Abstract: A light emitting device includes a first electrode, a second electrode disposed on the first electrode, at least one functional layer which is disposed between the first electrode and the second electrode and includes an amine compound represented by Formula 1: wherein C1 is a substituted or unsubstituted cycloalkyl group having 6 to 12 ring-forming carbon atoms, and nx is an integer of 2 to 5.

Abstract: A light emitting device includes a first electrode, a second electrode on the first electrode, and at least one functional layer between the first electrode and the second electrode and including an amine compound represented by Formula 1.

Abstract: A light-emitting device that includes a diamine-based compound represented by Formula 1, and an electronic apparatus that includes the light-emitting device are provided.

Abstract: The disclosed technology can execute rules for an ambient avatar to perform physical interactions based on a status of a represented user and/or a context of a viewing user. The disclosed technology can further evaluate and select movement points that support avatar movement in an artificial reality environment. The disclosed technology can yet further detect trigger conditions and transition a user presence in a shared communication session. And the disclosed technology can generate stylized 3D avatars from 2D images.

Abstract: An amine-based compound is represented by Formula 1. A light-emitting device includes a first electrode, a second electrode, an interlayer between the first electrode and the second electrode and including an emission layer, and the amine-based compound. An electronic apparatus includes the light-emitting device: Formula 1 may be understood by referring to the description of Formula 1 provided herein.

Abstract: According to various embodiments, an electronic device includes a memory storing deep learning models for determining a force touch, a touchscreen, and a processor configured to identify a touch input of a user through the touchscreen, receive touch pixel data for frames having a time difference based on the touch input, and identify whether the touch input is a force touch based on the touch pixel data. The processor is configured to identify whether the touch input is the force touch using a first determination model among the deep learning models in response to identifying that the touch input is reinputted a designated first number of times or more within a designated time, and otherwise, identify whether the touch input is the force touch using a determination model having a lower computation load than the first determination model among the deep learning models.

Abstract: The present invention relates to a multi-response photosensitive nanocomposite film that may exhibit independent responses of bending and light emission by light stimuli with different wavelengths. The multi-response photosensitive nanocomposite film may induce independent optical bending and light emission when light stimuli with different wavelengths are applied, such that electrical stimulation and location detection may be independently and simultaneously performed in vivo.

Abstract: An example electronic device according to various embodiments may include a fingerprint sensor, a touch sensor, a memory storing at least one instruction, and a processor operatively connected to the fingerprint sensor, the touch sensor, and the memory.

Abstract: An electronic device is provided. The electronic device includes a memory, and a processor including a resource management unit and a neural processing unit. The processor may be configured to obtain an execution request for a specific function operating based on a specific neural network model, identify an available bandwidth of the memory through the resource management unit, and quantize the specific neural network model based on the available bandwidth of the memory through the neural processing unit.

Abstract: A conversation augmentation system can automatically augment a conversation with content items based on natural language from the conversation. The conversation augmentation system can select content items to add to the conversation based on determined user “intents” generated using machine learning models. The conversation augmentation system can generate intents for natural language from various sources, such as video chats, audio conversations, textual conversations, virtual reality environments, etc. The conversation augmentation system can identify constraints for mapping the intents to content items or context signals for selecting appropriate content items. In various implementations, the conversation augmentation system can add selected content items to a storyline the conversation describes or can augment a platform in which an unstructured conversation is occurring.

Abstract: A light-emitting device and an electronic apparatus including the light-emitting device include a novel condensed cyclic compound represented by Formula 1, in which Z1 is a group represented by Formula 2. The condensed cyclic compound represented by Formula 1 may have a high glass transition temperature (Tg) and/or a high melting point, and may thus provide high durability during storage and driving.

Abstract: A conversation augmentation system can automatically augment a conversation with content items based on natural language from the conversation. The conversation augmentation system can select content items to add to the conversation based on determined user “intents” generated using machine learning models. The conversation augmentation system can generate intents for natural language from various sources, such as video chats, audio conversations, textual conversations, virtual reality environments, etc. The conversation augmentation system can identify constraints for mapping the intents to content items or context signals for selecting appropriate content items. In various implementations, the conversation augmentation system can add selected content items to a storyline the conversation describes or can augment a platform in which an unstructured conversation is occurring.

