Currently, users enjoying media programs often desire to control those media programs during their presentation, such as to pause, mute, or stop the presentation. A user, for example, may wish to stop a television show while he or she gets up from watching the television show to get a snack. To do so, conventional controls, such as a remote control, may require that the user find the remote control, find a stop button on the remote control, press the button, and, once he or she returns with the snack, again find the remote control, find the play button, and press the play button.
A user may instead be listening to a music program and a friend listening with the user may start talking to the user during the music program. In such a case, the user may wish to pause, reduce the volume, or mute the music program until the discussion is done. Conventional methods permit the user to pause, reduce the volume, or mute the program through intentional, active control by the user, such as through a volume dial on a stereo amplifier or through a remote control. Controlling the music program through these methods, however, may be slow, thereby causing the user to miss some of what the friend said, or otherwise not provide a good user experience.
Further still, the user may have a young son or daughter that unexpectedly steps into the room where there is playing a song or movie that the user does not wish the son or daughter to hear or see. In such a case, the user may attempt to stop the media program, though this attempt at control may be undesirably slow.
These are but a few examples of ways in which conventional methods for controlling media programs may fail to provide a good user experience.
This document describes techniques and apparatuses for controlling a media program based on a media reaction. In some embodiments, the techniques pause, mute, or stop a media program when a user leaves the room, when a user in the room is talking or is otherwise not paying attention to the program, or when a child walks into the room.
This summary is provided to introduce simplified concepts for controlling a media program based on a media reaction, which is further described below in the Detailed Description. This summary is not intended to identify essential features of the claimed subject matter, nor is it intended for use in determining the scope of the claimed subject matter.
Embodiments of techniques and apparatuses for controlling a media program based on a media reaction are described with reference to the following drawings. The same numbers are used throughout the drawings to reference like features and components:
This document describes techniques and apparatuses for controlling a media program based on a media reaction. Consider, for example, a case where two people, Bob and Janet, are watching a television drama. Assume that Janet turns to Bob to talk. At this point, or responsive to Bob turning to Janet to talk back to her, the techniques pause the program. Neither of these two people hunted for, found, nor selected a remote control to pause the program. Further still, assume that Bob or Bob and Janet turn their heads back toward the television. At this point the techniques resume the program. Note again that neither of these two people had to use the remote control for the program to resume.
Consider also a case where the television drama is not appropriate for children. As Bob and Janet do not wish their children to see this program, they are watching the program after the children have gone to bed. Assume, however, that their daughter, Abigail, who is six years old, gets up from bed and walks into the room where Bob and Janet are watching the drama. Rather than hunt for, find, and quickly press a stop button on a remote control, the techniques instead black out the screen and mute the audio as soon as Abigail walks into the room. When Abigail leaves the room, the techniques may wait a short period, rewind the drama to a point at or prior to when the drama was blacked out and muted, and resume the drama.
These are but two examples of how the techniques and/or apparatuses may control a media program based on a media reaction, though many others are contemplated herein. Techniques and/or apparatuses are referred to herein separately or in conjunction as the “techniques” as permitted by the context. This document now turns to an example environment in which the techniques can be embodied and then various example methods that can, but are not required to, work in conjunction with the techniques. Some of these various methods include methods for sensing and determining reactions to media and building a reaction history for a user. After these various example methods, this document turns to example methods for controlling a media program based on a media reaction.
Example Environment
Environment 100 includes a media presentation device 102, an audience-sensing device 104, a state module 106, an interest module 108, an interface module 110, and a user interface 112.
Media presentation device 102 presents a media program to an audience 114 having one or more users 116. A media program can include, alone or in combination, a television show, a movie, a music video, a video clip, an advertisement, a blog, a web page, an e-book, a computer game, a song, an album or program of songs, a slideshow or other arrangement of images, a tweet, or other audio and/or video media. Audience 114 can include one or more users 116 that are in locations enabling consumption of a media program presented by media presentation device 102 and measurement by audience-sensing device 104, whether separately or within one audience 114. In audience 114 three users are shown: user 116-1, user 116-2, and user 116-3. While only three users are shown sensor data can be sensed and media reactions determined at many locations and for tens, hundreds, thousands, or even millions of users.
Audience-sensing device 104 is capable of sensing audience 114 and providing sensor data for audience 114 to state module 106 and/or interest module 108 (sensor data 118 shown provided via an arrow). The data sensed can be sensed passively, actively, and/or responsive to an explicit request.
Passively sensed data is passive by not requiring active participation of users in the measurement of those users. Actively sensed data includes data recorded by users in an audience, such as with handwritten logs, and data sensed from users through biometric sensors worn by users in the audience. Sensor data sensed responsive to an explicit request can be sensed actively or passively. One example where the techniques, prior to or during control of a media program, request that a user perform a particular action to produce a particular result, such as raise a hand if the user wishes the techniques to cease to pause or mute a media program. In such a case, the user is expressing a reaction of raising a hand, though this can be passively sensed by not requiring the user to actively participate in the measurement of the reaction. The techniques sense this raised hand in various manners as set forth below.
Sensor data can include data sensed using emitted light or other signals sent by audience-sensing device 104, such as with an infrared sensor bouncing emitted infrared light off of users or the audience space (e.g., a couch, walls, etc.) and sensing the light that returns. Examples of sensor data measuring a user and ways in which it can be measured are provided in greater detail below.
Audience-sensing device 104 may or may not process sensor data prior to providing it to state module 106 and/or interest module 108. Thus, sensor data may be or include raw data or processed data, such as: RGB (Red, Green, Blue) frames; infrared data frames; depth data; heart rate; respiration rate; a user's head orientation or movement (e.g., coordinates in three dimensions, x, y, z, and three angles, pitch, tilt, and yaw); facial (e.g., eyes, nose, and mouth) orientation, movement, or occlusion; skeleton's orientation, movement, or occlusion; audio, which may include information indicating orientation sufficient to determine from which user the audio originated or directly indicating which user, or what words were said, if any; thermal readings sufficient to determine or indicating presence and locations of one of users 116; and distance from the audience-sensing device 104 or media presentation device 102. In some cases audience-sensing device 104 includes infrared sensors (webcams, Kinect cameras), stereo microphones or directed audio microphones, and a thermal reader (in addition to infrared sensors), though other sensing apparatuses may also or instead be used.
State module 106 receives sensor data and determines, based on the sensor data, states 120 of users 116 in audience 114 (shown at arrow). States include, for example: sad, talking, disgusted, afraid, smiling, scowling, placid, surprised, angry, laughing, screaming, clapping, waving, cheering, looking away, looking toward, leaning away, leaning toward, asleep, newly arrived, or departed, to name just a few.
The talking state can be a general state indicating that a user is talking, though it may also include subcategories based on the content of the speech, such as talking about the media program (related talking) or talking that is unrelated to the media program (unrelated talking). State module 106 can determine which talking category through speech recognition.
State module 106 may also or instead determine, based on sensor data, a number of users, a user's identity and/or demographic data (shown at 122), or engagement (shown at 124) during presentation. Identity indicates a unique identity for one of users 116 in audience 114, such as Susan Brown. Demographic data classifies one of users 116, such as 5 feet, 4 inches tall, young child, and male or female. Engagement indicates whether a user is likely to be paying attention to the media program, such as based on that user's presence or head orientation. Engagement, in some cases, can be determined by state module 106 with lower-resolution or less-processed sensor data compared to that used to determine states. Even so, engagement can be useful in measuring an audience, whether on its own or to determine a user's interest using interest module 108.
Interest module 108 determines, based on sensor data 118 and/or a user's engagement or state (shown with engagement/state 126 at arrow) and information about the media program (shown at media type 128 at arrow), that user's interest level 130 (shown at arrow) in the media program. Interest module 108 may determine, for example, that multiple laughing states for a media program intended to be a serious drama indicate a low level of interest and conversely, that for a media program intended to be a comedy, that multiple laughing states indicate a high level of interest.
As illustrated in
State module 106 and interest module 108 can be local to audience 114, and thus media presentation device 102 and audience-sensing device 104, though this is not required. An example embodiment where state module 106 and interest module 108 are local to audience 114 is shown in
Interface module 110 receives media reactions and demographics/identity information, and determines or receives some indication as to which media program or portion thereof that the reactions pertain. Interface module 110 may present, or causes to be presented, a media reaction 132 to a media program through user interface 112, though this is not required.
Interface module 110 can be local to audience 114, such as in cases where one user is viewing his or her own media reactions or those of a family member. In many cases, however, interface module 110 receives media reactions from a remote source.
Note that sensor data 118 may include a context in which a user is reacting to media or a current context for a user for which ratings or recommendations for media are requested. Thus, audience-sensing device 104 may sense that a second person is in the room or is otherwise in physical proximity to the first person, which can be context for the first person. Contexts may also be determined in other manners described in
CRM 206 includes an operating system 208, state module 106, interest module 108, media program(s) 210, each of which may include or have associated program information 212 and portions 214, interface module 110, user interface 112, history module 216, reaction history 218, and control module 220.
Each of media programs 210 may have, include, or be associated with program information 212 and portions 214. Program information 212 can indicate the name, title, episode, author or artist, type of program, and other information, including relating to various portions within each media program 210. Thus, program information 212 may indicate that one of media programs 210 is a music video, includes a chorus portion that is repeated four times, includes four verse portions, includes portions based on each visual presentation during the song, such as the artist singing, the backup singers dancing, the name of the music video, the artist, the year produced, resolution and formatting data, and so forth.
Portions 214 of one of media programs 210 make up the program, each of which may have metadata or other information about each portion (e.g., an “R” rating for a particular portion but not others of a same movie). These portions may represent particular time-ranges in the media program, such as two, five, or fifteen-second periods. These portions may instead represent cohesive portions in the media program, which may be based on content in the cohesive portion, such as a complete song being played in a radio-like program, a possession or play in a sporting event, an act in a theatrical play, an advertisement of a block of advertisements, or a scene of a movie, to name a few.
History module 216 includes or has access to reaction history 218. History module 216 may build and update reaction history 218 based on ongoing reactions by the user (or others as noted below) to media programs. In some cases history module 216 determines various contexts for a user, though this may instead be determined and received from other entities. Thus, in some cases history module 216 determines a time, a locale, weather at the locale, and so forth, during the user's reaction to a media program or request for ratings or recommendations for a media program. History module 216 may determine ratings and/or recommendations for media based on a current context for a user and reaction history 218. Reaction history 218, as noted elsewhere herein, may be used along with a media reaction as a basis for controlling presentation of a media program.
Control module 220 is capable of controlling presentation of a media program based on a media reaction. Control module 220 may exercise control based a media reaction indicating as little as a person being present and having an identity or demographic, for example. Thus, control module 220 may control a media program based on a media reaction indicating that a person has walked into a room and that the person is a child. Or, control module 220 may obscure a currently-playing scene of a movie showing a horse with a broken leg based on the person's identity indicating, through that person's reaction history 218, that the person is likely to be sensitive to scenes showing animals in distress.
As shown in
Note that in this illustrated example, entities including media presentation device 102, audience-sensing device 104, state module 106, interest module 108, interface module 110, history module 216, and control module 220 are included within a single computing device, such as a desktop computer having a display, forward-facing camera, microphones, audio output, and the like. Each of these entities, however, may be separate from or integral with each other in one or multiple computing devices or otherwise. As will be described in part below, media presentation device 102 can be integral with audience-sensing device 104 but be separate from state module 106, interest module 108, interface module 110, history module 216, or control module 220. Further, each of these modules may operate on separate devices or be combined in one device.
As shown in
Remote computing device 302 includes one or more processors 306 and remote computer-readable storage media (“remote CRM”) 308. Remote CRM 308 includes state module 106, interest module 108, media program(s) 210, each of which may include or have associated program information 212 and/or portions 214, history module 216, reaction history 218, and control module 220.
Note that in this illustrated example, media presentation device 102 and audience-sensing device 104 are physically separate from state module 106 and interest module 108, with the first two local to an audience viewing a media program and the second two operating remotely. Thus, sensor data is passed from audience-sensing device 104 to one or both of state module 106 or interest module 108, which can be communicated locally (
These and other capabilities, as well as ways in which entities of
Block 402 senses or receives sensor data for an audience or user, the sensor data passively sensed during presentation of a media program to the audience or user. This sensor data may include a context of the audience or user or a context may be received separately, though a context is not required.
Consider, for example, a case where an audience includes three users 116, users 116-1, 116-2, and 116-3 all of
Sensor data is received for all three users 116 in audience 114; for this example consider first user 116-1. Assume here that, over the course of Incredible Family, that audience-sensing device 104 measures, and then provides at block 402, the following at various times for user 116-1:
Block 404 determines, based on the sensor data, a state of the user during the media program. In some cases block 404 determines a probability for the state or multiple probabilities for multiple states, respectively. For example, block 404 may determine a state likely to be correct but with less than full certainty (e.g., 40% chance that the user is laughing). Block 404 may also or instead determine that multiple states are possible based on the sensor data, such as a sad or placid state, and probabilities for each (e.g., sad state 65%, placid state 35%).
Block 404 may also or instead determine demographics, identity, and/or engagement. Further, methods 400 may skip block 404 and proceed directly to block 406, as described later below.
In the ongoing example, state module 106 receives the above-listed sensor data and determines the following corresponding states for user 116-1:
At Time 1 state module 106 determines, based on the sensor data indicating a 3-degree deviation of user 116-1's head from looking directly at the LCD display and a rule indicating that the looking toward state applies for deviations of less than 20 degrees (by way of example only), that user 116-1's state is looking toward the media program. Similarly, at Time 2, state module 106 determines user 116-1 to be looking away due to the deviation being greater than 20 degrees.
At Time 3, state module 106 determines, based on sensor data indicating that user 116-1 has skeletal movement in his arms and audio that is high amplitude that user 116-1 is clapping. State module 106 may differentiate between clapping and other states, such as cheering, based on the type of arm movement (not indicated above for brevity). Similarly, at Time 4, state module 106 determines that user 116-1 is cheering due to arm movement and high-amplitude audio attributable to user 116-1.
