Abstract: A system may include a data classification module configured to classify data into classified data based on predefined sensitivity levels and regulatory compliance requirements. A system may include an access control module configured to manage permissions for different user roles within an enterprise, granting access to the classified data in accordance with the sensitivity levels and regulatory compliance requirements. A system may include a data formatting module configured to format classified data into formatted data with customized presentations for various enterprise departments. A system may include an integration module configured to interface with at least one of an Enterprise Resource Planning (ERP) system or a Customer Relationship Management (CRM) system to retrieve and classify the data. A system may include a user interface module configured to present the formatted data within the host application, providing a seamless user experience for accessing the embedded marketplace.

Abstract: An information technology generally including a set of monitoring facilities that are configured to monitor the value chain network entities; a set of applications that are configured to direct an enterprise to manage the value chain network entities of the platform from a point of origin to a point of customer use; and a machine learning/artificial intelligence system configured to generate recommendations for placing at least one of an additional sensor and a camera on and/or in proximity to a value chain network entity of the value chain network entities, and wherein data from the at least one of the additional sensor and the camera feeds into a digital twin that represents the value chain network entities.

Abstract: An information technology system generally includes a cloud-based management platform with a micro-services architecture deploying a set of adaptive intelligence facilities that can be configured to automate a set of capabilities of the platform related to at least one of the value chain network entities and the features of the platform and a set of data storage facilities that can be configured to store data collected and handled by the platform. The data can be related to at least one of the value chain network entities and the features of the platform. A set of monitoring facilities can be configured to monitor the value chain network entities. The platform can be configured to host a set of applications for directing an enterprise to manage the value chain network entities from a point of origin of a product of the enterprise to a point of customer use.

Abstract: A value chain system that provides recommendations for designing a logistics system generally includes a machine learning system that trains machine-learned models that output logistics design recommendations based on training data sets that each respectively defines one or more features of a respective logistic system and an outcome relating to the respective logistics system; an artificial intelligence system that receives a request for a logistics system design recommendation and determines the logistics system design recommendation based on one or more of the machine-learned models and the request; and a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation, and one or more physical asset digital twins of physical assets. The digital twin system executes a simulation based on the logistics environment digital twin, the one or more physical asset digital twins.

Abstract: A method for providing personalized non-profit venue visit recommendations to a visitor at a non-profit venue, comprising: providing a management interface for management of a set of multimedia assets; receiving a mapping of multimedia assets to a display in at least one site plan; receiving metadata for the set of multimedia assets; providing an interface for receiving data indicating personal interests of a visitor, including visitor-provided preference data and passively-collected visitor interaction data; receiving personal interest data for a plurality of non-profit venue visitors; receiving preference data relating to a prospective visitor; applying a machine learning system to analyze the metadata, the data indicating personal interests of the visitor, the personal interest data for the plurality of non-profit venue visitors, and the preference data relating to the prospective visitor; and generating a selection or sequence of non-profit venue location recommendations for the prospective visitor.

Abstract: A value chain system that provides recommendations for designing a logistics system generally includes a machine learning system that trains machine-learned models that output logistics design recommendations based on training data sets that each respectively defines one or more features of a respective logistic system and an outcome relating to the respective logistics system; an artificial intelligence system that receives a request for a logistics system design recommendation and determines the logistics system design recommendation based on one or more of the machine-learned models and the request; and a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation, and one or more physical asset digital twins of physical assets. The digital twin system executes a simulation based on the logistics environment digital twin, the one or more physical asset digital twins.

Abstract: A method for providing personalized non-profit venue visit recommendations to a visitor at a non-profit venue, comprising: providing a management interface for management of a set of multimedia assets; receiving a mapping of multimedia assets to a display in at least one site plan; receiving metadata for the set of multimedia assets; providing an interface for receiving data indicating personal interests of a visitor, including visitor-provided preference data and passively-collected visitor interaction data; receiving personal interest data for a plurality of non-profit venue visitors; receiving preference data relating to a prospective visitor; applying a machine learning system to analyze the metadata, the data indicating personal interests of the visitor, the personal interest data for the plurality of non-profit venue visitors, and the preference data relating to the prospective visitor; and generating a selection or sequence of non-profit venue location recommendations for the prospective visitor.

