifying may include retrieving a first activity log from the first mobile device and a first browser timeout history. The method may further include retrieving from the second mobile device a second activity log and a second browser timeout history. The method may further include verifying that a first mobile application was active when a browser on the first mobile device was in timeout and that a second mobile application was active when a browser on the second mobile device was in timeout.
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
G06Q 20/06 - Circuits privés de paiement, p. ex. impliquant de la monnaie électronique utilisée uniquement entre les participants à un programme commun de paiement
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
The invention relates to adaptation/alteration of a digital file referenced to a Non-Fungible Token (NFT) via NFT metadata-defined adaptation rules and criteria, i.e., predefined conditions or events that trigger the occurrence of digital file adaptation. The adapted digital file may be presented to indicate a change in a state associated with a specific type of NFT. The NFT may be an authentication NFT used to verify the identity of a user/users, and the digital file may be presented within computing networks or applications for conveying user authenticity to other users within the computing network or application. The predefined conditions are security events that have a positive/negative impact on the authenticity of the user or group of users. Occurrence of one or more of the predefined security events triggers adaptation and presentation of the adapted digital file indicating an increase/decrease in the level of authenticity associated with the user/users.
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
H04L 9/00 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité
79.
Method for Dynamic AI Supported Graph-Analytics Self Learning Templates
Methods and systems described herein for addressing issues associated with varying graph analytics tools that require different tool-specific coding languages. An artificial intelligence (AI) sub-system of various modules extracts metadata from a dataset and identifies nodes and relationships in the dataset using the metadata. The dataset is matched with a corresponding graph-analytics template in a data store, and a dynamic template modifier modifies the corresponding graph-analytics template. In some examples, the AI system generates smart guided videos with logical breakpoints that are embedded along with templates for quick learning and to build faster graphical analytics. The AI system includes a dynamic template modifier and a cognitive smart AI engine that includes a graph.
An apparatus may comprise a memory communicatively coupled to a processor. The processor may be configured to receive an evaluation request to perform multiple data processing operations; determine a microservice configured to fulfill the first evaluation request; and determine multiple publishing commands associated with the microservice. The one or more microservices may be integrated in multiple Interactive Voice Response (IVR) operations. The processor may be further configured to obtain input parameters of the one or more input parameters corresponding to the microservice; generate a response to the evaluation request associated with the microservice, compare the response to an expected response of the one or more expected responses; and determine whether the response matches the expected response. In response to determining that the response does not match the expected response, the processor is configured to update the publishing commands in accordance with multiple rules and policies, and publish the microservice.
A plurality of data interactions are monitored and a plurality of potential exceptions relating to the data interactions are identified. Each potential exceptions is analyzed based on a respective pre-configured logic to determine a result relating to the potential exception. A plurality of data objects are generated, wherein each data object includes one or more of the potential exceptions, the results determined for the potential exceptions and other related data. The data objects are passed as nodes of a tangle associated with a tangle network. A federated machine learning (ML) model is deployed with each node passed to the tangle to determine whether the results are correct. A report is generated including a plurality of potential exceptions determined as valid.
Aspects of the disclosure relate to s computing system that is configured to use heuristic and/or metaheuristic algorithms based on swarm learning (SL) intelligence frameworks and combine SL with blockchain and edge computing frameworks to provide a technologically efficient, responsive, and/or adaptable solution to detecting and preventing defects in software applications.
G06F 11/22 - Détection ou localisation du matériel d'ordinateur défectueux en effectuant des tests pendant les opérations d'attente ou pendant les temps morts, p. ex. essais de mise en route
G06F 11/34 - Enregistrement ou évaluation statistique de l'activité du calculateur, p. ex. des interruptions ou des opérations d'entrée–sortie
83.
Intuitive Defect Prevention with Swarm Learning Intelligence over Blockchain Network
Aspects of the disclosure relate to s computing system that is configured to use heuristic and/or metaheuristic algorithms based on swarm learning (SL) intelligence frameworks and combine SL with blockchain and edge computing frameworks to provide a technologically efficient, responsive, and/or adaptable solution to detecting and preventing defects in software applications.