Abstract: The present embodiments relate to automated memory creation and retrieval from moment content items. In some implementations, the automated memory creation and retrieval system can obtain moment content items from user-designated sources with a single user perspective or multiple user perspectives. The moment content items can be assigned tags and arranged in chronological order. The arranged moment content items can be clustered into memory content items based on clustering conditions. Once memory content items are created, they can be arranged into a memory hierarchy made up of connected nodes. In some implementations, the memory content items are also linked together based on similarity in various dimensions in a memory graph. The automated memory creation and retrieval system can receive search criteria for memories from a user interface and provide the user with memories from matched nodes in the memory hierarchy or linked memories in the memory graph.

Abstract: The present embodiments relate to automated memory creation and retrieval from moment content items. In some implementations, the automated memory creation and retrieval system can obtain moment content items from user-designated sources with a single user perspective or multiple user perspectives. The moment content items can be assigned tags and arranged in chronological order. The arranged moment content items can be clustered into memory content items based on clustering conditions. Once memory content items are created, they can be arranged into a memory hierarchy made up of connected nodes. In some implementations, the memory content items are also linked together based on similarity in various dimensions in a memory graph. The automated memory creation and retrieval system can receive search criteria for memories from a user interface and provide the user with memories from matched nodes in the memory hierarchy or linked memories in the memory graph.

Abstract: An electronic device is provided. The electronic device includes a microphone, and at least one processor operatively connected to the microphone, wherein the at least one processor may include a buffer memory configured to store a first feature vector for a first voice signal obtained from the microphone as an inverse value, and an operation circuit configured to perform a norm operation for a first feature vector and a second feature vector, based on the second feature vector, based on a second voice signal streamed from the microphone and an inverse value of the first feature vector stored in the buffer memory, or calculate a similarity between the first feature vector and the second feature vector. In addition, various embodiments identified through the specification are possible.

Abstract: Disclosed herein a system, a method and a device for generating a voice model for a user. A device can include an encoder and a decoder to generate a voice model for converting text to an audio output that resembles a voice of the person sending respective text. The encoder can includes a neural network and can receive a plurality of audio samples from a user. The encoder can generate a sequence of values and provide the sequence of values to the decoder. The decoder can establish, using the sequence of values and one or more speaker embeddings of the user, a voice model corresponding to the plurality of audio samples of the user.

Abstract: The present embodiments relate to automated memory creation and retrieval from moment content items. In some implementations, the automated memory creation and retrieval system can obtain moment content items from user-designated sources with a single user perspective or multiple user perspectives. The moment content items can be assigned tags and arranged in chronological order. The arranged moment content items can be clustered into memory content items based on clustering conditions. Once memory content items are created, they can be arranged into a memory hierarchy made up of connected nodes. In some implementations, the memory content items are also linked together based on similarity in various dimensions in a memory graph. The automated memory creation and retrieval system can receive search criteria for memories from a user interface and provide the user with memories from matched nodes in the memory hierarchy or linked memories in the memory graph.

Abstract: An automatic memory system can automatically identify and present content items that match a user's natural language (NL) input. The automatic memory system can compute a match score between the NL input and each of multiple potential memory content items. The automatic memory system can compute the match score using a variety of algorithms and/or machine learning models such as an image/NL matching process to get a first match score, a tag matching process to get a second match score, and/or a combination match score from the first and second match scores. The automatic memory system can select one or more of the content items with the highest match score(s). The automatic memory system can provide the selected content items, such as by suggesting them to the NL providing user, automatically displaying or playing them, inserting them into the conversation thread, etc.

Abstract: A display device includes a display panel including a first area of a constant shape, and at least one shock absorber located on a surface of the first area of the display panel, the at least one shock absorber supporting the display panel, wherein the at least one shock absorber includes a first member located on the first area of the display panel, and a second member facing the first member. The second member is connected to the first member and disposed only on a part of the first member.

Abstract: Disclosed herein a system, a method and a device for generating a voice model for a user. A device can include an encoder and a decoder to generate a voice model for converting text to an audio output that resembles a voice of the person sending respective text. The encoder can includes a neural network and can receive a plurality of audio samples from a user. The encoder can generate a sequence of values and provide the sequence of values to the decoder. The decoder can establish, using the sequence of values and one or more speaker embeddings of the user, a voice model corresponding to the plurality of audio samples of the user.