UPA Israel event 2011 - Eran Aharonson

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

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UPA Israel event 2011 - Eran Aharonson

  1. 1. Eran Aharonson UPA Israel 2011Intuitive User Interfaces
  2. 2. Topics Introduction to Intuitive User Interfaces What is one touch ? Use case – integrating one touch into smartphones Underlying technology Challenges Overcoming 2
  3. 3. 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
  4. 4. Evolution of ComplexitySimplicity Complexity 4
  5. 5. Vision 5
  6. 6. What went wrong? Endless applications.... Endless information.... Screens away... Manus away… Fun…Endless scrolls and touches… 6
  7. 7. Quiz: How many clicks to setupyour alarm Clock ? Set alarm to 7AM
  8. 8. Persistent changes – are hard to use 8
  9. 9. 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
  10. 10. ‹#›
  11. 11. NTT DoCoMo Eye-Controlled Phones 11
  12. 12. One Touch – the vision 12
  13. 13. 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
  14. 14. One Touch in action (Android) 14
  15. 15. One Touch ExperienceText Debra Go to VVMCall Ron Set Alarm Clock Open a network connection 15
  16. 16. 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
  17. 17. 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
  18. 18. One Touch - the Technology 18
  19. 19. 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
  20. 20. 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
  21. 21. Learning Engine Virtual Learning Statistical Event Log Engine Prediction Model Creating statistical model from events 21
  22. 22. 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
  23. 23. Android User Experience 23
  24. 24. Challenges 24
  25. 25. Challenges Black Box approach Existing predictors Multiple channels of communication Different roles User Expectations Not enough data / Boot strapping User Interfaces 25
  26. 26. Black Box Device “senses” the world Many sensors  Time / Location  Connectivity  Device status … Correlation to reality  Silence ~ meeting  BT ~ car  … 26
  27. 27. Why known predictors work? Last call  Returning a call ProbabilityProbability Incoming Missed Outgoing Calls distance Hours 27
  28. 28. 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
  29. 29. Uneven distribution Web Morning Night 10% 23%Applications Calls Afternoon 34% Evening 33% SMS Action type Time of day 29
  30. 30. Communication channels 30
  31. 31. 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
  32. 32. 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
  33. 33. 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
  34. 34. User Interfaces 34
  35. 35. Guideline to Solution 35
  36. 36. 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
  37. 37. 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
  38. 38. Summary 38
  39. 39. 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
  40. 40. ‹#›
  41. 41. Thank youEran Aharonsoneran.aharonson@intuitiveui.com

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