Abstract: Apparatus, methods and systems for securely trade data from one node by another node is provided. Methods may include receiving raw data at a node. Methods may include categorizing the data. Methods may include classifying the data. Methods may include assigning data grades to the data. Methods may include assigning security levels to the data. Methods may include storing the data at a data mesh. Methods may include determining a resource consumption metric (“RCM”) for the data. Methods may include transmitting the data. The dataset may be acquired for a value specified by the RCM. Methods may include improving the data. Methods may include recategorizing, reclassifying and reassigning a security level to the data. Methods may include storing the improved data in the data mesh. Methods may include determining a new RCM for the improved data. Methods may include transmitting the improved data. Methods may include managing the transmission of the data with a third node.

Abstract: Methods, systems and apparatus for controlled data transmission within leveled data environments is provided. Methods may include transmitting a dataset, using a data driver, from a first data environment to a second data environment. Methods may include intercepting the dataset at a reception gateway. Methods may include transmitting an identification. The identification request may request an original data environment from which the data originated; and a destination data environment to which the data is being transmitted. Methods may include in response to receiving a reply to the identification request, authenticating the reply. Methods may include initiating a security level compatibility check. Methods may include determining whether the first data environment is compatible with the second data environment. Methods may include transmitting the dataset through an auto-sanitization engine. Methods may include fragmenting the data into a plurality of data segments.

Abstract: A processor is configured to access a virtual environment comprising a plurality of virtual locations. The processor is configured to generate a primary avatar in a first virtual location based on a superset of the user data associated with a first set of the interaction criteria objects. The processor is configured to receive an interaction request from a user device for an interaction with the entity in a second virtual location. The processor is configured to generate a secondary set of user data based on the superset of the user data and the second set of the interaction criteria objects. The processor is configured to generate a secondary avatar associated with the user device in the second virtual location. The processor is further configured to conduct the interaction between the secondary avatar and the entity in the second virtual location based on the second set of the interaction criteria objects.

Abstract: Aspects of the disclosure relate to real-time management of data container generation, authorization, and throttling. In some embodiments, a computing platform may receive a first data container and retrieve a first data container management file associated with the first data container. The computing platform may then perform a first preliminary analysis of the first data container, including analyzing the first data container and the first data container management file to determine whether a first predetermined communication path requires additional throttling analysis. If additional throttling analysis is required, the computing platform may send analysis messages to a data container throttling module to extract a first communication entity listed in the first predetermined communication path.

Abstract: A computing platform having at least one processor, a memory, and a communication interface may receive, via the communication interface, a transaction request from a participant. The computing platform may identify a user and one or more linked digital wallets associated with the transaction request. The computing platform then may execute an algorithm for comparing the transaction request to predetermined criteria associated with the one or more linked digital wallets. The computing platform may establish, via the communication interface, a first connection with a user computing device and, while the first connection is established, transmit to the user computing device transaction information which, when executed by the user computing device, causes a notification to be displayed on the user computing device.

Abstract: 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.

Abstract: Methods and system are provided for tuning an artificial intelligence (“AI”) system. The methods and system may include training the AI system to identify distinct users their distinct interactions. The training may include receiving datatsets related to a plurality of distinct users and their plurality of distinct interactions. The AI system may parse the datasets to arrive at a plurality of sets of distinct interactions performed by respective distinct users, each distinct interaction may be associated with a level of security clearance. The AI system may receive a request from a user to access information. The methods and system may include the AI system initiating a communication with the user. The communication may be compared with the each set of distinct interactions until a user and level of security can be identified. The AI system may prompt the user to access information on the level of security clearance identified.

Abstract: A computing platform having at least one processor, a memory, and a communication interface may receive, via the communication interface, a transaction request from a participant. The computing platform may identify a user and one or more linked digital wallets associated with the transaction request. The computing platform then may execute an algorithm for comparing the transaction request to predetermined criteria associated with the one or more linked digital wallets. The computing platform may establish, via the communication interface, a first connection with a user computing device and, while the first connection is established, transmit to the user computing device transaction information which, when executed by the user computing device, causes a notification to be displayed on the user computing device.

Abstract: Arrangements for machine learning-based requirements predictions are provided. In some examples, user data may be received from a plurality of sources. In some arrangements, the user data may be segmented according to item type and anonymized to protect private information. The segmented, anonymized data may be analyzed using machine learning to output one or more requirements predictions. In some examples, an entity may request access to requirements prediction data. The request and/or entity may be validated and, if validated, access to the requirements prediction data may be provided. In some arrangements, additional analysis may be performed on the data to identify secondary entities associated with additional materials related to a particular item type. A prediction associated with volumes or amounts of additional materials may be generated and, in some examples, transmitted to the identified secondary entities.

Abstract: A predictive AI model generation and execution system with a plurality of engines is provided. A data exchange computing engine may receive and process one or more data streams to generate processed data, and send the processed data to a model generation and execution computing engine. The model generation and execution computing engine may receive the processed data and update a first predictive artificial intelligence model using the processed data. The client interface computing engine may receive a model execution request, generate and send a first graphical user interface, receive model execution data from the external client computing system, and send the model execution data to the workflow management computing engine. The workflow management computing engine may generate one or more model execution instructions based on the model execution data, and send the one or more model execution instructions to the model generation and execution computing engine.

