The present invention provides a processing apparatus (10) including: an acquisition unit (11) that acquires an image including a product display shelf; a display location determination unit (12) that determines, based on the image, a display location of each of products; a computation unit (13) that computes, based on the determined display location of each of the products, a sales-related score relating to sales; and an output unit (14) that outputs the computed sales-related score.

A business support device includes: a storage unit that stores an appearance image of an article included in a first shelf image obtained by capturing an image of a display shelf of the article and a position of an arrangement of the article indicated by the appearance image on the display shelf in association with each other; and a processing unit configured to detect the appearance image stored in the storage unit from a second shelf image obtained by capturing an image of the display shelf after the first shelf image is obtained, and control a notification related to the arrangement of the article indicated by the appearance image based on a detection result thereof.

Aspects of the present disclosure include methods, systems, and non-transitory computer readable media for receiving initial planogram data and one or more of traffic data, inventory data, sales data, staff data, or shrink data, wherein the initial planogram data includes initial merchandise placements in a retail store, generating updated planogram data including updated merchandise placements in the retail store, wherein an updated sales revenue associated with the updated merchandise placements is projected to be higher than an initial sales revenue associated with the initial merchandise placements, and providing the updated planogram data.

The concepts and technologies disclosed herein are a photography composition service. According to one aspect disclosed herein, a requester device can create, on behalf of a requester user, a request for a photograph to be captured by a provider user via a provider device. The request can specify a composition template to be used by the provider user to capture the photograph. The request can further specify an offer to the provider user in compensation for capturing the photograph. The requester device can provide the request to the provider device. The requester device can determine if the offer was accepted. In response to determining that the offer was accepted, the requester device can notify the requester user that the offer was accepted. The requester device can receive the photograph and fulfill the offer to the provider user in compensation for the provider user capturing the photograph.

Described is a service provider marketing system for use to market services provided in various areas and locations. The system allows services provides to enter locations of services provided for viewing by those seeking for such services. The users seeking service may then based on the location entered by the user, view service providers who provide services within the area of the entered location, such as the users neighborhood. This allows the user to identify the properties for which the services are being rendered and see who is most popular with neighbors or see the provider of services that the user has identified as pleasing or liking. The system then provides information for the user to contact the service provider.

This application relates to apparatus and methods for training machine learning models, and applying the trained models to determine an item's relevance to a search query. In some examples, features are generated based on item engagement data associated with a search query. First values are generated for each of a plurality of items based on the search query, a first attribute of each of the items, and the item engagement data. Second values are generated for the items based on the search query, the first attributes, a second attribute of each item, and the item engagement data. Third values are generated for the items based on the search query, the first attributes, a third attribute of each item, and the item engagement data. Features are generated based on the first, second, and third values, and a machine learning model is applied to the features to rank items.

This application relates to apparatus and methods for training machine learning models, and applying the trained models to determine an item's relevance to a search query. In some examples, features are generated based on item engagement data associated with a search query. First values are generated for each of a plurality of items based on the search query, a first attribute of each of the items, and the item engagement data. Second values are generated for the items based on the search query, the first attributes, a second attribute of each item, and the item engagement data. Third values are generated for the items based on the search query, the first attributes, a third attribute of each item, and the item engagement data. Features are generated based on the first, second, and third values, and a machine learning model is applied to the features to rank items.

A network of interconnected digital mirrors within a mall. A plurality of digital mirrors are installed in a participating stores, each comprising a digital screen, a video camera positioned to generate video stream of a user in front of the digital mirror, and a microprocessor. A centralized database stores merchandize data of participating stores. A controller is coupled to the mirrors and the centralized database and preprogrammed to perform the operations comprising: receiving the video stream from the video camera of one of the plurality of digital mirrors; identifying a merchandize item within the video stream; activating a recommendation engine to identify a complementary item from the centralized database that is complementary to the identified merchandize item; sending data regarding the complementary item and a store offering the complementary item to the digital mirror for display.

A virtual repository system with robust item management automatically derives item data from accessed current and past transactions. The system interfaces with merchant systems to receive current and archived transaction data, scans emails for current and past transaction data, monitors browser data for online transaction data, and accepts manual input. Data obtained from all sources is collated and stored in a cache for user validation, whereupon it is added to a virtual repository. Triggers prompt the delivery of responsive results including information from shared virtual repositories.

An autonomous App for selecting color is disclosed. The App includes (i) receiving an oral query concerning a color from a user, (ii) converting the oral query into a digital signal, (iii) associating the queried color with indicia of said queried color in a color database, and (iv) communicating an oral response to the user based on the queried color. The App resides in a general computing machine, such as a smart phone, a smart speaker or a computer tablet. The App can also guide the user to color merchandises on a color display by the general computing machine capable of augmenting an image of the color display with color information.