Enterprise resource planning database extraction, transformation, and analysis

Patent metadata

Field	Value
Publication number	US-12461933-B2
Application number	US-202217958980-A
Country	US
Kind code	B2
Filing date	Oct 3, 2022
Priority date	Oct 3, 2022
Publication date	Nov 4, 2025
Grant date	Nov 4, 2025

Abstract

In an embodiment, a transaction dataset is analyzed to identify a set of transaction events, each transaction event from the set of transaction events associated with a transaction event type. The set of transaction events is analyzed to identify a plurality of sets of grouped transaction events, each set of grouped transaction events from the plurality of sets of grouped transaction events associated with a subset of transaction events from the set of transaction events. To identify a plurality of sets of variants, each set of grouped transaction events from the plurality of sets of grouped transaction events including an identifier of a variant for that set of grouped transaction events and from the plurality of sets of variants is analyzed, each set of variants from the plurality of sets of variants differing from each remaining set of variants from the plurality of sets of variants.

First claim

The invention claimed is:

1 . A non-transitory processor-readable medium storing code representing instructions to be executed by one or more processors, the instructions comprising code to cause the one or more processors to:
receive transaction data associated with a process, the transaction data filtered from a database including data associated with a plurality of processes that includes the process; analyze the transaction data to identify a set of transaction events, each transaction event from the set of transaction events associated with a transaction event type from a set of transaction event types; analyze the set of transaction events to identify a plurality of sets of grouped transaction events, each set of grouped transaction events from the plurality of sets of grouped transaction events including a subset of transaction events from the set of transaction events; analyze, to identify a plurality of sets of variants, each set of grouped transaction events from the plurality of sets of grouped transaction events including an identifier of a variant for that set of grouped transaction events, each set of variants from the plurality of sets of variants (1) differing from each remaining set of variants from the plurality of sets of variants and (2) representing a sequence of transaction event types for a set of grouped transaction events from the plurality of sets of grouped transaction events that are associated with that set of variants; analyze the plurality of sets of variants to identify a set of variants from the plurality of sets of variants not having a standard sequence of transaction event types; calculate a first set of metric values associated with the set of variants; calculate a second set of metric values associated with a set of grouped transaction events from the plurality of sets of grouped transaction events that are associated with the set of variants; determine that at least one of (1) the first set of metric values is outside a first predetermined acceptable range, or (2) the second set of metric values is outside a second predetermined acceptable range; send, in response to the determining, a signal to cause a remedial action to be performed; determine that a total number of variants in the set of variants from the plurality of sets of variants not having the standard sequence of transaction event types is greater than a predetermined threshold; and prevent, without human intervention, a future transaction in response to determining that the total number is greater than the predetermined threshold.

2 . The non-transitory processor-readable medium of claim 1 , wherein the transaction data is stored in a text file, and the database is an enterprise resource planning database.

3 . The non-transitory processor-readable medium of claim 1 , wherein the instructions further comprise code to cause the one or more processors to:
send a first signal to cause display of a representation quantifying the set of variants not having the standard sequence of transaction event types; send a second signal to cause display of the first set of metric values; and send a third signal to cause display of the second set of metric values.

4 . The non-transitory processor-readable medium of claim 1 , wherein the process is one of a procurement process, a production process, a distribution process, an accounting process, a human resource process, a sales process, or a customer service process.

5 . A method, comprising:
analyzing, via a processor, a transaction dataset to identify a set of transaction events, each transaction event from the set of transaction events associated with a transaction event type from a set of transaction event types; analyzing, via the processor and based on associations between transaction events from the set of transaction events, the set of transaction events to identify a plurality of sets of grouped transaction events, each set of grouped transaction events from the plurality of sets of grouped transaction events associated with a subset of transaction events from the set of transaction events; analyzing, via the processor and to identify a plurality of sets of variants, each set of grouped transaction events from the plurality of sets of grouped transaction events including an identifier of a variant for that set of grouped transaction events and from the plurality of sets of variants, each set of variants from the plurality of sets of variants (1) differing from each remaining set of variants from the plurality of sets of variants and (2) representing a sequence of transaction event types for a set of grouped transaction events from the plurality of sets of grouped transaction events that are associated with that set of variants; analyzing, via the processor, the plurality of sets of variants to identify (1) a subset of standard variants from the plurality of sets of variants having a standard sequence of transaction event types, (2) a subset of non-standard variants from the plurality of sets of variants having a non-standard sequence of transaction event types, and (3) a subset of incomplete variants from the plurality of sets of variants having an incomplete sequence of transaction event types; causing, via the processor, display of at least one of a representation quantifying the subset of standard variants, a representation quantifying the subset of non-standard variants, or a representation quantifying the subset of incomplete variants; identifying, via the processor, a set of metrics associated with each set of variants from the plurality of sets of variants; causing, via the processor, display of a representation of the set of metrics; determining that a total number of variants from the plurality of sets of variants not having the standard sequence of transaction event types is greater than a predetermined threshold; and preventing, without human intervention, a future transaction in response to determining that the total number is greater than the predetermined threshold.

6 . The method of claim 5 , wherein, for each set of variants from the plurality of sets of variants, the set of metrics includes a net monetary value associated with that set of variants.

7 . The method of claim 5 , wherein each transaction event from the set of transaction events is further associated with a different transaction identifier, a document number, and a time.

8 . The method of claim 5 , wherein the set of transaction event types includes at least one of a purchase requisition, a purchase order, a goods receipt, a post good return, or an invoice.

9 . The method of claim 5 , further comprising:
identifying, via the processor, for each set of grouped transaction events from the plurality of sets of grouped transaction events, and for each pair of subsequent transaction events included in that set of grouped transaction events, a duration of time between that pair of subsequent transaction events; and causing, via the processor, display of a representation of the duration of time between that pair of subsequent transaction events.

10 . The method of claim 9 , wherein the predetermined threshold is a first predetermined threshold, the method further comprising:
identifying, via the processor, that the duration of time between that pair of subsequent transaction events is greater than a second predetermined threshold; and sending, via the processor and in response to the identifying that the duration of time between that pair of subsequent transaction events is greater than the second predetermined threshold, a signal to cause at least one remedial action to be performed.

11 . The method of claim 5 , further comprising:
for

Assignees

• Ey Global Delivery Services India Llp
• Ernst & Young Llp
• Eygs Llp

Inventors

• Parameswaran Ramesh
• Choubey Sukesh
• Srinivasan Ramkumar
• Bhattacharya Debajyoti

Classifications

• G06F16/2358

  Change logging, detection, and notification (replication G06F16/27) · CPC title

• G06F16/258

  Data format conversion from or to a database · CPC title

• G06Q40/12

  Accounting · CPC title

• G06Q10/0635

  Risk analysis of enterprise or organisation activities · CPC title

• G06F16/26 Primary

  Visual data mining; Browsing structured data · CPC title