At Time 5, state module 106 determines, based on sensor data indicating that user 116-1 has head movement, facial-feature changes of 20%, and moderate-amplitude audio, that user 116-1 is laughing. Various sensor data can be used to differentiate different states, such as screaming, based on the audio being moderate-amplitude rather than high-amplitude and the facial-feature changes, such as an opening of the mouth and a rising of both eyebrows.
For Time 6, audience-sensing device 104 processes raw sensor data to provide processed sensor data, and in this case facial recognition processing to provide detailed facial orientation data. In conjunction with no audio, state module 106 determines that the detailed facial orientation data (here upturned lip corners, amount of eyelids covering eyes) that user 116-1 is smiling.
At Time 7, state module 106 determines, based on sensor data indicating that user 116-1 has skeletal movement moving away from the audience-sensing device 104, that user 116-1 is departed. The sensor data may indicate this directly as well, such as in cases where audience-sensing device 104 does not sense user 116-1's presence, either through no skeletal or head readings or a thermal signature no longer being received.
At Time 8, state module 106 determines, based on sensor data indicating that user 116-1's facial orientation has not changed over a certain period (e.g., the user's eyes have not blinked) and a steady, slow respiration rate that user 116-1 is asleep.
These eight sensor readings are simplified examples for purpose of explanation. Sensor data may include extensive data as noted elsewhere herein. Further, sensor data may be received measuring an audience every fraction of a second, thereby providing detailed data for tens, hundreds, and thousands of periods during presentation of a media program and from which states or other media reactions may be determined.
Returning to methods 400, block 404 may determine demographics, identity, and engagement in addition to a user's state. State module 106 may determine or receive sensor data from which to determine demographics and identity or receive, from audience-sensing device 104, the demographics or identity. Continuing the ongoing example, the sensor data for user 116-1 may indicate that user 116-1 is John Brown, that user 116-2 is Lydia Brown, and that user 116-3 is Susan Brown. Or sensor data may indicate that user 116-1 is six feet, four inches tall and male (based on skeletal orientation), for example. The sensor data may be received with or include information indicating portions of the sensor data attributable separately to each user in the audience. In this present example, however, assume that audience-sensing device 104 provides three sets of sensor data, with each set indicating the identity of the user along with the sensor data.
Also at block 404, the techniques may determine an engagement of an audience or user in the audience. As noted, this determination can be less refined than that of states of a user, but nonetheless is useful. Assume for the above example, that sensor data is received for user 116-2 (Lydia Brown), and that this sensor data includes only head and skeletal orientation:
State module 106 receives this sensor data and determines the following corresponding engagement for Lydia Brown:
At Times 1, 2, 7, and 8, state module 106 determines, based on the sensor data indicating a 5-degree-or-less deviation of user 116-2's head from looking directly at the LCD display and skeletal orientation of upper torso forward of lower torso (indicating that Lydia is leaning forward to the media presentation) that Lydia is highly engaged in Incredible Family at these times.
At Time 3, state module 106 determines that Lydia's engagement level has fallen due to Lydia no longer leaning forward. At Time 4, state module 106 determines that Lydia's engagement has fallen further to medium based on Lydia leaning back, even though she is still looking almost directly at Incredible Family.
At Times 5 and 6, state module 106 determines Lydia is less engaged, falling to Medium-Low and then Low engagement based on Lydia still leaning back and looking slightly away (16 degrees) and then significantly away (37 degrees), respectively. Note that at Time 7 Lydia quickly returns to a High engagement, which media creators are likely interested in, as it indicates content found to be exciting or otherwise captivating.
Methods 400 may proceed directly from block 402 to block 406, or from block 404 to block 406 or block 408. If proceeding to block 406 from block 404, the techniques determine an interest level based on the type of media being presented and the user's engagement or state. If proceeding to block 406 from block 402, the techniques determine an interest level based on the type of media being presented and the user's sensor data, without necessarily first or independently determining the user's engagement or state.
Continuing the above examples for users 116-1 and 116-2, assume that block 406 receives states determined by state module 106 at block 404 for user 116-1 (John Brown). Based on the states for John Brown and information about the media program, interest module 108 determines an interest level, either overall or over time, for Incredible Family. Assume here that Incredible Family is both an adventure and a comedy program, with portions of the movie marked as having one of these media types. While simplified, assume that Times 1 and 2 are marked as comedy, Times 3 and 4 are marked as adventure, Times 5 and 6 are marked as comedy, and that Times 7 and 8 are marked as adventure. Revisiting the states determined by state module 106, consider the following again:
Based on these states, state module 106 determines for Time 1 that John Brown has a medium-low interest in the content at Time 1—if this were of an adventure or drama type, state module 106 may determine John Brown to instead be highly interested. Here, however, due to the content being comedy and thus intended to elicit laughter or a similar state, interest module 108 determines that John Brown has a medium-low interest at Time 1. Similarly, for Time 2, interest module 108 determines that John Brown has a low interest at Time 2 because his state is not only not laughing or smiling but is looking away.
At Times 3 and 4, interest module 108 determines, based on the adventure type for these times and states of clapping and cheering, that John Brown has a high interest level. At time 6, based on the comedy type and John Brown smiling, that he has a medium interest at this time.
At Times 7 and 8, interest module 108 determines that John Brown has a very low interest. Here the media type is adventure, though in this case interest module 108 would determine John Brown's interest level to be very low for most types of content.
As can be readily seen, advertisers, media providers, builders or augmenters of media, and media creators can benefit from knowing a user's interest level. Here assume that the interest level is provided over time for Incredible Family, along with demographic information about John Brown. With this information from numerous demographically similar users, a media creator may learn that male adults are interested in some of the adventure content but that most of the comedy portions are not interesting, at least for this demographic group.
Consider, by way of a more-detailed example,
This can be valuable information—the user stayed for the first advertisement, left for the middle advertisement and the beginning of the last advertisement, and returned, with medium interest, for most of the last advertisement. Contrast this resolution and accuracy of interest with some conventional approaches, which likely would provide no information about how many of the people that watched the movie actually watched the advertisements, which ones, and with what amount of interest. If this example is a common trend with the viewing public, prices for advertisements in the middle of a block would go down, and other advertisement prices would be adjusted as well. Or, advertisers and media providers might learn to play shorter advertisement blocks having only two advertisements, for example. Interest levels 502 also provide valuable information about portions of the movie itself, such as through the very high interest level at time period 7 (e.g., a particularly captivating scene of a movie) and the waning interest at time periods 35-38.
Note that, in some cases, engagement levels, while useful, may be less useful or accurate than states and interest levels. For example, state module 106 may determine, for just engagement levels, that a user is not engaged if the user's face is occluded (blocked) and thus not looking at the media program. If the user's face is blocked by that user's hands (skeletal orientation) and audio indicates high-volume audio, state module 106, when determining states, may determine the user to be screaming A screaming state indicates, in conjunction with the content being horror or suspense, an interest level that is very high. This is but one example of where an interest level can be markedly different from that of an engagement level.
As noted above, methods 400 may proceed directly from block 402 to block 406. In such a case, interest module 108, either alone or in conjunction with state module 106, determines an interest level based on the type of media (including multiple media types for different portions of a media program) and the sensor data. By way of example, interest module 108 may determine that for sensor data for John Brown at Time 4, which indicates skeletal movement (arms and body), and high-amplitude audio, and a comedy, athletics, conflict-based talk show, adventure-based video game, tweet, or horror types, that John Brown has a high interest level at Time 4. Conversely, interest module 108 may determine that for the same sensor data at Time 4 for a drama, melodrama, or classical music, that John Brown has a low interest level at Time 4. This can be performed based on the sensor data without first determining an engagement level or state, though this may also be performed.
Block 408, either after block 404 or 406, provides the demographics, identity, engagement, state, and/or interest level. State module 106 or interest module 108 may provide this information to various entities, such as interface module 110, history module 216, control module 220, as well as others.
Providing this information to a builder of a highlight program can enable the highlighter to build a program with portions that are actual highlights, such as a well-received joke in a comedy or an amazing sports play in a sporting program. Providing this information to an augmenter of media programs can enable the augmenter to add media reactions to a presentation of a media program, which may improve the experience for a user. A user may enjoy a comedy more when accompanied with real laughter and at correct times in a comedy program, for example, as compared to a laugh track.
Providing this information to an advertiser after presentation of an advertisement in which a media reaction is determined can be effective to enable the advertiser to measure a value of their advertisements shown during a media program. Providing this information to a media creator can be effective to enable the media creator to assess a potential value of a similar media program or portion thereof. For example, a media creator, prior to releasing the media program to the general public, may determine portions of the media program that are not well received, and thus alter the media program to improve it.
Providing this information to a rating entity can be effective to enable the rating entity to automatically rate the media program for the user. Still other entities, such as control module 220, may use the information to control presentation of media.
Providing media reactions to history module 216 can be effective to enable history module 216 to build and update reaction history 218. History module 216 may build reaction history 218 based on a context or contexts in which each set of media reactions to a media program are received, or the media reactions may, in whole or in part, factor in a context into the media reactions. Thus, a context for a media reaction where the user is watching a television show on a Wednesday night after work may be altered to reflect that the user may be tired from work.
As noted herein, the techniques can determine numerous states for a user over the course of most media programs, even for 15-second advertisements or video snippets. In such a case block 404 is repeated, such as at one-second periods.
Furthermore, state module 106 may determine not only multiple states for a user over time, but also various different states at a particular time. A user may be both laughing and looking away, for example, both of which are states that may be determined and provided or used to determine the user's interest level.
Further still, either or both of state module 106 and interest module 108 may determine engagement, states, and/or interest levels based on historical data in addition to sensor data or media type. In one case a user's historical sensor data is used to normalize the user's engagement, states, or interest levels (e.g., dynamically for a current media reaction). If, for example, Susan Brown is viewing a media program and sensor data for her is received, the techniques may normalize or otherwise learn how best to determine engagement, states, and interest levels for her based on her historical sensor data. If Susan Brown's historical sensor data indicates that she is not a particularly expressive or vocal user, the techniques may adjust for this history. Thus, lower-amplitude audio may be sufficient to determine that Susan Brown laughed compared to amplitude of audio used to determine that a typical user laughed.
In another case, historical engagement, states, or interest levels of the user for which sensor data is received are compared with historical engagement, states, or interest levels for other people. Thus, a lower interest level may be determined for Lydia Brown based on data indicating that she exhibits a high interest for almost every media program she watches compared to other people's interest levels (either generally or for the same media program). In either of these cases the techniques learn over time, and thereby can normalize engagement, states, and/or interest levels.
Methods for Building a Reaction History
As noted above, the techniques may determine a user's engagement, state, and/or interest level for various media programs. Further, these techniques may do so using passive or active sensor data. With these media reactions, the techniques may build a reaction history for a user. This reaction history can be used in various manners as set forth elsewhere herein.
The information about the respective media programs can include, for example, the name of the media (e.g., The Office, Episode 104) and its type (e.g., a song, a television show, or an advertisement) as well as other information set forth herein.
In addition to the media reactions and their respective media programs, block 602 may receive a context for the user during which the media program was presented as noted above.
Further still, block 602 may receive media reactions from other users with which to build the reaction history. Thus, history module 216 may determine, based on the user's media reactions (either in part or after building an initial or preliminary reaction history for the user) other users having similar reactions to those of the user. History module 216 may determine other persons that have similar reactions to those of the user and use those other persons' reactions to programs that the user has not yet seen or heard to refine a reaction history for the user.
Block 604 builds a reaction history for the user based on sets of reactions for the user and information about the respective media programs. As noted, block 604 may also build the user's reaction history using other persons' reaction histories, contexts, and so forth. This reaction history can be used, in some embodiments described elsewhere herein, to control media programs.
Methods for Controlling a Media Program
Block 702 receives a current media reaction to a media program that is currently being presented to an audience having one or more persons, the media reaction determined based on sensor data passively sensed during the presentation. These media reactions may include one or more of the many described herein, which may be determined as noted above.
Block 704 determines, based on the current media reaction, that the person is not paying attention to the media program. Media reactions that may indicate that a person is not paying attention include a very low interest level, a low engagement, a departed state, an unrelated talking state, and a looking away state, to name a few.
Assume, for example, that two people named Bob and Janet are watching a movie together in a room in their home. Assume that Janet turns to Bob and comments about the movie. Here assume that control module 220 receives a talking state from state module 106 of
Continuing the ongoing example, assume that right after Janet turns to Bob and talks, that Bob looks away from the movie and talks back to Janet. At block 704, control module 220, soon after receiving Janet's related talking and looking away state, receives a looking away state and a talking state for Bob. Control module 220 then determines that Bob is not paying attention to the movie, instead he is paying attention to Janet. Control module 220 may determine that Bob is not paying attention based on his looking away state and his talking state. Control module 220 may instead also consider Janet's states or a reaction history of Bob, such as a reaction history indicating that Bob rarely talks or looks away when watching a media program, for example.
Block 706, responsive to the determination that the person is not paying attention to the media program, controls the presentation of the media program. This control can be performed in real time, quickly, and in various manners. Control module 220 may pause the presentation of the media program, mute or reduce audio of the media program, or stop the presentation of the media program.
Control module 220 may also or instead record a marker at a time or location in the media program commensurate with the current media reaction. This marker can be used later in “rewinding” the media program, as noted below.
Continuing the ongoing example, control module 220 pauses the movie. Control module 220 may wait for another media reaction, as noted below. Control module 220 may also or instead explicitly request a media reaction responsive to which control module 220 ceases to control (e.g., pause) the media program. Here assume that control module 220 pauses the movie and presents a request over the paused movie stating “Please wave your hand to continue the program.” Bob or Janet may wave their hand to continue the movie, though here we assume that they continue their conversation while the movie remains paused.
Block 708 receives a second media reaction of the person. The media reaction being received can be the same or a similar media reaction, responsive to which methods 700 continue to control presentation of the media program. Methods 700, therefore, may repeat blocks 702, 704, and 706. While Bob and Janet continue to talk and look away from the movie, for example, control module 220 continues to pause the movie.
