Disclosed is a system for tracking user activities with a user identification tag stored at the user's Internet-capable device. For example, the system can store a pixel having a color scheme that identifies the user but that is hidden to the user and identifiable by an external computer. The pixel can be stored in the user device's cache for later identification of the user.

The present invention enables an image selected by a user on the server side to be sent to a client in a client-server protocol. An image supply apparatus includes a storage unit storing images; a communication unit; and a processing unit that returns a response by performing processing in accordance with a type of a request received from a client. Here, the processing unit includes a display unit that displays, when a predetermined request is received, a list of images stored in the storage unit; a generation unit that generates, when a user has selected an image, information specifying the selected image for enabling the client to make a sending request for the selected image; and a sending unit that sends the generated information to the client as a response to the predetermined request.

Various implementations disclosed herein include devices, systems, and methods for generating a dimensionally accurate computer-generated reality (CGR) environment with a scaled CGR object. In some implementations, a method includes obtaining environmental data corresponding to a physical environment. A known physical article located within the physical environment is identified based on the environmental data. The known physical article is associated with a known dimension. A physical dimension of the physical environment is determined based on the known dimension of the known physical article. A CGR environment is generated that represents the physical environment. A virtual dimension of the CGR environment is a function of the physical dimension of the physical environment.

An object authentication device includes a speech recognition unit configured to obtain candidates for a speech recognition result for an input speech and a likelihood of the speech as a speech likelihood and an image model generation unit configured to obtain image models of a predetermined number of candidates for the speech recognition result in descending order of speech likelihoods, wherein the image model generation unit initially performs retrieval from an image model database storing the image models when the image models for the candidates for the speech recognition result are generated and generates an image model from information acquired from a network if the image model is not stored in the image model database.

Provided are an electronic device and an operation method thereof. The electronic device includes a memory that stores one or more instructions, and a processor that executes the one or more instructions stored in the memory, wherein the processor is configured to execute the one or more instructions to: divide original image data into a plurality of image sequences; determine a predetermined number of image sequences among the plurality of image sequences as an input image group, select one of the image sequences included in the input image group and add the selected image sequence to the highlight image group based on one or more image sequences pre-classified as a highlight image group, by using a trained model trained using an artificial intelligence algorithm; and generate summary image data extracted from the original image data, by using the image sequence included in the highlight image group.

Embodiments described herein disclose methods and systems for dynamic object recognition. Using location and information from the recognition process, historical data for a dynamic object, and associated data from secondary objects, a more complete data set can be generated for the dynamic object. The dynamic object data set can then be stored for later recognition and a more complete and human-like understanding of the dynamic object, useful in a variety of automated tasks.

Embodiments described herein disclose methods and systems for dynamic object recognition. Using location and information from the recognition process, historical data for a dynamic object, and associated data from secondary objects, a more complete data set can be generated for the dynamic object. The dynamic object data set can then be stored for later recognition and a more complete and human-like understanding of the dynamic object, useful in a variety of automated tasks.

Systems and methods are described for disambiguating user input based on a physical location of items in a vicinity of a user. The system determines that a query received from a user contains an ambiguity. In response, the system identifies several items in the physical vicinity of the user. Then, the system analyzes the identified plurality of items to determine whether the plurality of items forms a first ensemble of items or a second ensemble of items. If the plurality of items forms a first ensemble of items, the system performs a search using the search query and a first keyword related to the first ensemble of items. If the plurality of items forms a second ensemble of items, the system performs a search using the search query and a second keyword related to the second ensemble of items. The system then outputs results of the performed search.

Systems and methods are described for disambiguating user input based on a physical location of items in a vicinity of a user. The system determines that a query received from a user contains an ambiguity. In response, the system identifies several items in the physical vicinity of the user. Then, the system analyzes the identified plurality of items to determine whether the plurality of items forms a first ensemble of items or a second ensemble of items. If the plurality of items forms a first ensemble of items, the system performs a search using the search query and a first keyword related to the first ensemble of items. If the plurality of items forms a second ensemble of items, the system performs a search using the search query and a second keyword related to the second ensemble of items. The system then outputs results of the performed search.

Aspects of the current disclosure include systems and methods for identifying an entity in a query image by comparing the query image with digital images in a database. In one or more embodiments, a query feature may be extracted from the query image and a set of candidate features may be extracted from a set of images in the database. In one or more embodiments, the distances between the query feature and the candidate features are calculated. A feature, which includes a set of shortest distances among the calculated distances and a distribution of the set of shortest distances, may be generated. In one or more embodiments, the feature is input to a trained model to determine whether the entity in the query image is the same entity associated with one of the set of shortest distances.