Abstract: A method for generating predictive and event-based action recommendations includes receiving, from at least one data source, input data representative of a user; generating, using a first machine learning model, a user behavior pattern for the user based on the input data representative of the user; classifying the user behavior pattern based on one or more personas representative of user characteristics; identifying, based on at least the input data and the one or more personas, an inefficiency in the user behavior pattern impacting a goal of the user; and generating, using a second machine learning model, a personalized recommendation for the user to remediate the inefficiency.

Abstract: A method for generating predictive and event-based action recommendations includes receiving, from at least one data source, input data representative of a user; classifying, using a first machine learning model, the input data based on one or more personas representative of user characteristics to generate classified input data; identifying, from the input data, at least one event associated with the user; and generating, based on the at least one event and the classified input data, a personalized recommendation for the user using one or more second machine learning models, wherein the personalized recommendation comprises a set of actions predicted to achieve the goal based on the impact on the goal caused by the at least one event.

Abstract: A system includes a memory, and a processing device, operatively coupled to the memory, to perform operations including receiving, from a user device associated with a user, a request to generate a personalized recommendation for the user, in response to receiving the request, obtaining user data associated with the user from a set of data sources, generating, using at least one machine learning model based on the user data, the personalized recommendation and an extended reality (XR) representation of the personalized recommendation accessible to the user via an XR system, and causing the personalized recommendation and the XR representation to be sent to the user.

Abstract: A system includes a memory, and a processing device, operatively coupled to the memory, to perform operations including obtaining user data associated with a user, generating, using at least one machine learning (ML) model processing the user data, a virtual environment associated with an extended reality (XR) representation corresponding to a personalized recommendation for the user, and creating, based on the user data, a virtual avatar for the user reflecting a representation of the user within the virtual environment. The virtual avatar corresponds to a set of attributes representing visual features of the user.

Abstract: Disclosed are various embodiments for using a distributed ledger to track the evolution or deployment of feature sets for machine-learning. An approval request to approve creation of a feature set record in a distributed ledger can be received. The approval request can include a feature set hash representing a feature set, a code hash representing a feature set generator, and a data hash representing a data set. An approval decision can be generated for the feature set record based at least in part on the approval request and at least one approval rule stored in an approval data store. The approval decision can be transmitted to a distributed agent. The approval decision can approve or reject creation of the feature set record in the distributed ledger.

Abstract: Disclosed are various approaches for managing the status of machine-learning models using distributed ledgers. A registration request for a machine-learning model can be received. The registration request can include a model name for the machine-learning model, a version identifier for the machine-learning model, a network address from which the machine-learning model can be retrieved, a source code hash for a source code version of the machine learning model, and a runtime hash for a binary executable version of the machine-learning model. A registration identifier can then be created based at least in part on the source code hash and the runtime hash. Subsequently, an entry in the distributed ledger can be created for the machine-learning model. The entry can include the registration identifier, the model name, the model version, the network address, the source code hash, and the runtime hash.

Abstract: Disclosed are various embodiments for using a distributed ledger to track the evolution or deployment of feature sets for machine-learning. An approval request to approve creation of a feature set record in a distributed ledger can be received. The approval request can include a feature set hash representing a feature set, a code hash representing a feature set generator, and a data hash representing a data set. An approval decision can be generated for the feature set record based at least in part on the approval request and at least one approval rule stored in an approval data store. The approval decision can be transmitted to a distributed agent. The approval decision can approve or reject creation of the feature set record in the distributed ledger.

Abstract: Systems and methods for automated data extraction and adaptation are disclosed. The system may receive a data input from an external source using various different input channels. The system may determine a data quality of the data input by comparing data fields of the data input to known metadata in the system. The system may reformat the data input based on the comparison to a format consumable by downstream applications and services. The system may apply various machine learning operations on the data input including a descriptive analytics analysis, a predictive learning analysis, and/or a prescriptive intelligence analysis.

Abstract: Systems and methods for identity-based interconnected transactions are disclosed. The system may receive an interconnected transaction request comprising a first financial subsystem identifier, a second financial subsystem identifier, and a transaction request. The first financial subsystem and the second financial subsystem may not be in direct communication with each other. The first financial subsystem may write the interconnected transaction request to an interconnected transaction ledger. The second financial subsystem may retrieve the interconnected transaction request from the interconnected transaction ledger. In response to retrieving the interconnected transaction request the second financial subsystem may complete the transaction request.