G06F 11/07 - Réaction à l'apparition d'un défaut, p. ex. tolérance de certains défauts
G06N 3/006 - Vie artificielle, c.-à-d. agencements informatiques simulant la vie fondés sur des formes de vie individuelles ou collectives simulées et virtuelles, p. ex. simulations sociales ou optimisation par essaims particulaires [PSO]
84.
Intuitive Defect Prevention with Swarm Learning Intelligence over Blockchain Network
Aspects of the disclosure relate to s computing system that is configured to use heuristic and/or metaheuristic algorithms based on swarm learning (SL) intelligence frameworks and combine SL with blockchain and edge computing frameworks to provide a technologically efficient, responsive, and/or adaptable solution to detecting and preventing defects in software applications.
A hybrid system for natural language processing is provided. The system may include a transceiver operable to receive a query from a user. The query may include a stock portion integrated with a set of real-time conditions specific to the user. The transceiver may transmit the query to a model and to a response application. The model may receive the query and communicate with a data store. Based on historical data stored at the data store, the model may separate the stock portion from the set of real-time conditions, formulate a response to the stock portion and insert placeholders into the response for responses to the real-time conditions portion. The response application may receive the query and the response. The response application may communicate with private data stores to formulate responsive elements for the placeholders. The response application may insert the responsive elements into the placeholders to complete the response.
Systems and methods for validating the accuracy of an authenticated, end-to-end, digital response system are provided. Methods may include curating a database of training data, including historical profile data and historical interaction data. Profile data may include a name, an identifier, and a set of financial instruments for a plurality of system users. Interaction data may include records of multi-step interactions between the system users and the digital response system. Methods may include generating, via a machine-learning (ML) engine and based on the training data: a test profile including a fictitious name, a fictitious identifier, and a fictitious set of financial instruments; authentication data for the test profile including a username and password that are operational for authenticating the test profile to the digital response system; and a simulated conversation for the test profile including an utterance that is associated with an intended request. Methods may include: initiating a validation session by logging the test profile into the digital response system using the authentication data; feeding the simulated conversation as an input to the digital response system; receiving a response from the digital response system; scoring the accuracy of the response vis-à-vis the intended request; generating an accuracy report based on the accuracy score; and submitting the accuracy report to a system administrator.
G06F 21/57 - Certification ou préservation de plates-formes informatiques fiables, p. ex. démarrages ou arrêts sécurisés, suivis de version, contrôles de logiciel système, mises à jour sécurisées ou évaluation de vulnérabilité
The invention relates to adaptation/alteration of a digital file referenced to a Non-Fungible Token (NFT) via NFT metadata-defined adaptation rules and criteria, i.e., predefined conditions or events that trigger the occurrence of digital file adaptation. The adapted digital file may be presented to indicate a change in a state associated with a specific type of NFT. The NFT may be an authentication NFT used to verify the identity of a user/users, and the digital file may be presented within computing networks or applications as a means of conveying user authenticity to other users within the computing network or application. The predefined conditions are security events that have a positive/negative impact on the authenticity of the user or group of users. Occurrence of one or more of the predefined security events triggers adaptation and presentation of the adapted digital file indicating an increase/decrease in the level of authenticity associated with the user/users.
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
H04L 9/00 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité
The invention relates to adaptation/alteration of a digital file referenced to a Non-Fungible Token (NFT) via NFT metadata-defined adaptation rules and criteria, i.e., predefined conditions or events that trigger the occurrence of digital file adaptation. The adapted digital file may be presented to indicate a change in a state associated with a specific type of NFT. The NFT may be an authentication NFT used to verify the identity of a user/users, and the digital file may be presented within computing networks or applications for conveying user authenticity to other users within the computing network or application. The predefined conditions are security events that have a positive/negative impact on the authenticity of the user or group of users. Occurrence of one or more of the predefined security events triggers adaptation and presentation of the adapted digital file indicating an increase/decrease in the level of authenticity associated with the user/users.
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
H04L 9/00 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité
89.
