AU2009233605B2 - Processing Engine - Google Patents

Abstract

A processing engine which automatically "tags" (also 5 commonly referred to as "maps" or "allocates") information from an already existing financial database with newly created and defined function codes, groups or fields; thus identifying and populating new and usable ways of filtering and aggregating the financial database information. The o biggest advantages of this are elimination of time for manual creation; and also reliable and consistent treatment of these new populations thus significantly reducing errors. -4 PROCESSING ENGINE UNSTRUCTURED TRANSLATOR PARTLY STRUCTURED DATA TABLE DATA TABLE 18f 18f qnq MAP MAPPABLE -k-DATA UNSTRUCTURED DESCRIP-T-01 18 MANUAL DESCRIPTOR ALLOCATION ELEMENT$ STEP 17 - FULLY STRUCTURED F-- -- \14 DATA TABLE q1 q2 MAPPABLE DATA g13 DESCRIPTOR TABLE qn / Fig. 1

Description

PROCESSING ENGINE

The present invention relates to the processing and categorisation of large volumes of data and, more particularly but not exclusively to financial reporting data .

BACKGROUND

Computers are known for their ability to process large volumes of data. However, in the case where the data, or at least parts of it, is unstructured there can be difficulties in the computer recognising the information contained in the data and knowing how to handle it.

There are many instances in industry where unstructured data presents a processing problem to data processing devices. For example hccounting information in the form of trial balances from general ledger systems will present to a data processing system in an unstructured way and typically a different way depending on the general ledger system from which the data is derived.

It is an object of the present invention to address or at least ameliorate some of the above disadvantages.

Notes 1. The term "comprising" (and grammatical variations thereof) is used in this specification in the inclusive sense of "having" or "including", and not in the exclusive sense of "consisting only of". 2. The above discussion of the prior art in the Background of the invention, is not an admission that any information discussed therein is citable prior art or part of the common general knowledge of persons skilled in the art in any country.

BRIEF DESCRIPTION OF INVENTION

In a broad form the processing engine of a preferred embodiment of the present invention when applied to a financial database automatically "tags" (also commonly referred to as "maps" or "allocates") information from an already existing financial database with newly created and defined function codes, groups or fields; thus identifying and populating new and usable ways of filtering and aggregating the financial database information. The biggest advantages of this are elimination of time for manual creation; and also reliable and consistent treatment of .these new populations thus significantly reducing errors and wherein and wherein a unique code or alias comprising a financial reporting code selected from a graded code element set is used to identify individual elements in said at least one trial balance and in said at least one client disclosure document and in said financial reporting content thereby to reorder and re-present said elements in arranging said individual elements in an array together with descriptor elements and interposing said individual elements into said report.

Accordingly, in a further broad form of the present invention there is provided a processing engine for processing unstructured data located in an unstructured data table; said data comprising quantity data associated with an unstructured descriptor element; said engine comparing each said unstructured descriptor element against a master list of mappable data descriptors to create a single data description per entry; in the event of a match between an unstructured descriptor element and a mappable data descriptor in the mappable data descriptors list then quantity data associated with said unstructured data element is associated with the matching mappable data descriptor and copied into a partly structured data table and wherein a unique code or alias comprising a financial reporting code selected from a graded code element set is used to identify individual elements in said at least one trial balance and in said at least one client disclosure document and in said financial reporting content thereby to reorder and re-present said elements in arranging said individual elements in an array together with descriptor elements and interposing said individual elements into said report.

Preferably said partly structured data table is reviewed manually

Preferably those unstructured descriptor elements for which no match is available are manually reviewed and a manual match made so as to produce a fully structured data table from said partially structured data table.

Preferably those unstructured descriptor elements for which no match is available comprise less than 25 per cent of said unstructured data.

Preferably said unstructured data comprises accounting data.

Preferably the structured data table can be filtered and . aggregated by all data fields, including newly created data descriptions, thereby to provide new ways of viewing, summarising and disseminating data.

Preferably advantages of employing the processing engine are elimination of time for manual creation; and also reliable and consistent treatment of these new data descriptions thus significantly reducing errors.

In yet a further broad form of the invention there is provided a processing engine for processing unstructured data located in an unstructured data table in a computer memory; said data comprising quantity data associated with an unstructured descriptor element; said engine including a comparator which compares each said unstructured descriptor element against a master list of mappable data descriptors to create a single data description per entry; in the event of a match signal from said comparator indicating a match between an unstructured descriptor element and a mappable data descriptor in the mappable data descriptors list then quantity data associated with said unstructured data element is associated with the matching mappable data descriptor and copied into a partly structured data table in said computer memory.

Preferably said database and said comparator are implemented via a microprocessor in communication with a computer memory.

BRIEF DESCRIPTION OF DRAWINGS

Embodiments of the present invention will now be described with reference to the accompanying drawings wherein:

Figure 1, is a block diagram of dataflow through a processing engine in accordance with a first preferred embodiment of the present invention,

Figure 2, is a logic flow diagram of processing steps applied to data in accordance with the first embodiment,

Figure 3, illustrates data mapping for a particular instance of data,

Figure 4 is an example .of a fully structured data table,

Fig 5 illustrates a further example of the tagging process where translation is to a common descriptor base -in this case an XBRL common descriptor set,

Fig 6 is a simplified explanation in table form of an embodiment of the technology process showing how the Master Chart Of Accounts tag may be determined.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

With reference to Figure 1 there is illustrated a processing engine 10 in accordance with a first preferred embodiment of the present invention. In this instance the processing engine 10 comprises a translator 11 which receives input from comparator 12. The comparator 12 receives and compares data in unstructured data table 13 and mappable data descriptor table 14. The output of translator 11 is input to partly structured table 15. A manual processing operation is then applied to table 15, the output of which results in a fully structured data table 16.

