Abstract: A method includes curating CRM data by employing a type system of a model-driven architecture and selecting an AI CRM application from a group of applications. Each CRM application may generate one or more use case insights with one or more objectives. The method also includes obtaining one or more data models including an industry-specific data model from the curated CRM data and orchestrating a plurality of machine learning models for the selected CRM application with the obtained data model(s) to determine one or more machine learning models effective for at least one objective of the selected CRM application. The method further includes applying the determined machine learning model(s) and the obtained data model(s) to predict probabilities that optimize the at least one objective and using the predicted probabilities to apply at least one of the one or more use case insights that optimizes the at least one objective.

Abstract: In certain embodiments, content items may be obtained, where each of the content items may include multiple data types. Machine learning models may be caused to be trained based on the content items to map data in a vector space by providing at least a first portion of each of the content items as input to at least one of the machine learning models and providing at least a second portion of each of the content items as input to at least another one of the machine learning models. A search request for results may be obtained, where the search request includes search parameters. One or more locations within the vector space may be predicted (e.g., by one or more of the machine learning models) based on the search parameters. Information (indicating content items mapped to or proximate the predicted locations) may be provided as a request response.

Abstract: A system for wear and aging evaluation of a paper-based resource described herein is structured for determining a fitness state of a physical body structure of a paper resource item and for adaptive processing of the paper resource item in accordance with the fitness state. Specifically, the system comprises an item transfer unit configured to receive one or more paper resource items and a scanning device structured to retrieve a fitness state of a paper resource item from a physical state sensing component of the paper resource item. The system is structured to adaptively process the paper resource item based on determining whether (i) a wear level and (ii) an item age level of the paper resource item are associated with either a fit item type or an unfit item type.

Abstract: In certain embodiments, content items may be obtained, where each of the content items may include multiple data types. Machine learning models may be caused to be trained based on the content items to map data in a vector space by providing at least a first portion of each of the content items as input to at least one of the machine learning models and providing at least a second portion of each of the content items as input to at least another one of the machine learning models. A search request for results may be obtained, where the search request includes search parameters. One or more locations within the vector space may be predicted (e.g., by one or more of the machine learning models) based on the search parameters. Information (indicating content items mapped to or proximate the predicted locations) may be provided as a request response.

Abstract: A system for wear and aging evaluation of a paper-based resource described herein is structured for determining a fitness state of a physical body structure of a paper resource item and for adaptive processing of the paper resource item in accordance with the fitness state. Specifically, the system comprises an item transfer unit configured to receive one or more paper resource items and a scanning device structured to retrieve a fitness state of a paper resource item from a physical state sensing component of the paper resource item. The system is structured to adaptively process the paper resource item based on determining whether (i) a wear level and (ii) an item age level of the paper resource item are associated with either a fit item type or an unfit item type.

Abstract: Resource exchange platform for integrating and facilitating direct resource exchanges and resource tracking between multiple organizations. The platform may establish an exchange program directly between organizations to identify, trade, and settle the transfer of physical resources physically and/or electronically. The resource exchange platform provides a more efficient exchange of physical resources, more secure trackable resource exchanges, and more agency insight into resource exchanges in order to identify potential interactions involving misappropriated resources.

Abstract: This disclosure illustrates that resource exchange requests may be identified and sent directly to ATMs. The service providers access the ATMs and are able to select the resource exchange request from the ATMs (e.g., electronically, or the like). The service providers transfer the resources at the ATMs, and reconcile the resources at the ATMs. The ATMs may send service provider and/or organization reconciliation notices confirming the reconciliations. In some embodiments, the reconciliation may be stored in a distributed ledger within a blockchain system.

Abstract: This disclosure illustrates that resource exchange requests may be identified and sent directly to ATMs. The service providers access the ATMs and are able to select the resource exchange request from the ATMs (e.g., electronically, or the like). The service providers transfer the resources at the ATMs, and reconcile the resources at the ATMs. The ATMs may send service provider and/or organization reconciliation notices confirming the reconciliations. In some embodiments, the reconciliation may be stored in a distributed ledger within a blockchain system.

Abstract: Resource exchange platform for integrating and facilitating direct resource exchanges and resource tracking between multiple organizations. The platform may establish an exchange program directly between organizations to identify, trade, and settle the transfer of physical resources physically and/or electronically. The resource exchange platform provides a more efficient exchange of physical resources, more secure trackable resource exchanges, and more agency insight into resource exchanges in order to identify potential interactions involving misappropriated resources.

Abstract: In certain embodiments, content items may be obtained, where each of the content items may include multiple data types. Machine learning models may be caused to be trained based on the content items to map data in a vector space by providing at least a first portion of each of the content items as input to at least one of the machine learning models and providing at least a second portion of each of the content items as input to at least another one of the machine learning models. A search request for results may be obtained, where the search request includes search parameters. One or more locations within the vector space may be predicted (e.g., by one or more of the machine learning models) based on the search parameters. Information (indicating content items mapped to or proximate the predicted locations) may be provided as a request response.

Abstract: Described is a system for catastrophe prediction. The system generates a time series of observables at multiple time steps from data observed from a complex system. A surrogate time series based on the time series of observables is then generated. Inferred network structures for both the time series of observables and the surrogate time series are reconstructed. Next, spatial autocorrelation for each inferred network structure in both the time series of observables and the surrogate time series is computed. A statistical test of a detected trend between the time series of observables and the surrogate time series is computed to determine if the detected trend occurred by chance. Finally, an early warning signal of the detected trend occurring by chance is generated.