AUTOMATED TELLER MACHINE ("ATM") COLOR TECHNOLOGY FOR MASS TRANSACTION PRE-STAGING
Wait times at ATMs may undermine the utility of these self-service machines. ATMs are configured to provide faster self-service kiosks that allow users to quickly perform common financial transactions. However, it has been increasingly common for users to have to wait in a queue to access an ATM. Systems and methods are provided for an ATM that pre-stages transactions by extracting account balance, account status, account fraud, and other necessary information from a plurality of smart cards, before a user begins interacting with the ATM. Information extracted from the plurality of smart cards may be transferred to the ATM wirelessly. Color changes may then be sent from the ATM to the plurality of smart cards. The color changes indicate whether the plurality of smart cards may transact with the ATM without any additional information, thereby improving the transaction processing efficiency of the plurality of smart cards and enhancing user satisfaction.
G06Q 20/10 - Architectures de paiement spécialement adaptées aux systèmes de transfert électronique de fondsArchitectures de paiement spécialement adaptées aux systèmes de banque à domicile
90.
SYSTEMS AND METHODS FOR FAST AND SIMULTANEOUS MULTI-FACTOR AUTHENTICATION USING VECTOR COMPUTING
Aspects of the disclosure relate to multi-factor authentication. A computing system may receive, from an authenticated entity, a plurality of authenticated inputs. Normalized authenticated inputs may be generated. An n-dimensional authenticated signature vector corresponding to the plurality of normalized authenticated inputs may be generated. A plurality of multi-factor authentication (MFA) inputs may be received. A plurality of normalized multi-factor authentication inputs may be generated. An n-dimensional multi-factor authentication vector corresponding to the plurality of normalized MFA input may be generated. There may be a determination of whether a distance between the n-dimensional authenticated signature vector and the multi-factor authentication vector not exceeding an authentication distance threshold. Based on the distance between the n-dimensional authenticated signature vector and the multi-factor authentication vector not exceeding the authentication distance threshold, an indication that the plurality of multi-factor authentication inputs have been authenticated may be generated.
Systems, computer program products, and methods are described herein for improving computing performance by implementing an application package orchestrator in an electronic environment. The present disclosure is configured to identify an instruction(s) associated with a current application(s); apply the instruction(s) to an NLP engine; generate, by the NLP engine, a stepwise metadata packet, wherein the stepwise metadata packet comprises a standardized set of computer-readable instructions; apply the stepwise metadata packet to an application package orchestrator model; query, by the application package orchestrator model, a programming template database based on the stepwise metadata package, wherein the programming template database comprises pre-generated programming actions; determine, based on the query, whether the pre-generated programming actions resolve each of the standardized set of computer-readable instructions of the stepwise metadata packet; and generate an application package based on a combination of the pre-generated programming actions that resolve each of the standardized set of computer-readable instructions.
Systems, computer program products, and methods are described herein for dynamic task management across a network. The method includes receiving a task to be completed by one or more end-point devices. The method also includes determining one or more task actions that are part of the task. The method further includes receiving a task action input indicating a change to at least one of the one or more task actions or an additional task action to be added to the one or more task actions. The method still further includes updating the one or more task actions based on the task action input. The method also includes determining a task action order for each of the one or more task actions. The method further includes assigning at least one of the task actions to at least one of the end-point devices on the network based on the task action order.
Systems, computer program products, and methods are described herein for a secure transactional process utilizing a novel approach to data management within resource instruments. The described technology specifically addresses the need for enhanced security in electronic transactions by incorporating a system that dynamically retrieves, encrypts, stores, and subsequently erases transactional data in a secure manner. At the core of these systems is the use of electrically erasable programmable read-only memory (EEPRAM) which temporarily houses transaction details in an encrypted form. The details are securely fetched from an entity server following the verification of a transaction terminal integrity through unique cryptographic keys.
G06Q 20/20 - Systèmes de réseaux présents sur les points de vente
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
94.
system and method for digitally marking artificial intelligence (AI) generated content
A processor receives request to generate content using an AI tool and generates the requested content using the AI tool. The processor detects that the AI tool used a first AI module and a second AI module associated with the AI tool to generate the requested content. In response, the processor accesses, from a memory, a first digital signature associated with the first AI module and a second digital signature associated with the second AI module. The processor generates a combined digital signature based on the first digital signature and the second digital signature and embeds the combined digital signature in the generated content to generate a marked content.