Abstract: An information technology generally including a set of monitoring facilities that are configured to monitor the value chain network entities; a set of applications that are configured to direct an enterprise to manage the value chain network entities of the platform from a point of origin to a point of customer use; and a machine learning/artificial intelligence system configured to generate recommendations for placing at least one of an additional sensor and a camera on and/or in proximity to a value chain network entity of the value chain network entities, and wherein data from the at least one of the additional sensor and the camera feeds into a digital twin that represents the value chain network entities.

Abstract: An information technology system generally including a cloud-based management platform with a micro-services architecture having a unified set of robotic process automation systems that provide coordinated automation among at least two types of applications from among a set of demand management applications, a set of supply chain applications, a set of intelligent product applications, and a set of enterprise resource management applications for a category of goods with respect to the value chain network entities of the platform.

Abstract: A system can include a backend system and a sensor kit configured to monitor an industrial setting. The sensor kit can include an edge device and a plurality of sensors that capture sensor data and transmit the sensor data via a self-configuring sensor kit network. At least one sensor can capture sensor measurements and output instances of sensor data, generate and output reporting packets, and transmit the reporting packets to the edge device via the self-configuring sensor kit network in accordance with a first communication protocol. The edge device receives reporting packets from the plurality of sensors via the self-configuring sensor kit network and transmits sensor kit packets to the backend system via a public network. The backend system can include a processing system and a storage system, where the processing system performs backend operations on the sensor data and the storage systems stores the sensor data.

Abstract: An information technology platform for personalizing an experience of a visitor at a non-profit venue comprising: a network; a server in communication with the network; a mobile computing device to be carried by the visitor, the mobile computing device in communication with the server via the network, wherein the information technology platform determines personalized information about the visitor; a recommendation engine associated with the server, wherein the recommendation engine is to receive the personalized information about the visitor and to output a suggestion for a personalized experience for the visitor, wherein the information technology platform conveys the suggestion for the personalized experience to the visitor, wherein the personalized experience is to encourage philanthropic activity; and a social networking system for digital communication between the visitor and other visitors, and for sharing information or media related to the non-profit venue or to a philanthropic campaign associated wi

Abstract: A value chain system that provides recommendations for designing a logistics system generally includes a machine learning system that trains machine-learned models that output logistics design recommendations based on training data sets that each respectively defines one or more features of a respective logistic system and an outcome relating to the respective logistics system; an artificial intelligence system that receives a request for a logistics system design recommendation and determines the logistics system design recommendation based on one or more of the machine-learned models and the request; and a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation, and one or more physical asset digital twins of physical assets. The digital twin system executes a simulation based on the logistics environment digital twin, the one or more physical asset digital twins.

Abstract: A method of cross-non-profit venue referral for a visitor to a non-profit venue in a group of cooperating non-profit venues, comprising: receiving personalized interest data from among a set of visitor-provided preference data, passively-collected visitor interaction data, and visitor route data for a plurality of visitors to non-profit venues across a plurality of non-profit venues; receiving preference data relating to a specific set of visitors; applying a machine learning algorithm to analyze the personalized interest data and the preference data relating to the specific set of visitors; and generating a recommendation of a selection or sequence of non-profit venue locations for the specific set of visitors relating to at least a portion of the plurality of the non-profit venues.

Abstract: A system can include a backend system and a sensor kit configured to monitor an industrial setting. The sensor kit can include an edge device and a plurality of sensors that capture sensor data and transmit the sensor data via a self-configuring sensor kit network. At least one sensor can capture sensor measurements and output instances of sensor data, generate and output reporting packets, and transmit the reporting packets to the edge device via the self-configuring sensor kit network in accordance with a first communication protocol. The edge device receives reporting packets from the plurality of sensors via the self-configuring sensor kit network and transmits sensor kit packets to the backend system via a public network. The backend system can include a processing system and a storage system, where the processing system performs backend operations on the sensor data and the storage systems stores the sensor data.

