Validating electronic content by users includes providing, by a data processing system, electronic content to users for rating the electronic content based on rating metric(s), receiving, by the data processing system, a rating of the electronic content by at least some of the users based on the rating metric(s), each rating being a raw rating having a default weight, cognitively adjusting, by the data processing system, the default weight for each raw rating based on one or more of pre-rating user behaviors, a user rating history and a user credibility rating to arrive at an adjusted rating, using, by the data processing system, the adjusted rating to arrive at a total rating, and providing the total rating to the users.

A computer system for remote interactive graphical display and data management includes a data storage device storing data records, a remote data acquisition computer configured to selectively trigger display actions for the data records based on at least a time-based rule and a time-independent rule; a classification engine configured to classify a response received from a remote display interface having user-selectable options arranged to define a scale of values, in one of two categories, a first category and a second category, being below a first threshold value being classified as being in the first category, and responses on the scale above a second threshold value being in the second category, and a display interface generator configured to selectively generate a supplemental interface or a conclusion message dependent on the category.

A computer system for remote interactive graphical display and data management includes a data storage device storing data records, a remote data acquisition computer configured to selectively trigger display actions for the data records based on at least a time-based rule and a time-independent rule; a classification engine configured to classify a response received from a remote display interface having user-selectable options arranged to define a scale of values, in one of two categories, a first category and a second category, being below a first threshold value being classified as being in the first category, and responses on the scale above a second threshold value being in the second category, and a display interface generator configured to selectively generate a supplemental interface or a conclusion message dependent on the category.

A system for business feedback includes a first feedback recipient feedback module, a system operation module, a cash flow module, and a supply module. The feedback recipient feedback module includes an initialization unit for receiving personal data and a recommendation unit connected to the first initialization unit for receiving recommendation data. The system operation module receives feedback information from the recommendation unit and collects cash flow information or gift information which is sent to the cash flow module or the supply module. The system operation module can increase the number of the first feedback recipient feedback module connected thereto based on records of recommendation information. The cash flow module receives the cash flow information and sends feedback money to the user of the first feedback recipient feedback module. The supply module receives the gift information from the system operation module and send gifts to the user.

A system for business feedback includes a first feedback recipient feedback module, a system operation module, a cash flow module, and a supply module. The feedback recipient feedback module includes an initialization unit for receiving personal data and a recommendation unit connected to the first initialization unit for receiving recommendation data. The system operation module receives feedback information from the recommendation unit and collects cash flow information or gift information which is sent to the cash flow module or the supply module. The system operation module can increase the number of the first feedback recipient feedback module connected thereto based on records of recommendation information. The cash flow module receives the cash flow information and sends feedback money to the user of the first feedback recipient feedback module. The supply module receives the gift information from the system operation module and send gifts to the user.

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.

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 risk relationship package data store may contain electronic records, each electronic record representing a risk relationship package between an enterprise and an entity, and including, for each package, an electronic record identifier and an overall attribute value. A computer server may transmit, to a remote device associated with the entity, a set of potential types of risk coverage based on information in an available type of risk coverage data store. The server may then receive an indication of a selected subset of the potential types of risk coverage and, based on the received indication and a pre-determined rule, generate a risk relationship package for the entity using a flexible structure framework. The server may calculate an overall attribute value for the package based on an attribute algorithm (such that at least one reduction to the overall attribute value is applied based on an amount associated with the selected subset).

A risk relationship package data store may contain electronic records, each electronic record representing a risk relationship package between an enterprise and an entity, and including, for each package, an electronic record identifier and an overall attribute value. A computer server may transmit, to a remote device associated with the entity, a set of potential types of risk coverage based on information in an available type of risk coverage data store. The server may then receive an indication of a selected subset of the potential types of risk coverage and, based on the received indication and a pre-determined rule, generate a risk relationship package for the entity using a flexible structure framework. The server may calculate an overall attribute value for the package based on an attribute algorithm (such that at least one reduction to the overall attribute value is applied based on an amount associated with the selected subset).

The Multidimensional Machine Learning Data and User Interface Segment Tagging Engine Apparatuses, Methods and Systems (“MLUI”) transforms ambient condition data, sales data, user interface selections, cognitive intelligence question input inputs via MLUI components into project projections, campaigns, user interface visualizations, cognitive intelligence question output outputs. A category identifier selection is obtained via a category selection interaction interface mechanism. Entity segment identifier selections are obtained via entity segment selection interaction interface mechanisms. A set of visualization cognitive intelligence (CI) datapoint identifiers is determined as CI datapoint identifiers associated with each combination of a selected entity segment identifier and the selected category identifier. CI datapoint values corresponding to the set of visualization CI datapoint identifiers are retrieved from a NoSQL database configured to act as cache for generating visualizations based on metrics calculated using survey data. A visualization is generated using the retrieved CI datapoint values.