A method for providing pre-data breach monitoring provides information to businesses that is useful to predict portions of the company data that may not be secured well enough and other risks associated with data breaches, such as employees that may not be trustworthy.

The present disclosure relates to a method and system for electronically forming a plurality of local areas of commerce on a computerized map. In one aspect, the method includes obtaining, by a processor, mail volume data, census data and vacancy data for the geographical area. The method also includes computing, by the processor, data relationships among the mail volume data, the census data and the vacancy data, identifying, by the processor, longitude and latitude values of the geographical area, and creating, by the processor, an appropriate grid of the geographical area using decimal precision. The method further includes combining, by the processor, the computed data relationships and the created appropriated grid to produce a combined data set and dividing, by the processor, the geographical area into a plurality of strategically plotted locations over time (SPLOTs) based on the combined data set.

In some examples, a location of a merchant is updated as the merchant moves. A server receives the location of the merchant, and compares that location to the location of a user, so as to determine whether the merchant is located within a first threshold distance or a second, smaller threshold distance from the location of the user. If the user is within the first threshold distance, the server presents a first point of sale (POS) interface to initiate an order from the merchant and present the user with an option to fulfill that order through delivery. If the merchant is located within the second, smaller threshold distance from the user, the server presents the user with a second POS interface that gives the user an option to fulfill the order through pickup instead of delivery.

Systems, computer program products, and methods are described herein for implementing predictive resource transfers. The present invention is configured to initiate a remote access to a computing device associated with a merchant; request, via the remote access, information associated with one or more resources associated with the merchant; electronically receive information associated with one or more historical resource transfers executed by one or more users within a predetermined distance from a location of the merchant; predict the market need for the one or more resources associated with the merchant based on at least initiating the machine learning engine on the one or more historical resource transfers; and generate one or more resource transfer requests associated with at least a subset of the one or more resources associated with the merchant based on at least the market need.

A server that includes first circuitry is provided. The first circuitry receives first information from an electronic apparatus. The first information indicates a geographical region of interest to render advertisement content. The geographical region of interest includes a plurality of nodes to render the advertisement content. The first circuitry further receives second information from the electronic apparatus. The second information is about a user selection of a node from the plurality of nodes located in the region of interest. Further, the first circuitry transmits the advertisement content to the selected node. The advertisement content is rendered on the selected node.

Product provisioning and recommender systems and methods are described for generating product-based recommendations for geographically distributed physical stores based on mobile device movement. In various embodiments, a server aggregates mobile device movement data originating from a plurality of mobile devices geographically distributed with respect to a physical store of a geographic trade area. A provisioning application (app) executing on the server and analyzing the mobile device movement data, assigns geographic region descriptors defining a geographical consumer region for each mobile device of the plurality of mobile devices. The provisioning app generates a network store graph model based on a set of regional probabilities and a set of trade area probabilities. The provisioning app implements the network store graph model to generate, and provide to physical store operator(s), product-based recommendations for a target geographic consumer region selected from the geographical consumer region(s) and in a proximity to the geographic trade area.

A method is provided that includes receiving, in a server, a request from a service provider, the request including a consumer identification code associated with a consumer, and obtaining a personalized list of universal product codes based on the consumer identification code and a purchase history log in a database. The method also includes providing the personalized list of universal product codes to the service provider, and receiving a tracking pixel indicative that the consumer has interacted with a consumer payload, wherein the consumer payload is associated with at least one product from the personalized list of universal product codes. A system and a non-transitory, computer-readable medium storing instructions which cause the system to perform the above method are also disclosed.

An information processing device acquires a reference predicted value that is a predicted value of sales figure of a prediction target period and is calculated based on an actual value of sales figure of a past period corresponding to the prediction target period regarding each of target item groups having a trend that sales figures periodically vary in a predetermined repetition cycle; acquires a value of a contextual parameter envisaged to vary in a period shorter than the repetition cycle and affect the sales figures of the target item groups; calculates a difference value between a predicted value of the sales figure of the prediction target period predicted based on the acquired value of the contextual parameter and the reference predicted value regarding each of the target item groups; and selects an item to be recommended to a user based on the difference value calculated regarding each of the target item groups.

Provided in the embodiments of the present disclosure are a data processing method, device and storage medium. The method comprises: acquiring visit data of a target customer in a group of customers in a target area, wherein the visit data includes location data used for reflecting a location of the target customer and time for generating the location data; determining a residence time duration of the target customer in the target area according to the time for generating the location data; determining data of interest of the target customer based on the residence time duration; and determining the data of interest of the target customer as the data of interest of the group of customers.

Embodiments of sales generation, including sales generation employing reverse lead generation using vehicle data systems and methods, are presented herein. In particular, in certain embodiments a user may utilize the vehicle data system to obtain pricing data corresponding to a desired vehicle configuration. When the user is presented with the pricing data associated with the specified vehicle configuration the user may additionally be presented with an upfront price offered by a dealer, where by providing their personal information the user may obtain the name of the dealer offering the upfront price and may additionally be offered the opportunity to purchase the desired, or similar, vehicle at the upfront price.