A method, computer system, and computer program product for accelerating class data loading in a containers environment are provided. In response to a first container in a containers environment being created from a first image, at least one archive file containing a set of classes from the first image can be loaded. Then a respective class sharing file for each of the at least one archive file can be generated. The class sharing file is stored in a shared location. A second container in the containers environment is created from a second image. If a class sharing file from the archive is found in the shared location, that class sharing file can be used.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for distributing content items. In one aspect, a method includes presenting an immersive content item at a client device. The immersive content item includes a first presentation interface that includes a scrollable image portion that presents an image having a first interaction point and a preview graphic indicating the availability of other images for presentation. The immersive content item also includes a second presentation interface that is initiated upon interaction with the first interaction point. The second presentation interface includes a scrollable image portion that presents an image from a plurality of images and an image sequencing indicator.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for distributing content items. In one aspect, a method includes presenting an immersive content item at a client device. The immersive content item includes a first presentation interface that includes a scrollable image portion that presents an image having a first interaction point and a preview graphic indicating the availability of other images for presentation. The immersive content item also includes a second presentation interface that is initiated upon interaction with the first interaction point. The second presentation interface includes a scrollable image portion that presents an image from a plurality of images and an image sequencing indicator.

The present invention is directed to a tool that enables songwriters to efficiently solicit and analyze listener feedback during the songwriting process. To begin, a songwriter uploads a song to an online platform. The online platform presents the song and a feedback interface to a music listener. As the song plays, the listener provides feedback about the song using the feedback interface. For example, the listener may indicate that she likes or dislikes a particular element of the song, such as an instrument, at a specific point in time. The feedback is timestamped, processed, and presented to the songwriter for analysis. Provided feedback may also be used for recommending subsequent songs to the listener. The interactive music feedback system described herein may integrate with a variety of music streaming platforms.

A user interacts with an inventory location, such as a lane on a shelf, picking items or placing items to that inventory location. A neural network (NN) is trained using a subset of reference images to determine parameters, such as node weights. An image of the user interaction is acquired and processed by the NN to determine if an item in a test image matches an item in a candidate set of items. During use, the parameters are used to dynamically instantiate an ad hoc neural network having a graph that is based on a number of items in a candidate set. The candidate set of items may comprise items within reach of a user at the time the test image was acquired, such as on a nearby shelf or tote. The NN output indicates if the test image is deemed to match one of the candidate set of items.

A user interacts with an inventory location, such as a lane on a shelf, picking items or placing items to that inventory location. A neural network (NN) is trained using a subset of reference images to determine parameters, such as node weights. An image of the user interaction is acquired and processed by the NN to determine if an item in a test image matches an item in a candidate set of items. During use, the parameters are used to dynamically instantiate an ad hoc neural network having a graph that is based on a number of items in a candidate set. The candidate set of items may comprise items within reach of a user at the time the test image was acquired, such as on a nearby shelf or tote. The NN output indicates if the test image is deemed to match one of the candidate set of items.

A system and method utilizing three-dimensional (3D) data to identify objects. A database of profiles can be created for goods, product, object, or part information by producing object representations that permit rapid, highly-accurate object identification, matching, and obtaining information about the object. The database can be part of a different recognition system than the system used to identify the object. The profiles can be compared to a profile of an unknown object to identify, match, or obtain information about the unknown object, and the profiles can be filtered to identify or match the profiles of known objects to identify and/or gather information about an unknown object. Comparison, filtering, and identification may be performed prior to, subsequent to, or in conjunction with other systems, such as image-based machine learning algorithms.

An information processing apparatus includes a processor configured to acquire operation history of data used in a target operation, and arrange and display images representing the data in accordance with a chronological order represented by the operation history on a display.

An information processing apparatus includes a processor configured to acquire operation history of data used in a target operation, and arrange and display images representing the data in accordance with a chronological order represented by the operation history on a display.

A method and system for automated generation of photographic metadata in response to a machine learning algorithm including an input for receiving an image, a processor configured to perform an image recognition algorithm to generate a first metadata, to predict a second set of metadata in response to the first set of metadata and a set of rules generated in response to a machine learning training process wherein the set of rules are generated in response to a plurality of images having user generated metadata, and an output for transmitting the second set of metadata and the image to a storage medium.