Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and method to present missed revenue based on competitor data. The program and method provide for receiving indication of a user request to present missed revenue for at least one product made available for purchase by a website; determining a set of competitor products for the at least one product, each competitor product in the set of competitor products having a competitor product price; identifying, based on the determining, a first competitor product within the set of competitor products with a lowest competitor product price; calculating a missed revenue for the at least one product based at least in part on a difference between the product price and the lowest competitor product price for a given time period; and causing display of the missed revenue in association with the at least one product.

A system and a method of using property taxes to create an asset (property tax easement), which has a fair market value, that can serve as an underlying instrument or security for purpose of linking it to a new type of option (property tax put or call option) that can be bought, sold, and/or traded on, preferably, a major publicly-traded exchange. The present invention includes several, most or all of the following: a property tax easement, a Multi-Listing Subscription-type (MLS-type) listing, property tax options, a publicly-traded market exchange, and a Property Tax Refund Program that—collectively, provides the owners of residential real estate property and/or commercial real estate property with the means to potentially obtain a property tax refund each and every year.

Systems and methods for identifying a pattern in data to detect a behavior of interest. The systems and methods receive a data stream representing a series of events occurring over a time interval. The systems and methods determine, for the interval, a depth indicating the amount of interest during the interval, where the interest represents an amount of a selected activity during that interval for a selected parameter. The systems and methods also generate a depth chart for the activities at different values of the selected parameter over a series of intervals, train a machine-learning image-classification model using depth chart images; identify the behavior of interest using predictions from the trained machine-learning model and target pattern; and provide an indication of the presence or absence of the behavior of interest. The parameter can represent a quantity and associated price of a commodity in a market.

Automated property value calculation is provided. The method comprises receiving historic transaction data for a group of real estate properties over a specified time and receiving characteristic data regarding the properties for a number of defined categories. Historic data is also received for a number of demographic parameters over the specified time. The demographic data corresponds to regions in which the properties are located. A predictive valuation model is built with the financial transaction data, characteristic data, and demographic data. Individual values are calculated with the predictive valuation model for a new group of real estate properties according to their characteristics. The individual values are then aggregated. Financial transaction data is received for the new group of properties, and a net asset value of the new group of properties is calculated according to the aggregated valuations and financial transaction data.

A system for selection and configuration of an automated robotic process includes a media input module structured to receive at least one functional media, a media analysis module structured to analyze the at least one functional media and identify an action parameter; and a solution selection module structured to select at least one component of an AI solution for use in an automated robotic process, wherein the selection is based, at least in part, on the action parameter.

A system for managing objects is provided. The system comprises a data collection gateway and a plurality of apparatus. Each of the plurality of apparatus obtains parameters relating to objects in a farm from a server through the data collection gateway. The parameters include a reference threshold weight of the objects. Each of the plurality of apparatus collects weights of the objects through sensors. Also, each of the plurality of apparatus adjusts the reference threshold weight based on the obtained parameters to generate an adjusted threshold weight. Further, each of the plurality of apparatus sorts the objects to different areas in the farm based on the adjusted threshold weight and the collected weights.

Methods and systems for controlling production resources in a supply chain are described. The system automatically generates predicted supply chain operational metrics across a nodes of a supply chain. The system automatically infers causal factors that impact the predicted supply chain operational metrics. The causal factors include a change to a utilization of the production resource. The system communicates a user interface including production runs being scheduled on the production resource including a user interface element representing the scheduling of the production run associated with a value at risk. The system receives input causing a change to the utilization of the production resource. The change to the utilization of the production resource impacts the predicted supply chain operational metrics including the value at risk associated with the scheduling of the production run.

An Artificial Intelligence (AI)-based attribute prediction system generates predictions for attributes of highly customized equipment in response to received user requests. Processed historical data is initially used to generate feature combinations which are then employed along with a plurality of statistical and machine learning (ML) models in order to identify a best scoring model-feature combination in two selection cycles using multiple selection criteria. The predictions for an attribute are generated by the best scoring model and feature combination. Various insights regarding the features affecting the attribute can be additionally derived to provide recommendations to the user.

Disclosed herein are embodiments for automated intelligent price guidance of listings for a for sale object (FSO) being offered by a seller. Some embodiments may operate by: receiving information relating to the FSO, including specifications for selling the FSO, wherein the specifications include an original offer price and a time window for selling the FSO; determining a category of the FSO; generating an optimal offer price for the FSO based on one or more of: (a) past listings of previously sold FSOs that have a same or similar category of the FSO; (b) the specifications, including the time window; (c) a category decay curve applicable to the category; and (d) seller flexibility curve of the seller; and through the use of a machine learning neural networking analysis suggesting the optimal offer price to the seller as an offer price for a listing corresponding to the FSO, wherein this price is evaluated over time and suggestions are made accordingly.

Apparatus and associated methods relate to presenting for selection services comprising at least one bundled set of healthcare services to be performed separately by respective providers, determining a bundle price for the at least one bundled set of healthcare services, and in response to receiving payment in an amount of the bundle price, generating a purchase data record selectively redeemable to receive each of the at least one bundled set of healthcare services, and transmitting a unique confirmation number generated for the purchase data record. One or more service of the bundled set may be a dental or veterinary service. The bundle price may be based on a location or time at which at least one service will be performed and may be determined using a user's remaining insurance deductible. Payment may be disbursed to multiple providers of the bundled set of healthcare services. A payment may be virtual funds.