A memory stores therein, with respect to a database containing records each having a first data item and a second data item, an index that includes, in association with each candidate value that is used as the first data item, record specification information specifying two or more records with the candidate value and a statistical value obtained from values of the second data item registered in the two or more records. A processor receives a query including a search condition specifying a requested value of the first data item and a command requesting statistical processing of values of the second data item registered in records satisfying the search condition, retrieves the statistical value associated with a candidate value satisfying the search condition from the index, and outputs a processing result based on the retrieved statistical value for the query.

The present technology pertains to a distributed server system for verifying vendors. The distributed server system comprises one or more nodes on a distributed network; a communication interface of a first node that communicates over a communication network with the one or more nodes on the distributed network, wherein the communication interface receives information about a unique seal associated with a product in response to a query; and a processor of the first node that executes instructions stored in memory, wherein execution of the instructions by the processor verifies that a vendor is associated with the unique seal has been appended to a distributed ledger, determines a match between the unique seal and the vendor; and, after determining the match, confirms that the vendor is a certified vendor of the product.

Embodiments disclosed provide a virtual currency system within a messaging application by performing operations comprising: launching, within a messaging application, a third-party application; receiving, by the messaging application from the third-party application, a request to perform an ecommerce transaction in relation to an item available for consumption on the third-party application; determining, by the messaging application, an expected attribute for the item based on accessing a database that stores expected attribute information for multiple items; verifying, by the messaging application, that an attribute of the item specified by the request corresponds to the expected attribute for the item; and processing, by the messaging application, the ecommerce transaction in response to successfully verifying that the attribute of the item satisfies a verification criterion based on the expected attribute for the item.

A system and method for the aesthetic design of a modular assemblage, comprising means for providing a client graphic user interface for receiving an input for defining parameters of the modular assemblage, and for presenting an image of the defined modular assemblage; communicating a code to a server representing the defined parameters; at the server, in dependence on the communicated code, defining a set of graphic elements corresponding to the defined modular assemblage; communicating the graphic elements from the server to the client; and displaying, at the client, the graphic elements received from the server to represent the defined modular assemblage.

A computer-implemented method of operating a user-searchable database system of curated, themed digital content data, which method includes operating a Connects function to process and store heterogeneous digital content comprising topic data as curated topic data and identified connections between each topic and other topics, and operating a user interface to enable users to search the curated topics and present a search results page in a way that invites further investigation of topics searched, including a list or compilation of topics matching terms defining the search. The method also includes displaying topic elements corresponding to the list or compilation of matching topics in prioritized order based on each topic's relatedness to the search.

Database requests are processed in a distributed database system comprising a central database subsystem and a plurality of local database subsystems comprising a first local database subsystem. The central database subsystem receives a second database request indicating at least one unique identifier of at least one database result of a plurality of database results which have been stored with corresponding unique identifiers by the first local database subsystem in response to a first database request received at the first local database subsystem. The at least one database result identified by the at least one unique identifier is retrieved from the first local database subsystem, processed and stored persistently in a database of the central database subsystem. At least the first local database subsystem and the central database subsystem may operate according to the New Distribution Capability (NDC) standard.

Disclosed are systems, methods, and non-transitory computer-readable storage media for semi-opaque sales with bundles of items. An example bundle can include hotel lodging and a bonus service, status, or item associated with that hotel. An system can identify a first bundle of a first base and bonus item, and a second bundle of a second base and bonus item. The system can present a semi-opaque offer comprising the first bundle, the second bundle, and a discount price, wherein the semi-opaque offer does not identify which of the first bundle and the second bundle will be sold at the discount price to the user upon acceptance of the semi-opaque offer, and wherein the discount price is a full price of the first base item. After receiving acceptance of the semi-opaque offer from the user, the system can disclose that the user has purchased the first bundle at the discount price.

Methods, systems, and apparatuses are described herein for providing purchase recommendations by analyzing social networks using machine learning. A machine learning model may be trained to select one or more of the first plurality of users. Purchase intention data that indicates an intention of a first user to acquire a type of asset may be received. Social networking data that comprises a plurality of associations between a second plurality of users may be received. Purchase history data indicating one or more purchases, of one or more assets associated with the type of asset, made by the second plurality of users may be received. The trained machine learning model may be provided the data. In return, the trained machine learning model may provide an indication of a second user. A notification may be sent to the second user.

Provided is a method for providing product data to a user. The method may include receiving query data associated with a plurality of queries of a database by a user. A classification for at least two queries of the plurality of queries may be determined. A product associated with the classification of the at least two queries may be determined. A potential revenue associated with the product may be calculated based on the user. A probability that the user will purchase the product may be calculated. A score may be calculated based on the potential revenue and the probability that the user will purchase the product. Product data associated with the product may be transmitted to the user if the score exceeds a threshold. A system and computer program product are also disclosed.

This application relates to apparatus and methods for training machine learning models, and applying trained machine learning models to generate item ranking values. In some examples, user session data for multiple users is received. Based on the user session data, user engagement data is generated characterizing engagements of corresponding items for a search query. Further, a number of examines is determined for each of the corresponding items. The user engagement data for each item is normalized based on the corresponding number of examines, and ranking data is generated based on the normalized user engagement data. The ranking data characterizes a ranking of at least a subset of the items. Further, a machine learning model is trained based on the ranking data. In some examples, the trained machine learning model is applied to a query to generate a ranking of items, and the ranking data is transmitted to a web server.