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System for best software update moment prediction Disclosure Number: IPCOM000247339D
Publication Date: 2016-Aug-25
Document File: 2 page(s) / 23K

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The Prior Art Database


Most of the times software updates are run in bad moments. During presentation, when background jobs are run/scheduled leading to running program closure or system restart. For example, when a background job is scheduled to run during the night and update restart the computer (and use has bios/disk password) the job will not start. There are more situations like that leading to brakes in fluent work. The core of the idea is to introduce module that in first phase will learn using user feedback (user behavior pattern recognition), and in the next step it will predict the best update moment. Such module can be part of each installer/update independently of the product.

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System for best software update moment prediction


Train dataset - historical data consisting of samples and features. Each sample contains set of features (attributes).

Feature set - is a set of attributes that are used to determine if particular sample belongs to class A or B.

Module consists of the following elements/steps: 1. Monitoring and storing user activities including:
a) user activities (computer)

  - open files
- open ports
- open sockets
- running programs
- time spend on running particular program
- keyboard activity
- mouse activity
- typing speed
- clicking speed
- front program (program window opened in front)
- time spend on front program
- created media names
- created media types (for example: java file, presentation) - any other user activities related data
b) user social activities (social media, chats)

  - open chat windows
- active chat persons
- social media activities
- social media contact persons
- chat status (in a meeting, DO not disturb)
- any other communication / sharing tooling and artifacts c) user planned activities
- calendar related data: day of week, day of month, holidays
- calendar events
- calendar events persons - TO DO tasks / notes
- any other events related artifacts d) product / project related data:
- release schedules
- iteration/sprint time frame
- defects / code related repositories and artifacts (for example: defect due date, defects assignment: team, person) - any other software development tooling/artifacts

2. Based on currently existing solutions where user is asked to confirm/cancel/postpone incoming update the TARGET label can be provided. The TARGET label can...