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Operational business intelligence measurement and learning system

  • US 10,467,550 B1
  • Filed: 04/29/2017
  • Issued: 11/05/2019
  • Est. Priority Date: 07/12/2013
  • Status: Active Grant
First Claim
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1. An automated method of executing machine self-learning methods to detect and report measurements of a plurality of real-world processes, said real world processes comprising a plurality of real-world things and/or real-world activities, and wherein at least some of said real-world things and/or activities are linked together to form real-world processes, said method comprising:

  • receiving, by at least one computer processor, raw data properties pertaining to said real-world things and activities, said raw data properties comprising when-where data, attribute data, identifier data, and quantity data associated with said real-world things and activities;

    wherein at least some attribute data comprise link attribute data, and wherein at least some identifier data comprise thing-activity identifier data;

    creating and storing, by said at least one computer processor, in real time as said raw data properties are received, said raw data properties, thing-activity identifier data and any link attribute data in a process aganostic database system (PADS) database;

    as PADS database objects, said PADS database objects comprising object header information and at least one set of when-where data, attribute data, thing-activity identifier data, quantity data and object exception information;

    continually executing measures rules to select data from said PADS database objects, irrespective of process, according to any of date, quantity/amount, and location based parameters, and continually operating on said measures rules selected data with measures rules selected transformations, thus continually creating measures rules selected and transformed data, and continually storing said measures rules selected and transformed data in a process agnostic measure store (PAMS) database;

    wherein said measurements rules comprise algorithms for producing time-adjusted and corrected data obtained from said PADS database; and

    wherein said transformations comprise algorithms to fit at least some of said measurements into at least one model showing trends in said measurements over time;

    and executing machine self-leaning methods to analyze said PAMS database and output a plurality of measurements according to said measures rules and measures transformations.

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