At some point, however, assume that Bob looks back at a display presenting the paused movie. In such a case, control module 220 receives a looking toward state rather than receive additional looking away states.
Block 710 determines, based on the second media reaction, that the person is paying, or is ready to pay, attention to the media program. The media reactions on which block 710 determines that a person is paying attention or is ready to pay attention may vary, including based on the person's reaction history. Media reactions that may indicate that a user is paying attention include a medium or higher interest level, a medium or higher engagement, a looking toward state, a leaning toward state, and a newly arrived state, to name a few.
Continuing the ongoing example, at block 710 control module 220 receives a looking toward state for Bob. Control module 220 determines that Bob is now paying attention because he is looking at the paused movie.
Block 712, responsive to determining that the user is, or is about to be paying, attention to the media program, ceases to control and/or resumes the media program. As noted above, controlling the presentation of the media program may include pausing, muting, or stopping the media program, among others. Thus, control module 220 may cease to pause the media program, cease to mute the audio of the media program, or resume the media program.
In some cases, however, control module 220 rewinds the media program a particular amount of time, such as two seconds, or presents the media program at a beginning of a cohesive portion of the media program during which the control occurred.
In the example of Bob and Janet above, assume that control module 220 paused the movie during a particular scene during which Bob was not paying attention. Control module 220 may rewind the media program to the beginning of that same scene. This may depend on the length of the distraction of the audience. Control module 220 may rewind and begin play at a beginning of a scene when the distraction was more than momentary. Thus, assume that Bob and Janet talk for five minutes. In such a case control module 220 may rewind to the beginning of the scene. If Bob and Janet instead talked only for four seconds, control module 220 may instead simply cease to pause the movie or rewind just a few seconds.
As noted above, control module 220 may record a marker at a time or location in the media program. This marker may aid control module 220, such as in cases where control module 220 does not pause the media program, but instead mutes or turns down the volume but allows the media program to continue to be presented.
Assume, by way of a different example, that two people are listening to an album having thirty songs. Control module 220 may turn the volume down, but not off or stop the songs, responsive to the two people beginning to talk. Control module 220 may mark this location and, if the lack of attention lasts for a few minutes or more, rewind to the beginning of the song to replay it at regular volume when the talking stops.
Control module 220 may analyze metadata associated with the location or time to determine a cohesive portion at which the control was exercised. Thus, control module 220 may determine, based on metadata for the movie at the marked location in the media program, that the current portion of the media program is part of a scene having a beginning at a particular time in the media program. Control module 220 may then resume presentation of the media program at the particular time to replay the beginning of the scene.
By way of illustration, consider
Methods 700 may operate alone or in conjunction with other methods described herein, such as methods 400, 600, 900, and/or 1000. This description continues with other methods also describing techniques for controlling a media program.
Block 902 receives an identity or a demographic of a person, the identity or demographic determined based on sensor data passively sensed during a current presentation of a media program. As noted herein, the identity or demographic can be determined from a media reaction, though in some cases it may also be determined based on sensor data without also determining a media reaction for the person.
Block 904 determines, based on the identity or the demographic and information about the media program, to control the current presentation of the media program. This information may indicate, for example, that the media program is of a particular type, such as being a horror or suspense program, or that the program is rated unsuitable for children, and so forth.
Block 906 controls the current presentation of the media program. This control of the current presentation can include those noted for methods 700, though block 906 may alter the presentation in additional ways not set forth above.
Assume, for example, that the identity or the demographic of the person indicates that the person is a minor and the information indicates that the media program or a currently presented portion of the media program is not appropriate for presentation to minors. In such a case, control module 220 may pause, stop, or mute the media program as above, though control module 220 may instead alter the presentation by blacking out or substantially reducing the resolution of video portion of the presentation (e.g., pixilation of the video) and lowering the volume. For the movie example above, assume that Bob and Janet's six-year-old daughter walks in unexpectedly into the room while the movie is playing. In response, control module 220 may partially obscure or reduce the resolution of the movie while also lowering the volume but may forgo stopping or pausing the program.
Furthermore, if an identity is received, control module 220 may determine a reaction history associated with the person. In such a case, control module 220 may determine, at block 904, whether or not to control the current presentation and, if control is determined, how to control the presentation. For example, assume that the information about the portion of the media program indicates that coarse language is about to be presented for the media program and that the reaction history indicates that a person in the audience has a history of dislike or sensitivity to coarse language. In such an example, control module 220 may lower the volume of the presentation during the coarse language.
Consider again Bob and Janet from the above movie example. Here assume that control module 220 receives or determines Janet's identity and an associated reaction history. Control module 220 determines, based on this reaction history that Janet is very sensitive to, and is offended by animals shown in distress. Control module 220, for much of a media program, may determine that no control is needed. Assume for a particular upcoming scene, however, that control module 220 determines, based on metadata for the media program associated with portions of the movie (e.g., information 212 about portions 214 of media program 210 all of
Thus, assume that a child's parents have decided that a popular comedy show would be fine for the child to watch except for the coarse language, as the comedy otherwise has many redeeming qualities. It may be nearly impossible for the child to enjoy the comedy without hearing the coarse language—the alternative is having a parent attentively holding a mute button on a remote (which may still fail to mute all the coarse language) or watch the comedy without any audio (which would likely make watching the comedy pointless). The techniques, however, enable the child to watch the comedy without the parent needing to actively control the program.
Control module 220, after the portion of the media program being controlled is no longer being presented, such as by being fully presented or skipped, ceases to control the media program. Thus, control module 220 may cease to mute the coarse language when the course language is done or skipped, or obscure the scene showing the animal in distress when that scene is done.
Block 1002 receives a first media reaction to a media program that is currently being presented to an audience having one or more persons, the first media reaction determined based on sensor data passively sensed during the current presentation and of a first person of the one or more persons.
Block 1004 determines, based on the first media reaction, that the first person is not paying attention to the media program. Control module 220 may do so in one or more of the various manner described above. Some media reactions do not indicate that a person is not paying attention, and some that do indicate that a person is not paying attention may be ignored in some cases. A departed state, for example, may indicate that control of the media program is warranted, though this is not always the case. If an advertisement has just begun, a departed state may not indicate that ceasing a current presentation of the media program (and thus the advertisement) is warranted. Similarly, a low-interest level during an advertisement also may not indicate that control module 220 should cease the presentation of the media program.
Block 1006, responsive to determining that the first person is not paying attention to the media program, ceases or alters the current presentation of the media program, such as by stopping, pausing, or muting the current presentation.
Block 1008 receives a second media reaction of the first person. Control module 220 may determine that this media reaction is from the same person as the first media reaction, though the media reactions received may also be labeled or otherwise include an indicator to associate the media reaction with a person in the audience, whether or not the identities of the persons are known.
Block 1010 determines, based on the second media reaction, that the first person is paying or is about to pay attention to the media program.
Block 1012 presents a second presentation of the media program at or prior to a point at which the current presentation ceased or was altered. As described above, presenting a media program at or prior to the point at which the current presentation ceased or was altered can be performed based on determining a cohesive portion, such as a scene or a song, at which to begin presentation. Presenting the media program can be performed automatically and without user interaction, though this is not required.
Block 1014 receives or determines that a second person of the one or more persons in the audience is of an approximate age. The approximate age can be based on an identity or demographic determined for the second person, which may be received or determined. If determined, the approximate age may be based on a media reaction of the second person, which can be as simple as receiving a “newly present state” as noted above.
Block 1016 determines, based on information about the media program and that the second person is of the approximate age, to cease or alter the second presentation of the media program.
Block 1018 ceases or alters the second presentation of the media program. Control module 220 may resume the media program by presenting the media program again, in the various manners set forth above, responsive to determining that the second person is no longer present, such as by receiving a departed state from state module 106 of
These are but a few of the many ways that the techniques may enable people to better enjoy or control media programs.
The preceding discussion describes methods relating to controlling a media program based on a media reaction, as well as other methods and techniques. Aspects of these methods may be implemented in hardware (e.g., fixed logic circuitry), firmware, software, manual processing, or any combination thereof A software implementation represents program code that performs specified tasks when executed by a computer processor. The example methods may be described in the general context of computer-executable instructions, which can include software, applications, routines, programs, objects, components, data structures, procedures, modules, functions, and the like. The program code can be stored in one or more computer-readable memory devices, both local and/or remote to a computer processor. The methods may also be practiced in a distributed computing mode by multiple computing devices. Further, the features described herein are platform-independent and can be implemented on a variety of computing platforms having a variety of processors.
These techniques may be embodied on one or more of the entities shown in
Example Device
Device 1100 includes communication devices 1102 that enable wired and/or wireless communication of device data 1104 (e.g., received data, data that is being received, data scheduled for broadcast, data packets of the data, etc.). Device data 1104 or other device content can include configuration settings of the device, media content stored on the device (e.g., media programs 210), and/or information associated with a user of the device. Media content stored on device 1100 can include any type of audio, video, and/or image data. Device 1100 includes one or more data inputs 1106 via which any type of data, media content, and/or inputs can be received, such as human utterances, user-selectable inputs, messages, music, television media content, media reactions, recorded video content, and any other type of audio, video, and/or image data received from any content and/or data source.
Device 1100 also includes communication interfaces 1108, which can be implemented as any one or more of a serial and/or parallel interface, a wireless interface, any type of network interface, a modem, and as any other type of communication interface. Communication interfaces 1108 provide a connection and/or communication links between device 1100 and a communication network by which other electronic, computing, and communication devices communicate data with device 1100.
Device 1100 includes one or more processors 1110 (e.g., any of microprocessors, controllers, and the like), which process various computer-executable instructions to control the operation of device 1100 and to enable techniques for controlling a media program based on a media reaction and other methods described herein. Alternatively or in addition, device 1100 can be implemented with any one or combination of hardware, firmware, or fixed logic circuitry that is implemented in connection with processing and control circuits which are generally identified at 1112. Although not shown, device 1100 can include a system bus or data transfer system that couples the various components within the device. A system bus can include any one or combination of different bus structures, such as a memory bus or memory controller, a peripheral bus, a universal serial bus, and/or a processor or local bus that utilizes any of a variety of bus architectures.
Device 1100 also includes computer-readable storage media 1114, such as one or more memory devices that enable persistent and/or non-transitory data storage (i.e., in contrast to mere signal transmission), examples of which include random access memory (RAM), non-volatile memory (e.g., any one or more of a read-only memory (ROM), flash memory, EPROM, EEPROM, etc.), and a disk storage device. A disk storage device may be implemented as any type of magnetic or optical storage device, such as a hard disk drive, a recordable and/or rewriteable compact disc (CD), any type of a digital versatile disc (DVD), and the like. Device 1100 can also include a mass storage device 1116.
Computer-readable storage media 1114 provides data storage mechanisms to store device data 1104, as well as various device applications 1118 and any other types of information and/or data related to operational aspects of device 1100. For example, an operating system 1120 can be maintained as a computer application with computer-readable storage media 1114 and executed on processors 1110. Device applications 1118 may include a device manager, such as any form of a control application, software application, signal-processing and control module, code that is native to a particular device, a hardware abstraction layer for a particular device, and so on.
Device applications 1118 also include any system components, engines, or modules to implement techniques for controlling a media program based on a media reaction. In this example, device applications 1118 can include state module 106, interest module 108, interface module 110, history module 216, and/or control module 220.
Although embodiments of techniques and apparatuses for controlling a media program based on a media reaction have been described in language specific to features and/or methods, it is to be understood that the subject of the appended claims is not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as example implementations for controlling a media program based on a media reaction.
