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Swift Document Frame Charter Disclosure Number: IPCOM000250458D
Publication Date: 2017-Jul-20
Document File: 4 page(s) / 327K

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The Prior Art Database

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Swift Document Frame Charter


Disclosed is a unique proposition of building the context from Topic chosen and Sections

of template combined with Natural Language Classification to have a more comprehensive

inclusion of relevant information which is not feasible with existing solution. The proposed idea

supports user defined Template and addition of new templates as and when required.


The idea is based on a collaboration concept (framework) wherein if user should prepare a

document for further maintenance or reference which supposed to be a crux of the reference

documents. In this situation either user must go through all the references and get one by one

relevant sections to keep in new document or mix all those sections to create one in the new


e.g. User should create one AID (Application Information Document) out of the Knowledge

transfer recordings, earlier and old reference documents in different formats

like .DOC, .PPT, .JPG, .PDF etc.

The references may have details like Application History, Architect diagrams, Flow charts,

Application Description, Application Interfaces, Change requests, Business Benefits etc.

Some of the reference documents have Application details, Interfaces. Some might have Change

requests and Incidents details. Some are Snapshots. Further to this some documents may have

old details about application.

The proposed idea will ask user to select format of the outcome document e.g. Word Document

(.doc) and provide the Template/Outline/Table of Contents based on which the contents of

outcome document will be arranged.

First the framework will put all the reference documents into same number of tabs with different

colors and name the tab/sheet as a filename.

Next the Content Analytics will analyze and collect all the Main headings and keep the content

from all the relevant reference documents into new tab/sheet called Final Doc.

The Content Analytics will sort/filter the required details from all the references and keep the

relevant, most logical and recent data under the sections in the Final Doc as per the outline

mentioned by user.

All the reference document may have positive as well as negative approach about any section e.g.

Business Benefits/Pros & Cons etc. In this case based on the settings, Sentiment Analysis will

analyze the Business Benefits and neglect those proposed benefits or future requirement from the

outcome document.

The Sentiment Analysis will work based on the User Settings and the topics in the

Template/Outline/Table of Content. E.g. if Content Outline consist of Business Benefits of

Application which is a combination of Subject + Quality (Subject=Application Overview,

Quality=Business Benefits), so it will gather only the positive portion and ignore the negative


Once the Outcome Document ready, it can be provided to intended users or uploaded for


Based on the feedback/comments from users the Machine Learning builds the pattern. This

pattern will be cons...