• Save
UPA Israel event 2011 - Eran Aharonson
Upcoming SlideShare
Loading in...5
×
 

UPA Israel event 2011 - Eran Aharonson

on

  • 306 views

Eran Aharonson's talk on "Intuitive User-Interface" at UPA Israel's 2011 event

Eran Aharonson's talk on "Intuitive User-Interface" at UPA Israel's 2011 event

Statistics

Views

Total Views
306
Views on SlideShare
301
Embed Views
5

Actions

Likes
0
Downloads
1
Comments
0

1 Embed 5

http://www.linkedin.com 5

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    UPA Israel event 2011 - Eran Aharonson UPA Israel event 2011 - Eran Aharonson Presentation Transcript

    • Eran Aharonson UPA Israel 2011Intuitive User Interfaces
    • Topics Introduction to Intuitive User Interfaces What is one touch ? Use case – integrating one touch into smartphones Underlying technology Challenges Overcoming 2
    • Intuitive User Interfaces Established 1 January 2009 The company’s mission is to simplify the use of devices (mobile phones first) via One Touch Experience Founded by industry veterans and experts in the fields of Mobile, Machine learning and User Experience Patent pending:  “System and Method for intuitive User Interaction”  Priority date: 26 June 2008 3
    • Evolution of ComplexitySimplicity Complexity 4
    • Vision 5
    • What went wrong? Endless applications.... Endless information.... Screens away... Manus away… Fun…Endless scrolls and touches… 6
    • Quiz: How many clicks to setupyour alarm Clock ? Set alarm to 7AM
    • Persistent changes – are hard to use 8
    • Mobile User Experience ChallengesComplexity: • More features and applications high and with deeper menu trees increasing • To ‘call John Smith’ you need to “Silos” of open contacts, search activities contact, select location, place callSmall screen • Current solutions (predictive and text, speech recognition) don’tlimited data help entry • Mobile is not PC Impersonal • The user interface does not adapt according to location,and Static UI status, usage history etc. 9
    • ‹#›
    • NTT DoCoMo Eye-Controlled Phones 11
    • One Touch – the vision 12
    • The Vision:What you need, when you need it Situation • Adaptive to • Options the user • Time • Fast, simple • Location • Intuitive • Past events Personal One Touch …One Touch Away 13
    • One Touch in action (Android) 14
    • One Touch ExperienceText Debra Go to VVMCall Ron Set Alarm Clock Open a network connection 15
    • One Touch Calls & SMS Examp;eIntuitive: Touchone touch contact iconStandard Scroll for Select home / Click ‘phone’Android name contact mobileIntuitive: Click Scroll for Select home / Click ‘Home’fallback ‘phone’ name contact mobile In most cases: the action is there, saves the user many touches If the action is not there: 1 more touch than Standard Android 16
    • Dynamic UI: One Touch for any applicationIntuitive: Touchone touch applicationStandard Click Scroll for SelectAndroid ‘Applications’ appIntuitive: Click Scroll forfallback Select ‘Applications’ app In most cases: application is there, saves the user many touches If the application is not there: same as Standard Android 17
    • One Touch - the Technology 18
    • Solution flow Log – Black Box Learn One Touch • Calls, SMS, web, • Patterns • Personal and applications • Habits situation based • Time, location, • Situations and prediction network info scenarios • Simple and • Phone events and Intuitive 3D UI sensors 19
    • Black Box Event Logger Situation Events Information • Calls, SMS, IM, Email Contact • Incoming/outgoing Time • Web page, Playlist, Items Destination Location Virtual Apps • Games, Camera, ... Event Connectivity Log Social • Facebook, twitter Sensors System • Settings, General/Silent 20
    • Learning Engine Virtual Learning Statistical Event Log Engine Prediction Model Creating statistical model from events 21
    • Prediction Engine Time Location Connectivity  Call Ron’s mobile Sensors  SMS to InbalCurrent Situation Information Prediction Engine  Start the alarm clock Statistical  Start service Actions Last Model Actions Generating personalized, situation based actions 22
    • Android User Experience 23
    • Challenges 24
    • Challenges Black Box approach Existing predictors Multiple channels of communication Different roles User Expectations Not enough data / Boot strapping User Interfaces 25
    • Black Box Device “senses” the world Many sensors  Time / Location  Connectivity  Device status … Correlation to reality  Silence ~ meeting  BT ~ car  … 26
    • Why known predictors work? Last call  Returning a call ProbabilityProbability Incoming Missed Outgoing Calls distance Hours 27
    • Frequent actions - contact prediction Prediction of contacts based on frequency Usually one very strong contact Probability A few contacts that always have high probability to be used (usually 3 to 5) Random Different Contacts 28
    • Uneven distribution Web Morning Night 10% 23%Applications Calls Afternoon 34% Evening 33% SMS Action type Time of day 29
    • Communication channels 30
    • Usage pattern (Roles) Personal  Incoming ~ outgoing calls  Most from address book  Last calls a good predictor VC  Incoming >> outgoing calls  Many unknown – used once  Lot of meetings – many missed  Most calls are done in the car (other device) 31
    • Expectations I always call my mom in the morning  Well not always … I never spoke to that person  What about yesterday ? Why this person does not appear?  Well… because last communication was e-mail checked on other device Those are all last calls….  But only 60% is last No one can read my mind… 32
    • Data - missing Average ~ 50 per day Texting is mostly ping- pong chats Very few are beyond last or frequent Takes time to learn – what we do in the evening at home … 33
    • User Interfaces 34
    • Guideline to Solution 35
    • Think positive Learn from first appearance  Users know the value – we don’t … Forget fast  Compensate the fast learning Find the reason with time  Location  Time  Missed call Compare to other options 36
    • Use the person brain … Present enough options ~ 10  Miller – short memory < 7  In web people can do more Build a graphic language  Images  Icons Selection is fast  We know what we look for …  Reminder Magic / Fun 37
    • Summary 38
    • One Touch - highlights Actions are predicted based on various probabilistic criteria  Above “black box” sensors Normalization is performed on received data  Data is part of conversation or usage pattern User behavior shows:  Strong tendency for the short period history (i.e. last calls)  Few frequent actions with high probability – usually also inside the last actions history  It takes long time to learn behavior of non frequent actions Using Intuitive UI saves clicks There is still work to do 39
    • ‹#›
    • Thank youEran Aharonsoneran.aharonson@intuitiveui.com