Number | Name | Date | Kind |
---|---|---|---|
4288078 | Lugo | Sep 1981 | A |
4627620 | Yang | Dec 1986 | A |
4630910 | Ross et al. | Dec 1986 | A |
4645458 | Williams | Feb 1987 | A |
4695953 | Blair et al. | Sep 1987 | A |
4702475 | Elstein et al. | Oct 1987 | A |
4711543 | Blair et al. | Dec 1987 | A |
4751642 | Silva et al. | Jun 1988 | A |
4796997 | Svetkoff et al. | Jan 1989 | A |
4809065 | Harris et al. | Feb 1989 | A |
4817950 | Goo | Apr 1989 | A |
4843568 | Krueger et al. | Jun 1989 | A |
4893183 | Nayar | Jan 1990 | A |
4901362 | Terzian | Feb 1990 | A |
4925189 | Braeunig | May 1990 | A |
4931865 | Scarampi | Jun 1990 | A |
5101444 | Wilson et al. | Mar 1992 | A |
5148154 | MacKay et al. | Sep 1992 | A |
5175641 | Boerstler et al. | Dec 1992 | A |
5184295 | Mann | Feb 1993 | A |
5229754 | Aoki et al. | Jul 1993 | A |
5229756 | Kosugi et al. | Jul 1993 | A |
5239463 | Blair et al. | Aug 1993 | A |
5239464 | Blair et al. | Aug 1993 | A |
5288078 | Capper et al. | Feb 1994 | A |
5295491 | Gevins | Mar 1994 | A |
5320538 | Baum | Jun 1994 | A |
5347306 | Nitta | Sep 1994 | A |
5385519 | Hsu et al. | Jan 1995 | A |
5405152 | Katanics et al. | Apr 1995 | A |
5417210 | Funda et al. | May 1995 | A |
5423554 | Davis | Jun 1995 | A |
5454043 | Freeman | Sep 1995 | A |
5469740 | French et al. | Nov 1995 | A |
5495576 | Ritchey | Feb 1996 | A |
5516105 | Eisenbrey et al. | May 1996 | A |
5524637 | Erickson | Jun 1996 | A |
5528263 | Platzker et al. | Jun 1996 | A |
5534917 | MacDougall | Jul 1996 | A |
5563988 | Maes et al. | Oct 1996 | A |
5577981 | Jarvik | Nov 1996 | A |
5580249 | Jacobsen et al. | Dec 1996 | A |
5581276 | Cipolla et al. | Dec 1996 | A |
5594469 | Freeman et al. | Jan 1997 | A |
5597309 | Riess | Jan 1997 | A |
5616078 | Oh | Apr 1997 | A |
5617312 | Iura et al. | Apr 1997 | A |
5638300 | Johnson | Jun 1997 | A |
5641288 | Zaenglein | Jun 1997 | A |
5682196 | Freeman | Oct 1997 | A |
5682229 | Wangler | Oct 1997 | A |
5690582 | Ulrich et al. | Nov 1997 | A |
5703367 | Hashimoto et al. | Dec 1997 | A |
5704837 | Iwasaki et al. | Jan 1998 | A |
5715834 | Bergamasco et al. | Feb 1998 | A |
5801704 | Oohara et al. | Sep 1998 | A |
5805167 | van Cruyningen | Sep 1998 | A |
5828779 | Maggioni | Oct 1998 | A |
5875108 | Hoffberg et al. | Feb 1999 | A |
5877503 | Neriishi | Mar 1999 | A |
5877803 | Wee et al. | Mar 1999 | A |
5904484 | Burns | May 1999 | A |
5913727 | Ahdoot | Jun 1999 | A |
5933125 | Fernie et al. | Aug 1999 | A |
5980256 | Carmein | Nov 1999 | A |
5989157 | Walton | Nov 1999 | A |
5995649 | Marugame | Nov 1999 | A |
5999766 | Hisatomi et al. | Dec 1999 | A |
6002808 | Freeman | Dec 1999 | A |
6005548 | Latypov et al. | Dec 1999 | A |
6009210 | Kang | Dec 1999 | A |
6054991 | Crane et al. | Apr 2000 | A |
6057909 | Yahav et al. | May 2000 | A |
6066075 | Poulton | May 2000 | A |
6072494 | Nguyen | Jun 2000 | A |
6073489 | French et al. | Jun 2000 | A |
6075895 | Qiao et al. | Jun 2000 | A |
6077201 | Cheng | Jun 2000 | A |
6098458 | French et al. | Aug 2000 | A |
6100517 | Yahav et al. | Aug 2000 | A |
6100896 | Strohecker et al. | Aug 2000 | A |
6101289 | Kellner | Aug 2000 | A |
6111580 | Kazama et al. | Aug 2000 | A |
6115482 | Sears | Sep 2000 | A |
6128003 | Smith et al. | Oct 2000 | A |
6130677 | Kunz | Oct 2000 | A |
6141463 | Covell et al. | Oct 2000 | A |
6147678 | Kumar et al. | Nov 2000 | A |
6152856 | Studor et al. | Nov 2000 | A |
6159100 | Smith | Dec 2000 | A |
6173066 | Peurach et al. | Jan 2001 | B1 |
6181343 | Lyons | Jan 2001 | B1 |
6181472 | Liu | Jan 2001 | B1 |
6188777 | Darrell et al. | Feb 2001 | B1 |
6215890 | Matsuo et al. | Apr 2001 | B1 |
6215898 | Woodfill et al. | Apr 2001 | B1 |
6222465 | Kumar et al. | Apr 2001 | B1 |
6226388 | Qian et al. | May 2001 | B1 |
6226396 | Marugame | May 2001 | B1 |
6229913 | Nayar et al. | May 2001 | B1 |
6256033 | Nguyen | Jul 2001 | B1 |
6256400 | Takata et al. | Jul 2001 | B1 |
6283860 | Lyons et al. | Sep 2001 | B1 |
6289112 | Jain et al. | Sep 2001 | B1 |
6291816 | Liu | Sep 2001 | B1 |
6299308 | Voronka et al. | Oct 2001 | B1 |
6308565 | French et al. | Oct 2001 | B1 |
6316934 | Amorai-Moriya et al. | Nov 2001 | B1 |
6363160 | Bradski et al. | Mar 2002 | B1 |
6377296 | Zlatsin et al. | Apr 2002 | B1 |
6384819 | Hunter | May 2002 | B1 |
6411744 | Edwards | Jun 2002 | B1 |
6421453 | Kanevsky et al. | Jul 2002 | B1 |
6430997 | French et al. | Aug 2002 | B1 |
6476834 | Doval et al. | Nov 2002 | B1 |
6496598 | Harman | Dec 2002 | B1 |
6498628 | Iwamura | Dec 2002 | B2 |
6502515 | Burckhardt et al. | Jan 2003 | B2 |
6503195 | Keller et al. | Jan 2003 | B1 |
6512838 | Rafii et al. | Jan 2003 | B1 |
6514081 | Mengoli | Feb 2003 | B1 |
6525827 | Liu | Feb 2003 | B2 |
6539931 | Trajkovic et al. | Apr 2003 | B2 |
6570555 | Prevost et al. | May 2003 | B1 |
6591236 | Lewis et al. | Jul 2003 | B2 |
6594616 | Zhang et al. | Jul 2003 | B2 |
6615177 | Rapp et al. | Sep 2003 | B1 |
6622119 | Ramaswamy et al. | Sep 2003 | B1 |
6633294 | Rosenthal et al. | Oct 2003 | B1 |
6640202 | Dietz et al. | Oct 2003 | B1 |
6661918 | Gordon et al. | Dec 2003 | B1 |
6674877 | Jojic et al. | Jan 2004 | B1 |
6681031 | Cohen et al. | Jan 2004 | B2 |
6714665 | Hanna et al. | Mar 2004 | B1 |
6730913 | Remillard et al. | May 2004 | B2 |
6731799 | Sun et al. | May 2004 | B1 |
6738066 | Nguyen | May 2004 | B1 |
6750848 | Pryor | Jun 2004 | B1 |
6765726 | French et al. | Jul 2004 | B2 |
6771277 | Ohba | Aug 2004 | B2 |
6778171 | Kikinis | Aug 2004 | B1 |
6788809 | Grzeszczuk et al. | Sep 2004 | B1 |
6801637 | Voronka et al. | Oct 2004 | B2 |
6856827 | Seeley et al. | Feb 2005 | B2 |
6868383 | Bangalore et al. | Mar 2005 | B1 |
6873723 | Aucsmith et al. | Mar 2005 | B1 |
6876496 | French et al. | Apr 2005 | B2 |
6881526 | Bobeck et al. | Apr 2005 | B2 |
6937742 | Roberts et al. | Aug 2005 | B2 |
6950534 | Cohen et al. | Sep 2005 | B2 |
7003134 | Covell et al. | Feb 2006 | B1 |
7006236 | Tomasi et al. | Feb 2006 | B2 |
7007236 | Dempski et al. | Feb 2006 | B2 |
7028001 | Muthuswamy et al. | Apr 2006 | B1 |
7036094 | Cohen et al. | Apr 2006 | B1 |
7038855 | French et al. | May 2006 | B2 |
7039676 | Day et al. | May 2006 | B1 |
7042440 | Pryor et al. | May 2006 | B2 |
7042442 | Kanevsky et al. | May 2006 | B1 |
7050177 | Tomasi et al. | May 2006 | B2 |
7050606 | Paul et al. | May 2006 | B2 |
7058204 | Hildreth et al. | Jun 2006 | B2 |
7060957 | Lange et al. | Jun 2006 | B2 |
7096454 | Damm et al. | Aug 2006 | B2 |
7113918 | Ahmad et al. | Sep 2006 | B1 |
7120880 | Dryer et al. | Oct 2006 | B1 |
7121946 | Paul et al. | Oct 2006 | B2 |
7134130 | Thomas | Nov 2006 | B1 |
7145330 | Xiao | Dec 2006 | B2 |
7151530 | Roeber et al. | Dec 2006 | B2 |
7155305 | Hayes et al. | Dec 2006 | B2 |
7162082 | Edwards | Jan 2007 | B2 |
7170492 | Bell | Jan 2007 | B2 |
7170605 | Cromwell et al. | Jan 2007 | B2 |
7184048 | Hunter | Feb 2007 | B2 |
7202898 | Braun et al. | Apr 2007 | B1 |
7212665 | Yang et al | May 2007 | B2 |
7214932 | Brunfeld et al. | May 2007 | B2 |
7217020 | Finch | May 2007 | B2 |
7222078 | Abelow | May 2007 | B2 |
7224384 | Iddan et al. | May 2007 | B1 |
7227526 | Hildreth et al. | Jun 2007 | B2 |
7259747 | Bell | Aug 2007 | B2 |
7293356 | Sohn et al. | Nov 2007 | B2 |
7308112 | Fujimura et al. | Dec 2007 | B2 |
7310431 | Gokturk et al. | Dec 2007 | B2 |
7317836 | Fujimura et al. | Jan 2008 | B2 |
7340077 | Gokturk et al. | Mar 2008 | B2 |
7348963 | Bell | Mar 2008 | B2 |
7359121 | French et al. | Apr 2008 | B2 |
7367887 | Watabe et al. | May 2008 | B2 |
7379563 | Shamaie | May 2008 | B2 |
7379566 | Hildreth | May 2008 | B2 |
7389591 | Jaiswal et al. | Jun 2008 | B2 |
7412077 | Li et al. | Aug 2008 | B2 |
7421093 | Hildreth et al. | Sep 2008 | B2 |
7430312 | Gu | Sep 2008 | B2 |
7435941 | Ayres | Oct 2008 | B2 |
7436496 | Kawahito | Oct 2008 | B2 |
7450736 | Yang et al. | Nov 2008 | B2 |
7452275 | Kuraishi | Nov 2008 | B2 |
7460690 | Cohen et al. | Dec 2008 | B2 |
7487375 | Lourie et al. | Feb 2009 | B2 |
7489812 | Fox et al. | Feb 2009 | B2 |
7512889 | Newell et al. | Mar 2009 | B2 |
7536032 | Bell | May 2009 | B2 |
7538782 | Kuroki et al. | May 2009 | B2 |
7555142 | Hildreth et al. | Jun 2009 | B2 |
7559841 | Hashimoto | Jul 2009 | B2 |
7560701 | Oggier et al. | Jul 2009 | B2 |
7568116 | Dooley et al. | Jul 2009 | B2 |
7570805 | Gu | Aug 2009 | B2 |
7574020 | Shamaie | Aug 2009 | B2 |
7576727 | Bell | Aug 2009 | B2 |
7590262 | Fujimura et al. | Sep 2009 | B2 |
7593552 | Higaki et al. | Sep 2009 | B2 |
7598942 | Underkoffler et al. | Oct 2009 | B2 |
7607509 | Schmiz et al. | Oct 2009 | B2 |
7620202 | Fujimura et al. | Nov 2009 | B2 |
7627139 | Marks et al. | Dec 2009 | B2 |
7636456 | Collins et al. | Dec 2009 | B2 |
7640304 | Goldscheider | Dec 2009 | B1 |
7643056 | Silsby | Jan 2010 | B2 |
7668340 | Cohen et al. | Feb 2010 | B2 |
7680298 | Roberts et al. | Mar 2010 | B2 |
7683954 | Ichikawa et al. | Mar 2010 | B2 |
7684592 | Paul et al. | Mar 2010 | B2 |
7701439 | Hillis et al. | Apr 2010 | B2 |
7702130 | Im et al. | Apr 2010 | B2 |
7704135 | Harrison, Jr. | Apr 2010 | B2 |
7710391 | Bell et al. | May 2010 | B2 |
7729530 | Antonov et al. | Jun 2010 | B2 |
7739140 | Vinson et al. | Jun 2010 | B2 |
7746345 | Hunter | Jun 2010 | B2 |
7752633 | Fleming | Jul 2010 | B1 |
7760182 | Ahmad et al. | Jul 2010 | B2 |
7762665 | Vertegaal et al. | Jul 2010 | B2 |
7764311 | Bill | Jul 2010 | B2 |
7770136 | Beeck et al. | Aug 2010 | B2 |
7809167 | Bell | Oct 2010 | B2 |
7814518 | Ducheneaut et al. | Oct 2010 | B2 |
7834846 | Bell | Nov 2010 | B1 |
7836480 | Harvey et al. | Nov 2010 | B1 |
7852262 | Namineni et al. | Dec 2010 | B2 |
7889073 | Zalewski | Feb 2011 | B2 |
7895076 | Kutaragi et al. | Feb 2011 | B2 |
RE42256 | Edwards | Mar 2011 | E |
7898522 | Hildreth et al. | Mar 2011 | B2 |
8035612 | Bell et al. | Oct 2011 | B2 |
8035614 | Bell et al. | Oct 2011 | B2 |
8035624 | Bell et al. | Oct 2011 | B2 |
8072470 | Marks | Dec 2011 | B2 |
8081302 | Paluszek et al. | Dec 2011 | B2 |
8096660 | Vertegaal et al. | Jan 2012 | B2 |
8102422 | Kenderov et al. | Jan 2012 | B1 |
8132187 | Klyuchevskyy | Mar 2012 | B2 |
8141775 | Aidasani et al. | Mar 2012 | B1 |
8189053 | Pryor | May 2012 | B2 |
8260740 | Davis et al. | Sep 2012 | B2 |
8322856 | Vertegaal et al. | Dec 2012 | B2 |
8327395 | Lee et al. | Dec 2012 | B2 |
8332883 | Lee et al. | Dec 2012 | B2 |
8418085 | Snook et al. | Apr 2013 | B2 |
8471868 | Wilson et al. | Jun 2013 | B1 |
8499245 | Froment et al. | Jul 2013 | B1 |
8620113 | Yee | Dec 2013 | B2 |
8635637 | Krum | Jan 2014 | B2 |
8660303 | Izadi et al. | Feb 2014 | B2 |
8760395 | Kim et al. | Jun 2014 | B2 |
20010021994 | Nash | Sep 2001 | A1 |
20020041327 | Hildreth et al. | Apr 2002 | A1 |
20020072952 | Hamzy et al. | Jun 2002 | A1 |
20020073417 | Kondo et al. | Jun 2002 | A1 |
20020120925 | Logan | Aug 2002 | A1 |
20020144259 | Gutta et al. | Oct 2002 | A1 |
20020157095 | Masumitsu et al. | Oct 2002 | A1 |
20020174230 | Gudorf et al. | Nov 2002 | A1 |
20020174445 | Miller et al. | Nov 2002 | A1 |
20020178446 | Sie et al. | Nov 2002 | A1 |
20030001846 | Davis et al. | Jan 2003 | A1 |
20030005439 | Rovira | Jan 2003 | A1 |
