Systems and methods are disclosed for generating analytics for products placed in virtual or physical shopping carts. In particular, the present systems and methods disclose various ways to generate and use analytics describing the relation between products that are stored in shopping carts (e.g., indicating a user’s interest in a product), and products that are actually purchased. One method includes: detecting, using a processor, a shopping cart associated with a registered user; receiving, by the processor, indication of a selected product associated with the shopping cart; receiving, by the processor, transaction data associated with the registered user; and if the transaction data includes purchase of the selected product, generating, using the processor, a first cart abandonment score; if the transaction data includes purchase of a product other than the selected product, generating, using a processor, a second cart abandonment score, the second cart abandonment score being different from the first cart abandonment score.

An information processing apparatus includes an acquiring unit configured to acquire product identification information from a reference source web page, and a setting unit configured to set, in a case where the product identification information is acquired, a URL based on the acquired product identification information in a hyperlink in a product common page which is commonly used among a plurality of products, and wherein, in a case where the hyperlink is selected, a web page related to a product corresponding to the product identification information is displayed.

An information providing device includes: a storage that stores: provided information; potential queries expected to be input from a user terminal and each associated with properties; and a correspondence among the properties; and a control unit that is connected to the storage and that: based on the correspondence among the properties, collates each of the properties with the provided information, instead of a query or a potential query that are input from the user terminal, rates the provided information with respect to each of the potential queries to obtain a rating result, and upon receiving the query from the user terminal, includes, to the provided information, the rating result with respect to at least one of the potential queries corresponding to the received query, and outputs, to the user terminal, the provided information including the rating result.

An online concierge system generates an item graph connecting item nodes with attribute nodes of the items. Example attributes include a brand, a category, a department, or any other suitable information about the item. When the online concierge system receives a search query to identify one or more items from a customer, the online concierge system parses the search query into combinations of terms and identifies item nodes and attribute nodes related to the search query. The online concierge system identifies item nodes and attribute nodes that are likely to result in a conversion. Information about the identified nodes is presented to the customer. The customer may select an item node to purchase the item, or an attribute node to execute a new search query based on terms associated with the attribute node.

A product purchase system includes a first communication terminal carried by an advertising user, a second communication terminal provided to be movable with a vehicle, and a server. The first communication terminal sends advertisement product information indicating a product worn or held by the advertising user to the server. The second communication terminal sends a product search request including second position information and second time information to the server. The second position information indicates a position of the vehicle when a specific action performed by an occupant in the vehicle is detected while the vehicle is moving. The second time information indicates a clock time when the specific action is detected. The server extracts candidate product information from the advertisement product information, based on the second position information and the second time information. The server then sends the extracted candidate product information to the second communication terminal.

Systems and methods enabling marketing and distribution of motion pictures and other media content by content creators and other content providers are described herein. A platform is provided by which a plurality of content providers can market and distribute media content to users. Information about activity of the users on the platform is obtained in relation to the item of media content or in relation to media content related to the item of media content. A request is received for an activity report comprising information related to a user demographic or a media content characteristic. Responsive to the activity report request, the activity report is provided to the content provider.

Methods, apparatus, systems, and articles of manufacture are disclosed for processing an image using visual and textual information. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to execute the instructions to detect regions of interest corresponding to a product promotion of an input digital leaflet, extract textual features from the product promotion by applying an optical character recognition (OCR) algorithm to the product promotion and associating output text data with corresponding ones of the regions of interest, determine a search attribute corresponding to the product promotion, generate a first dataset of candidate products corresponding to the product in the product promotion by comparing the search attribute against a second dataset of products, and select a product from the first dataset of candidate products to associate with the product promotion, the product selected based on a match determination.

A labor marketplace exchange (LME) computing system and method are provided. The LME computing system communicates with communications devices of service providers and service users. The LME computing system receives service provider profile data from the service providers. Service users can submit service queries to the LME computing system. Based on the parameters of the service query, service providers are identified by the LME computing system. A service request is then sent to a service provider identified by the LME computing system and selected by the service user.

Disclosed herein are embodiments for automated intelligent price guidance of listings for a for sale object (FSO) being offered by a seller. Some embodiments may operate by: receiving information relating to the FSO, including specifications for selling the FSO, wherein the specifications include an original offer price and a time window for selling the FSO; determining a category of the FSO; generating an optimal offer price for the FSO based on one or more of: (a) past listings of previously sold FSOs that have a same or similar category of the FSO; (b) the specifications, including the time window; (c) a category decay curve applicable to the category; and (d) seller flexibility curve of the seller; and through the use of a machine learning neural networking analysis suggesting the optimal offer price to the seller as an offer price for a listing corresponding to the FSO, wherein this price is evaluated over time and suggestions are made accordingly.

Techniques are provided for identifying objects (such as products within a physical store) within a captured video scene and indicating which of object in the captured scene matches a desired object requested by a user. The matching object is then displayed in an accentuated manner to the user in real-time (via augmented reality). Object identification is carried out via a multimodal methodology. Objects within the captured video scene are identified using a neural network trained to identify different types of objects. The identified objects can then be compared against a database of pre-stored images of the desired product to determine if a close match is found. Additionally, text on the identified objects is analyzed and compared to the text of the desired object. Based on either or both identification methods, the desired object is indicated to the user on their display, via an augmented reality graphic.