Operational business intelligence system and method

• US 9,639,595 B2
• Filed: 07/10/2014
• Issued: 05/02/2017
• Est. Priority Date: 07/12/2013
• Status: Active Grant

• Current Assignee: OpsVeda, Inc.
• Original Assignee: OpsVeda, Inc.
• Inventors: Appanna, Venkatesh, Gupta, Sanjiv, Somani, Dinesh
• Primary Examiner(s): Brown, Sheree
• Application Number:
  US14/328,123
• Publication Number:
  US 20150019475A1
• Time in Patent Office: 1,027 Days
• Field of Search: 707600
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP

Abstract

An automated method of detecting and reporting exceptions to a plurality of real-world processes, designed to combine the real-time speeds of automated transaction processing systems with the in depth analysis of automated business intelligence systems. The system operates by storing input data in the form of process agnostic database system (PADS) objects, configured to accept data without limitation as to specific process, and design to allow flexible automated linking between various things, activities, and processes to any desired depth. The system operates by analyzing real-time input data against various exception criteria, thereby determining exception marked objects, and then using linkages between different PADS objects to immediately determine the impact of these exceptions on other activities and processes. The system can then estimate the severity of the impact, automatically notify users when an impact to even distantly connected processes are detected, and also estimate the severity of the impact.

Claims

1. An automated method of using machine self-learning to detect and report exceptions to 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, using 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;

a) creating and storing, using 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 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;

said PADS database objects being process agnostic objects that handle said raw data according to generic data models and machine self-learning software executed on said at least one computer processor;

wherein said object header information is received along with said raw data properties, or wherein said object header information is obtained by using said at least one computer processor to compare said raw data properties against a previously defined set of categories of real-world things and activities, and classifying said raw data according to said previously defined categories of real-world things and activities, thereby determining said object header information;

wherein said object exception information is obtained by using said at least one computer processor to compare said raw data properties against a previously defined baseline properties of said previously defined set of real-world things and activities to further determine if any of said raw data properties represent an exception from said baseline properties, and if said exception is found, storing it as object exception information;

wherein PADS database objects comprising at least one stored object exception information are exception marked PADS database objects;

linking, using said at least one computer processor, at least some different PADS database objects comprising data pertaining to real-world things and activities together to form real-world processes by setting said link attribute data in said different PADS database objects to create said links according to at least one set of process linking rules;

wherein PADS database objects linked together by setting said linked attribute data are linked PADS database objects;

b) further analyzing, using said at least one computer processor and at least one algorithm, said exception marked PADS database objects and linked PADS database objects and determining an impact of said exception marked PADS database objects and linked PADS database objects on a status of at least some of said real world processes, thereby determining real-world process exceptions;

and outputting, using said at least one computer processor, data pertaining to at least some of said real world process exceptions.

2. The method of claim 1, wherein at least some of said raw data properties are received from a human or automated user using a remote computerized device and a network connection;

wherein said remote computerized device has at least one remote processor;

wherein said at least one remote processor is configured to obtain said raw data properties from said user, and automatically link said raw data properties to said object header information;

thereby using said at least one remote processor and said network connection to receive raw data properties from said user along with said object header information.

3. The method of claim 1, wherein at least some of said raw data properties are received from a human or automated user using a remote computerized device and a network connection;

wherein said remote computerized device has at least one remote processor;

wherein said at least one remote processor is configured to obtain said raw data properties from said user, and automatically link said raw data properties to said thing-activity identifier data;

thereby using said at least one remote processor and said network connection to receive raw data properties from said user along with said thing-activity identifier data.

4. The method of claim 1, wherein using said at least one computer processor and at least one algorithm to analyze said exception marked PADS database objects and linked PADS database objects and determine an impact of said exception marked PADS database objects and linked PADS database objects on a status of at least some of said real world processes, thereby determining real-world process exceptions, is done either on a real-time basis as said data properties are received, or on a non-real time basis as determined by schedules set according to other criteria.

5. The method of claim 1, wherein a given PADS database object comprising data pertaining to real world activities is linked with zero or more different PADS database objects comprising data pertaining to real world things, and wherein said given PADS database object is also linked with zero or more different PADS database objects comprising data pertaining to different real world activities;

and

further using said at least one processor to alter at least some of said links according to at least one set of business process link configuration rules.