20030007018 | Seni et al. | Jan 2003 | A1 |
20030033600 | Cliff et al. | Feb 2003 | A1 |
20030066071 | Gutta et al. | Apr 2003 | A1 |
20030074661 | Krapf et al. | Apr 2003 | A1 |
20030093784 | Dimitrova et al. | May 2003 | A1 |
20030118974 | Obrador | Jun 2003 | A1 |
20030141360 | De Leo et al. | Jul 2003 | A1 |
20030167358 | Marvin et al. | Sep 2003 | A1 |
20040001616 | Gutta et al. | Jan 2004 | A1 |
20040046736 | Pryor et al. | Mar 2004 | A1 |
20040056907 | Sharma et al. | Mar 2004 | A1 |
20040068409 | Tanaka et al. | Apr 2004 | A1 |
20040070573 | Graham | Apr 2004 | A1 |
20040113933 | Guler | Jun 2004 | A1 |
20040155962 | Marks | Aug 2004 | A1 |
20040168190 | Saari et al. | Aug 2004 | A1 |
20040189720 | Wilson et al. | Sep 2004 | A1 |
20040193413 | Wilson et al. | Sep 2004 | A1 |
20040207597 | Marks | Oct 2004 | A1 |
20050059488 | Larsen et al. | Mar 2005 | A1 |
20050076365 | Popov et al. | Apr 2005 | A1 |
20050082480 | Wagner et al. | Apr 2005 | A1 |
20050190973 | Kristensson et al. | Sep 2005 | A1 |
20050212767 | Marvit et al. | Sep 2005 | A1 |
20050215319 | Rigopulos et al. | Sep 2005 | A1 |
20050223237 | Barletta et al. | Oct 2005 | A1 |
20050229199 | Yabe | Oct 2005 | A1 |
20050234998 | Lesandrini et al. | Oct 2005 | A1 |
20050289582 | Tavares et al. | Dec 2005 | A1 |
20060031776 | Glein et al. | Feb 2006 | A1 |
20060031786 | Hillis et al. | Feb 2006 | A1 |
20060055685 | Rimas-Ribikauskas et al. | Mar 2006 | A1 |
20060073816 | Kim et al. | Apr 2006 | A1 |
20060101349 | Lieberman et al. | May 2006 | A1 |
20060123360 | Anwar et al. | Jun 2006 | A1 |
20060158307 | Lee et al. | Jul 2006 | A1 |
20060174313 | Ducheneaut et al. | Aug 2006 | A1 |
20060184800 | Rosenberg | Aug 2006 | A1 |
20060188144 | Sasaki et al. | Aug 2006 | A1 |
20060188234 | Takeshita | Aug 2006 | A1 |
20060200780 | Iwema et al. | Sep 2006 | A1 |
20060210958 | Rimas-Ribikauskas | Sep 2006 | A1 |
20060218573 | Proebstel | Sep 2006 | A1 |
20060239558 | Rafii et al. | Oct 2006 | A1 |
20060253793 | Zhai et al. | Nov 2006 | A1 |
20060262116 | Moshiri et al. | Nov 2006 | A1 |
20060271207 | Shaw | Nov 2006 | A1 |
20060280055 | Miller et al. | Dec 2006 | A1 |
20060282856 | Errico et al. | Dec 2006 | A1 |
20060282859 | Garbow et al. | Dec 2006 | A1 |
20070013718 | Ohba | Jan 2007 | A1 |
20070018973 | Shih et al. | Jan 2007 | A1 |
20070060336 | Marks et al. | Mar 2007 | A1 |
20070075978 | Chung | Apr 2007 | A1 |
20070098222 | Porter et al. | May 2007 | A1 |
20070140532 | Goffin | Jun 2007 | A1 |
20070143715 | Hollins et al. | Jun 2007 | A1 |
20070143787 | Cankaya | Jun 2007 | A1 |
20070150281 | Hoff | Jun 2007 | A1 |
20070150916 | Begole et al. | Jun 2007 | A1 |
20070203685 | Takano | Aug 2007 | A1 |
20070214292 | Hayes et al. | Sep 2007 | A1 |
20070216894 | Garcia et al. | Sep 2007 | A1 |
20070219430 | Moore | Sep 2007 | A1 |
20070260984 | Marks et al. | Nov 2007 | A1 |
20070271580 | Tischer et al. | Nov 2007 | A1 |
20070279485 | Ohba et al. | Dec 2007 | A1 |
20070283296 | Nilsson | Dec 2007 | A1 |
20070298882 | Marks et al. | Dec 2007 | A1 |
20080001951 | Marks et al. | Jan 2008 | A1 |
20080016544 | Lee et al. | Jan 2008 | A1 |
20080018591 | Pittel et al. | Jan 2008 | A1 |
20080026838 | Dunstan et al. | Jan 2008 | A1 |
20080027984 | Perdomo | Jan 2008 | A1 |
20080033790 | Nickerson et al. | Feb 2008 | A1 |
20080059578 | Albertson et al. | Mar 2008 | A1 |
20080062257 | Corson | Mar 2008 | A1 |
20080065243 | Fallman et al. | Mar 2008 | A1 |
20080081694 | Hong et al. | Apr 2008 | A1 |
20080091512 | Marci et al. | Apr 2008 | A1 |
20080092159 | Dmitriev et al. | Apr 2008 | A1 |
20080100620 | Nagai et al. | May 2008 | A1 |
20080100825 | Zalewski | May 2008 | A1 |
20080124690 | Redlich | May 2008 | A1 |
20080126937 | Pachet | May 2008 | A1 |
20080134102 | Movold et al. | Jun 2008 | A1 |
20080151113 | Park | Jun 2008 | A1 |
20080152191 | Fujimura et al. | Jun 2008 | A1 |
20080163130 | Westerman | Jul 2008 | A1 |
20080163283 | Tan et al. | Jul 2008 | A1 |
20080178126 | Beeck | Jul 2008 | A1 |
20080215972 | Zalewski et al. | Sep 2008 | A1 |
20080215973 | Zalewski et al. | Sep 2008 | A1 |
20080234023 | Mullahkhel et al. | Sep 2008 | A1 |
20080310707 | Kansal et al. | Dec 2008 | A1 |
20090013366 | You et al. | Jan 2009 | A1 |
20090025024 | Beser et al. | Jan 2009 | A1 |
20090027337 | Hildreth | Jan 2009 | A1 |
20090037945 | Greig et al. | Feb 2009 | A1 |
20090051648 | Shamaie et al. | Feb 2009 | A1 |
20090070798 | Lee et al. | Mar 2009 | A1 |
20090072992 | Yun | Mar 2009 | A1 |
20090073136 | Choi | Mar 2009 | A1 |
20090085864 | Kutliroff et al. | Apr 2009 | A1 |
20090089225 | Baier et al. | Apr 2009 | A1 |
20090094627 | Lee et al. | Apr 2009 | A1 |
20090094628 | Lee et al. | Apr 2009 | A1 |
20090094629 | Lee et al. | Apr 2009 | A1 |
20090094630 | Brown | Apr 2009 | A1 |
20090106645 | Knobel | Apr 2009 | A1 |
20090112817 | Jung et al. | Apr 2009 | A1 |
20090116684 | Andreasson | May 2009 | A1 |
20090133051 | Hildreth | May 2009 | A1 |
20090141933 | Wagg | Jun 2009 | A1 |
20090146775 | Bonnaud et al. | Jun 2009 | A1 |
20090157472 | Burazin et al. | Jun 2009 | A1 |
20090167679 | Klier et al. | Jul 2009 | A1 |
20090175540 | Dariush et al. | Jul 2009 | A1 |
20090178097 | Kim et al. | Jul 2009 | A1 |
20090183125 | Magal et al. | Jul 2009 | A1 |
20090183220 | Amento | Jul 2009 | A1 |
20090195392 | Zalewski | Aug 2009 | A1 |
20090217315 | Malik et al. | Aug 2009 | A1 |
20090221368 | Yen et al. | Sep 2009 | A1 |
20090234718 | Green | Sep 2009 | A1 |
20090235195 | Shin et al. | Sep 2009 | A1 |
20090251425 | Sohn et al. | Oct 2009 | A1 |
20090252423 | Zhu et al. | Oct 2009 | A1 |
20090259960 | Steinle et al. | Oct 2009 | A1 |
20090296002 | Lida et al. | Dec 2009 | A1 |
20090303231 | Robinet et al. | Dec 2009 | A1 |
20090320055 | Langille et al. | Dec 2009 | A1 |
20090327977 | Bachfischer et al. | Dec 2009 | A1 |
20100007801 | Cooper et al. | Jan 2010 | A1 |
20100026914 | Chung et al. | Feb 2010 | A1 |
20100033427 | Marks et al. | Feb 2010 | A1 |
20100070913 | Murrett et al. | Mar 2010 | A1 |
20100070987 | Amento et al. | Mar 2010 | A1 |
20100070992 | Morris et al. | Mar 2010 | A1 |
20100073329 | Raman et al. | Mar 2010 | A1 |
20100083373 | White et al. | Apr 2010 | A1 |
20100086204 | Lessing | Apr 2010 | A1 |
20100093435 | Glaser et al. | Apr 2010 | A1 |
20100095206 | Kim | Apr 2010 | A1 |
20100095332 | Gran et al. | Apr 2010 | A1 |
20100107184 | Shintani | Apr 2010 | A1 |
20100122286 | Begeja et al. | May 2010 | A1 |
20100138780 | Marano et al. | Jun 2010 | A1 |
20100138797 | Thorn | Jun 2010 | A1 |
20100138798 | Wilson et al. | Jun 2010 | A1 |
20100146389 | Yoo et al. | Jun 2010 | A1 |
20100151946 | Wilson et al. | Jun 2010 | A1 |
20100153856 | Russ | Jun 2010 | A1 |
20100153984 | Neufeld | Jun 2010 | A1 |
20100169157 | Muhonen et al. | Jul 2010 | A1 |
20100169905 | Fukuchi et al. | Jul 2010 | A1 |
20100191631 | Weidmann | Jul 2010 | A1 |
20100207874 | Yuxin et al. | Aug 2010 | A1 |
20100207875 | Yeh | Aug 2010 | A1 |
20100211439 | Marci et al. | Aug 2010 | A1 |
20100235667 | Mucignat et al. | Sep 2010 | A1 |
20100248832 | Esaki et al. | Sep 2010 | A1 |
20100251280 | Sofos et al. | Sep 2010 | A1 |
20100251300 | Fahey et al. | Sep 2010 | A1 |
20100271802 | Recker et al. | Oct 2010 | A1 |
20100278393 | Snook et al. | Nov 2010 | A1 |
20100286983 | Cho | Nov 2010 | A1 |
20100295782 | Binder | Nov 2010 | A1 |
20100295783 | El Dokor et al. | Nov 2010 | A1 |
20100306712 | Snook et al. | Dec 2010 | A1 |
20100327766 | Recker et al. | Dec 2010 | A1 |
20100332842 | Kalaboukis et al. | Dec 2010 | A1 |
20110007142 | Perez et al. | Jan 2011 | A1 |
20110016102 | Hawthorne et al. | Jan 2011 | A1 |
20110026765 | Ivanich | Feb 2011 | A1 |
20110037866 | Iwamoto | Feb 2011 | A1 |
20110038547 | Hill | Feb 2011 | A1 |
20110066682 | Aldunate et al. | Mar 2011 | A1 |
20110072448 | Stiers et al. | Mar 2011 | A1 |
20110077513 | Rofougaran | Mar 2011 | A1 |
20110084983 | Demaine | Apr 2011 | A1 |
20110085705 | Izadi et al. | Apr 2011 | A1 |
20110115887 | Yoo et al. | May 2011 | A1 |
20110126154 | Boehler et al. | May 2011 | A1 |
20110145040 | Zahn et al. | Jun 2011 | A1 |
20110145041 | Salamatov et al. | Jun 2011 | A1 |
20110157009 | Kim et al. | Jun 2011 | A1 |
20110161912 | Eteminan et al. | Jun 2011 | A1 |
20110164143 | Shintani et al. | Jul 2011 | A1 |
20110173589 | Guttman et al. | Jul 2011 | A1 |
20110208582 | Hoyle | Aug 2011 | A1 |
20110214141 | Oyaizu | Sep 2011 | A1 |
20110216059 | Espiritu et al. | Sep 2011 | A1 |
20110246572 | Kollenkark et al. | Oct 2011 | A1 |
20110254859 | Matsuda | Oct 2011 | A1 |
20110263946 | el Kaliouby et al. | Oct 2011 | A1 |
20110264531 | Bhatia et al. | Oct 2011 | A1 |
20110282745 | Meoded et al. | Nov 2011 | A1 |
20110316845 | Roberts et al. | Dec 2011 | A1 |
20110321096 | Landow et al. | Dec 2011 | A1 |
20120005632 | Broyles, III et al. | Jan 2012 | A1 |
20120011530 | Bentolila et al. | Jan 2012 | A1 |
20120030637 | Dey et al. | Feb 2012 | A1 |
20120047525 | Campagna et al. | Feb 2012 | A1 |
20120051719 | Marvit | Mar 2012 | A1 |
20120060176 | Chai et al. | Mar 2012 | A1 |
20120079521 | Garg et al. | Mar 2012 | A1 |
20120084812 | Thompson et al. | Apr 2012 | A1 |
20120105257 | Murillo et al. | May 2012 | A1 |
20120105473 | Bar-Zeev et al. | May 2012 | A1 |
20120109726 | Ruffini | May 2012 | A1 |
20120124603 | Amada | May 2012 | A1 |
20120192233 | Wong | Jul 2012 | A1 |
20120209715 | Lotan et al. | Aug 2012 | A1 |
20120226981 | Clavin | Sep 2012 | A1 |
20120268362 | Yee | Oct 2012 | A1 |
20120280897 | Balan et al. | Nov 2012 | A1 |
20120290508 | Bist | Nov 2012 | A1 |
20120304059 | McCloskey | Nov 2012 | A1 |
20120304206 | Roberts et al. | Nov 2012 | A1 |
20120306734 | Kim et al. | Dec 2012 | A1 |
20130007671 | Hammontree et al. | Jan 2013 | A1 |
20130014144 | Bhatia et al. | Jan 2013 | A1 |
20130054652 | Antonelli et al. | Feb 2013 | A1 |
20130117771 | Lee et al. | May 2013 | A1 |
20130136358 | Dedhia et al. | May 2013 | A1 |
20130145384 | Krum | Jun 2013 | A1 |
20130145385 | Aghajanyan | Jun 2013 | A1 |
20130159555 | Rosser | Jun 2013 | A1 |
20130198690 | Barsoum et al. | Aug 2013 | A1 |
20130232515 | Rivera et al. | Sep 2013 | A1 |
20130268955 | Conrad | Oct 2013 | A1 |
20130298146 | Conrad | Nov 2013 | A1 |
20130298158 | Conrad | Nov 2013 | A1 |
20140109121 | Krum | Apr 2014 | A1 |
20140247212 | Kim | Sep 2014 | A1 |
Number | Date | Country |
---|---|---|
2775700 | Jul 2012 | CA |
2775814 | Sep 2013 | CA |
101095055 | Dec 2007 | CN |
101202994 | Jun 2008 | CN |
101254344 | Jun 2010 | CN |
102713788 | Oct 2012 | CN |
0583061 | Feb 1994 | EP |
1315375 | May 2003 | EP |
2423808 | Jun 2006 | GB |
2459707 | Nov 2009 | GB |
08044490 | Feb 1996 | JP |
WO-9310708 | Jun 1993 | WO |
WO-9717598 | May 1997 | WO |
WO-9915863 | Apr 1999 | WO |
WO-9944698 | Sep 1999 | WO |
WO-0159975 | Aug 2001 | WO |
WO-0163916 | Aug 2001 | WO |
WO-0169799 | Sep 2001 | WO |
WO-02082249 | Oct 2002 | WO |
WO-03001722 | Jan 2003 | WO |
WO-03015056 | Feb 2003 | WO |
WO-03046706 | Jun 2003 | WO |
WO-03054683 | Jul 2003 | WO |
WO-03071410 | Aug 2003 | WO |
WO-03073359 | Sep 2003 | WO |
WO-2007128507 | Nov 2007 | WO |
WO-2008001287 | Jan 2008 | WO |
WO-2009059065 | May 2009 | WO |