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
A system is provided for generating an electronic data fabric using virtualized photocrosslinking-based parallel computing process. In particular, the system may, using an artificial intelligence (“AI”) model, identify one or more users and a subject or description of a communication in order to determine the data needs of the one or more users, where such data may reside within one or more databases, websites, applications, tools, and/or the like. The system may then use a simulated photocrosslinking based process to assimilate various combinations of datasets and place the data sets within a virtualized environment. The virtualized data may then be linked to a data fabric to identify the linkages and/or sublinkages between the virtualized data and the underlying data. Subsequently, upon receiving a data request from a user, the system may use the metadata within the data fabric to dynamically recall the relevant data in real time.
Systems, computer program products, and methods are described herein for the implementation of an alternative data management system. The method includes dividing a process into one or more tasks, including a first task, to be completed. The method also includes determining a first task availability status for the first task. The first task availability status indicates whether the first task can be started by a user. The first task availability is designated as waiting in an instance in which at least one prerequisite task exists for the first task. The method includes causing the first task availability status to be changed from waiting to ready in an instance in which at least a portion of the prerequisite task(s) is completed. The method further includes assigning the first task to a first user in an instance in which the first user finishes one of the one or more tasks.
Systems, computer program products, and methods are described herein for securing data transfers across multiple devices on a network. The method includes receiving a transfer identifier associated with a data transfer request. The method also includes generating a transfer record associated with the transfer identifier for the data transfer request. The transfer record indicates node(s) in which the data transfer request has transmitted. The method further includes receiving a transfer data packet associated with a receiving node. The receiving node received the data transfer request from a transmitting node. The transfer data packet includes the transfer identifier. The method still further includes causing a verification signal to be transmitted to the receiving node in an instance in which the transfer data packet is verified. The method also includes updating the transfer record based on the transfer data packet.
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
G06Q 20/38 - Protocoles de paiementArchitectures, schémas ou protocoles de paiement leurs détails
98.
SYSTEMS AND METHODS TO TOKENIZE AND CATEGORIZE DATA TRANSACTIONS
An interactive distributive platform apparatus configured to be a single source of truth. The platform may include entity identifier data stored in a database. The data may be placed in a grid through an AI algorithm. The AI algorithm may place the data in the grid using one or more rules. The data may be extracted into datasets. The datasets may be placed in corresponding selected nodes. Placement of the datasets may occur using smart contracts. The nodes may be part of a holochain. The datasets and rules may be validated in an interactive distributive platform. The interactive distributive platform may be the single source of truth. The interactive distributive platform may direct any request that enters the system to the correct node.
H04L 67/1097 - Protocoles dans lesquels une application est distribuée parmi les nœuds du réseau pour le stockage distribué de données dans des réseaux, p. ex. dispositions de transport pour le système de fichiers réseau [NFS], réseaux de stockage [SAN] ou stockage en réseau [NAS]
Provided herein is a system. The system may include a parallel metaverse creator layer. The system may include a parallel metaverse verifier layer. The system may include a data sharing interface. The system may include a data deciphering interface. The system may include an NFT stacker. The system may include an NFT validator. The system may include a payment decision interface. the system may include a transaction decision interface. The system may include a payment initiator interface. The system may include a transaction initiator interface.
Provided herein is a system. The system may include a parallel metaverse creator layer. The system may include a parallel metaverse verifier layer. The system may include a data sharing interface. The system may include a data deciphering interface. The system may include an NFT stacker. The system may include an NFT validator. The system may include a payment decision interface. the system may include a transaction decision interface. The system may include a payment initiator interface. The system may include a transaction initiator interface.
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G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
Systems and methods may include contributing to a quantum-resistant blockchain that stores a quantum-resistant audit trail of changes to a dataset. A non-transitory computer readable medium may store instructions that are run on a computer processor to populate a block with a log of a dataset, run a quantum-resistant cryptographic algorithm to encrypt the block, distribute the block to and receive approval from blockchain nodes to add the block to the blockchain, and run the algorithm to encrypt the block onto the blockchain. The method may include repeating the steps of running the non-transitory medium on the computer processor to populate a second block with a second log of the dataset, run the algorithm to encrypt the second block, distribute the second block to and receive approval from the blockchain nodes to add the block to the blockchain, and run the algorithm to encrypt the second block onto the blockchain.
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
H04L 9/00 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité
