Browse Prior Art Database

Automatic computer display arrangement suggestion by analyzing user behavior (P)

IP.com Disclosure Number: IPCOM000247945D
Publication Date: 2016-Oct-13
Document File: 2 page(s) / 30K

Publishing Venue

The IP.com Prior Art Database

Abstract

This article presents a method to auto-detect computer screen arrangement. Detection is based on user actions performed using any kind of pointing device like mouse. Precisely speaking based on how user intends pointer to move, how it moves in "incorrect" screen arrangement and what corrective actions user takes.

This text was extracted from a PDF file.
This is the abbreviated version, containing approximately 51% of the total text.

Page 01 of 2

Automatic computer display arrangement suggestion by analyzing user behavior (

(P

PP))

Nowadays computer systems are frequently connected to more than one display screen. To fully use their potential users want to arrange them to form one, huge space - virtual desktop spanning all the screens. For this to work graphic card driver/operating system needs to be aware of the relative location of screens (which is main, which is on the left of main, on the right, below etc.). In case of permanent setups this is easier, as you need to define this only once - but still needs user involvement. It is more complicated if you are using a mobile computer workstation and connect it in different locations with different screen setups. This idea solves two problems: semi-automatic detection of relative screen positions and remembering/restoring screen arrangement setups.

We propose to "guess" screen arrangement by analyzing user actions - mainly mouse/pointing device/pointer movements. If screens are not placed correctly users

will need to correct their actions as in nature they will expect to have one continuous virtual desktop space. This includes "pointing actions" (when user just moves mouse pointer to reach certain point on virtual desktop) window and other element dragging (user wants to move window or other "object"). If we see that user needs to correct his actions frequently we try to find screen positions that would be correct from user point of view - and propose user the results to be applied. Such detection can be carried out either via simple algorithm depending on counting "misses" or via supervised machine learning.

Second problem is mobility - when user uses his mobile computer in several places he expects it to behave correctly in each of them. That is: in the office the main device screen is off and user uses three external monitors placed side by side, middle one acting as "main" screen, at home user uses one external monitor...