WO-2011069035 | Jun 2011 | WO |
Entry |
---|
“Foreign Office Action”, Canadian Application No. 2775814, (Aug. 24, 2012), 3 pages. |
“Foreign Office Action”, Canadian Application No. 2775700, (Aug. 24, 2012), 2 pages. |
“Future Media Internet Research Challenges and the Road Ahead”, Retrieved at <<http://www.gatv.ssr.upm.es/nextmedia/images/fmi-tf-white—paper—042010.pdf>>, Apr. 2010, pp. 31. |
Minge, Michael, “Dynamics of User Experience”, Retrieved at <<http://www.cs.uta.fi/˜ux-emotion/submissions/Minge.pdf>>, Workshop on Research Goals and Strategies for Studying User Experience and Emotion, 2008, pp. 5. |
“Advisory Action”, U.S. Appl. No. 10/396,653, (May 2, 2007),3 pages. |
“Advisory Action”, U.S. Appl. No. 10/396,653, (May 23, 2008),3 pages. |
“Affdex: Measuring Emotion over the Web”, Affectiva, Retrieved from: <http://www.affectiva.com/affdex/> on Nov. 4, 2011,3 pages. |
“Application Titled “Controlling Electronic Devices in a Multimedia System Through a Natural User Interface””, U.S. Appl. No. 13/038,024, filed Mar. 2, 2011, pp. 1-46. |
“Application Titled “Interaction with Networked Screen Content Via Motion Sensing Device in Retail Setting””, U.S. Appl. No. 13/025,180, filed Feb. 11, 2011, pp. 1-23. |
“Commanding Overview”, MSDN, retrieved from <http://msdn.microsoft.com/en-us/library/ms752308.aspx> on Sep. 27, 2011,11 pages. |
“Designing CEC into your next HDMI Product”, Quantum Data White Paper, Retrieved from the Internet:<URL:http://www.quantumdata.com/pdf/CEC—white—paper.pdf> Quantum Data, Inc., Elgin, IL, USA, (May 13, 2006),12 pages. |
“Final Office Action”, U.S. Appl. No. 10/396,653, (Feb. 20, 2009),12 pages. |
“Final Office Action”, U.S. Appl. No. 10/396,653, (Feb. 25, 2008),20 pages. |
“Final Office Action”, U.S. Appl. No. 10/396,653, (Feb. 26, 2007),18 pages. |
“Final Office Action”, U.S. Appl. No. 11/626,794, (Jun. 11, 2009),14 pages. |
“Final Office Action”, U.S. Appl. No. 12/474,453, (May 10, 2012),14 pages. |
“GWindows: Light-Weight Stereo Vision for Interaction”, http://research.microsoft.com/˜nuria/gwindows/htm, (Jul. 8, 2005),2 pages. |
“International Search Report”, PCT Application No. PCT/US2010/036005, (Dec. 24, 2010),3 pages. |
“KinEmote uses Kinect to translate key strokes for Windows applications”, techshout.com [online], Retrieved from the Internet:<URL:http://www.techshout.com/gaming/2010/28/kinemote-uses-kinect-to-translate-key-strokes-for-windows-applications/>,(Dec. 28, 2010),2 pages. |
“Non-Final Office Action”, U.S. Appl. No. 10/396,653, (Sep. 6, 2007),17 pages. |
“Non-Final Office Action”, U.S. Appl. No. 10/396,653, (Sep. 8, 2008),13 pages. |
“Non-Final Office Action”, U.S. Appl. No. 10/396,653, (Sep. 19, 2006),24 pages. |
“Non-Final Office Action”, U.S. Appl. No. 11/626,794, (Oct. 27, 2009),15 pages. |
“Non-Final Office Action”, U.S. Appl. No. 11/626,794, (Dec. 23, 2008),18 pages. |
“Non-Final Office Action”, U.S. Appl. No. 12/474,453, (Sep. 6, 2011),10 pages. |
“Notice of Allowance”, U.S. Appl. No. 10/396,653, (Nov. 19, 2009),7 pages. |
“Notice of Allowance”, U.S. Appl. No. 11/626,794, (May 13, 2010),4 pages. |
“Signal Processing Institute”, http://Itswww.epfl.ch/˜alahi/student—projects/proposals.shtml#4, Downloaded Feb. 2, 2009,4 pages. |
“Simulation and Training”, Division Incorporated,(1994),6 Pages. |
“The Case for Kinect”, Eurogamer [online] Retrieved from the Internet on Aug. 20, 2010: URL:<http://www.eurogamer.net/articles/digitalfoundry-the-case-for-kinect-article?page=2>., (Aug. 7, 2010),pp. 1-7. |
“U.S. Appl. No. 12/794,406”, filed Jun. 4, 2010, 37 pages. |
“Virtual High Anxiety”, Tech update, (Aug. 1995),1 Page. |
Agarwal, Ankur et al., “High Precision Multi-touch Sensing on Surfaces using Overhead Cameras”, Second Annual IEEE International Workshop on Horizontal Interactive Human-Computer System, available at <<http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4384130>>,(Nov. 19, 2007),4 pages. |
Aggarwal, et al., “Human Motion Analysis: A Review”, IEEE Nonrigid and Articulated motion Workshop, University of Texas at Austin, Austin, TX.,(1997),pp. 90-102. |
Ali, Azarbayejani et al., “Real-Time Self-Calibrating Stereo Person Tracking Using 3-D Shape Estimation from Blob Features”, Proceedings of ICPR, Vienna, Austria, (Aug. 1996),pp. 627-632. |
Althoff, Frank et al., “Using Multimodal Interaction to Navigate in Arbitrary Virtual VRML Worlds”, PUI 2001 Orlando, FL USA, available at <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.16.8034&rep=rep1&type=pdf>,(2001),8 pages. |
Argyros, et al., “Vision-Based Interpretation of Hand Gestures for Remote Control of a Computer Mouse”, Retrieved from: <http://www.ics.forth.gr/˜argyros/mypapers/2006—05—hci—virtualmouse.pdf> on Oct. 31, 2007, (2006),pp. 40-51. |
Azarbayejani, et al., “Visually Controlled Graphics”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, No. 6, (Jun. 1993),pp. 602-605. |
Azoz, Yusuf et al., “Reliable Tracking of Human Arm Dynamics by Multiple Cue Integration and Constraint Fusion”, IEEE Conference on Computer Vision and Pattern Recognition, (1998),6 pages. |
Baudel, Thomas et al., “Charade: Remote Control of Objects using Free-Hand Gestures”, Communications of the ACM, vol. 36. No. 7, (Jul. 1993),10 pages. |
Becker, David A., “Sensei: A Real-Time Recognition, Feedback and Training System for T'ai Chi Gestures”, http://citeseer.ist.psu.edu/cache/papers/cs/405/ftp:zSzzSzwhitechapel.media.mit.eduzSzpubzSztech-reporterzsSzTR-426pdf/becker97sensei.pdf, (Jun. 1993),50 pages. |
Berard, Francois “The Perceptual Window-Head Motion as a New Input Stream”, Proceedings of the Seventh IFIP Conference of Human-Computer Interaction, (1999),238-244. |
Bhuiyan, Moniruzzaman et al., “Gesture-controlled user interfaces, what have we done and what's next?”, Retrieved at <<http://www.newi.ac.uk/computing/research/pubs/SEIN—BP.pdf>>, (Nov. 27, 2009),10 pages. |
Bobic, Nick “Rotating Objects Using Quaternions”, Retrieved from the Internet on Aug. 20, 2010: URL http://www.gamasutra.com/view/feature/3278/rotating—objects—quarternions.php?page=2>., (Jul. 5, 1998),14 pages. |
Boverie, S. et al., “Comparison of Structured Light and Stereovision Sensors for New Airbag Generations”, Control Engineering Practice vol. 11, Issue 12 (2003), available at <<http://homepages.laas.fr/lerasle/pdf/cep03.pdf>>,(Dec. 2003),pp. 1413-1421. |
Bowman, Doug A., et al., “New Directions in 3D User Interfaces”, The International Journal of Virtual Reality, retrieved from <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.99.1121&rep=rep1&type=pdf> on Nov. 15, 2011,(2006),pp. 3-14. |
Breen, David et al., “Interactive Occlusion and Collision of Real and Virtual Objects in Augmented Reality”, Technical report ECRC-95-02 European Computer-Industry Research Centre GmbH, Munich, Germany, (1995),22 Pages. |
Brogan, David et al., “Dynamically Simulated Characters in Virtual Environments”, vol. 18, Issue 5, IEEE Computer Graphics and Applications, (Sep./Oct. 1998),pp. 58-69. |
Buxton, William et al., “A Study of Two-Handed Input”, Proceedings of CHI'86,(1986),pp. 321-326. |
Cedras, Claudette et al., “Motion-based Recognition: A Survey”, IEEE Proceedings, Image and Vision Computing, vol. 13, No. 2, (Mar. 1995),pp. 129-155. |
Crawford, Stephanie “How Microsoft Kinect Works”, Howstuffworks[online] Retrieved from the Internet on Aug. 19, 2010: URL: <http://electronics.howstuffworks.com/microsoft-kinect.htm/printable>., pp. 1-5. |
Dalton, Angela B., et al., “Sensing User Intention and Context for Energy Management”, Duke University, Department of Computer Science, Retrieved from the Internet:<URL:http://www.cs.duke/edu/ari/millywatt/faceoff.pdf>, (Feb. 23, 2003),5 pages. |
Darrell, T et al., “Integrated Person Tracking Using Stereo, Color and Pattern Detection”, Proceedings of the Conference on Computer Vision and Pattern Recognition, (1998),pp. 601-609. |
Fisher, et al., “Virtual Environment Display System”, ACM Workshop on Interactive 3D Graphics, Chapel Hill, NC, (Oct. 1986),12 Pages. |
Fitzgerald, et al., “Integration of Kinematic Analysis into Computer Games for Exercise”, Proceedings of CGames 2006—9th International Conference on Computer Games: Al, Animation, Mobile, Educational and Serious Games, Dublin Ireland, (Nov. 2006),pp. 24-28. |
Fitzgerald, Will et al., “Multimodal Event Parsing for Intelligent User Interfaces”, IUI Conference, (Jan. 2003),8 pages. |
Freed, Natalie “Toys Keeping in Touch: Technologies for Distance Play”, Retrieved from <<http://people.ischool.berkeley.edu/˜daniela/tei2010/gsc09e-freed.pdf>>, (Jan. 24, 2010),2 pages. |
Freeman, William et al., “Television Control by Hand Gestures”, International Workshop on Automatic Face and Gesture Recognition, (1995),pp. 179-183. |
Gonzalez, Barb “HDMI CEC”, Home Theater University [online] Retrieved from the Internet:<URL:http://www.hometheatre.com/hookmeup/208hook>, (Mar. 24, 2008),3 pages. |
Granieri, John P., et al., “Simulating Humans in VR”, The British Computer Society, Academic Press, (Oct. 1994),15 Pages. |
Grunder, Alexander “Updated: Xbox 360 Kinect Hand Gesture Media Controls, Voice Control, TV Video Chat.”, eHomeUpgrade [online] retrieved from the internet:<URL:http://www.ehomeupgrade.com/2010/06/14/updated-xbox-360-kinect-hand-gesture-media-controls-voice-control-tv-video-chat/>, (Jun. 14, 2010),8 pages. |
Guiard, Yves “Asymmetric Division of Labor in Human Skilled Bimanual Action: The Kinematic Chain as a Model”, Journal of Motor Behavior, vol. 19 Issue 4, (1987),486-517. |
Guler, Sadiye Z., “Split and Merge Behavior Analysis and Understanding Using Hidden Markov Models”, (Oct. 8, 2002),21 pages. |
Hardin, Winn “Machine Vision Makes the Leap to Consumer Gaming”, Machine Vision Online, retrieved from <<http://www.machinevisiononline.org/vision-resources-details.cfm?content—id=2398>> on Mar. 14, 2011,(Dec. 8, 2010),3 pages. |
Hasegawa, Shoichi et al., “Human-Scale Haptic Interaction with a Reactive Virtual Human in a Real-Time Physics Simulator”, ACM Computers in Entertainment, vol. 4, No. 3, (Jul. 2006),12 Pages. |
He, Lei “Generation of Human Body Models”, University of Auckland, New Zealand (Apr. 2005),111 Pages. |
Hongo, Hitoshi et al., “Focus of Attention for Face and Hand Gesture Recognition Using Multiple Cameras”, 4th IEEE International Conference on Automatic Face and Gesture Recognition, Grenoble, France, (Mar. 2000),pp. 156-161. |
Horvitz, Eric “Principles of Mixed-Initiative User Interfaces”, Proceedings of CHI, (1999),8 pages. |
Horvitz, Eric et al., “A Computational Architecture for Conversation”, Proceedings of the Seventh International Conference on User Modeling, (1999),pp. 201-210. |
Hourcade, Juan P., “Architecture and Implementation of Java Package for Multiple Input Devices (MID)”, HCIL Technical Report No. 99-08 (May 1999); http://www.cs.umd.edu/hcil, (May 1999),7 pages. |
Isard, Michael et al., “Condensation—Conditional Density Propagation for Visual Tracking”, International Journal of Computer Vision 29(1), Netherlands, (1998),pp. 5-28. |
Jacko, “HDI Dune Prime 3.0 Part 2.”, Retrieved from the internet: <URL:http://www.jacko.my/2010/06/hdi-dune-prime-30-part-2.html>, (Jun. 19, 2010),15 pages. |
