Abstract: In some examples, a system may be configured to obtain a set of features of a set of users including one or more features of transactional data of the set of users and one or more features of engagement data of the set of users. Additionally, the system may be configured to implement a first set of operations that generate output data including a plurality of trial membership scores, based on the set of features. In some examples, each trial membership score of the plurality of trial membership scores are associated with a particular user of the set of users and characterize a likelihood of an acquisition event of the corresponding user changing from a non-member status to a trial member status.

Abstract: This application relates to apparatus and methods for automatically determining and providing digital customer insights based on historical customer data. In some examples, a computing device obtains user data for a user. In response, the computing device receives a plurality of product types relevant to the user and their corresponding relevance scores. For each product type, the computing device then receives a set of attributes, where each attribute is associated with an affinity score for the user. The computing device determines, for each product type, an overall score for each attribute and product type pair based on the relevance score for the product type and the affinity score for the corresponding attribute. At least one attribute and product type pair is presented to the user based on the corresponding overall score.

Abstract: Systems and methods for generating and using seasonal affinity scores is disclosed. A set of user-specific historical transaction data is obtained and a user-specific affinity score including at least one of a user-specific season affinity score or a user-specific seasonal theme affinity score is determined by determining one or more product affinity scores for a set of product taxonomies and combining the one or more product affinity scores with one or more product index scores to generate the user-specific affinity score. The product affinity scores are determined by a trained scoring calculation model configured to receive the set of user-specific historical transaction data. One or more interface elements are selected based on the user-specific affinity score and an interface is generated including the one or more interface elements.

Abstract: This application relates to apparatus and methods for automatically determining and providing digital recommendations including discounted items with associated savings. In some examples, a computing device obtains savings data including savings items. In response to obtaining the savings data, the computing device obtains a plurality of clusters. Each cluster of the plurality of clusters includes a set of product types. The computing device obtains user data associated with a user, and based on the savings data and the plurality of clusters, determines a set of initial items for recommendation. The set of initial items includes a subset of the savings items. The computing device also determines final recommendation items for the user based on the set of initial items and the user data.

Abstract: A method can include retrieving product information from a website database to identify a first product as a value-sensitive product identified with at least a value price tag. The method can include determining second users who are not value conscious about the first product. The method can also include preparing first and second recommendations and promotions for the first product, wherein the first recommendation comprises one or more value-sensitive products. The method additionally can include transmitting machine readable instructions to display the first recommendations and promotions for the first product for viewing by the first user. The method also can include transmitting machine readable instructions to display the second recommendations and promotions for the first product for viewing by the second user. Other embodiments are disclosed herein.

Abstract: Systems and methods for attribute recommendation are disclosed. Transaction data related a user is received and attribute recommendations for the user are generated based on the transaction data. The attribute recommendations are generated by a variational inference model configured using a transaction matrix and a loyalty matrix. A set of N recommendations is generated by ranking the generated attribute recommendations based on a combined transaction score and loyalty score and a user interface is generated including the set of N recommendations.

Abstract: In some implementations, a provider edge device associated with a link aggregation group (LAG) may maintain, according to a link aggregation control protocol (LACP), a set of links that connect the PE device to a consumer edge device. The provider edge device may determine that the provider edge device and another provider edge device associated with the LAG are not receiving link aggregation control protocol data units (LACPDUs) from the consumer edge device. The provider edge device may cause the set of links to have a maintain LAG status, which causes the provider edge device to keep up the set of links and to cease maintaining the set of links according to the LACP. The provider edge device may route, based on causing the set of links to have the maintain LAG status, one or more packets to or from the consumer edge device via the set of links.

Abstract: An apparatus of a user equipment (UE) comprises one or more baseband processors to encode a unique UE identifier to be transmitted to an Access and Mobility Function (AMF) of a Fifth Generation (5G) network, wherein the unique identifier identifies UE capabilities to be stored in a UE capabilities database. An apparatus of Access and Mobility Function (AMF) of a Fifth Generation (5G) network comprises one or more processors to receive a user equipment (UE) Capabilities update from a remote server to update a UE capability database that stores UE capability information corresponding to one or more UEs identified with a unique identifier, and to process the UE Capabilities update via one or more Application Programming Interfaces (APIs).

Abstract: A seasonal recommendation system can include a computing device that is configured to receive a request to identify a set of recommendations associated with a season, obtain historical data over a threshold period for a set of product types, and compute a seasonality index score based on the historical data over a target period and the threshold period. The computing device is also configured to select a subset of product types based on the seasonality index score and by applying a theme-aware model to the product types and identify and store a set of items corresponding to at least one product type of the subset of product types. The computing device is configured to, in response to a user navigating to a webpage using a user device, select and display at least one item of the set of items on a user interface of the user device.

Abstract: This application relates to systems and methods for providing search results based on a primary intent. In some examples, a disclosed system includes a memory resource storing instructions; and one or more processors coupled to the memory resource. The one or more processors are configured to execute the instructions to: receive, from a user, a search query including a plurality of words, identify a plurality of intention terms from the words of the search query, compute, for each of the plurality of intention terms, a compatibility score between the intention term and a query context associated with the intention term, determine, from the plurality of intention terms, a primary intention term having a maximum compatibility score among the plurality of intention terms, and generate, based on the primary intention term, a set of intent-based search results in response to the search query, the set of intent-based search results identifying a set of items associated with the primary intention term.

