In some implementations, a device may receive data identifying products and encoded data identifying smart tags of the products. The device may map the data and the encoded data to generate encoded product data. The device may receive encoded data provided by smart tags of products received by a store. The device may receive images of the products. The device may compare the encoded data and the encoded product data to identify a set of the products received by the store. The device may correlate the images with the set of the products. The device may process the correlated data to identify locations of the set of the products in the store. The device may generate an instruction to relocate a product to a new location and may provide the instruction to a device, associated with the store, to cause the product to be relocated to the new location.

Systems and methods to facilitate a customization and proliferation of models are described. The system receives, via a first interface, table information and communicates the table information to a first model. The first model includes logic to process the values to generate a column of predicted values. The system receives a column of predicted values from the first model. The system appends the column of predicted values to the table information to generate appended table information. The system communicates, via a second interface, the appended table information to a second state including a second plurality of models. The sequence of states is associated with a plurality of interfaces including the first interface and the second interface. The interfaces facilitate a customization and proliferation of models.

Systems and methods are provided for object identifier translation using a key pairs platform in a virtualized or cloud-based computing system. A key pair refers to a pair of identifiers held by an entity. Each key pair includes at least one anonymized object identifier. Advantageously, the key pair system protects privacy and provides anonymity for objects by not disclosing the identity of the objects or the underlying data associated with the objects.

The present disclosure describes transaction-enabling systems and methods. A system can include a facility including a core task including a customer relevant output and a controller. The controller may include a facility description circuit to interpret a plurality of historical facility parameter values and corresponding facility outcome values and a facility prediction circuit to operate an adaptive learning system, wherein the adaptive learning system is configured to train a facility production predictor in response to the historical facility parameter values and the corresponding outcome values. The facility description circuit also interprets a plurality of present state facility parameter values, wherein the trained facility production predictor determines a customer contact indicator in response to the plurality of present state facility parameter values and a customer notification circuit provides a notification to a customer in response.

Aspects described herein may allow for generating a customized price rating using a machine learning algorithm. This may have the effect of improving the display of information about merchants by including customized, personalized price ratings that better reflect the tastes and preferences of a user or group of users. According to some aspects, these and other benefits may be achieved by using a machine learning model, trained to receive input corresponding to both user data and merchant data and output an indication of a customized price rating for the merchant that is specific to the user, and then to generate information about the merchant for display that includes the customized price rating.

The present disclosure provides methods and systems for quantifying item performance in a digital shelf. A method for quantifying item performance in a digital shelf may comprise: calculating a value associated with a shelf share of the given item; determining a set of factors for calculating a score indicative of the item performance on the digital shelf, wherein the set of factors includes the shelf share; generating, using a trained machine learning algorithm, the score based on the set of factors; and displaying the score within a graphical user interface (GUI) on an electronic device.

A method may include obtaining at least one dataset that includes information corresponding to periods of usage of a plurality of components within a cloud infrastructure and usage cost for each component of the plurality of components within the cloud infrastructure. The method may include comparing the information corresponding to the periods of usage with at least a portion of the information corresponding to the usage cost for components. The method may include determining a cost for one or more of the components for a period of time. The cost may be determined based on the comparison of the information corresponding to the periods of usage of the components with at least the portion of the information corresponding to the usage cost for the components. The method may include generating a visualization that includes information representative of the cost of the components and displaying the visualization via a display screen.

A method and a computer system for performing the method of determining an initial value or lifetime value for a domain name is provided. The method for determining an initial value includes obtaining, over a communication network, a domain name from requestor; obtaining, over the communication network, one or more inputs from one or more domain name data sources; applying the one or more inputs and the domain name to an initial lifetime worth computer model, wherein the one or more inputs comprise data related to comparable historical domain names, data from a linguistic model analysis, data from a linguistic frequency list, and data related to a second-level domain to top-level domain relationship analysis; determining, by a hardware processor, an initial lifetime worth for the domain name based on the initial lifetime worth computer model; and providing the initial lifetime worth for the domain name to the requestor.

The invention describes a distributed merchandise management system, in which the client, retailer and the manufacturer are linked by a network. This is implemented by a cloud storage (105), the cloud storage (105) comprising a means (105a) for storing data, a means for receiving first data from a first network node (110), the first data being associated with a physical object, a means for receiving request data from a second network node (120), a means for receiving second data from a third network node (130), the second data being associated with the first data and comprising at least one data piece adapted to change the first data depending on the received request data, a means for changing the first data based at least in part on the second data and the request data, and a means for sending a changed portion of the first data from the cloud storage (105) to the first network node (110).

An online concierge system trains a user interaction model to predict a probability of a user performing an interaction after one or more content items are displayed to the user. This provides a measure of an effect of displaying content items to the user on the user performing one or more interactions. The user interaction model is trained from displaying content items to certain users of the online concierge system and withholding display of the content items to other users of the online concierge system. To train the user interaction model, the user interaction model is applied to labeled examples identifying a user and value based on interactions the user performed after one or more content items were displayed to the user and interactions the user performed when one or more content items were not used.