Jojic, Nebojsa et al., “Detection and Estimation of Pointing Gestures in Dense Disparity Maps”, Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition, (2000),pp. 1000-1007. |
Kabbash, P et al., “The “Prince” Technique: Fitts' Law and Selection Using Area Cursors”, Proceedings of CHI'95, http://www.billbuxton.com/prince.html, (1995),pp. 273-279. |
Kanade, et al., “Development of Video-Rate Stereo Machine”, Proceedings of 94 ARPA Image Understanding Workshop, (1994),pp. 549-558. |
Kanade, Takeo et al., “A Stereo Machine for Video-rate Dense Depth Mapping and Its New Applications”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA,(1996),pp. 196-202. |
Kim, Song-Gook et al., “Multi-Touch Tabletop Interface Technique for HCI”, retrieved from <<http://210.119.33.7/apis6/paper/data/63-multi-touch%20tabl.pdf>> on Mar. 16, 2011,4 pages. |
Kjeldsen, Frederik “Visual Interpretation of Hand Gestures as Practical Interface Modality”, Ph.D. Dissertation, Columbia University Department of Computer Science, (1997),168 pages. |
Klompmaker, Florian “D5.1—State of the art analysis and recommendations on ‘Context Awareness’, ‘Human Computer Interaction’ and ‘Mobile Users Interfaces’”, Information Technology for European Advancement (ITEA), Local Mobile Services, Retrieved from the Internet:<URL:http://www.loms-itea.org/deliverables/LOMS—D5.1—v1.0.pdy>, (Jul. 2, 2007),55 pages. |
Kohler, Marcus “Technical Details and Ergonomical Aspects of Gesture Recognition applied in Intelligent Home Environments”, Germany, (1997),35 Pages. |
Kohler, Markus “Special Topics of Gesture Recognition Applied in Intelligent Home Environments”, In Proceedings of the Gesture Workshop, Germany, (1998),12 Pages. |
Kohler, Markus “Vision Based Remote Control in Intelligent Home Environments”, University of Erlangen-Nuremberg, Germany, (1996),8 Pages. |
Kolsch, Mathias et al., “Vision-Based Interfaces for Mobility”, Retrieved from <<http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1331713>>, (Aug. 22, 2004),9 pages. |
Kwon, et al., “Combining Body Sensors and Visual Sensors for Motion Training”, Computer Graphics Laboratory, http://graphics.ethz.ch/˜dkwon/downloads/publications/ace05—ace.pdf, Downloaded 2009,(2005),pp. 1-8. |
Latoschik, Marc E., “A User Interface Framework for Multimodal VR Interactions”, ICMI'05, Trento, Italy, Oct. 4-6, 2005, available at <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.91.2941&rep=rep1&type=pdf>,(Oct. 4, 2005),8 pages. |
Le, Nguyen T., “EmuPlayer: Music Recommendation System Based on User Emotion Using Vital-sensor”, Thesis, Keio University, Available at <http://www.sfc.wide.ad.jp/thesis/2011/files/sunny-publish-thesis.pdf>,(2010),85 pages. |
Leal, Anamary et al., “Initial Explorations into the User Experience of 3D File Browsing”, Proceedings of HCI 2009, retrieved from <http://www.eecs.ucf. edu/isuelab/publications/pubs/p339-leal-3dfiles.pdf> on Nov. 15, 2011,(Sep. 2009),pp. 339-344. |
Li, Stan Z., et al., “A Near-Infrared Image Based Face Recognition System”, available at <<http://www.cbsr.ia.ac.cn/Li%20Group/papers/IR-Face-FG06.pdf>>,(Apr. 2006),6 pages. |
Livingston, Mark A., “Vision-based Tracking with Dynamic Structured Light for Video See-through Augmented Reality”, TheUniversity of NorthCarolina at ChapelHill, North Carolina, USA, (1998),145 Pages. |
Long, Jr., Allan C., et al., “Implications for a Gesture Design Tool”, Proceedings of CHI'99, (1999),pp. 40-47. |
Maes, Pattie et al., “The Alive System: Wireless, Full-body, Interaction with Autonomous Agents”, ACM Multimedia Systems, Special Issue on Multimedia and Multisensory Virtual Worlds, (Nov. 1995),17 pages. |
Maltby, John R., “Using Perspective in 3D File Management: Rotating Windows and Billboarded Icons”, Proceedings of the International Conference on Computer Graphics, Imaging and Visualisation (CGIV'06), available at <http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1663764>,(Jul. 28, 2006),8 pages. |
Martin, Benoit “VirHKey: A VIRtual Hyperbolic KEYboard with Gesture Interaction and Visual Feedback for Mobile Devices”, http://delivery.acm.org/10.1145/1090000/1085794/p99-martin.pdf?key1=1085794&key2=4890534611&coll=portal&dl=ACM&CFID=11111111&CFTOKEN=2222222, (Sep. 2005),8 pages. |
McCrae, James et al., “Exploring the Design Space of Multiscale 3D Orientation”, AVI '10, retrieved from <http://www.autodeskresearch.com/pdf/avi2010-final.pdf> on Nov. 15, 2011,(May 29, 2010),8 pages. |
Mignot, Christopher et al., “An Experimental Study of Future ‘Natural’ Multimodal Human-Computer Interaction”, Proceedings of INTERCHI93, (1993),pp. 67-68. |
Millan, Maria S., et al., “Unsupervised Defect Segmentation of Patterned Materials under NIR Illumination”, Journal of Physics: Conference Series 274 (2011) 012044, available at <<http://iopscience.iop.org/1742-6596/274/1/012044/pdf/1742-6596—274—1—012044.pdf>>,(2011),9 pages. |
Miyagawa, Ryohei et al., “CCD-Based Range-Finding Sensor”, IEEE Transactions on Electron Devices, vol. 44, No. 10, (Oct. 1997),pp. 1648-1652. |
Moeslund, Thomas B., et al., “A Survey of Computer Vision-Based Human Motion Capture”, Computer Vision and Image Understanding: CVIU, vol. 81, No. 3, (2001),pp. 231-269. |
Morency, Louis-Philippe et al., “Contextual Recognition of Head Gestures”, Trento, Italy http://delivery.acm.org/10.1145/1090000/1088470/p18—morency.pdf?key1=1088470&key2=8870534611&coll=portal&dl=ACM&CFID=11111111&CFTOKEN=2222222, 7 pages. |
Morrison, Gerald D., “A Camera-Based Touch Interface for Pervasive Displays”, Retrieved from <<http://ubicomp.algoritmi.uminho.pt/perdisplay/docs/Morrison-Camera%20Touch—SV—Rev1.pdf>> on Mar. 16, 2011,7 pages. |
Moscovich, Tomer “Multi-touch Interaction”, Brown University, CHI 2006, Apr. 22-27, 2006, Montreal, Quebec, Canada, (Apr. 22, 2006),4 pages. |
Moyle, et al., “Gesture Navigation: An Alternative ‘Back’ for the Future”, Proceedings of CHI'02, (2002),pp. 882-823. |
Nielsen, Michael et al., “A Procedure for Developing Intuitive and Ergonomic Gesture Interfaces for Man-Machine Interaction”, Technical Report CVMT 03-01, ISSN 1601-3646. CVMT, Aalborg University, (Mar. 2003),12 pages. |
Oh, Alice et al., “Evaluating Look-to-talk: A Gaze-Aware Interface in a Collaborative Environment”, CHI'02 (2002),650-651. |
Oviatt, Sharon “Ten Myths of Multimodal Interaction”, Communications of the ACM. vol. 42, No. 11, (Nov. 1999),8 pages. |
Paquit, Vincent et al., “Near-Infrared Imaging and Structured Light Ranging for Automatic Catheter Insertion”, Proceedings of SPIE vol. 6141, 61411T,(2006), available at <<http://www.cs.rpi.edu/˜chakrn2/work/catheter—plan/paguit—06.pdf>>,(2006),9 pages. |
Parrish, Kevin “Microsoft Does Want Core Games, FPS for Kinect”, Tom's Guide: Tech for Real Life [online], Retrieved from the Internet on Aug. 20, 2010: URL: <http://www.tomsguide.com/us/Core-Gamers-Kinect-FPS-Action.news-7195.html>., (Jun. 23, 2010),1 page. |
Pavlou, Paul A., et al., “Measuring the Effects and Effectiveness of Interactive Advertising: A Research Agenda”, Journal of Interactive Advertising, vol. 1, No. 1 (Fall 2000), Available at <http://scholar.google.co.in/scholar—url?hl=en&q=http://jiad.org/download%3Fp%3D6&sa=X&scisig=AAGBfm3He5PA4sgMGDXTyQuqaVQn4Q3nZw&oi=scholarr>,(Oct. 2000),pp. 62-78. |
Pavlovic, Vladimir et al., “Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, No. 7, (Jul. 1997),pp. 667-695. |
Qian, et al., “A Gesture-Driven Multimodal Interactive Dance System”, IEEE International Conference on Multimedia and Expo, Taipei, (Jun. 2004),pp. 1579-1582. |
Raymer, A “Gestures and Words: Facilitating Recovery in Aphasia”, The ASHA Leader, http://www.asha.org/about/publications/leader-online/archives/2007/070619/f070619a.htm, (Jun. 19, 2007),6 pages. |
Rigoll, Gerhard et al., “High Performance Real-Time Gesture Recognition Using Hidden Markov Models”, Gesture and Sign Language in Human-Computer Interaction, vol. LNAI 1371, Frohlich, ed., (1997),pp. 69-80. |
Rosenhahn, Bodo et al., “Automatic Human Model Generation”, University of Auckland (CITR), New Zealand, (2005),pp. 41-48. |
Sakir, Samit “Kinect is your personal trainer in EA Sports Active 2”, Gamerss [online] Retrieved from the Internet on Aug. 20, 2010: URL:<http://www.gamerss.co.uk/kinect-is-your-personal-trainer-in-ea-sports-active-2>., (Jul. 26, 2010),4 pages. |
Schick, Alexander et al., “Extending Touch: Towards Interaction with Large-Scale Surfaces”, ITS '09, Nov. 23-25, 2009, Banff, Alberta, Canada, available at <<http://www.iosb.fraunhofer.de/servlet/is/33404/urn—nbn—de—0011-n-1159494.pdf>>,(Nov. 23, 2009),8 pages. |
Schielel, Seth “A Home System Leaves Hand Controls in the Dust, Kinect by Microsoft Keeps You Entertained Hands Free”, The New York Times [online] Retrieved from the Internet:<URL:http://www.nytimes.com/2010/11/04/arts/television/04kinect.html>, (Nov. 4, 2010),3 pages. |
Shao, Jiang et al., “An Open System Architecture for a Multimedia and Multimodal User Interface”, Japanese Society for Rehabilitation of Persons with Disabilities (JSRPD), Japan, (Aug. 24, 1998),8 Pages. |
Sharma, et al., “Method of Visual and Acoustic Signal Co-Analysis for Co-Verbal Gesture Recognition”, U.S. Appl. No. 60/413,998, (Sep. 19, 2002),16 pages. |
Sharma, Rajeev M., et al., “Speech-Gesture Driven Multimodal Interfaces for Crisis Management”, Proceedings of IEEE Special Issue on Mulitmodal Human-Computer Interface, (2003),28 pages. |
Shen, Guobin et al., “Dita: Enabling Gesture-Based Human-Device Interaction using Mobile Phone”, Retrieved at <<:http://research.microsoft.com/en-us/people/jackysh/dita.pdf>>, (Oct. 1, 2010),pp. 1-14. |
Sheridan, Thomas et al., “Virtual Reality Check”, Technology Review, vol. 96, No. 7, (Oct. 1993),9 Pages. |
Shivappa, et al., “Person Tracking with Audio-Visual Cues Using the Iterative Decoding Framework”, IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance, AVSS 08, Santa Fe, NM, (Sep. 2008),260-267. |
Simeone, Luca et al., “Toys++ AR Embodied Agents as Tools to Learn by Building”, Retrieved from <<http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=05572598>>, (Jul. 5, 2010),2 pages. |
Stevens, Jane “Flights into Virtual Reality Treating Real World Disorders”, The Washington Post, Science Psychology, (Mar. 27, 1995),2 Pages. |
Tep, S. P., et al., “Web Site Quality Evaluation Combining Eyetracking and Physiologicial Measures to Self-Reported Emotions: An Exploratory Research”, Proceedings of Measuring Behavior 2008 (Maastricht, The Netherlands, Aug. 26-29, 2008), Retrieved from: <http://www.noldus.com/mb2008/individual—papers/FPS—eye—tracking/FPS—eye—tracking—Prom-Tep.pdf> on Oct. 4, 2011,(Aug. 26, 2008), pp. 224-225. |
Tilley, Steve “E3 09: Project Natal exposed”, Load This [online] Retrieved from the Internet:<URL:http://blogs.canoe.ca/loadthis/general/e3-09-project-natal-exposed/>, (Jun. 1, 2009),4 pages. |
Todd, Paul “Google Campaign Insights: Better Measurement for Display Advertising”, Retrieved from: <http://adwordsagency.blogspot.com/2009/10/campaign-insights-better-measurement.html> on Nov. 14, 2011,(Oct. 19, 2009),3 pages. |
Toyama, Kentaro et al., “Probabilistic Tracking in a Metric Space”, Eighth International Conference on Computer Vision, Vancouver Canada, vol. 2, (Jul. 2001),8 pages. |