6. The method of claim 1, wherein said real-world processes are business processes, said real world things comprise products that are goods, and wherein said activities comprise activities selected from the group consisting of putting products on hold, releasing products for delivery, shipping products, product sales, product returns, changing one or more raw materials to work-in-process goods or finished goods, creating purchase orders for products, creating purchase orders for raw materials for a product, delay in receiving products, and delay in receiving raw materials for a product;

or

wherein said real world things are selected from the group consisting of customers, vendors, raw materials, materials, and finished goods

or wherein said real world activities comprise various material movement &

manufacturing steps of a manufacturing &

distribution process, marketing activities, financial activities, real estate activities, personnel activities, regulatory activities, and quality assurance activities.

7. The method of claim 1, further analyzing, using said at least one computer processor, said machine self-learning software, and impact analysis rules, said real world process exceptions according to said object exception information, and said link attribute data, of their respective PADS database objects, thereby determining at least one of an urgency, magnitude of impact, and cost of inaction the impact of said exceptions on said real world processes, and outputting data pertaining to at least some of said real world process exceptions according to at least one of said urgency, magnitude of impact, and cost of inaction of said exceptions on said real world processes.

8. The method of claim 7, wherein said at least one algorithm analyzes the impact of said exception marked PADS database objects and linked PADS database objects on the status of at least some real world processes by:

using at least one processor and linked attribute data in said exception marked PADS database objects and linked PADS database object to determine any potential real world process exceptions;

using said at least one processor and a likely impact database comprising the likely impact that said exception marked PADS database objects will have on said potential real world process exceptions, determine if said exception marked PADS database objects have a projected impact on said potential real world process exceptions that exceeds at least one threshold of significance; and

if said likely impact exceeds said at least one threshold of significance, then categorize said potential real world process exception as a real world process exception and output data pertaining to said real world process exception.

9. The method of claim 8 wherein said projected impact exceeds said at least one threshold of significance, further using said at least one threshold of significance to also categorize a priority or severity of said real world process exception, and also output data pertaining to said priority or severity of said real world process exception.

10. The method of claim 8, wherein said likely impact database is based on said machine self-learning software executed on said at least one computer processor, and at least one of impact rules, historical impact data, or historical impact patterns.

11. The method of claim 10, wherein said impact rules, historical impact data, or historical impact patterns are business process rules, business process historical data, or business process historical patterns.

12. The method of claim 1, wherein said baseline properties further comprise computer data pertaining to object specific baseline properties of key dates, locations, and quantities;

wherein using said at least one computer processor to determine if any of said raw data properties represent an exception from said baseline properties comprises any of;

using the object header information and the object specific baseline properties of key dates to determine if said when-where data corresponds to said key dates;

or

using the object header information, and said object specific baseline properties of said locations to determine if said when-where data corresponds to said locations;

or

using the object header information, and said object specific baseline properties of said quantities to determine if said quantity data corresponds to said quantities;

and if one or more determinations find a lack of correspondence, then storing the lack of correspondence as at least one object exception information.

13. The method of claim 1, wherein if said raw data properties pertain to an activity, and if no previously defined baseline properties of said raw data properties are found, then using said at least one computer processor to further designate said activity as a non-planned activity;

marking the PADS database objects without said previously defined baseline properties as affected PADS database objects;

further using pre-defined rules for non-planned activity to analyze said affected PADS database objects and linked PADS database objects and to determine an impact of said affected PADS database and linked PADS database objects on a status of at least some of said real world processes, thereby determining real-world process exceptions.

14. The method of claim 1, wherein at said at least one computer processors are located on a computer network connected server, and wherein said method is performed on said computer network connected server.

15. The method of claim 1, wherein said PADS database and PADS database objects are configured to simultaneously handle both online transaction processing and online analytic processing.

21. The method of claim 1, wherein said PADS database is a columnar database.

16. An automated method of using machine self-learning to detect and report exceptions to 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, using 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;

a) creating and storing, using 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 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;

said PADS database objects being process agnostic objects that handle said raw data according to generic data models and machine self-learning software executed on said at least one computer processor;

wherein said object header information is received along with said raw data properties, or wherein said object header information is obtained by using said at least one computer processor to compare said raw data properties against a previously defined set of categories of real-world things and activities, and classifying said raw data according to said previously defined categories of real-world things and activities, thereby determining said object header information;

wherein said object exception information is obtained by using said at least one computer processor to compare said raw data properties against a previously defined baseline properties of said previously defined set of real-world things and activities to further determine if any of said raw data properties represent an exception from said baseline properties, and if said exception is found, storing it as object exception information;

wherein PADS database objects comprising at least one stored object exception information are exception marked PADS database objects;

