Abstract: A method includes: providing a hosted digital wallet that mirrors at least a portion of data of a user digital wallet, the hosted digital wallet comprising first data indicative of a first portion of a private key; associating the hosted digital wallet with a smart contract storing second data indicative of at least a remainder of the private key that is not the first portion of the private key stored in the hosted digital wallet; determining that the user digital wallet is not compliant with one or more predetermined conditions specified via the smart contract; acquiring authentication data from one or more entities; requiring that the authentication data satisfy a security measure; and in response to the authentication data satisfying the security measure, performing one or more actions authorized by the smart contract associated with recovering the private key using the first data and second data.

Abstract: A computerized method comprising: tracking user behavior data of users each using a respective mobile device within a channel; detecting a demand surge in the channel based on channel analysis data for the channel, wherein the channel analysis data for the channel are based channel events for the channel; and generating one or more first incentives for one or more first users of the users based on the demand surge and incentive metrics, wherein the one or more first incentives are configured to change user behavior of the one or more first users. Other embodiments are disclosed herein.

Abstract: This disclosure relates to improved techniques for personalizing vehicle routes and operator sessions using pre-trained machine learning language models. In certain embodiments, a language model is trained on operator interaction data to learn operator route preferences for vehicle operators. These learned operator route preferences can be leveraged to optimize and personalize vehicle routes and operator sessions in various ways. Other embodiments are disclosed herein as well.

Abstract: A computerized method comprising: tracking user behavior data of users each using a respective mobile device within a channel; detecting a demand surge in the channel based on channel analysis data for the channel, wherein the channel analysis data for the channel are based channel events for the channel; and generating one or more first incentives for one or more first users of the users based on the demand surge and incentive metrics, wherein the one or more first incentives are configured to change user behavior of the one or more first users. Other embodiments are disclosed herein.

Abstract: This disclosure relates to improved techniques for personalizing vehicle routes and operator sessions using pre-trained machine learning language models. In certain embodiments, a language model is trained on operator interaction data to learn operator route preferences for vehicle operators. These learned operator route preferences can be leveraged to optimize and personalize vehicle routes and operator sessions in various ways. Other embodiments are disclosed herein as well.

Abstract: An embodiment provides for private key management for digital assets. A method provides a hosted digital wallet that mirrors at least a portion of data of a user digital wallet. The hosted digital wallet includes first data indicative of a first portion of a private key. The hosted digital wallet is associated with a smart contract storing second data indicative of at least the remainder of the private key that is not the first portion of the private key stored in the hosted digital wallet. It is determined that the user digital wallet is not compliant with condition(s) specified via the smart contract, and the data indicative of the second portion of the private key is obtained from the smart contract, recovering the private key using the first data and second data.

Abstract: This disclosure relates to artificial intelligence (AI) and machine learning networks for predicting or determining demand metrics across multiple channels. An analytics platform can receive channel events from multiple channels corresponding to geographic areas, and channel features related to demand conditions in the channels can be extracted from the channel events. During a training phase, the channel features can be accumulated into one or more training datasets for training one or more demand prediction models. The one or more demand prediction models can be trained to predict or determine demand metrics for each of the channels. The demand metrics can indicate or predict demand conditions based on the current conditions in the channels and/or based on future, predicted conditions in the channels. Other embodiments are disclosed herein as well.

Abstract: An embodiment provides for private key management for digital assets. A method provides a hosted digital wallet that mirrors at least a portion of data of a user digital wallet. The hosted digital wallet includes first data indicative of a first portion of a private key. The hosted digital wallet is associated with a smart contract storing second data indicative of at least the remainder of the private key that is not the first portion of the private key stored in the hosted digital wallet. It is determined that the user digital wallet is not compliant with condition(s) specified via the smart contract, a response to a third-party request is obtained prior to releasing the remainder, and the data indicative of the second portion of the private key is obtained from the smart contract, recovering the private key using the first data and second data.

Abstract: This disclosure relates to decentralized computing networks, architectures and techniques for collecting, analyzing, and processing data over multiple channels. A decentralized computing network comprises a plurality of computing nodes, each of which is dedicated to analyzing and processing events for a particular channel corresponding to a geographic region. Each node of the decentralized computing network can operate independently to process channel analysis data for a corresponding channel. The decentralized configuration of the nodes enables efficient processing of data collected over large geographic areas, increases the reliability of the system, and facilitates easy scaling of the system. Other embodiments are disclosed herein as well.

Abstract: A computer system for accessing a decentralized user controlled social media platform receives, from an user computing device, a user authentication token for the decentralized user controlled social media platform. The computer system determines that the user authentication token is valid for access to a user account at the decentralized user controlled social media platform. Additionally, the computer system requests, through a network connection, content from a plurality of remote servers. Each of the plurality of remote servers requires a unique remote server authentication token to access a portion of the content stored at the respective remote server. Further, the computer system communicates, to the user computing device, the content received from the plurality of remote servers.

