The present disclosure belongs to the field of network technologies, and discloses a resource deduction method and apparatus, an intelligent terminal, and a deduction server. The method includes: sending a rule obtaining request for a deduction rule of a user-specified deduction item to the deduction server; receiving the deduction rule returned by the deduction server; calculating a second resource value that is obtained after applying the deduction rule to a first resource value associated with the user-specified deduction item; and sending a resource transfer request carrying the second resource value to the deduction server. The present disclosure resolves a problem that a merchant needs to manually calculate expense that is obtained after applying the deduction rule to the first resource, and payment efficiency is relatively low. Expense that is obtained after applying the deduction rule to the first resource is automatically calculated by using an intelligent terminal, and payment efficiency is improved.

Provided herein are systems, methods and computer readable media for facilitating pricing. An example method may include determining a price adjustable promotion from a plurality of promotions, the price adjustable promotion having a first promotional price, a forecasted demand that provides an indication of a promotion inventory exhaustion period and a plurality of redemption parameters that comprise at least a promotional period, a promotional value and a redemption expiration and causing the price adjustable promotion to be offered at the second promotional price while at least one of the plurality of redemption parameters for the price adjustable promotion remain constant.

A system for customizing an end user gaming experience is disclosed herein. The system includes a processor. The processor is configured to create a campaign by receiving a campaign creation request from an end user device, the campaign creation request comprising one or more parameters of a campaign associated with the campaign request, creating the campaign based on the one or more parameters associated with the campaign request, and personalizing one or more offers for an end user, each of the one or more offers comprising a game having one or more hurdles, wherein completion of each of the one or more hurdles leads to a reward. The processor is further configured to map a subset of the one or more personalized offers to the end user. The processor is further configured to monitor progress of the campaign.

A system for customizing an end user gaming experience is disclosed herein. The system includes a processor. The processor is configured to create a campaign by receiving a campaign creation request from an end user device, the campaign creation request comprising one or more parameters of a campaign associated with the campaign request, creating the campaign based on the one or more parameters associated with the campaign request, and personalizing one or more offers for an end user, each of the one or more offers comprising a game having one or more hurdles, wherein completion of each of the one or more hurdles leads to a reward. The processor is further configured to map a subset of the one or more personalized offers to the end user. The processor is further configured to monitor progress of the campaign.

A discount service method carried out by a discount service device. The method includes: obtaining discount rate information based on changes in variable values of respective discount elements of an object being sold; on the basis of the discount rate information, identifying, for a plurality of sales records of the object being sold, a consumer distribution corresponding to changes in the discount rates of the respective discount elements of the object being sold; and determining a final discount rate for a predetermined consumer of the object being sold, by applying weights to the respective discount elements, calculated on the basis of the identified consumer distribution. When the consumer distribution changes, the weights of the respective discount elements change, and thus the final discount rate is determined differently, even when the variable values of the respective discount elements for the predetermined consumer are the same.

This document describes the autonomous collection, generation, distribution, and management of online web content. The devices, systems, and methods described herein can be used to collect and generate online web content and communications in an automatic and autonomous manner. Specifically, the disclosed methods, devices, and systems may be employed to produce one or more communications and/or advertising campaigns, as well as for monitoring, managing, defining the efficiency, effectiveness, and workability of the campaign with respect to generating predicted user engagements, thereby accurately determining the cost benefits of the communication campaign. The system may track, evaluate, and provide analytic results that may then be used to better guide the system parameters for customizing autonomous communications directed one or more characteristics of a defined target audience.

The present disclosure relates to a device that calculates a fire insurance premium discount rate applied to a distribution board so as to provide the same to an insurance company server, and provides a device for calculating a fire insurance premium discount rate for a distribution board, comprising: a control unit for receiving distribution board information from the distribution board and controlling the distribution board; and a discount rate calculation unit for receiving, from the insurance company server, fire insurance premium information applied to the distribution board, and calculating a discount rate of the fire insurance premium on the basis of the distribution board information and the fire insurance premium information.

Computer-readable media, methods, and systems are disclosed for electronically creating an improved timeline of events for product-location pairs. A machine learning model is trained on historical data to generate a demand forecast. Input requirements including an initial timeline of events for product-location pairs are received. Using combinatorial branch-and-bound along with the trained machine learning model, the improved timeline of events for product-location pairs is generated.

Computer-readable media, methods, and systems are disclosed for electronically creating an improved timeline of events for product-location pairs. A machine learning model is trained on historical data to generate a demand forecast. Input requirements including an initial timeline of events for product-location pairs are received. Using combinatorial branch-and-bound along with the trained machine learning model, the improved timeline of events for product-location pairs is generated.

Systems and methods for a collaborative offer portal is provided. A proposed offer is received from a manufacturer, including an offer structure and a number of consumers they wish to target. Transaction logs of a retailer are accessed to determine an audience for the offer by calculating a return on investment (ROI) for the customer base using the retailer's records given the offer type. The consumers are then grouped by their ROI distribution, and the ROI for the deal is calculated based upon the offer size in light of this distribution. From the offer ROI a discount percentage to be paid by the retailer versus the merchant can be created. The retailer may then choose to accept the offer for deployment.