Abstract: Arrangements for generating geo-fences for automatically authenticating a user or authorizing event processing are provided. In some examples, user data associated with a location of the user or mobile device of the user may be received. Further, data associated with successful processing of an event may be received. In some arrangements, this data may be used to generate a first geo-fence. The geo-fenced area may be considered a trusted zone within which a user may be automatically authenticated and/or events may be automatically authorized and/or processed. In some examples, additional user data may be received and analyzed using machine learning to predict second or subsequent geo-fence locations. Based on the analyzed data, one or more additional geo-fences or geo-fenced areas may be generated and interlinked with the initial geo-fence.

Abstract: Provided herein is a method of storing an incoming dataset in a data mesh. The method may include a plurality of steps. The steps may include associating a metadata tag with a classifying feature and a storage instruction in a (first) relational database. The steps may include scanning incoming datasets to identify datasets characterized by the classifying feature. The steps may include tagging an incoming dataset to generate a tagged dataset. The steps may include storing the tagged dataset in the data mesh, according to the storage instruction. The steps may include associating, in a second relational database, the metadata tag with the initial storage location. The steps may include modifying the storage instruction. The steps may include storing the incoming dataset an additional time in the data mesh, according to the modified storage instruction.

Abstract: Arrangements for controlling data retrieval are provided. In some aspects, a data request may be received by a computing platform. A pre-fetch engine may be executed to analyze the data request and generate, using a machine learning model, a pre-fetch template identifying data sets responsive to the request. The pre-fetch template may be transmitted to one or more data repositories and at least one data repository may transmit response data sets to the computing platform. An issue in the mesh network through which the data sets are transmitted may be detected and the data sets may be stored at a data silo associated with a node of the mesh network. An alternate transmission path may be generated and the data may be transmitted from the data silo of the node to another node in the mesh network based on the alternate transmission path.

Abstract: Apparatus and methods for an artificial intelligence implemented termination by an auditor artificial intelligence (“aAI”) of a malicious artificial intelligence (“mAI”) are provided. The aAI may detect a mAI on a network and determine which data the mAI can access on the network. The aAI may then degrade all or part of the data in various ways to prevent the mAI from producing valid content based on the data. The aAI may also create code and inject the code into the mAI to degrade the mAI's operations. As the mAI may rely on valid data to produce valid output, degrading the data may degrade the mAI.

Abstract: A personal artificial intelligence (AI) system may be used to conduct business with one or more entities. The personal AI system may automatically replicate security rules from another AI system to provide initial security rules and may adjust the initial security rules based on information about the user to generate revised security rules. The rules adjustments may also be based on the entities with which the user does business. An AI firewall may be generated to control access to the personal AI system by a second AI system, with which business may be conducted. To permit interactions with the second AI system, the AI firewall may require that the second AI system provide the personal AI system with data relating to previous transactions between the user and the operator of the second AI system. The data may include the monetary amount of each of multiple recent transactions.

Abstract: Apparatus and methods to verify human interaction through an encrypted channel are provided. A human verification program may create an encrypted channel between two or more devices when a communication between users of the devices is started. Data may be transferred over the encrypted channel when a user performs a verifiably human action. The data may be analyzed to determine whether the user is more likely than not a bot. The channel may be collapsed when a pre-determined condition is satisfied.

Abstract: Artificial intelligence (AI) systems may manipulate search results about an entity to inject a predetermined bias into the search results. A bias reduction artificial intelligence (AI) system and method may perform the same automated search about the entity on open source and closed source AI systems. The AI-generated search results for the open and closed source AI systems may be compared to determine differences in results. The differences may be analyzed to determine attempts by the AI systems to manipulate search results about the entity to inject predetermined bias. If an attempt at predetermined bias is identified, the bias reduction AI system may reduce the predetermined bias by sharing data about the entity between the open source AI system and the closed source AI system to cause the system to use machine learning to update the algorithms and the first and second data sets. Incentives may also be provided to the open and closed source AI systems to reduce biases.

Abstract: Apparatus and methods to verify human interaction through digital tokens are provided. A human verification program may create a principal digital token and assign it to the profiles of a plurality of users. When a first user of the plurality of users requests verification that a second user of the plurality of users is human and not a bot, the program may transmit the principal token to the second user. The second user may perform a verifiably human action and create a secondary token. The secondary token may be transmitted to the program. The program may analyze the secondary token and create a limited verification token when the second user is human and not a bot. The verification token may be transmitted to the first user.

Abstract: Artificial intelligence (AI) impersonation detection using an AI model is provided. Methods may train an AI model. The training may provide the AI model with a first dataset including a communication between two or more human users, a second and third dataset including an impersonation of the communication by a public AI model and a private AI model respectively. Methods may identify a first identifier set in the first dataset, a second identifier set in the second dataset and a third identifier set in the third dataset. Methods may create a parameter range for the first, second and third identifier set. The methods may include monitoring a production communication using the AI model. The AI model may compare the production communication with the parameter ranges. Methods may identify a smallest comparison value. Methods may, based on the identified smallest comparison value, identify the origin of the production communication.

Abstract: Systems and methods for an artificial intelligence-based auditor of artificial intelligence may be provided. The artificial-intelligence-based auditor may operate on one or more hardware processors. The artificial intelligence-based auditor may continually scan one or more operating artificial intelligence systems for productivity and operability. The scan may identify a data source powering the one or more operating artificial intelligence systems. The scan may measure a delta between an output from each of the one or more operating artificial intelligence systems. The scan may identify trends in quality of the one or more operating artificial intelligence systems. The scan may analyze instances where the one or more operating artificial intelligence systems outputted data outside of a parameter range. Based on the scan, the auditor may label each of the one or more operating artificial intelligence systems as positive, neutral or negative.