System and method for using artificial intelligence to identify and respond to information from non-hierarchical business structures
View Patent ↗System and method to flatten a hierarchical organizational structure by applying Magic Grid, specifically by generating a trained machine learning model using artificial intelligence using training data comprising a history of an organization's deep value customer satisfaction ratings and innovation (CSI) and associated operating status data of said organization, outputting indications of whether an alarm should be triggered, wherein said training model weights one or more nodes of an artificial neural network; providing said model with current operating status data, outputting a value indicating whether an alarm should be triggered, triggering said alarm based upon said value, receiving user input via a software interface, and further training said model based upon said user input.
1. A system configured to generate messages and initiate tangible alarm signals comprising:
a flat, non-hierarchical organization chart,
at least two phones,
at least two micro business unit users,
at least one phone line disposed between said at least two micro business unit users,
at least one wiretap device,
a digital computer,
visual heat map device,
wherein said visual heat map device is adapted to display elements in marked contrast against a background,
at least one alarm device configured to generate said tangible alarm signal,
wherein said tangible alarm signal is a multicolored map comprising vertical input bars,
said map is configured to allow said users to zoom in and zoom out, and
wherein said at least two phones are connected by said at least one phone line operated by said users, and
wherein said users are people disclosed on said organization chart, and
wherein said at least one wiretap device is disposed upon and adapted to intercept communications on said at least one phone line, and further adapted to sending said communications to said digital computer, and
wherein said digital computer is adapted to receiving signals from said at least one wiretap device, said at least one wiretap device adapted to detect, record and communicate messages between said people, using artificial intelligence to process said communications, and adapted to sending at least one signal to said at least one alarm device, and
wherein said at least one alarm device is adapted to receiving said signals and displaying at least one high chroma, pulsing or tangible warning message in near real-time on said visual heat map device to alert at least one member of an organization,
communicating, between said at least two micro business unit users:
generating, output data base on said communicating;
providing, to the trained machine learning model, input data comprising current operating status data, wherein said input data are generated by application program interfaces or wiretaps on said at least one phone line;
receiving, from the trained machine learning model, output data, based on the input data, comprising a value that indicates that said alarm should be triggered;
triggering, based on the output data, said alarm;
receiving, via a user interface, user input associated with said alarm; and
further training, based on the user input, the trained machine learning model
thereby transforming said organization's structure over time from a hierarchical to a flat, horizontal structure resulting in increased customer and employee satisfaction
wherein said system is configured to enable an alarm augmented trained machine learning model trained using training data comprising a history of an organization's customer satisfaction information data, a machine learning model to output, based on input operating status data, an indication of whether an alarm should be triggered, wherein training the machine learning model comprises modifying one or more weights of one or more nodes of an artificial neural network; and
wherein said system is configured to generate said tangible alarm signals as directed by said trained machine learning model.