Abstract: Disclosed are various embodiments for using a distributed ledger to track the evolution or deployment of feature sets for machine-learning. A registration request is received from a first node of a distributed ledger, the registration request comprising a code hash representing a feature set generator and a data hash representing a data set. The registration request is then relayed to a second node of the distributed ledger for approval by the second node. Next, an approval for the registration request is received from the second node. Subsequently, an entry in the distributed ledger comprising the code hash and the data hash is created.

Abstract: Systems and methods for automated data extraction and adaptation are disclosed. The system may receive a data input from an external source using various different input channels. The system may determine a data quality of the data input by comparing data fields of the data input to known metadata in the system. The system may reformat the data input based on the comparison to a format consumable by downstream applications and services. The system may apply various machine learning operations on the data input including a descriptive analytics analysis, a predictive learning analysis, and/or a prescriptive intelligence analysis.

Abstract: Disclosed are various approaches for managing the status of machine-learning models using distributed ledgers. A registration request for a machine-learning model can be received. The registration request can include a model name for the machine-learning model, a version identifier for the machine-learning model, a network address from which the machine-learning model can be retrieved, a source code hash for a source code version of the machine learning model, and a runtime hash for a binary executable version of the machine-learning model. A registration identifier can then be created based at least in part on the source code hash and the runtime hash. Subsequently, an entry in the distributed ledger can be created for the machine-learning model. The entry can include the registration identifier, the model name, the model version, the network address, the source code hash, and the runtime hash.

Abstract: Disclosed are various approaches for managing the status of machine-learning models using distributed ledgers. A registration request for a machine-learning model can be received. The registration request can include a model name for the machine-learning model, a version identifier for the machine-learning model, a network address from which the machine-learning model can be retrieved, a source code hash for a source code version of the machine learning model, and a runtime hash for a binary executable version of the machine-learning model. A registration identifier can then be created based at least in part on the source code hash and the runtime hash. Subsequently, an entry in the distributed ledger can be created for the machine-learning model. The entry can include the registration identifier, the model name, the model version, the network address, the source code hash, and the runtime hash.

Abstract: Systems and methods for data Process an optimized analytics environment are disclosed. The system may enable users to create data processing requests, interact with various data sources and datasets, and generate data processing outputs. The system may receive a data processing request from an audio-enabled input source or a UI-based input source. The system may determine whether the data processing request at least partially matches a stored data processing request. The system may receive a data processing request selection comprising the data processing request or the stored data processing request. The system may execute the data processing request selection on a data source.

Abstract: Systems and methods for data processing in an optimized analytics environment are disclosed. The system may enable users to create data processing requests, interact with various data sources and datasets, and generate data processing outputs. The system may receive a data processing request from an audio-enabled input source or a UI-based input source. The system may determine whether the data processing request at least partially matches a stored data processing request. The system may receive a data processing request selection comprising the data processing request or the stored data processing request. The system may execute the data processing request selection on a data source.

Abstract: A master processor may retrieve historical and real time machine and human data related to computer system health. The master processor may utilize machine learning and artificial intelligence to predict potential computer malfunctions. The master processor may output notifications regarding the potential computer malfunctions in order to prevent the computer malfunctions from occurring.

Abstract: Systems and methods for identity-based interconnected transactions are disclosed. The system may receive an interconnected transaction request comprising a first financial subsystem identifier, a second financial subsystem identifier, and a transaction request. The first financial subsystem and the second financial subsystem may not be in direct communication with each other. The first financial subsystem may write the interconnected transaction request to an interconnected transaction ledger. The second financial subsystem may retrieve the interconnected transaction request from the interconnected transaction ledger. In response to retrieving the interconnected transaction request the second financial subsystem may complete the transaction request.

Abstract: Systems and methods for data processing in an optimized analytics environment are disclosed. The system may enable users to create data processing requests, interact with various data sources and datasets, and generate data processing outputs. The system may receive a data processing request from an audio-enabled input source or a UI-based input source. The system may determine whether the data processing request at least partially matches a stored data processing request. The system may receive a data processing request selection comprising the data processing request or the stored data processing request. The system may execute the data processing request selection on a data source.

Abstract: Systems and methods for automated data extraction and adaptation are disclosed. The system may receive a data input from an external source using various different input channels. The system may determine a data quality of the data input by comparing data fields of the data input to known metadata in the system. The system may reformat the data input based on the comparison to a format consumable by downstream applications and services. The system may apply various machine learning operations on the data input including a descriptive analytics analysis, a predictive learning analysis, and/or a prescriptive intelligence analysis.

Abstract: A master processor may retrieve historical and real time machine and human data related to computer system health. The master processor may utilize machine learning and artificial intelligence to predict potential computer malfunctions. The master processor may output notifications regarding the potential computer malfunctions in order to prevent the computer malfunctions from occurring.