Abstract: An enhanced product recommendation service observes a user engaging in an activity to automatically recommend products that facilitate performance of the activity. Photographs and/or video of the user performing the activity may be analyzed to identify an output that results from the activity and/or an activity task sequence that includes multiple tasks associated with completing the activity. Then, the enhanced product recommendation service may identify a product that is usable to generate the output(s) of the activity and/or complete the activity without performing one or more individual tasks of the activity task sequence. The product may be an existing product. Alternatively, the product may be a customized product that is designed based on observing the user engage in the activity. Physical measurements of the customized product may be determined based on various measurements determined by analyzing the photographs and/or video of the user performing the activity.

Abstract: Example methods and systems are directed to a managed inventory. A database may store information regarding items owned by a user. The information regarding an item may include a quantity owned and one or more triggering events. Based on the occurrence of a triggering event, an order for the item may be placed without user intervention. Data to the database may be provided by one or more sensors. Triggering events may be defined in terms of sensor data. The triggering event may be defined by a user or through machine learning. The order may be placed using a predetermined modality or a dynamically-determined modality based on one or more criteria, such as price, shipping speed, and the urgency of the order.

Abstract: Example methods and systems are directed to a managed inventory. A database may store information regarding items owned by a user. The information regarding an item may include a quantity owned and one or more triggering events. Based on the occurrence of a triggering event, an order for the item may be placed without user intervention. Data to the database may be provided by one or more sensors. Triggering events may be defined in terms of sensor data. The triggering event may be defined by a user or through machine learning. The order may be placed using a predetermined modality or a dynamically-determined modality based on one or more criteria, such as price, shipping speed, and the urgency of the order.

Abstract: The disclosed technologies identify opportunities to display relevant three-dimensional (“3D”) model data within a real-world environment as a user wears a wearable device. The 3D model data can be associated with objects, or items, and the 3D model data rendered for display is relevant in the sense that the items are determined to be of interest to the user and the items fits within the real-world environment in which the user is currently located. For instance, the techniques described herein can recognize items typically found in a kitchen or a dining room of a user's house, an office space at the user's place of work, etc. The characteristics of the recognized items can be identified and subsequently analyzed together to determine preferred characteristics of a user. In this way, the disclosed technologies can retrieve and display an item that correlates to (e.g., matches) the preferred characteristics of the user.

Abstract: A method and a system convert the value of a gift card for use with a mobile device. The value of the gift card is determined from an issuer of the gift card. The value of the gift card is stored in a digital payment account in a storage device. The digital payment account stores values of gift cards from issuers. A financial transaction corresponding to the issuer of the gift card is performed on the digital payment account. The value of a first gift card from a first issuer of a first digital payment account is exchanged with the value of a second gift card from a second issuer of a second digital payment account.

Abstract: In various example embodiments, systems and methods to provide proximity recommendations are provided. In example embodiments, data representing prioritized recommendations for a user is received. The data representing the prioritized recommendations is used to generated a composite visual representation by embedding select prioritized recommendations into a visual representation. The composite visual representation presents a higher prioritized recommendation in a more spatially prominent manner than a lower prioritized recommendation. The composite visual representation is then presented to the user.

Abstract: Example methods and systems are directed to a managed inventory. A database may store information regarding items owned by a user. The information regarding an item may include a quantity owned and one or more triggering events. Based on the occurrence of a triggering event, an order for the item may be placed without user intervention. Data to the database may be provided by one or more sensors. Triggering events may be defined in terms of sensor data. The triggering event may be defined by a user or through machine learning. The order may be placed using a predetermined modality or a dynamically-determined modality based on one or more criteria, such as price, shipping speed, and the urgency of the order.

Abstract: An indication of a touch on a touch-enabled screen of a device is received that indicates a selection of a date facet in a listing of items. The date facet denotes a temporal indicator, other facets denote non-temporal indicators. An indication of a pinch gesture is received. The date facet is scaled to produce a scaled-facet listing. The scaling includes modifying a degree of a characteristic of the date facet in an increasing amount including producing a scaled-out listing implemented as an updated portion of item data having a different temporal indicator than an initial display of the item data by an amount proportional to a magnitude of a pinch gesture characteristic of the pinch gesture. The scaled-facet listing is displayed by exposing an updated set of the item data having the modified degree of the characteristic and replacing previous item data with the scaled-out listing.

Abstract: A method and a system convert the value of a gift card for use with a mobile device. The value of the gift card is determined from an issuer of the gift card. The value of the gift card is stored in a digital payment account in a storage device. The digital payment account stores values of gift cards from issuers. A financial transaction corresponding to the issuer of the gift card is performed on the digital payment account. The value of a first gift card from a first issuer of a first digital payment account is exchanged with the value of a second gift card from a second issuer of a second digital payment account.

Abstract: An apparatus is provided including a processor, a memory, and a vehicle tracking portal. The vehicle tracking portal is configured to obtain vehicle identification information regarding a vehicle. Then diagnostic information regarding the vehicle can be obtained. Then, based on the vehicle identification and the diagnostic information, a recommended service for the vehicle can be determined. A user can then be alerted to the recommended service.

