Abstract: A system for administering regionally-based data storage and control is provided. The system may include a computer processor and a distributed memory. The distributed memory may be distributed across a plurality of regions. The distributed memory may provide data storage. The system may build a plurality of artificial intelligence (AI) models. Each of the models may correspond to a set of data rules. Each of the data rules governs the storage of data in each of the plurality of regions. The system may further create, or otherwise access, a plurality of data keys. Each of the data keys preferably corresponds to one of the regions. The system preferably centralizes the location of the plurality of data keys in a central location such that control of the data in the plurality of regions can be administered by the processor, via the plurality of data keys, from the central location.

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: 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: A method for facilitating transfer of a new dataset across a network may include the following steps: (a) querying the global dataset via a computer processor to identify an existing dataset having data points corresponding to the new dataset; (b) identifying historical data rules previously used for a set of data transfers relating to the existing dataset; and (c) using the historical data rules to assist a machine learning engine in generating data rules for use with a data transfer relating to the new dataset.

Abstract: Apparatus and methods to automatically filter data at an edge of a network are provided. An edge data filter may intercept incoming data before the data is downloaded onto the network. The edge data filter may analyze the data and metadata of the data through one or more artificial intelligence/machine learning (“AI/ML”) algorithms to determine if any of the data is relevant to the network. When any of the data is relevant to the network, the edge data filter may release the relevant data to the network.

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: A system for administering regionally-based data storage and control is provided. The system may include a computer processor and a distributed memory. The distributed memory may be distributed across a plurality of regions. The distributed memory may provide data storage. The system may build a plurality of artificial intelligence (AI) models. Each of the models may correspond to a set of data rules. Each of the data rules governs the storage of data in each of the plurality of regions. The system may further create, or otherwise access, a plurality of data keys. Each of the data keys preferably corresponds to one of the regions. The system preferably centralizes the location of the plurality of data keys in a central location such that control of the data in the plurality of regions can be administered by the processor, via the plurality of data keys, from the central location.

Abstract: A method for facilitating transfer of a new dataset across a network may include the following steps: (a) querying the global dataset via a computer processor to identify an existing dataset having data points corresponding to the new dataset; (b) identifying historical data rules previously used for a set of data transfers relating to the existing dataset; and (c) using the historical data rules to assist a machine learning engine in generating data rules for use with a data transfer relating to the new dataset.

Abstract: There is provided a method for increasing available memory in an edge layer of a network, where the network stores data and has a default retention period for datasets. The method may include the steps of: (a) receiving the dataset by the edge layer of the network; (b) analyzing the dataset by the front-end filter to identify disposable data points in the dataset, (c) instructing a computer processor to remove the disposable data points from the dataset, upon passage of a specified time interval.

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: Systems, computer program products, and methods are described herein for an intelligent processing engine for acquisition, storage, and subsequent refresh of data from a distributed network. The present invention is configured to receive, from one or more distributed computing systems, an enterprise dataset; convert the enterprise dataset to a standardized format; store the enterprise dataset in one or more storage locations based on a characteristic of the enterprise dataset; provide the one or more distributed computing systems with remote access to the one or more storage locations; monitor, via an artificial intelligence engine, usage data from the one or more distributed computing systems, wherein the usage data is associated with remote access of the one or more storage locations; generate, via the artificial intelligence engine, a use score associated with the one or more storage locations; and automatically refresh the one or more storage locations based on the use score.

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 cryptocurrency. A computing platform may synchronize a database with another database to store account information for a user, which may include establishing a distributed ledger to store the account information, and where the account information includes a balance of fiat currency for the user. The computing platform may receive a request to transfer custody of cryptocurrency, on behalf of the user, to an enterprise organization corresponding to the computing platform. The computing platform may store, using a wrapper database integrated with the database, information of the custody of the cryptocurrency. The computing platform may receive, from a user device of the user, an event processing request. The computing platform may process, using the distributed ledger, the event processing request using one or more of: the fiat currency or the cryptocurrency.

Abstract: An apparatus for sorting and ordering data based on filtering parameters comprises a memory operable to store a digital folder and a processor operably coupled to the memory. The processor is configured to receive a transmission comprising verification of an interaction session established between a first user and an entity and to receive file information corresponding to one or more files stored in the digital folder from an external exchange. The processor is further configured to generate a file vector comprising the one or more files of the digital folder that is sorted based on applying a selection factor to the received file information for each of the one or more files. The processor is further configured to send a transmission to a user device comprising the file vector and an indication to utilize a first file of the file vector during the interaction session.

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: An apparatus that matches one or more affiliates to a user includes a memory, a geofencing engine, an artificial intelligence engine, and processing circuitry communicatively coupled to each. The geofencing engine determines a current geofenced area of a mobile device of the user. The artificial intelligence engine determines anticipated needs of the user. The processing circuitry determines, using the affiliate database, a list of candidate affiliates for the current geofenced area, and transmits a query to an affiliate feed for the list of candidate affiliates, where the query is based at least in part on the anticipated need of the user. The processing circuitry determines a first affiliate and a second affiliate based on the query and receives query results for the first affiliate and the second affiliate from the affiliate feed. The results are transmitted to the mobile device for presentation in a user interface.

Abstract: An entity data record includes, for each of a plurality of entities, a plurality of attribute data entries. Each attribute data entry indicates a characteristic of the entity. For each entity of the plurality of entities, an authentication score is determined indicating an extent to which the characteristics indicated by the attribute data entries for the entity are authenticated. The authentication score is stored in memory for each entity in the entity data record. A device provides a request for information about a first entity of the plurality of entities. After receiving the request, one or more attribute entries associated with the first entity and a first authentication score of the first entity are provided to the device.

Abstract: A system comprises a processor configured to receive user criteria associated with an operation and to obtain a plurality of segment candidates from a plurality of sources, each source configured to provide a segment of the operation that corresponds to a respective segment candidate obtained from said source. The processor is configured to determine a plurality of operation structure candidates for the operation. Each operation structure candidate is determined based on aggregating a respective subset of the plurality of segment candidates. The processor is configured to determine a priority for each operation structure candidate based on prioritization rules stored in memory of the system and based on the user criteria. The processor is configured to communicate an output indicating one or more of the operation structure candidates for the operation. The output is based on the priority.

Abstract: An apparatus for sorting and ordering data based on filtering parameters comprises a memory operable to store a digital folder and a processor operably coupled to the memory. The processor is configured to receive a transmission comprising verification of an interaction session established between a first user and an entity and to receive file information corresponding to one or more files stored in the digital folder from an external exchange. The processor is further configured to generate a file vector comprising the one or more files of the digital folder that is sorted based on applying a selection factor to the received file information for each of the one or more files. The processor is further configured to send a transmission to a user device comprising the file vector and an indication to utilize a first file of the file vector during the interaction session.

Abstract: Aspects of the disclosure relate to cognitive automation-based engine processing to propagate data across multiple systems via a private network to overcome technical system, resource consumption, and architecture limitations. Data to be propagated can be manually input or extracted from a digital file. The data can be parsed by analyzing for correct syntax, normalized into first through sixth normal forms, segmented into packets for efficient data transmission, validated to ensure that the data satisfies defined formats and input criteria, and distributed into a plurality of data stores coupled to the private network, thereby propagating data without repetitive manual entry. The data may also be enriched by, for example, correcting for any errors or linking with other potentially related data. Based on data enrichment, recommendations of additional target(s) for propagation of data can be identified. Reports may also be generated. The cognitive automation may be performed in real-time to expedite processing.