Abstract: This disclosure relates to artificial intelligence (AI) and machine learning networks for predicting or determining demand metrics across multiple channels. An analytics platform can receive channel events from multiple channels corresponding to geographic areas, and channel features related to demand conditions in the channels can be extracted from the channel events. During a training phase, the channel features can be accumulated into one or more training datasets for training one or more demand prediction models. The one or more demand prediction models can be trained to predict or determine demand metrics for each of the channels. The demand metrics can indicate or predict demand conditions based on the current conditions in the channels and/or based on future, predicted conditions in the channels. Other embodiments are disclosed herein as well.

Abstract: This disclosure relates to decentralized computing networks, architectures and techniques for collecting, analyzing, and processing data over multiple channels. A decentralized computing network comprises a plurality of computing nodes, each of which is dedicated to analyzing and processing events for a particular channel corresponding to a geographic region. Each node of the decentralized computing network can operate independently to process channel analysis data for a corresponding channel. The decentralized configuration of the nodes enables efficient processing of data collected over large geographic areas, increases the reliability of the system, and facilitates easy scaling of the system. Other embodiments are disclosed herein as well.

Abstract: This disclosure relates to improved techniques for monitoring activities across a plurality of channels and predicting conditions in the channels. In certain embodiments, channel events corresponding to the channels are collected and monitored by an analytics dashboard of a geolocation platform. A channel analysis function analyzes utilizes the channel events to generate demand predictions and/or other metrics. In some cases, the channel analysis function can execute an anomaly detection model, a time series forecasting model, and/or other learning model to generate the predictions and metrics. The analytics dashboard generate various displays to visualizing the demand predictions and/or other metrics. Other embodiments are disclosed herein as well.

Abstract: This disclosure relates to improved techniques for personalizing vehicle routes and operator sessions using pre-trained machine learning language models. In certain embodiments, a language model is trained on operator interaction data to learn operator route preferences for vehicle operators. These learned operator route preferences can be leveraged to optimize and personalize vehicle routes and operator sessions in various ways. Other embodiments are disclosed herein as well.

Abstract: This disclosure relates to improved techniques for accessing and presenting service offerings and/or services options from multiple service provider platforms. In some embodiments, a front-end of the user application includes a client interface that enables an end-user to communicate with a pre-trained language model. The language model can serve as an intermediary between the end-user and a plurality of service provider platforms. The language model can communicate with each of the of service provider platforms to identify and present service options to the end-user via the client interface. Other embodiments are disclosed herein as well.

Abstract: This disclosure relates to artificial intelligence (AI) and machine learning networks for predicting or determining demand metrics across multiple channels. An analytics platform can receive channel events from multiple channels corresponding to geographic areas, and channel features related to demand conditions in the channels can be extracted from the channel events. During a training phase, the channel features can be accumulated into one or more training datasets for training one or more demand prediction models. The one or more demand prediction models can be trained to predict or determine demand metrics for each of the channels. The demand metrics can indicate or predict demand conditions based on the current conditions in the channels and/or based on future, predicted conditions in the channels. Other embodiments are disclosed herein as well.

Abstract: A computerized method comprising: receiving channel events comprising (a) user-sourced data provided by users each using a respective mobile device within a channel and (b) location data associated with the respective mobile devices of the users with the channel; generating a correlation between the location data and the user-sourced data; and generating, using a predictive engine, channel analysis data for the channel based on the channel events and the correlation between the location data and the user-sourced data. Other embodiments are disclosed herein.

Abstract: A computerized method comprising: tracking user behavior data of users each using a respective mobile device within a channel; detecting a demand surge in the channel based on channel analysis data for the channel, wherein the channel analysis data for the channel are based channel events for the channel; and generating one or more first incentives for one or more first users of the users based on the demand surge and incentive metrics, wherein the one or more first incentives are configured to change user behavior of the one or more first users. Other embodiments are disclosed herein.

Abstract: This disclosure relates to decentralized computing networks, architectures and techniques for collecting, analyzing, and processing data over multiple channels. A decentralized computing network comprises a plurality of computing nodes, each of which is dedicated to analyzing and processing events for a particular channel corresponding to a geographic region. Each node of the decentralized computing network can operate independently to process channel analysis data for a corresponding channel. The decentralized configuration of the nodes enables efficient processing of data collected over large geographic areas, increases the reliability of the system, and facilitates easy scaling of the system. Other embodiments are disclosed herein as well.

Abstract: A method for implementing an arbiter module includes cryptographically binding a true identity for a particular real-world individual to an arbiter module and cryptographically binding one or more real-world entities to the arbiter module. The one or more real-world entities store third party information associated with the particular real-world individual. The method also includes cryptographically communicating one or more data packages, which include the third-party information, from the one or more real-world entities to the arbiter module. The method also includes cryptographically binding one or more metaidentities to the arbiter module such that the third-party information associated with the particular real-world individual is at least partially attributable to the one or more metaidentities via the arbiter module for facilitating one or more metaverse actions within the one or more metaverses without revealing the true identity that is bound to the arbiter module.