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Intelligent Analytics Solution for Mobile Data Disclosure Number: IPCOM000241519D
Publication Date: 2015-May-08
Document File: 5 page(s) / 129K

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The Prior Art Database


The luxury of analytics is currently limited only to enterprise world. A new operating system framework is proposed to run the analytics on data generated from apps running on a mobile device.

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Intelligent Analytics Solution for Mobile Data

In this age of excessively growing use of mobile devices like smart phones, tablets, there is an invincible need to extract and mine useful data from these devices. Be it a phone call, a message, or a reminder, these devices will store large pool of data unto themselves which may be useful as and when need arises. We, the users of these devices, would like to have an easy way out to pick and choose any piece of our activity record anytime. The existing solutions can just do a plain search but they are not intelligent enough to understand any complex query involving multiple data sets. If the query is complex we have to do lot of manual activity in searching the data .For example "If I would like to retrieve the birthday photos of my daughter taken in school on her 5th birthday", I would need to first go list of images in my smart phone and scroll down until the birthday date and sort out according to where the photos are taken to prepare a list of photos taken at school. This is very cumbersome process as there is lot of manual activity to be done on the data (here images) to retrieve the exact information needed. We propose a solution where user can run such complex queries on multiple data sets to easily get the desired information/data.

More examples of complex queries are given below:

1) For example, a salesperson's job demands him to make a number of calls to his customers in any given day and he uses a smart phone so he can attend his customer's needs effectively. Supposing the sales guy in the process of analyzing his customer calls wants to know the best response time from his customers in a month or in a year. His smart phone may just show him all call logs but doesn't help him any further. So our solution comes to his rescue applying an analytical algorithm on the data. the algorithm first calculates the average duration of the customer calls, filters the customer calls whose duration is more than the average duration, and comes up with a time range ( say 10.30 A.M to 11.30

2) For instance a person wants to have the list of incoming calls while he was in Pune. Our solution first extracts the dates on which the person was in Pune and then uses these dates to retrieve the incoming call list ( when he was in Pune) from the call log.

3) For example, a frequent traveller may use online maps, inquire people and use all the ways and means to arrive at a destination in the most convenient way. In the process, his smartphone app saves the route he's taken by means of GPS. Our analytics solution saves the routes in the cloud that the traveller had taken to different destinations over the years. Later if the traveller queries to get the route to an 'x' destination in 'y' year. Our solution pulls the data from the cloud and shows the exact route that the traveller has asked for.

4) Supposing a person makes numerous trips. He can query " Get me the travel expenses for the years bet...