Abstract: This disclosure is directed to a computer system and method to assist in identifying data feature intersection or overlap between private datasets without revealing any specific data items or data features in the datasets. Various technical components including natural language processing, lexical optimization, and encryption and key management technologies such as homomorphic encryption and secret sharing and coding, are integrated into the disclosed system and method to achieve the data feature intersection identification. Such a system and method may be employed in circumstances where data feature intersection is important for collaborative efforts between entities.

Abstract: A device may receive entity data identifying parameters needed by entities to generate a quotation, and entity encryption data identifying homomorphic encryption schemes supported by the entities. The device may receive, from a client device, a smart contract that includes a request for a quotation, client encryption data identifying homomorphic encryption schemes supported by the client device, and identification data identifying the entities. The device may encrypt the data included in the smart contract, based on the entity encryption data and the client encryption data, to generate encrypted data. The device may generate a modified smart contact by replacing the data included in the smart contract with references to the encrypted data. The device may provide the modified smart contract to server devices associated with the entities. The device may receive, from the server devices associated with the entities, the modified smart contract with quotations.

Abstract: A participant node of a distributed ledger network may identify a distributed federated learning (DFL) smart contract stored on a blockchain. The DFL smart contract may include an aggregation sequence. The aggregation sequence may include an ordered sequence of participant node identifiers. The participant node may generate a trained model by training a global model with training data. The participant node may detect, on the blockchain, a first transition token indicative of a first model previously aggregated by another participant node. The participant node may receive the first model. The participant node may aggregate the first model with the trained model to generate a second model. The participant node may store, on the blockchain, a second transition token indicative of the second model. A successor node identified in the aggregation sequence may further aggregate the second model with an additional model in response to detection of the second transition token.

Abstract: A device may receive entity data identifying parameters needed by entities to generate a quotation, and entity encryption data identifying homomorphic encryption schemes supported by the entities. The device may receive, from a client device, a smart contract that includes a request for a quotation, client encryption data identifying homomorphic encryption schemes supported by the client device, and identification data identifying the entities. The device may encrypt the data included in the smart contract, based on the entity encryption data and the client encryption data, to generate encrypted data. The device may generate a modified smart contact by replacing the data included in the smart contract with references to the encrypted data. The device may provide the modified smart contract to server devices associated with the entities. The device may receive, from the server devices associated with the entities, the modified smart contract with quotations.

Abstract: In some examples, distributed ledger technology and artificial intelligence-based energy trading may include ascertaining energy data that includes historical weather data for a plurality of units, future climate forecast data, household behavioral energy data, and distributed ledger technology energy marketplace data. Based on a specified time interval, a specified future time duration may be divided to generate a plurality of specified divided future time durations to determine a price of energy. A trading recommendation may be generated for a time during the specified future time duration to buy the energy from a distributed ledger technology energy marketplace, and/or another time during the specified future time duration to sell the energy to the distributed ledger technology energy marketplace. Further, instructions to implement the recommendation may be generated to buy the energy and/or sell the energy.

Abstract: A device may receive, from an augmented reality device, speech data identifying augmented reality interactions with an augmented reality robot. The device may receive, from a camera, video data identifying movements of a real robot based on the augmented reality interactions with the augmented reality robot. The device may process the speech data to obtain first action data identifying first actions that the real robot is to perform. The device may process the video data to obtain second action data identifying second actions that the real robot is to perform. The device may process the first action data and the second action data to generate pseudocode. The device may transform the pseudocode into code. The device may cause the real robot to execute the code, wherein executing the code causes the real robot to perform the first actions and the second actions.

Abstract: A participant node of a distributed ledger network may identify a distributed federated learning (DFL) smart contract stored on a blockchain. The DFL smart contract may include an aggregation sequence. The aggregation sequence may include an ordered sequence of participant node identifiers. The participant node may generate a trained model by training a global model with training data. The participant node may detect, on the blockchain, a first transition token indicative of a first model previously aggregated by another participant node. The participant node may receive the first model. The participant node may aggregate the first model with the trained model to generate a second model. The participant node may store, on the blockchain, a second transition token indicative of the second model. A successor node identified in the aggregation sequence may further aggregate the second model with an additional model in response to detection of the second transition token.