Abstract: In some examples, a system tomay be configured to, for at least a first user of the plurality of users, implement a first set of operations that generate, for each of a first set of item types, attribute value data Additionally, the system may implement a second set of operations that generate, for each of a second set of item types identified incatalogue data, clique data . Moreover, the system may, for the at least first user, implement a third set of operations that generate preference dependency data . Further, the system may , for the at least first user, based on the preference dependency data, the clique data, the attribute value data, generate, for each item type of a set of item types, output data including an affinity value for each item type of the first set of item types.

Abstract: This application relates to apparatus and methods for automatically identifying substitute items. A computing device can generate matrix data that identifies connection values between a plurality of items. The matrix data may be generated based on the application of one or more machine learning algorithms to historical data identifying accepted or denied item substitutions. The computing device may then receive item data identifying at least one second item and at least one attribute of that second item. The computing device may generate a graph based on the matrix data and the item data to determine connection values between the second item and the plurality of first items. The computing device may then determine a substitute item (e.g., a replacement item) for the second item based on the connection values between the second item and the plurality of first items.

Abstract: Disclosed herein is a method of provisioning a virtual experience of a building based on user preference. The method may include receiving an identity data associated with an identity of a user, retrieving a user profile data based on the identity data, analyzing the user profile data using a machine learning model, determining at least one preference data based on the analyzing, identifying at least one virtual utility object based on the at least one preference data, generating an interactive 3D model data comprising the at least one virtual utility object, transmitting the interactive 3D model data to a user device configured to present the interactive 3D model data, receiving a reaction data from the user device. Further, the user device may include at least one sensor configured to generate the reaction data based on a behavioral reaction of a user consuming the interactive 3D model data.

Abstract: In many embodiments, a system comprising one or more processor and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform: determining a set of questions associated with a campaign by extracting text from one or more advertisements; generating vector embeddings of one or more online activities of a user; receiving a search query from a graphical user interface of a computing device of the user; determining a second question from the set of questions to present to the user based on respective relevance probabilities; determining a confidence score associated with the second question; and when the confidence score associated with the second question exceeds a second predetermined threshold, presenting the second question to the user via the graphical user interface. Other embodiments of related methods and systems are also provided.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform receiving a sequence of actions entered into a GUI before an account action for a user account of a user during a user session initiated via a first user portal; dividing the sequence of actions into actions for real-time features and actions for periodic features; simultaneously (1) storing the actions for the periodic features in a distributed cloud storage system and (2) processing the actions for the real-time features into the real-time features; and simultaneously storing the periodic features and the real-time features in a database managed by real-time stream processing software.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform creating a standardized flavor list from flavors associated with items in a catalog; determining a respective score indicating a degree of affinity for a user for each flavor of each of one or more item types associated with the items; creating a respective ordered list of flavors for each of the one or more item types for the user in which the respective ordered list of flavors are ranked by the respective score for the each flavor in the ordered list; and re-ranking a non-personalized list of recommended items associated with an anchor item of the items using the respective ordered list of flavors for the user. Other embodiments are disclosed.

Abstract: A method including a user who has brand affinities about a product brand or a product brand category. The method can include analyzing whether the user has brand affinities for the product brand or the brand category with two binary classification models that can include (a) a model one to determine an affinity to a particular product brand and (b) a model two to determine an affinity to a particular product brand category. The method also can include analyzing whether the user has affinities for a similar product brand or a similar product brand category by a model three neural network. The method can include displaying recommendations and the promotions for a product brand, a product brand category, a similar product brand, or a similar product brand category to the user. Other embodiments are disclosed.

Abstract: Systems and methods for a user equipment (UE) operating in a multi-radio dual connectivity (MR-DC) mode with a master node (MN) and a secondary node (SN) are described herein. A UE receives master cell group (MCG) configuration information for an MCG link with the MN and secondary cell group (SCG) configuration information for an SCG link with the SN, attendant to an attempted MR-DC transition. The UE validates the MCG configuration information and determines that the SCG configuration cannot be applied. The UE applies the MCG configuration information and sends the MN a message indicating that the MCG configuration information and the SCG configuration information were each applied (thereby avoiding a determination of overall MR-DC transition failure due to the unusable SCG configuration information). The UE later sends the MN a message indicating that the SCG configuration information cannot be applied, allowing the network to appropriately react.

Abstract: Systems and methods including one or more processors and one or more non-transitory computer readable storage devices storing computing instructions configured to run on the one or more processing modules and perform: gathering first data comprising first interactions of a user with a first graphical user interface; storing the first data comprising the first interactions of the user with the first graphical user interface as at least one first vector by adding to the at least one first vector for each level of a hierarchical categorization of the first user interface; gathering second data comprising second interactions of the user with a second graphical user interface; storing the second data comprising the second interactions of the user with the second graphical user interface as at least one second vector; determining an intent of the user using the at least one first vector, the at least one second vector, and a predictive algorithm; and transmitting instructions to display a third graphical user inter

Abstract: System and method for generating a ranked list are disclosed. A plurality of prior interactions for a first customer are received by a computing device. Each of the prior interactions includes a product interaction and time. A ranked list of item recommendations is generated based on the plurality of prior interactions. The ranked list of item recommendations is generated by a trained prediction model trained using temporal information embedded into a finite-dimensional vector space. The ranked list of item recommendations is output by the computing device.