Abstract: In various example embodiments, systems and methods to provide proximity recommendations are provided. In example embodiments, data representing prioritized recommendations for a user is received. The data representing the prioritized recommendations is used to generated a composite visual representation by embedding select prioritized recommendations into a visual representation. The composite visual representation presents a higher prioritized recommendation in a more spatially prominent manner than a lower prioritized recommendation. The composite visual representation is then presented to the user.

Abstract: Model placement metadata is defined and stored for a three-dimensional (“3D”) model. The model placement metadata specifies constraints on the presentation of the 3D model when rendered in a view of a real-world environment, such as a view of a real-world environment generated by wearable computing device like an augmented reality (“AR”) or virtual reality (“VR”) device. A wearable computing device can analyze the geometry of a real-world environment to determine a configuration for the 3D model that satisfies the constraints set forth by the model placement metadata when the 3D model is rendered in a view of the environment. Once the configuration for the 3D model has been computed, the wearable device can render the 3D model according to the displayed configuration and display the rendering in a view of the real-world environment.

Abstract: An apparatus is provided including a processor, a memory, and a vehicle tracking portal. The vehicle tracking portal is configured to obtain vehicle identification information regarding a vehicle. Then diagnostic information regarding the vehicle can be obtained. Then, based on the vehicle identification and the diagnostic information, a recommended service for the vehicle can be determined. A user can then be alerted to the recommended service.

Abstract: Model placement metadata is defined and stored for a three-dimensional (“3D”) model. The model placement metadata specifies constraints on the presentation of the 3D model when rendered in a view of a real-world environment, such as a view of a real-world environment generated by wearable computing device like an augmented reality (“AR”) or virtual reality (“VR”) device. A wearable computing device can analyze the geometry of a real-world environment to determine a configuration for the 3D model that satisfies the constraints set forth by the model placement metadata when the 3D model is rendered in a view of the environment. Once the configuration for the 3D model has been computed, the wearable device can render the 3D model according to the displayed configuration and display the rendering in a view of the real-world environment.

Abstract: An attribute correlation system reduces network traffic and processing cycles associated with impromptu item selections by generating attribute preference models based on disparate attribute spectrums. The attribute correlation system deploys the attribute preference models to select individual items from various disparate “candidate item categories.” Generally described, the attribute preference models facilitate analyzing item sets across a wide variety of disparate “candidate” item categories to preemptively identify individual items for a user. In this way, the individual items may be identified and, ultimately, selected for the user even absent any indication that the user has searched for otherwise identified these items or even other items from within the disparate “candidate” item categories. The “candidate” item categories may be determined to be disparate from one another based on a relationship void existing such that predefined relationships are missing between these “candidate” item categories.

Abstract: A method for displaying content displayed on one or more first devices on a second device is provided. The method includes receiving a request to display content currently displayed on a first device on a second device, the request including a gesture made on a screen of the first device, and pairing the first device to the second device. The method further includes transmitting instructions to the second device to display the content currently displayed on the first device, and transmitting the content currently displayed on the first device to the second device for display thereon.

Abstract: An attribute correlation system reduces network traffic and processing cycles associated with impromptu item selections by generating attribute preference models based on disparate attribute spectrums. The attribute correlation system deploys the attribute preference models to select individual items from various disparate “candidate item categories.” Generally described, the attribute preference models facilitate analyzing item sets across a wide variety of disparate “candidate” item categories to preemptively identify individual items for a user. In this way, the individual items may be identified and, ultimately, selected for the user even absent any indication that the user has searched for otherwise identified these items or even other items from within the disparate “candidate” item categories. The “candidate” item categories may be determined to be disparate from one another based on a relationship void existing such that predefined relationships are missing between these “candidate” item categories.

Abstract: In various example embodiments, systems and methods to provide proximity recommendations are provided. In example embodiments, data representing prioritized recommendations for a user is received. The data representing the prioritized recommendations is used to generated a composite visual representation by embedding select prioritized recommendations into a visual representation. The composite visual representation presents a higher prioritized recommendation in a more spatially prominent manner than a lower prioritized recommendation. The composite visual representation is then presented to the user.

Abstract: A method for displaying content displayed on one or more first devices on a second device is provided. The method includes receiving a request to display content currently displayed on a first device on a second device, the request including a gesture made on a screen of the first device, and pairing the first device to the second device. The method further includes transmitting instructions to the second device to display the content currently displayed on the first device, and transmitting the content currently displayed on the first device to the second device for display thereon.

Abstract: In various example embodiments, systems and methods to provide proximity recommendations are provided. In example embodiments, data representing prioritized recommendations for a user is received. The data representing the prioritized recommendations is used to generated a composite visual representation by embedding select prioritized recommendations into a visual representation. The composite visual representation presents a higher prioritized recommendation in a more spatially prominent manner than a lower prioritized recommendation. The composite visual representation is then presented to the user.