Abstract: Eyetracking techniques and analysis techniques are disclosed. At least one interpretation of eyetracking data is received from an eye interpretation engine. A characteristic of an application is dynamically modified based on the interpretation of the eyetracking data. A portion of the application being viewed by a user may be determined. The determined portion may be modified in response to the interpretation of the eyetracking data for the user.

Abstract: Methods and apparatuses for evaluation of visual stimuli using existing viewing data. An embodiment of a method includes storing viewing data relating to the viewing of visual stimuli. In addition, parameters relating to the stored viewing data are stored. A first visual stimulus for analysis is received. In order to analyze the first visual stimulus, the target parameters for the first visual stimulus are matched with the parameters for the stored viewing data. If there is a sufficient match between the target parameters with the parameters for the stored viewing data, data to represent the first visual stimulus is generated based at least in part on the stored viewing data.

Abstract: Individual eye tracking data can be used to determine whether an individual has actually looked at a particular region of a visual field. Aggregation of data corresponding to multiple individuals can provide trends and other data useful for designers of graphical representations (e.g., Web pages, advertisements) as well as other items that can be viewed. Representation of the aggregated viewing data, which indicates both regions viewed and regions not viewed, can be accomplished using several different techniques. For example, percentages of the number of viewers that viewed a particular region can be represented as a particular color, or the underlying image being viewed can be blurred based on and acuity gradient and the number of individuals viewing various regions. The various regions represented as viewed can be selected based on the type of viewing activity (e.g., reading, gazing) is associated with a particular region.

Abstract: Individual eye tracking data can be used to determine whether an individual has actually looked at a particular region of a visual field. Aggregation of data corresponding to multiple individuals can provide trends and other data useful for designers of graphical representations (e.g., Web pages, advertisements) as well as other items that can be viewed. Representation of the aggregated viewing data, which indicates both regions viewed and regions not viewed, can be accomplished using several different techniques. For example, percentages of the number of viewers that viewed a particular region can be represented as a particular color, or the underlying image being viewed can be blurred based on and acuity gradient and the number of individuals viewing various regions. The various regions represented as viewed can be selected based on the type of viewing activity (e.g., reading, gazing) is associated with a particular region.

Abstract: Individual eye tracking data can be used to determine whether an individual has actually looked at a particular region of a visual field. Aggregation of data corresponding to multiple individuals can provide trends and other data useful for designers of graphical representations (e.g., Web pages, advertisements) as well as other items that can be viewed. Representation of the aggregated viewing data, which indicates both regions viewed and regions not viewed, can be accomplished using several different techniques. For example, percentages of the number of viewers that viewed a particular region can be represented as a particular color, or the underlying image being viewed can be blurred based on and acuity gradient and the number of individuals viewing various regions. The various regions represented as viewed can be selected based on the type of viewing activity (e.g., reading, gazing) is associated with a particular region.

Abstract: A software program stores during a recording session eye-tracking data and all other communication device activities of a test person that self-controlled confronts her/himself with display scenarios or virtual pages. The display scenarios are stored simultaneously either:1. after a predetermined elapse time interval,2. after recognizing a raised attention level of the test person,3. after a positive result of a scrolling detection process.In a consecutive processing cycle, the software program utilizes an eye interpretation engine to derive high level informations and visualizes them superimposed on the correlated image scenarios.

Abstract: A computer-implemented method infers mental states of a person from eye movements of the person. The method includes identifying elementary features of eye tracker data, such as fixations and saccades, and recognizing from the elementary features a plurality of eye-movement patterns. Each eye-movement pattern is recognized by comparing the elementary features with a predetermined eye-movement pattern template. A given eye-movement pattern is recognized if the elementary features satisfy a set of criteria associated with the template for that eye-movement pattern. The method further includes the step of recognizing from the eye-movement patterns a plurality of eye-behavior patterns corresponding to the mental states of the person. Because high level mental states of the user are determined in real time, the method provides the basis for reliably determining when a user intends to select a target.

Abstract: A software program stores during a recording session eye-tracking data and all other communication device activities of a test person that self-controlled confronts her/himself with display scenarios or virtual pages. The display scenarios are stored simultaneously either: 1. after a predetermined elapse time interval, 2. after recognizing a raised attention level of the test person, 3. after a positive result of a scrolling detection process. In a consecutive processing cycle, the software program utilizes an eye interpretation engine to derive high level information and visualizes them superimposed on the correlated image scenarios.

Abstract: A computer-implemented method infers mental states of a person from eye movements of the person. The method includes identifying elementary features of eye tracker data, such as fixations and saccades, and recognizing from the elementary features a plurality of eye-movement patterns. Each eye-movement pattern is recognized by comparing the elementary features with a predetermined eye-movement pattern template. A given eye-movement pattern is recognized if the elementary features satisfy a set of criteria associated with the template for that eye-movement pattern. The method further includes the step of recognizing from the eye-movement patterns a plurality of eye-behavior patterns corresponding to the mental states of the person. Because high level mental states of the user are determined in real time, the method provides the basis for reliably determining when a user intends to select a target.