An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

The size of human fingers and the lack of sensing precision can make precise touch screen interactions difficult. We present a set of five techniques, called Dual Finger Selections, which leverage the recent development of multi-touch sensitive displays to help users select very small targets. These techniques facilitate pixel-accurate targeting by adjusting the control-display ratio with a secondary finger while the primary finger controls the movement of the cursor. We also contribute a "clicking" technique, called SimPress, which reduces motion errors during clicking and allows us to simulate a hover state on devices unable to sense proximity. We implemented our techniques on a multi-touch tabletop prototype that offers computer vision-based tracking. In our formal user study, we tested the performance of our three most promising techniques (Stretch, X-Menu, and Slider) against our baseline (Offset), on four target sizes and three input noise levels. All three chosen techniques outperformed the control technique in terms of error rate reduction and were preferred by our participants, with Stretch being the overall performance and preference winner.

/pdf/precise-selection-techniques-for-multi-touch-screens-39j2jujtzf.pdf

Precise selection techniques for multi-touch screens

A virtual assistant uses context information to supplement natural language or gestural input from a user. Context helps to clarify the user's intent and to reduce the number of candidate interpretations of the user's input, and reduces the need for the user to provide excessive clarification input. Context can include any available information that is usable by the assistant to supplement explicit user input to constrain an information-processing problem and/or to personalize results. Context can be used to constrain solutions during various phases of processing, including, for example, speech recognition, natural language processing, task flow processing, and dialog generation.

Using context information to facilitate processing of commands in a virtual assistant

Retrieving the stylus of a pen-based device takes time and requires a second hand. Especially for short intermittent interactions many users therefore choose to use their bare fingers. Although convenient, this increases targeting times and error rates. We argue that the main reasons are the occlusion of the target by the user's finger and ambiguity about which part of the finger defines the selection point. We propose a pointing technique we call Shift that is designed to address these issues. When the user touches the screen, Shift creates a callout showing a copy of the occluded screen area and places it in a non-occluded location. The callout also shows a pointer representing the selection point of the finger. Using this visual feedback, users guide the pointer into the target by moving their finger on the screen surface and commit the target acquisition by lifting the finger. Unlike existing techniques, Shift is only invoked when necessary--over large targets no callout is created and users enjoy the full performance of an unaltered touch screen. We report the results of a user study showing that with Shift participants can select small targets with much lower error rates than an unaided touch screen and that Shift is faster than Offset Cursor for larger targets.

/pdf/shift-a-technique-for-operating-pen-based-interfaces-using-4l3jn2jhru.pdf

Shift: a technique for operating pen-based interfaces using touch

Recent advances in touch screen technology have increased the prevalence of touch screens and have prompted a wave of new touch screen-based devices. However, touch screens are still largely inaccessible to blind users, who must adopt error-prone compensatory strategies to use them or find accessible alternatives. This inaccessibility is due to interaction techniques that require the user to visually locate objects on the screen. To address this problem, we introduce Slide Rule, a set of audio-based multi-touch interaction techniques that enable blind users to access touch screen applications. We describe the design of Slide Rule, our interaction techniques, and a user study in which 10 blind people used Slide Rule and a button-based Pocket PC screen reader. Results show that Slide Rule was significantly faster than the button-based system, and was preferred by 7 of 10 users. However, users made more errors when using Slide Rule than when using the more familiar button-based system.

Slide rule: making mobile touch screens accessible to blind people using multi-touch interaction techniques

A study comparing the speed, accuracy, and user satisfaction of three different touch screen strategies was performed The purpose of the experiment was to evaluate the merits of the more intricate touch strategies that are possible on touch screens that return a continuous stream of touch data The results showed that a touch strategy providing continuous feedback until a selection was confirmed had fewer errors than other touch strategies The implications of the results for touch screens containing small, densely-packed targets were discussed

/pdf/improving-the-accuracy-of-touch-screens-an-experimental-4wmbqfwvy1.pdf

R. L. Potter

Papers

Improving the accuracy of touch screens: an experimental evaluation of three strategies