Computer-implemented systems and methods for determining the value of display space in a retail store are provided. These values of display space are determined based on measured traffic densities in the retail store. Traffic density is used as a metric for real-world customer activity and customer patterns in the retail store. To determine traffic density, measurements are obtained from one or more sensors in the retail store, where the measurements include spatial information pertaining to a region of the retail store. Traffic density can provide a quantitative, accurate and unbiased metric with which to determine a value associated with displaying products in a region of the retail store.

A computer-implemented method for anonymizing online transaction details may include performing a process to generate an identifier (ID) and reference address for a user, and receiving a request for transaction processing from an e-commerce server. The request may include first data including: information associated with a purchase of a product to be delivered to the user; the identifier; and the reference address. The method may further include: in response to the request, determining whether the ID and reference address are included in the request, and if so, obtaining the accurate address and shipping rate rules; applying, based on the first data and the accurate address, the shipping rate rules to determine accurate shipping rate cost information associated with delivering the product to the accurate address; performing an anonymizing process on the accurate information to obtain anonymous shipping rate information, and transmitting the anonymous shipping rate information to the e-commerce server.

Embodiments of the present invention include a crowdfunding platform comprising one or more computer systems, one or more memories, and one or more central processing units (CPUs). The crowdfunding platform has one or more alternative interfaces to one or more alternative networks. The alternative networks are not capable of operating directly with the Internet. There are also one or more Internet interfaces to the Internet. A crowdfunding platform receives information about a crowdfunding project from one of the alternative interfaces, processes the information into processed information, sends the processed information over one of the Internet interfaces. Further, the crowdfunding platform receives Internet information over one of the Internet interfaces, translates the Internet information into transformed information, and sends the transformed information over one of the alternative interfaces.

Method for predicting when rush hour demand for ride services exceeds supply, including, on a server, receiving ride requests from riders' devices, the requests including a ride price offer; receiving ride request responses from drivers' devices with a counteroffer; (i) analyzing number of ride requests within given period of time and number of unique riders sending ride requests within given period of time; (ii) analyzing number of drivers in the geographic area, number of drivers responding to a request within a given period of time, a number of unique riders whose requests have been responded to within a given period of time, and drivers' counteroffers; identifying true rush hour when demand for ride services exceeds supply and any false rush hours, based on (i) and (ii); raising pricing and/or increasing supply of ride services by calling in more drivers only for the true rush hour, ignoring false rush hours.

A data processing system for machine-learning driven price guidance implements obtaining location information indicative of a location associated with a user; obtaining prescription information for a first prescription and first cost prescription; obtaining, from one or more pharmacy benefits managers, prescription cost information for a prescription from a plurality of pharmacies; analyzing the prescription cost information, the location associated with the user, and the prescription information using a machine learning model to obtain a prescription cost information prediction indicating that a first subset of the plurality of pharmacies provide the prescription at a second prescription cost lower than the first prescription cost; providing the prescription cost information prediction to a pharmacy recommendation unit as an input; generating a prescription savings opportunity report that presents the prescription cost information; and causing a user interface of a display of a computing device to present the prescription savings opportunity report.

A computer-implemented method includes: training a machine learning model with training data to obtain a long-term value model, wherein the training data comprises a plurality of series of temporally-sequenced user bubbling events, wherein each of the user bubbling events corresponds to a historical transportation query from a user device and a historical response including a historical discount signal from a server, and wherein the long-term value model is configured to predict long-term values for respectively applying different discount signals to a given transportation plan of given bubbling features; obtaining a plurality of bubbling features of a transportation plan of a user, wherein the plurality of bubbling features; determining a discount signal based at least on feeding the plurality of bubbling features to the long-term value model; and transmitting the discount signal to a computing device of the user.

Methods and systems are described for optimizing markdown schedules for clearance items at physical retail stores. For example, price sensitivity of a current inventory item at a physical retail store may be modeled using techniques that account for differences in operating behavior and data availability at physical stores versus online channels for selling items. When the current item is placed on clearance at the physical retail store, a request for a markdown schedule, including goals for the clearance, may be received. The forecasted price sensitivity of the current item may be adjusted based on actual price sensitivity of one or more past clearance items (e.g., based on actual clearance sales data) determined to match the current item. An optimal markdown schedule for the item may then be determined based, at least in part, on the adjusted price sensitivity of the item and clearance goals.

Embodiments of systems and methods for the aggregation, analysis, display and monetization of pricing data for commodities in general, and which may be particularly useful applied to vehicles are disclosed. Specifically, in certain embodiments, historical transaction data associated with a particular vehicle configuration may be obtained and processed to determine pricing data associated with the vehicle configuration. The historical transaction data or determined pricing data may then be presented in an intuitive manner.

Systems and methods for facilitating the provision of goods using automated stores are provided. The automated stores are distributed across a geographical area and are remotely controlled to create an on-line marketplace. Users of the system can access the marketplace using an interactive portal to purchase items. Buyers can create purchase orders for items stocked in the marketplace and collect the items at one or more automated stores. The items are consigned to the buyers in real time at the automated store.

Embodiments of the invention are directed to systems, methods and computer program products for dynamic resource interaction allocation based on geographic positioning and usage category information associated with a resource interaction system. The invention receives a base positional location and category of usage for a resource interaction system. The invention determines a set of parameters to be used for interactions having characteristics meeting the determined base positional location and category of usage information. When it receives a proposed interaction from a requesting system containing geographic information and usage category information associated with the proposed interaction, it determines whether the geographic information and usage category information associated with the proposed interaction correspond to the base positional location and category of usage for the resource interaction system. If the information matches, the invention applies the set of parameters to the interaction to thereby process the interaction.