A method is provided of communication between a user and a database of Patents and also of the display and the interactive exploration of data on information of interest relating to Patents/Patent applications. The method comprises: the generation, by means of an access interface, of a request allowing the database to be interrogated based on at least one selection criterion entered into the access interface; the interrogation of the database by means of the request and the loading of bibliographical data for the Patents/Patent applications found, the downloaded bibliographical data comprising data on the technological category; the processing of the bibliographical data, the processing comprising an analysis of co-occurrences comprising the determination of a number of co-occurrences of data on the technological category for all of the Patents/Patent applications found; the displaying, in interactive graphical and/or textual form, of a result and/or of an interpretation of the analysis of co-occurrences.

A system for a product bundle for purchase may include a user device associated with a given user, and a promotional server. The promotional server may obtain historical online browsing data associated with the given user, and obtain historical shopping data associated with the given user. The promotional server may also generate the product bundle based upon the historical shopping data and the historical online browsing data. The product bundle may include complementary products for purchase each having a purchase price associated therewith. The product bundle may have a bundle price that is less than a sum of purchase prices of each of the complementary products. The promotional server may communicate the product bundle and the bundle price to the user device for display thereon, generate a digital promotion redeemable toward the purchase of the product bundle, and communicate the digital promotion to the user device.

A system for a product bundle for purchase may include a user device associated with a given user, and a promotional server. The promotional server may obtain historical online browsing data associated with the given user, and obtain historical shopping data associated with the given user. The promotional server may also generate the product bundle based upon the historical shopping data and the historical online browsing data. The product bundle may include complementary products for purchase each having a purchase price associated therewith. The product bundle may have a bundle price that is less than a sum of purchase prices of each of the complementary products. The promotional server may communicate the product bundle and the bundle price to the user device for display thereon, generate a digital promotion redeemable toward the purchase of the product bundle, and communicate the digital promotion to the user device.

Disclosed herein are systems and methods to efficiently execute predictions models to identify future values associated with various nodes. A server retrieves a set of nodes and generates a primary prediction model using data aggregated based on all nodes. The server then executes various clustering algorithms in order to segment the nodes into different clusters. The server then generates a secondary (corrective) prediction model to calculate a correction needed to improve the results achieved by executing the primary prediction model for each cluster. When a node with unknown/limited data and attributes is identified, the server identifies a cluster most similar the new node and further identifies a corresponding secondary prediction model. The server then executes the primary prediction model in conjunction with the identified secondary prediction model to populate a graphical user interface with an accurate predicted future attribute for the new node.

Disclosed herein are systems and methods to efficiently execute predictions models to identify future values associated with various nodes. A server retrieves a set of nodes and generates a primary prediction model using data aggregated based on all nodes. The server then executes various clustering algorithms in order to segment the nodes into different clusters. The server then generates a secondary (corrective) prediction model to calculate a correction needed to improve the results achieved by executing the primary prediction model for each cluster. When a node with unknown/limited data and attributes is identified, the server identifies a cluster most similar the new node and further identifies a corresponding secondary prediction model. The server then executes the primary prediction model in conjunction with the identified secondary prediction model to populate a graphical user interface with an accurate predicted future attribute for the new node.

Systems and methods for identifying content for an input query are presented. A mapping model is trained to map elements of an input query embedding vector for a received query into one or more elements of a destination embedding vector. In response to receiving an input query, an input query embedding vector is generated that projects into an input query embedding space. The input query embedding vector is processed by the mapping model to map the input query embedding vector into one or more elements of a destination embedding vector in a destination embedding space, resulting in a partial destination embedding vector. Items of a corpus of content are projected into the destination embedding space and the partial destination embedding vector is also projected into the destination embedding space. A similarity measure determines the most-similar items to the partial destination embedding vector and at least some of the most-similar items are returned in response to the input query.

Techniques are described for providing one or more clients with direct access to cached data blocks within a persistent memory cache on a storage server. In an embodiment, a storage server maintains a persistent memory cache comprising a plurality of cache lines, each of which represent an allocation unit of block-based storage. The storage server maintains an RDMA table that include a plurality of table entries, each of which maps a respective client to one or more cache lines and a remote access key. An RDMA access request to access a particular cache line is received from a storage server client. The storage server identifies access credentials for the client and determines whether the client has permission to perform the RDMA access on the particular cache line. Upon determining that the client has permissions, the cache line is accessed from the persistent memory cache and sent to the storage server client.

Techniques are described for providing one or more clients with direct access to cached data blocks within a persistent memory cache on a storage server. In an embodiment, a storage server maintains a persistent memory cache comprising a plurality of cache lines, each of which represent an allocation unit of block-based storage. The storage server maintains an RDMA table that include a plurality of table entries, each of which maps a respective client to one or more cache lines and a remote access key. An RDMA access request to access a particular cache line is received from a storage server client. The storage server identifies access credentials for the client and determines whether the client has permission to perform the RDMA access on the particular cache line. Upon determining that the client has permissions, the cache line is accessed from the persistent memory cache and sent to the storage server client.

The present invention relates to an artificial intelligence method and system for event predication, comprising: receiving, user messages, user activity data, event data, user identification information and transaction data; scraping webpages for additional event data; applying a natural language processing module to process the event data; constructing a training data set using the processed event data; constructing user preferences from the user messages, the user activity data, the user identification information and the transaction data; training a predictive model using the training data set to determine at least one upcoming event predictions determining to display the at least one event predictions based on the user profile; if it is determined to display one of the at least one event predictions, generating a graphical user interface display with a calendar depicting the at least one event prediction; and presenting the graphical user interface display to the user.

The present invention relates to an artificial intelligence method and system for event predication, comprising: receiving, user messages, user activity data, event data, user identification information and transaction data; scraping webpages for additional event data; applying a natural language processing module to process the event data; constructing a training data set using the processed event data; constructing user preferences from the user messages, the user activity data, the user identification information and the transaction data; training a predictive model using the training data set to determine at least one upcoming event predictions determining to display the at least one event predictions based on the user profile; if it is determined to display one of the at least one event predictions, generating a graphical user interface display with a calendar depicting the at least one event prediction; and presenting the graphical user interface display to the user.