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: 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: 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 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 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 system and method of identifying potential areas for mineral extraction is disclosed. The proposed systems and methods describe an autonomous mineral discovery platform that leverages robotics, X-Ray Florescence (XRF) technology, image analytics, smart devices, and IoT enabled devices to perform comprehensive field surveying and exploratory sampling. For example, by implementation of remote navigation and control, as well as field data capture and real-time data transmission capabilities, this platform can be configured to automatically identify rock types and their surface features and perform elemental composition analysis of surface while on-site and remote from the operator site.

Abstract: A system and method of identifying potential areas for mineral extraction is disclosed. The proposed systems and methods describe an autonomous mineral discovery platform that leverages robotics, X-Ray Florescence (XRF) technology, image analytics, smart devices, and IoT enabled devices to perform comprehensive field surveying and exploratory sampling. For example, by implementation of remote navigation and control, as well as field data capture and real-time data transmission capabilities, this platform can be configured to automatically identify rock types and their surface features and perform elemental composition analysis of surface while on-site and remote from the operator site.

Abstract: According to an example, with respect to blast reconciliation for mines, pre blast measurement data and post blast measurement data associated with a blasting operation for a mining site may be ascertained from a pre and post blast measurer. A blast reconciliation model may be generated using existing pre blast measurement data and existing post blast measurement data, and used to analyze the ascertained pre blast measurement data and the ascertained post blast measurement data. Based on the analysis of the ascertained pre blast measurement data and the ascertained post blast measurement data, a blast material yield for the mining site may be determined as a result of the blasting operation. An alert indicative of the blast material yield may be generated.

Abstract: According to an example, with respect to blast reconciliation for mines, pre blast measurement data and post blast measurement data associated with a blasting operation for a mining site may be ascertained from a pre and post blast measurer. A blast reconciliation model may be generated using existing pre blast measurement data and existing post blast measurement data, and used to analyze the ascertained pre blast measurement data and the ascertained post blast measurement data. Based on the analysis of the ascertained pre blast measurement data and the ascertained post blast measurement data, a blast material yield for the mining site may be determined as a result of the blasting operation. An alert indicative of the blast material yield may be generated.