Abstract: The current disclosure relates to a composition for promoting bone regeneration, preventing or treating bone diseases using a composition containing a hydrogel and a stem cell spheroid and a use thereof, and a method for preparing the same, wherein the composition for bone regeneration has the effect of promoting bone regeneration of injured bone tissue and alleviating pain while promoting the differentiation of the injected mesenchymal stem cells into osteoblasts and osteocytes at the same time, and thus, can be used for the prevention or treatment of bone diseases.

Abstract: Provided in the present disclosure are an electronic device and a natural language generation method thereof. The natural language generation method of the electronic device comprises: obtaining input data with information on a plurality of slots included for generating a response; obtaining a natural language corresponding to the input data by inputting the information on the plurality of slots to one of artificial intelligence models trained for obtaining a natural language generation template and a natural language; and outputting the obtained natural language.

Abstract: Provided is a drug delivery carrier including PLGA and ?-cyclodextrin containing a drug. According to the drug delivery carrier, the time during which a drug stays in the living body may be prolonged, and due to the biodegradation thereof, few side effects occur.

Abstract: An electronic device and a method for controlling the same are provided. The electronic device includes a microphone, a memory storing at least one instruction and dialogue history information, and a processor configured to be connected to the microphone and the memory and control the electronic device, in which the processor, by executing the at least one instruction, is configured to, based on a user's voice being input via the microphone, obtain response information for generating a response sentence to the user's voice, select at least one template phrase for generating the response sentence to the user's voice based on the stored dialogue history information, generate the response sentence using the response information and the at least one template phrase, and output the generated response sentence.

Abstract: A server and a control method thereof are disclosed. The control method of a server includes receiving knowledge information from a first electronic device, storing the received knowledge information in a personal knowledge database corresponding to a user using the first electronic device, transmitting a response to an inquiry to obtain the knowledge information to at least one second electronic device based on the knowledge information stored in the personal knowledge database, based on the inquiry being received from the at least one second electronic device, receiving feedback information to the response from the at least one second electronic device, and storing the knowledge information in a global knowledge database based on the feedback information. At least a part of a method of allowing a server to provide a response to a user inquiry may use an artificial intelligence model learned according to at least one of machine learning, neural networks, or deep learning algorithms.

Abstract: A method of controlling an electronic apparatus is provided. The controlling method of an electronic apparatus may include: receiving an original document, extracting a plurality of keywords from the received original document, structuralizing the plurality of extracted keywords, generating a summary for the received original document based on the plurality of structuralized keywords, providing the generated summary, receiving an inquiry for the provided summary, and providing a response to the inquiry based on the plurality of structuralized keywords.

Abstract: An electronic device and a method for controlling the same are provided. The electronic device includes a microphone, a memory storing at least one instruction and dialogue history information, and a processor configured to be connected to the microphone and the memory and control the electronic device, in which the processor, by executing the at least one instruction, is configured to, based on a user's voice being input via the microphone, obtain response information for generating a response sentence to the user's voice, select at least one template phrase for generating the response sentence to the user's voice based on the stored dialogue history information, generate the response sentence using the response information and the at least one template phrase, and output the generated response sentence.

Abstract: Provided in the present disclosure are an electronic device and a natural language generation method thereof. The natural language generation method of the electronic device comprises: obtaining input data with information on a plurality of slots included for generating a response; obtaining a natural language corresponding to the input data by inputting the information on the plurality of slots to one of artificial intelligence models trained for obtaining a natural language generation template and a natural language; and outputting the obtained natural language.

Abstract: A server and a control method thereof are disclosed. The control method of a server includes receiving knowledge information from a first electronic device, storing the received knowledge information in a personal knowledge database corresponding to a user using the first electronic device, transmitting a response to an inquiry to obtain the knowledge information to at least one second electronic device based on the knowledge information stored in the personal knowledge database, based on the inquiry being received from the at least one second electronic device, receiving feedback information to the response from the at least one second electronic device, and storing the knowledge information in a global knowledge database based on the feedback information. At least a part of a method of allowing a server to provide a response to a user inquiry may use an artificial intelligence model learned according to at least one of machine learning, neural networks, or deep learning algorithms.