Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis with respect to structured data objects. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis with respect to structured data objects by utilizing at least one of cross-table data similarity score generation machine learning models and various machine learning frameworks for cross-database similarity determination.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing augmented reality assistance mode functionalities. Certain embodiments utilize systems, methods, and computer program products that perform augmented reality assistance mode functionalities by using at least one of environment familiarity predictions, assistance mode triggering need determinations, and threat detection machine learning models.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for database integration. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform database integration by utilizing attention-based encoder-decoder machine learning models, such as by performing cross-row linking/similarity determination operations based at least in part on row-wise representations that are generated by combining column-wise representations that are generated by an encoder sub-model of an attention-based encoder-decoder machine learning model, and/or by performing cross-column linking/similarity determination operations based at least in part on column-wise representations that are generated based at least in part on attention scores generated by vertical self-attention sub-models of an attention-based encoder-decoder machine learning model.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis with respect to structured data objects. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis with respect to structured data objects by utilizing at least one of cross-table data similarity score generation machine learning models and various machine learning frameworks for cross-database similarity determination.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing augmented reality assistance mode functionalities. Certain embodiments utilize systems, methods, and computer program products that perform augmented reality assistance mode functionalities by using at least one of environment familiarity predictions, assistance mode triggering need determinations, and threat detection machine learning models.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis with respect to structured data objects. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis with respect to structured data objects by utilizing at least one of cross-table data similarity score generation machine learning models and unsupervised anomalous table row detection machine learning models.

Abstract: There is a need for more effective and efficient performing classification-based predictive data analysis on named data collections. This need can be addressed by, for example, solutions for performing classification-based predictive data analysis on named data collections that utilize at least one of techniques for generating column classification machine learning models to perform column classification, techniques for generating column classification machine learning models to perform anomaly detection, techniques for utilizing trained column classification machine learning models to perform column classification, and techniques for utilizing trained column classification machine learning models to perform anomaly detection.

Abstract: There is a need for more effective and efficient performing classification-based predictive data analysis on named data collections. This need can be addressed by, for example, solutions for performing classification-based predictive data analysis on named data collections that utilize at least one of techniques for generating column classification machine learning models to perform column classification, techniques for generating column classification machine learning models to perform anomaly detection, techniques for utilizing trained column classification machine learning models to perform column classification, and techniques for utilizing trained column classification machine learning models to perform anomaly detection.

Abstract: A system and a method for transforming a contract into a digital contract for deployment over a decentralized platform. The system uses blockchain and artificial intelligence to automatically execute contracts. The system is configured to capture the intent, milestones and events of a contract in smart contract codes. In order to achieve this without any imposing transition costs on users, natural language processing is utilized to draft contract and the system generates the same contract in a pre-defined standardized form. The contract in standardized form is converted to respective parse tree and abstract syntax tree. The abstract syntax tree is converted to digital contract. The digital contract is compiled and deployed on to a blockchain network.

Abstract: A system and a method for transforming a contract into a digital contract for deployment over a decentralized platform. The system uses blockchain and artificial intelligence to automatically execute contracts. The system is configured to capture the intent, milestones and events of a contract in smart contract codes. In order to achieve this without any imposing transition costs on users, natural language processing is utilized to draft contract and the system generates the same contract in a pre-defined standardized form. The contract in standardized form is converted to respective parse tree and abstract syntax tree. The abstract syntax tree is converted to digital contract. The digital contract is compiled and deployed on to a blockchain network.