NATURAL LANGUAGE METRIC CONDITION ALERTS ORCHESTRATION

• US 20140100923A1
• Filed: 10/05/2012
• Published: 04/10/2014
• Est. Priority Date: 10/05/2012
• Status: Abandoned Application

• Current Assignee: Successfactors Incorporated (SAP SE)
• Original Assignee: Successfactors Incorporated (SAP SE)
• Inventors: Haine, Dominic Philip, Krakovsky, Dmitri, Strezo, Michael, Ahlberg, Thor Axel, Hagar, Lyndal, Bolwell, Laesa, Ashton, Anthony, Capraro, Michel Angelo
• Application Number:
  US13/646,601
• Publication Number:
  US 20140100923A1
• Time in Patent Office: Days
• US Class Current: 705/7.39
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling

Abstract

Enterprise data sources can be monitored to detect metric conditions via rules, and alerts can be generated. The alerts can be presented as natural language descriptions of business metric conditions. From an alert, the reader can navigate to a story page that presents additional detail and allows further navigation within the data. Additional detail presented can include a drill down synopsis, strategies for overcoming a negative condition, links to discussions within the organization about the condition, options for sharing or collaborating about the condition, or the like.

Claims

1. A method comprising:

by a computing system;

via a plurality of business metric condition detection rules associated with a user identity, detecting a plurality of business metric conditions occurring in one or more enterprise data sources;

constructing natural language summary descriptions of respective of the business metric conditions; and

directing the natural language summary descriptions to the user identity.

3. The method of claim 1 further comprising:

storing a sample size threshold; and

filtering out consideration of observed business metrics for which the sample size threshold is not met.

4. The method of claim 1 wherein a given rule of the business metric condition detection rules comprises:

a condition trigger indicative of a target value.

5. The method of claim 4 wherein:

the given rule further comprises;

a tolerance value for the target value.

6. The method of claim 4 wherein:

the target value of the given rule indicates a business metric value observed in a prior period; and

for the given rule, the target value is compared to a business metric value observed for a current period.

7. The method of claim 4 wherein:

the target value of the given rule indicates an average business metric value observed in an organization or business unit of the organization; and

for the given rule the target value is compared to a business metric value observed for a current period.

8. The method of claim 4 further comprising:

calculating an industry benchmark based on aggregation of data from business enterprises availing themselves of the method; and

for the given rule, using the industry benchmark for the target value.

9. The method of claim 4 wherein:

the target value supports prior period, past period, enterprise average, and hard value comparisons.

10. The method of claim 1 further comprising:

ranking the plurality of business metric conditions according to a ranking based on severity;

wherein the natural language summary descriptions are ranked for display according to the ranking.

11. The method of claim 1 wherein constructing the natural language summary descriptions comprises:

for at least one of the natural language summary descriptions, constructing a user-centric description.

12. The method of claim 1 wherein:

a domain is specified for detecting a given business metric condition; and

the method further comprises;

automatically drilling down to a plurality of data segments within the domain, wherein the drilling down detects a business metric condition in one or more of the data segments; and

detecting at least one of the business metric conditions in at least one of the plurality of data segments.

13. The method of claim 12 wherein:

the drilling down is limited according to a level specified in a given rule.

2. One or more non-transitory computer-readable media comprising computer-executable instructions causing a computing system to perform a method comprising:

by the computing system;

via a plurality of business metric condition detection rules associated with a user identity, detecting a plurality of business metric conditions occurring in one or more enterprise data sources;

constructing natural language summary descriptions of respective of the business metric conditions; and

directing the natural language summary descriptions to the user identity.

14. A data processing system supporting a plurality of reading users, the system comprising:

one or more processors;

memory;

a plurality of stored business metric condition detection rules, wherein the stored business metric condition detection rules specify respective business metric conditions under which an alert is to be generated; and

a business metric monitoring system comprising an alert detection engine and a natural language engine;

wherein the alert detection engine is configured to apply the stored business metric condition detection rules against one or more enterprise data sources and detect occurrences of the business metric conditions specified in the stored business metric condition detection rules; and

wherein the natural language engine is configured to output natural language descriptions of the detected occurrences of the business metric conditions.

15. The system of claim 14 further comprising:

an alert distribution engine configured to distribute the natural language descriptions of the detected occurrences of the business metric conditions to users according to user preferences.

16. The system of claim 14 wherein:

the system is configured to communicate the natural language descriptions of the detected occurrences of the business metric conditions in natural language to a collaboration software system.

17. The system of claim 14, wherein:

the system is configured to switch between descriptions that are suitable for a tablet and descriptions that are suitable for a smartphone while using a single rule for a given description.

18. The system of claim 14 further comprising:

stored user configuration data comprising indications of a plurality of users comprising a particular user;

wherein the natural language engine accepts organizational perspective information for the particular user as input and is configured to describe at least one of the occurrences of the business metric conditions in natural language from an organizational perspective of the particular user.

19. The system of claim 14 further comprising:

a stored industry benchmark target for a business metric aggregated from observations of the business metric for a plurality of enterprises using the system;

wherein the alert detection engine is configured to compare an observed business metric observed for an enterprise against at least one of the stored industry benchmark targets and generate an alert responsive to detecting that the observed business metric is unusual in light of the stored industry benchmark target.

20. One or more non-transitory computer-readable media comprising computer-executable instructions causing a computing system to perform a method comprising:

by the computing system;

via a business metric condition detection rule comprising a business metric identifier, a domain indicating a population for which a business metric is to be monitored, a condition trigger, and automatic analysis dimensions, determining that a business metric condition is present in a segment of the domain, wherein determining that the business metric condition is present comprises comparing an observed business metric for a segment of the domain and a comparable, wherein the segment of the domain is determined via the automatic analysis dimensions;

constructing a natural language summary description of the business metric condition; and

directing the natural language summary description to a user associated in configuration information with the rule.