Provided is a sales management system configured to: acquire: sales data on a target product sold at a shop to be targeted, the sales data indicating a plurality of time-series values relating to sales of the target product; and a period of a bargain sale of the target product at the shop to be targeted; and output an image including a graph which is based on the plurality of time-series values indicated by the acquired sales data, and in which a display mode for an area corresponding to the period of the bargain sale is different from a display mode for another area.

Systems, methods and articles for optimizing the clearance of content, such as advertisements. The systems disclosed herein provide a greater chance that offers to buy advertisement placement opportunities from media providers are accepted by the media providers. This is achieved by matching advertisements to advertisement placement opportunities which match buyer goals and provider goals, and by using historical data related to advertisements that the seller has cleared. The system may optimize clearance for multiple buyers which are competing for the same advertisement placement opportunities at the same time.

Systems and methods for a merchant advertising platform for generating targeted advertisements to participants in a rewards program who are able to accumulate, manage, and redeem marketing incentives using a rewards platform. Users are able to designate at least one reward sub-account, and publish corresponding information such as purchasing preferences for the sub-account. The platform enables advertisers to view the published information, generate targeted advertisements based on the user's purchase preferences, and compete with other product and service providers for the user's business. The platform, therefore, has the potential to increase the value of the user's accumulated rewards through an open and competitive marketplace.

An apparatus, for a mobile object, that determines a bid-offer condition on an electricity transaction market: determines an optimal condition making the largest gain for the mobile object from an electricity transaction, on which a contract is executed for electricity that the mobile object directly supplies to an electricity demander, based on an upper limit of an offer electricity amount of the mobile object and a predicted value of a buy price of the electricity demander for an electricity amount, the upper limit determined based on a predicted value of an electricity amount demanded by the electricity demander in each time period and an electricity amount transferable from the mobile object; and determines, as the bid-offer condition, to place an offer on an electricity transaction market at a sell price for a to-be-discharged electricity amount that are determined for each time period in the optimal condition.

To automatically evaluate the reasonable price of real estate according to the housing data, the present invention discloses a novel intelligent property evaluation system. The system includes the following components: a housing data input system, a pre-processing filter, a feature extractor, a housing price trainer, and a housing price predictor, wherein the housing price predictor further includes a regression model generator and a decision integrator. The pre-processing filter is used to filter unreasonable samples from housing data and integrate synonymous features. The feature extractor is used to choose required variables of housing price model. The housing price trainer generates housing price model which is trained by a great amount of housing data. The housing price predictor then generates a prediction by the trained model. Furthermore, to maintain the accuracy of prediction under the social evolution, the housing price predictor could be regularly or irregularly updated by a rolling-based method.

The present invention is related to a network-based system and method for real-time online human or human-equivalent assistance that allows a user to connect with an online helper in real-time, wherein the online helper assists the user in doing things, both on or offline, including using apps of a personal digital device with or without the click of a button, searching, getting information, transacting, communicating, emoting, connecting with others, getting expertise, learning, multi-tasking, navigating the digital world, and much more. The network-based system comprising a mobile/web-based application for the personal digital device having a program and circuitry, is installed by the user and online helper in their respective personal digital devices for the real-time online interaction, creating a peer-to-peer connection therebetween via a server and the Internet. This provides new hope for many to be able to do a wide range of things, both on and offline, more easily.

A software management system includes a demand releasing unit configured to release a demand on software from a user to one or more developers, a demand identification unit configured to identify a demand from the user, which is the same or similar to a demand to be resolved by software created by the developer, and a fee determining unit configured to determine a usage fee for the software management system, to be paid by the developer that has created the software to resolve the demand that is the same or similar to the demand from the user.

In one aspect, an example methodology implementing the disclosed techniques includes receiving a corpus of historical recycling settlement data regarding a plurality of recycled assets, the historical recycling settlement data including information pertaining to a recycling of each asset of the plurality of recycled assets, wherein the information pertaining to the recycling includes a recovery value of each recycled asset. The method also includes generating a training dataset from the corpus of historical recycling settlement data, the training dataset including a plurality of training samples, each training sample of the plurality of training samples corresponding to a recycled asset, and training a recovery value prediction module using the plurality of training samples. Once trained, the recovery value prediction module can predict a recovery value of a provided asset.

A method for predicting a financial product price based on an accumulation distribution indicator includes the following steps: step S1: inputting, by a user, a ticker symbol; step S2: calculating, by a system, an accumulation distribution indicator value based on daily trade data; step S3: calculating an accumulation distribution interval and an average candlestick; step S4: determining whether the accumulation distribution indicator breaks through the accumulation distribution interval upward or downward; if the accumulation distribution indicator breaks through the accumulation distribution interval upward or downward, going to step S6, otherwise, going to step S5; step S5: displaying a chart with the average candlestick; and step S6: displaying a breakout mark, where the breakout mark is a rising mark if an upward breakout occurs or is a falling mark if a downward breakout occurs, and displaying the chart with the average candlestick.

A computer system and computer-implemented method for retail merchandise planning, including promotional product selection, price optimization and planning. According to an embodiment, the computer system for generating an electronic retail plan for a retailer comprises, a data staging module configured to input retail sensory data from one or more computer systems associated with the retailer; a data processing module configured to pre-process the inputted retail sensory data; a data warehouse module configured to store the inputted retail sensory data and the pre-processed retail sensory data; a state model module configured to generate a retailer state model for modeling operation of the retailer based on the retail sensory data; a calibration module configured to calibrate the state model module according to one or more control parameters; and an output module for generating an electronic retail plan for the retailer based on the retailer state model.