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Browse Prior Art Database

Life Event Feed

IP.com Disclosure Number: IPCOM000246435D
Publication Date: 2016-Jun-06
Document File: 4 page(s) / 104K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed is the Life Event system for managing multiple data sources and devices, filtering only alerts that are relevant in the current context, and presenting only immediately relevant alerts to the user. The life event stream brings together multiple event streams into one usable stream that sorts the useful events from the noise.

This text was extracted from a PDF file.
This is the abbreviated version, containing approximately 52% of the total text.

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Life Event Feed

As the use of Application Programming Interfaces (API) increases, the systems that people use every day will become increasingly disparate. Each of those systems will create alerts to the end user. Some of those will be relevant and some will be irrelevant. As users interact with more and more systems, it not only becomes increasingly difficult to filter the noise, but also to integrate with all the different APIs. For example, on a daily basis, everyone gets hundreds to thousands of notifications from tens of sources over multiple devices. With the rise of wearable devices, these numbers are growing.

In addition, while the number of life event notifications is growing, the screen size from which to view the notifications is shrinking. The screen of a smart watch, for example, is much smaller that of a smart phone or computer. Because of this wide variety of devices and notifications, a method is needed surface the most relevant events by device.

The novel contribution is the Life Event system for managing multiple data sources and filtering only alerts that are relevant in the current context. The life event stream brings together multiple event streams into one usable stream that sorts the useful events from the noise.

The core novelty of the system is comprised methods to use:


 Context and environment information to develop event streams


 Machine learning to the surface most relevant events (as opposed to presenting events in sequence)


 Different event streams by device, all linking to the same set of events


 A common meta data structure so anything can be turned into an event stream, which enables the use of machine learning

The system uses machine learning around multiple event streams to bring the most relevant life events to the top of the queue. It evaluates the environment, device, user, context, and many different event streams. (Figure 1)

Figure 1: System context

1


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By combining all of the different factors, the system creates a simple user interface through which the user can interact with the many life events that are streaming every day. (Figure 2)

Figure 2: Example user interface; the events come from multiple sources, and not every event surfaces

For example, a user is in a regular morning meeting (which is known by the user's calendar event stream), and receives an urgent email and a text from a friend. The system sorts the incoming...