Abstract: An information technology platform for personalizing an experience of a visitor at a non-profit venue, the platform comprising: a network; a server in communication with the network; a mobile computing device to be carried by the visitor, the mobile computing device in communication with the server via the network, wherein an information technology platform determines personalized information about the visitor; a recommendation engine associated with the server, wherein the recommendation engine is to receive the personalized information about the visitor and to output a suggestion for a personalized experience for the visitor, wherein the information technology platform conveys the suggestion for the personalized experience to the visitor, wherein the personalized experience is to encourage philanthropic activity; and a management system for managing the platform, wherein the management system includes a management interface for creating multimedia asset selections or sequences to be included as part of the

Abstract: A method for developing content for a personalized non-profit venue experience using a mobile computing device, comprising: providing an interface for management of a set of multimedia assets; receiving a mapping of multimedia assets to a set of locations on a site plan for the non-profit venue; receiving metadata for the set of multimedia assets; receiving personalized interest data indicating personal philanthropic interests of a visitor to the non-profit venue; and applying a machine learning algorithm to analyze the metadata and the personalized interest data to provide, to a mobile computing device, a selection of a multimedia asset from the set of multimedia assets for consumption by the visitor.

Abstract: A method for augmenting a photograph taken by a visitor at a non-profit venue, comprising: executing an application on a mobile computing device carried by the visitor, which, when executed, performs the steps of: analyzing an original photograph taken by a visitor to a non-profit venue to determine a location of a subject of the original photograph; determining a set of characteristics of the original photograph; accessing digital imagery from a database of high-resolution imagery of the non-profit venue; creating a high-resolution digital image having a corresponding set of characteristics to those of the original photograph by using the digital imagery from the database; masking the original photograph based on differences between the high-resolution digital image and the original photograph taken by the visitor; and compositing the original photograph with the high-resolution digital image to create a high-resolution composite photograph accentuating the characteristics that correspond to the philanthropi

Abstract: An information technology platform for personalizing an experience of a visitor at a non-profit venue comprising: a network; a server in communication with the network; a mobile computing device to be carried by a visitor to a non-profit venue, wherein the platform determines personalized information about the visitor and a recommendation engine outputs a suggestion for a personalized experience to the visitor to encourage philanthropic activity related to an ongoing philanthropic campaign; and a machine learning system for learning on a training set of photographic data or video data, the learning to train the machine learning system to predict a sentiment towards the ongoing philanthropic campaign of the visitor based, at least in part, on photographic images or video images that are taken by the visitor, that are shared to the visitor, that are shared by the visitor or that include the visitor as a subject.

Abstract: An information technology platform for personalizing an experience of a visitor at a non-profit venue comprising: a network; a server in communication with the network; a mobile computing device to be carried by a visitor to a non-profit venue, wherein the platform determines personalized information about the visitor and a recommendation engine outputs a suggestion for a personalized experience to the visitor to encourage philanthropic activity related to an ongoing philanthropic campaign; and a machine learning system for learning on a training set of photographic data or video data, the learning to train the machine learning system to predict a sentiment towards the ongoing philanthropic campaign of the visitor based, at least in part, on photographic images or video images that are taken by the visitor, that are shared to the visitor, that are shared by the visitor or that include the visitor as a subject.

Abstract: Systems and methods for integrating a gaming engine and a smart contract system in a platform are provided. The gaming engine is programmed with a software development environment and an architecture that provides a set of gaming engine services with predefined tools for digital content developers to create a set of game engine generated environments. The smart contract system programmed with smart contract services associated with transactions that are based on electronically verifiable conditions. The integration platform is programmed with an execution framework that is common to the gaming engine and to the smart contract system to integrate the smart contract services with at least one of the gaming engine and the set of game engine generated environments.