An apparatus and a method for controlling an interior of a vehicle are disclosed. The apparatus for controlling the interior of the vehicle may include an interface configured to receive, at predetermined cycles, information on at least one of video of an inside of the vehicle collected by a camera or sound of the inside of the vehicle collected from a microphone, and a processor configured to determine a relationship between passengers in the vehicle based on the information, and control the interior of the vehicle based on the relationship between the passengers. When determining the relationship between passengers, a passenger relationship estimation algorithm used may be a neural network model generated through machine learning, and may be stored in a memory in the apparatus for controlling the interior of the vehicle, or may be provided via a server in an artificial intelligence environment through a 5G network.

An information processing apparatus creates a dated image data list by classifying a plurality of dated image data, associates the dated image data list with a specific user, acquires the dated image data for a subject, which is similar to a subject of dateless image data, from the dated image data list, and derives a date to be added to the dateless image data, based on the date added to the acquired dated image data. The plurality of dated image data are image data of a plurality of users including the specific user. The dated image data list is created by classifying the plurality of dated image data for each subject. The dated image data list for a subject, which is similar to a subject of the dated image data, is associated with the specific user.

An information processing apparatus creates a dated image data list by classifying a plurality of dated image data, associates the dated image data list with a specific user, acquires the dated image data for a subject, which is similar to a subject of dateless image data, from the dated image data list, and derives a date to be added to the dateless image data, based on the date added to the acquired dated image data. The plurality of dated image data are image data of a plurality of users including the specific user. The dated image data list is created by classifying the plurality of dated image data for each subject. The dated image data list for a subject, which is similar to a subject of the dated image data, is associated with the specific user.

A system and method for reviewing and editing a series of time lapse photographs, using a machine learning system to review sequentially the individual photographs in the series, identify features in the photographs which features may have been classified as undesirable and flag an individual photograph as undesirable, remove photographs flagged as undesirable from the series set, review the remaining images from the series set of photographs for lighting and composition characteristics and further selection, process the selected photographs for image stabilization, and assembling the processed photographs into a single video for viewing.

In one embodiment, a method includes receiving at a head-mounted device a speech input from a user and a visual input captured by cameras of the head-mounted device, wherein the visual input comprises subjects and attributes associated with the subjects, and wherein the speech input comprises a co-reference to one or more of the subjects, resolving entities corresponding to the subjects associated with the co-reference based on the attributes and the co-reference, and presenting a communication content responsive to the speech input and the visual input at the head-mounted device, wherein the communication content comprises information associated with executing results of tasks corresponding to the resolved entities.

A mobile client device is localized based on a captured image by identifying where the client device is located from a set of known locations. The set of known locations is associated with a set of regions, where each region is associated with a set of key frames representing the important features of the region. Latent vectors and keypoints are calculated for each of the key frames and an image captured by the client device. The system compares the latent vectors of the captured image to the latent vectors associated with the regions to determine a subset of similar regions. The system compares the keypoints of the captured image to the keypoints associated with the regions in the subset to determine a best match. This determined location is considered the region of the client device and may be used with other localization information to maintain localization of the client device.

A mobile client device is localized based on a captured image by identifying where the client device is located from a set of known locations. The set of known locations is associated with a set of regions, where each region is associated with a set of key frames representing the important features of the region. Latent vectors and keypoints are calculated for each of the key frames and an image captured by the client device. The system compares the latent vectors of the captured image to the latent vectors associated with the regions to determine a subset of similar regions. The system compares the keypoints of the captured image to the keypoints associated with the regions in the subset to determine a best match. This determined location is considered the region of the client device and may be used with other localization information to maintain localization of the client device.

An image content determination device includes at least one processor, in which the processor is configured to execute first recognition processing of recognizing a character and a face of a first person from a first image including the character and the face of the first person, execute first acquisition processing of acquiring first person-related information related to the first person included in the first image based on the recognized character and face of the first person, execute second recognition processing of recognizing a face of a second person from a second image including the face of the second person, and execute second acquisition processing of acquiring second person-related information related to the second person included in the second image, in which the second person-related information is acquired using the first person-related information corresponding to the first image including the face of the first person similar to the face of the second person.

An image content determination device includes at least one processor, in which the processor is configured to execute first recognition processing of recognizing a character and a face of a first person from a first image including the character and the face of the first person, execute first acquisition processing of acquiring first person-related information related to the first person included in the first image based on the recognized character and face of the first person, execute second recognition processing of recognizing a face of a second person from a second image including the face of the second person, and execute second acquisition processing of acquiring second person-related information related to the second person included in the second image, in which the second person-related information is acquired using the first person-related information corresponding to the first image including the face of the first person similar to the face of the second person.

A method, system, and computer program product for discovering from user content, at least one tagged item that includes a product, includes identifying plural tags to be associated with each of the user-content item, and the corresponding probability that each of the plural tags is associated with products. There is also the feature of associating the plural tags and their corresponding probability of being associated with products. There are also the features of generating at least one subset of the tagged user content based upon the probability of a first one of the plural tags being associated with a product, and discovering the tagged user content comprising the product, from the subset of the tagged user content based upon the probability of the first one of the plural tags being associated with a product.