With reference to Figure 2 a logic flow diagram is provided for processing steps applied to data passing through the processing engine of Figure 1 wherein, in this instance, the data relates to trial balance data. In this instance the processing engine is termed the "autoallocator" in Figure 2. FRS AutoAllocator - outline

Using the FRS AutoAllocator (part of the FRS Process) , we can define the process as:

Importing Trial Balances from any General Ledger system and the FRS System (part of the FRS Process) automatically knows what Master Chart Of Account Allocation (based on data input from the International Financial Reporting Standards) to give it.

For example it would recognise: GL Account Name Master Chart

Till floats as Cash on hand

Westpac account as Cash at bank

Figure 3 illustrates data mapping in the instance where data is trial balance data and comprises a listing of unstructured descriptor elements 17 and a corresponding listing of mappable data descriptors 18. So, for example, unstructured descriptor element 17Δ comprising the descriptor "till floats" maps to a matched mappable data descriptor 18a being descriptor "cash on hand."

In summary the processing engine automatically "tags" (also commonly referred to as "maps" or "allocates") information from an already existing financial database with newly created and defined fgnction codes, groups or fields; thus identifying and populating new and usable ways of filtering and aggregating the financial database information. The biggest advantages of this are elimination of time for manual creation; and also reliable and consistent treatment of .these new populations thus significantly reducing errors .

Explanation of tagging process

The "tagging" (also commonly referred to as "mapping" or "allocating") process uses elements of existing financial database fields (such as the general ledger code, account name / description or balance) to predict a new field (such as a Master Chart of Accounts category or XBRL Value) using, amongst other describable processes, a matrix and array linear pattern. The technology also contains an inbuilt validation process, largely centred around the GL Code (with ranges input to match the current GL, which varies from system to system) and the GL Balance.

The tagging process is instantaneous - once you import the financial database (e.g. the trial balance), the new function codes (e.g. Master Chart of Accounts) appears immediately.

Fig 5 illustrates a further example of the tagging process where translation is to a common descriptor base - in this case an XBRL common descriptor set.

Fig 6 is a simplified explanation of the technology process as to how it determines the Master Chart Of Accounts tag.

In Use

With reference to Figure 4 a fully structured data table 16 is illustrated with reference to a particular example. In this instance the unstructured descriptor "Westpac account" is associated with quantity ql. It is mapped by way of the table of Figure 3 to descriptor "cash at bank" associated with quantity ql. Similarly, the descriptor "trade debtors" is initially associated with quantity q2 and is mapped to be associated with descriptor "trade receivables ."

In the case of the descriptor "special advance" there is no mapping available from the table of Figure 3 so, initially, no mapping takes place in the creation of the partially structured data table. The manual data entry step is then performed whereby the descriptor "special advance" is mapped to "other receivables" and remains associated with quantity qn.

Having produced the fully structured data table the structured data can then have accounting principles applied to it in an automated fashion. It will be appreciated that the more comprehensive the mappable data descriptor table 14 the smaller the amount of manual allocation will be as a proportion of total amount of data processed thereby resulting in the potential for relatively rapid processing of large volumes of data presenting as unstructured data from disparate sources .

The above describes only some embodiments of the present invention' and modifications, obvious to those skilled in the art, can be made thereto without departing from the scope of the present invention.

Claims (8)

1. A processing engine for processing unstructured data located in an unstructured data table; said data comprising quantity data associated with an unstructured descriptor element; said engine comparing each said unstructured descriptor element against a master list of mappable data descriptors to create a single data description per entry; in the event of a match between an unstructured descriptor element and a mappable data descriptor in the mappable data descriptors list then quantity data associated with said unstructured data element is associated with the matching mappable data descriptor and copied into a partly structured data table and wherein a unique code or alias comprising a financial reporting code selected from a graded code element set is used to identify individual elements in said at least one trial balance and in said at least one client disclosure document and in said financial reporting content thereby to reorder and re-present said elements in arranging said individual elements in an array together with descriptor elements and interposing said individual elements into said report.

2. The processing engine of claim 1 wherein said partly structured data table is reviewed manually.

3. The processing engine of claim 2 wherein those unstructured descriptor elements for which no match is available are manually reviewed and a manual match made so as to produce a fully structured data table from said partially structured data table.

4. The processing engine of claim 3 wherein those unstructured descriptor elements for which no match is available comprise less than 25 per cent of said unstructured data.

5. The processing engine of claim 3 wherein said unstructured data comprises accounting data.

6. The processing engine of any previous claim wherein the structured data table can be filtered and aggregated by all data fields, including newly created data descriptions, thereby to provide new ways of viewing, summarising and disseminating data. 7 The processing engine of any previous claim wherein advantages of employing the processing engine are elimination of time for manual creation; and also reliable and consistent treatment of these new data descriptions thus significantly reducing errors.

8. A processing engine for processing unstructured data located in an unstructured data table in a computer memory; said data comprising quantity data associated with an unstructured descriptor element; said engine including a comparator which compares each said unstructured descriptor element against a master list of mappable data descriptors to create a single data description per entry; in the event of a match signal from said comparator indicating a match between an unstructured descriptor element and a mappable data descriptor in the mappable data descriptors list then quantity data associated with said unstructured data element is associated with the matching mappable data descriptor and copied into a partly structured data table in said computer memory and wherein a unique code or alias comprising a financial reporting code selected from a graded code element set is used to identify individual elements in said at least one trial balance and in said at least one client disclosure document and in said financial reporting content thereby to reorder and re-present said elements in arranging said individual elements in an array together with descriptor elements and interposing said individual elements into said report.

9. The processing engine of any previous claim wherein said database and said comparator are implemented via a microprocessor in communication with a computer memory.