linking, using said at least one computer processor, at least some different PADS database objects comprising data pertaining to real-world things and activities together to form real-world processes by setting said link attribute data in said different PADS database objects to create said links according to at least one set of process linking rules;

wherein PADS database objects linked together by setting said linked attribute data are linked PADS database objects;

wherein a given PADS database object comprising data pertaining to real world activities is linked with zero or more different PADS database objects comprising data pertaining to real world things, and wherein said given PADS database object is also linked with zero or more different PADS database objects comprising data pertaining to different real world activities; and

further using said at least one processor to alter at least some of said links according to at least one set of business process link configuration rules;

b) further analyzing, using said at least one computer processor and at least one algorithm, said exception marked PADS database objects and linked PADS database objects and determining an impact of said exception marked PADS database objects and linked PADS database objects on a status of at least some of said real world processes, thereby determining real-world process exceptions is done either on a real time basis as said data properties are received, or on a non-real time basis as determined by schedules set according to other criteria;

further analyzing, using said at least one computer processor, said machine self-learning software, and impact analysis rules, said real world process exceptions according to said object exception information, and said link attribute data, of their respective PADS database objects, thereby determining at least one of an urgency, magnitude of impact, and cost of inaction the impact of said exceptions on said real world processes;

and outputting, using said at least one computer processor, data pertaining to at least some of said real world process exceptions according to at least one of said urgency, magnitude of impact, and cost of inaction of said exceptions on said real world processes.

17. The method of claim 16, wherein at least some of said raw data properties are received from a human or automated user using a remote computerized device and a network connection;

wherein said remote computerized device has at least one remote processor;

wherein said at least one remote processor is configured to obtain said raw data properties from said user, and automatically link said raw data properties to said object header information;

thereby using said at least one remote processor and said network connection to receive raw data properties from said user along with said object header information;

wherein at least some of said raw data properties are received from a human or automated user using a remote computerized device and a network connection;

wherein said remote computerized device has at least one remote processor;

wherein said at least one remote processor is configured to obtain said raw data properties from said user, and automatically link said raw data properties to said thing-activity identifier data;

thereby using said at least one remote processor and said network connection to receive raw data properties from said user along with said thing-activity identifier data.

18. The method of claim 16, wherein said PADS database and PADS database objects are configured to simultaneously handle both online transaction processing and online analytic processing, and

wherein at said at least one computer processor are located on a computer network connected server, and wherein said method is performed on said computer network connected server.

22. The method of claim 16, wherein said PADS database is a columnar database.

19. A machine self-learning system for automatically detecting and reporting exceptions to 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 system comprising:

at least one computer processor and memory;

PADS database object management software stored in said memory, said PADS database object management software configured to direct said at least one computer processor to receive 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;

said PADS database object management software further configured to direct said at least one computer processor to create and store in said memory, 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 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;

said PADS database objects being process agnostic objects that handle said raw data, according to generic data models and at least one computer processor configured to run machine self-learning software;

wherein said PADS database object management software is further configured to direct said at least one computer processor to receive said object header information along with said raw data properties, and/or wherein said PADS database object management software is further configured to direct said at least one computer processor to obtain said object header information by comparing said raw data properties against a previously defined set of categories of real-world things and activities, and to classify said raw data according to said previously defined categories of real-world things and activities, thereby determining said object header information;

wherein said PADS database object management software is further configured to direct said at least one computer processor to obtain said object exception information by comparing said raw data properties against a previously defined baseline properties of said previously defined set of real-world things and activities to further determine if any of said raw data properties represent an exception from said baseline properties, and if said exception is found, storing it as object exception information;

wherein PADS database objects comprising at least one stored object exception information are exception marked PADS database objects;

wherein said PADS database object management software is further configured to direct said at least one computer processor to link at least some different PADS database objects comprising data pertaining to real-world things and activities together to form real-world processes by setting said link attribute data in said different PADS database objects to create said links according to at least one set of process linking rules;

wherein PADS database objects linked together by setting said linked attribute data are linked PADS database objects.

20. The system of claim 19, wherein said PADS database object management software is further configured to direct said at least one computer processor configured to execute at least one algorithm to analyze said exception marked PADS database objects and linked PADS database objects and determine an impact of said exception marked PADS database objects and linked PADS database objects on a status of at least some of said real world processes, thereby determining real-world process exceptions;

and wherein said PADS database object management software is further configured to direct said at least one computer processor to output data pertaining to at least some of said real world process exceptions.

23. The system of claim 19, wherein said PADS database is a columnar database.