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Email categorisation for better control of Inbox

IP.com Disclosure Number: IPCOM000245854D
Publication Date: 2016-Apr-13
Document File: 2 page(s) / 30K

Publishing Venue

The IP.com Prior Art Database

Abstract

A Cognitive and Deep Learning system for handling emails or messages by connecting human wearables and email handling software to learn & understand human state of mind & emotions while reading & responding to it.

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Email categorisation for better control of Inbox


Emails & Email Clients were part and parcel of every individual & companies. Managing emails is always a big problem, most of the people doesn't have the their inbox clean and finding the right mails to respond is also becoming a key Art. Whoever excels in this art of picking the right mails & responding quickly excel in their career as well. Many email clients tries to solve this problem in their own way, for e.g. certain email clients tries to bundle the emails into categories based on the analytics they do on the email content, some emails categorises using sender names so that people can pick which sender they want to respond. But all these analytics today is pretty much static in nature hence leaving the problem space still wide open.

The core idea of the invention is to provide a methodology to personalise the analytics by taking into account the behaviour of the individual in responding to emails & not just the content of the email. And also provides a deep learning systems for clients that learns the individual's behaviour, state of mind and emotions to help him in better handling of the emails.

The cognitive and deep learning system works in the following way:


1. For the first time the individual opens an email it will be indifferent from reading email using any clients, as it doesn't learnt anything about the individual


2. When the individual starts reading through the email & responding to the same, the software will do the following analytics:

     - Profile the content of the email to understand & bucketise it - Profile the sender
- profile the date the email was received to understand the time taken by the individual to respond to that email, understand whether its below the average response time or above it or same

     - Profile the client he used to respond to the email to understand whether he uses the same client or a different one for this email

     - Profile the context switches he makes before he responds t...