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A feedback mechanism to the author of a Business Rule which provides a rating of how natural the rule is with respect to the English language

IP.com Disclosure Number: IPCOM000199492D
Publication Date: 2010-Sep-06
Document File: 1 page(s) / 47K

Publishing Venue

The IP.com Prior Art Database

Abstract

When developing a business rule, the developer needs to "verbalize" a term producing a list of possible usages of that term. This is so that when the user of a business rule types that term they are aided by this list of usages which should allow them to use the rule in such a way that it reads almost like a natural sentence, rather than like a logical statement (IF, THEN, ELSE etc). So there are 2 actors mentioned in this article; the business rule developer and the user of the business rule. So for example when the developer of the rule verbalizes the term "patient" they could come up with "a patient", "patients", "dentists customer" etc When the user makes uses of the rule and types in "patient" then they will be presented with a list of options automatically e.g IF patient (a patient, patients, dentist customer) THEN total = total +1 So the items in brackets above are shown automatically after the text "patient" has been typed into the system that is going to make use of the business rule. The business rule user then selects the most natural verbalization to use. IF a patient THEN total = total +1. This article addresses the difficulty of verbalizing off of a single word (or small phrase) and suggests that you can scan English language texts to determine a small list of popular uses of that word (or small phrase). This generated list would be presented to the business rule developer who can then add these suggestions to the business rule so the user of the business rule can then subsequently use them.

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A feedback mechanism to the author of a Business Rule which provides a rating of how natural the rule is with respect to the English language

This disclosure is in the area of business rules Our idea is to enhance the verbalization functions to provide more meaningful feedback to the rule author. The problem to be solved is that the verbalization text "the customer", "a customer" and "the customers" is manually entered and provides the author with no help as to how complete this phrase. Thus, the author has no idea in terms of natural language if the phrases chosen are suitable or consistent with previous usage in the product.This disclosure is particularly useful to people who are creating rules where their first language may not be English.

    The core idea is to provide automatic assistance for customers developing business rules. When a rule is being developed helpful entries would be suggested to show popular combinations of phrases which will help make the rule more readable.

    For example, consider the string "customer". When developing the rule you need to think up various usages of this in English language, which can be very difficult as an ad hoc process (especially as the focus is upon the underlying business rules being developed, not the presentation of those rules). With the solution disclosed, various popular usages of "customer" would be suggested based on the frequency of use in say newspapers,

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ournals or other recommended texts.

So the author would...