Tresadern, Philip A., et al., “Visual Analysis of Articulated Motion”, DPhil Thesis, University of Oxford, Oxford, U.K., (Oct. 12, 2006),1-171. |
Vaucelle, Cati et al., “Picture This! Film Assembly Using Toy Gestures”, Retrieved from <<http://web.media.mit.edu/˜cati/PictureThis—Ubicomp.pdf>>, (2008),10 pages. |
Walker, et al., “Age Related Differences in Movement Control: Adjusting Submovement Structure to Optomize Performance”, Journals of Gerontology, (Jan. 1997),pp. 40-52. |
Welford, Alan T., “Signal, Noise, Performance, and Age.”, Human Factors, vol. 23, Issue 1, http://www.ingentaconnect.com/content/hfes/hf/1981/00000023/00000001/art0009, (1981),pp. 97-109. |
Wilson, Andrew et al., “GWindows: Towards Robust Perception-Based UI”, Microsoft Research, (2003),pp. 1-8. |
Wilson, et al., “Hidden Markov Models for Modeling and Recognizing Gesture Under Variation”, Hidden Markov Model: Applications in Computer Vision., T.Caelli, ed. World Scientific, (2001),36 pages. |
Worden, Aileen et al., “Making Computers Easier for Older Adults to Use: Area Cursors and Sticky Icons”, CHI 97, Atlanta Georgia, USA, (1997),pp. 266-271. |
Wren, Christopher et al., “Pfinder: Real-Time Tracking of the Human Body”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, No. 7, (Jul. 1997),pp. 780-785. |
Yakut, Isil D., et al., “User and Task Analysis of Multi-Level 3D File Browser”, Dept. of Computer Engineering, Bilkent University, Ankara, Turkey, retrieved from <http://www.cs.bilkent.edu.tr/˜cansin/projects/cs560-3dui/multi-level-3d-file-browser/3dui-report.pdf> on Nov. 15, 2011,4 pages. |
Yoda, Ikushi et al., “Utilization of Stereo Disparity and Optical Flow Information for Human Interaction”, Proceedings of the Sixth International Conference on Computer Vision, IEEE Computer Society, Washington D.C., USA, (1998),5 pages. |
Zhai, Shumin et al., “The “Silk Cursor”: Investigating Transparency for 3D Target Acquisition”, CHI 94, (1994),pp. 273-279. |
Zhang, Zhengyou “A Flexible New Technique for Camera Calibration”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, No. 11, (Nov. 2000),pp. 1330-1334. |
Zhang, Zhengyou “Flexible Camera Calibration by Viewing a Plane from Unknown Orientations”, Microsoft Research, (1999),8 pages. |
Zhao, Liang “Dressed Human Modeling, Detection, and Parts Localization”, The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, (2001),121 Pages. |
“Foreign Notice of Allowance”, Canadian Application No. 2775700, (Jan. 3, 2013),1 page. |
“Foreign Office Action”, Canadian Application No. 2775814, (Dec. 14, 2012),3 pages. |
“International Search Report and Written Opinion”, Application No. PCT/US2012/034641, (Nov. 30, 2012), 9 pages. |
“Non-Final Office Action”, U.S. Appl. No. 12/794,406, (Sep. 14, 2012), 17 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/309,589, (Dec. 18, 2012), 10 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/316,351, (Feb. 14, 2013), 16 pages. |
“Notice of Allowance”, U.S. Appl. No. 12/474,453, (Dec. 12, 2012), 8 pages. |
“Final Office Action”, U.S. Appl. No. 13/441,228, (Sep. 11, 2013), 15 pages. |
“Non-Final Office Action”, U.S. Appl. No. 12/972,837, (Jun. 26, 2013), 10 pages. |
“Notice of Allowance”, U.S. Appl. No. 13/093,621, (Aug. 21, 2013), 7 pages. |
“Notice of Allowance”, U.S. Appl. No. 13/309,859, (Sep. 4, 2013), 7 pages. |
“Restriction Requirement”, U.S. Appl. No. 13/482,867, (Sep. 6, 2013), 6 pages. |
“Restriction Requirement”, U.S. Appl. No. 13/114,359, (Sep. 10, 2013), 6 pages. |
“Final Office Action”, U.S. Appl. No. 12/794,406, (Apr. 22, 2013),14 pages. |
“Final Office Action”, U.S. Appl. No. 13/316,351, (Jul. 31, 2013), 20 pages. |
“Foreign Office Action”, European Patent Application No. 12195349.1, (May 10, 2013), 5 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/093,621, (Jun. 20, 2013), 7 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/363,689, (Jul. 26, 2013),18 pages. |
“Response to Non-Final Office Action”, U.S. Appl. No. 12/794,406, (Feb. 14, 2013), 12 pages. |
“European Search Report”, European Patent Application No. 12195349.1, (Apr. 22, 2013), 3 pages. |
“Final Office Action”, U.S. Appl. No. 13/309,859, (May 15, 2013),13 pages. |
“Foreign Office Action”, European Patent Application No. 12194891.3, (Apr. 24, 2013), 5 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/025,180, (Apr. 5, 2013),17 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/441,228, (Mar. 20, 2013),12 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/488,046, (Jun. 13, 2013), 8 pages. |
“Recognizing Visual Focus of Attention from Head Pose in Natural Meetings”, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics—Special Issue on Human Computing, vol. 39, Issue 1, (Feb. 2009), 36 pages. |
“Restriction Requirement”, U.S. Appl. No. 13/488,046, (May 2, 2013), 5 pages. |
“Supplementary European Search Report”, European Patent Application No. 12194891.3, (Apr. 4, 2013), 3 pages. |
Asteriadis, Stylianos et al., “Estimation of Behavioral User State based on Eye Gaze and Head Pose—Application in an e-Learning Environment”, Journal of Multimedia Tools and Applications, vol. 41, Issue 3, (Feb. 2009), 25 pages. |
Ba, Sileye O., et al., “Head Pose Tracking and Focus of Attention Recognition Algorithms in Meeting Rooms”, Proceedings of the 1st International Evaluation Conference on Classification of Events, Activities and Relationships, (Apr. 6, 2006),12 pages. |
Boser, Bernhard E., et al., “A Training Algorithm for Optimal Margin Classifiers”, Proceedings of the Fifth Annual Workshop on Computational Learning Theory, (Jul. 27, 1992), 9 pages. |
Bradley, Margaret M., et al., “Measuring Emotion: The Self-Assessment Manikin and the Semantic Differential”, In Journal of Behavior Therapy and Experimental Psychiatry, vol. 25, Issue 1, (Mar. 1994),11 pages. |
Chang, Chih-Chung et al., “LIBSVM: A Library for Support Vector Machines”, retrieved from <http://www.csie.ntu.edu.tw/˜cjlin/libsvm/> on Apr. 1, 2013, 4 pages. |
El Kaliouby, Rana et al., “Real Time Inference of Complex Mental States from Facial Expressions and Head Gestures”, Proceedings of Conference on Computer Vision and Pattern Recognition Workshop, (Jun. 27, 2004), 20 pages. |
Grace, Richard et al., “A Drowsy Driver Detection System for Heavy Vehicles”, Proceedings of the 17th Digital Avionics Systems Conference, vol. 2, (Oct. 31, 1998), 8 pages. |
Guyon, Isabelle et al., “An Introduction to Variable and Feature Selection”, In Journal of Machine Learning Research, vol. 3, (Mar. 2003), pp. 1157-1182. |
Kapoor, Ashish et al., “Multimodal Affect Recognition in Learning Environments”, Proceedings of the 13th Annual ACM International Conference on Multimedia, (Nov. 6, 2005), 6 pages. |
Liang, Lin et al., “Face Alignment via Component-Based Discriminative Search”, Computer Vision, ECCV 2008, Lecture Notes in Computer Science vol. 5303, (2008),14 pages. |
McDuff, Daniel “Affective Storytelling: Automatic Measurement of Story Effectiveness from Emotional Responses Collected over the Internet”, PhD Thesis, retrieved from <http://web.media.mil.edu/˜djmcduff/documents/McDuff—Thesis—Proposal.pdf>pdf>>,(Jun. 6, 2012),16 pages. |
McDuff, Daniel et al., “Crowdsourcing Facial Responses to Online Videos”, Proceedings of the IEEE Transactions on Affective Computing, vol. 3, Issue 4, (Oct. 2012), pp. 456-468. |
McDuff, et al., “AffectAura: An Intelligent System for Emotional Memory”, In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Retrieved from <http://www.affectiva.com/assets/Q-Sensor-Microsoft-Publication.pdf>,(May 5, 2012),10 pages. |
Op Den Akker, Rieks et al., “Supporting Engagement and Floor Control in Hybrid Meetings”, In Cross-Modal Analysis of Speech, Gestures, Gaze, and Facial Expressions, (Jul. 2009),15 pages. |
Peacock, James et al., “Which Broadcast Medium Better Drives Engagement? Measuring the Powers of Radio and Television with Electromyography and Skin-Conductance Measurements”, In Journal of Advertising Research, vol. 51, Issue 4, (Dec. 2011), 8 pages. |
Poels, Karolien et al., “How to Capture the Heart? Reviewing 20 Years of Emotion Measurement in Advertising”, In the Journal of Advertising Research, vol. 46, Issue 1, (Mar. 2006), 48 pages. |
Viola, Paul et al., “Robust Real-Time Face Detection”, In International Journal of Computer Vision, vol. 57, Issue 2, (May 2004),18 pages. |
Voit, Michael et al., “Deducing the Visual Focus of Attention from Head Pose Estimation in Dynamic Multi-View Meeting Scenarios”, Proceedings of the 1oth International Conference on Multimodal Interfaces, (Oct. 20, 2008), 8 pages. |
Wedel, Michel et al., “Eye Fixations on Advertisements and Memory for Brands: A Model and Findings”, Journal of Marketing Science, vol. 19, Issue 4, (Oct. 2000), pp. 297-312. |
Wood, Orlando “Using Faces: Measuring Emotional Engagement for Early Stage Creative”, In ESOMAR, Best Methodology, Annual Congress, (Sep. 19, 2007), 29 pages. |
Zhang, Zhenqiu et al., “Head Pose Estimation in Seminar Room Using Multi View Face Detectors”, Proceedings of the 1st International Evaluation Conference on Classifications of Events, Activities and Relationships, (Mar. 30, 2006), 7 pages. |
“International Search Report”, Mailed Date: Jul. 5, 2013, Application No. PCT/US2013/035047, Filed Date: Apr. 3, 2013, pp. 10. |
“Notice of Allowance”, U.S. Appl. No. 12/972,837, Oct. 11, 2013, 10 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/118,884, Dec. 3, 2013, 10 pages. |
“Final Office Action”, U.S. Appl. No. 13/488,046, Dec. 10, 2013, 12 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/482,867, Nov. 5, 2013, 13 pages. |
“International Search Report and Written Opinion”, Application No. PCT/US2013/035348, Sep. 25, 2013, 16 pages. |
“Corrected Notice of Allowance”, U.S. Appl. No. 13/309,859, Oct. 29, 2013, 3 pages. |
“Restriction Requirement”, U.S. Appl. No. 13/039,024, Oct. 1, 2013, 5 pages. |
“Restriction Requirement”, U.S. Appl. No. 13/114,359, Dec. 18, 2013, 6 pages. |
“Response to Office Action”, U.S. Appl. No. 12/794,406, Jul. 22, 2013, 9 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/039,024, Apr. 7, 2014, 12 pages. |
“Final Office Action”, U.S. Appl. No. 13/488,046, May 1, 2014, 12 pages. |
“Final Office Action”, U.S. Appl. No. 13/025,180, Mar. 14, 2014, 21 pages. |
“Final Office Action”, U.S. Appl. No. 13/363,689, Feb. 11, 2014, 18 pages. |
“Final Office Action”, U.S. Appl. No. 13/482,867, Feb. 21, 2014, 15 pages. |
“International Search Report and Written Opinion”, Application No. PCT/US2013/038710, Jan. 8, 2014, 18 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/411,859, Mar. 11, 2014, 13 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/488,046, Mar. 14, 2014, 11 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/866,699, Feb. 7, 2014, 15 pages. |
“Notice of Allowance”, U.S. Appl. No. 13/118,884, Feb. 4, 2014, 7 pages. |
“Advisory Action”, U.S. Appl. No. 13/025,180, Jul. 3, 2014, 3 pages. |
“Final Office Action”, U.S. Appl. No. 13/411,859, Aug. 8, 2014, 16 pages. |
“International Search Report and Written Opinion”, Application No. PCT/US2013/039591, Aug. 1, 2014, 10 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/316,351, Jun. 19, 2014, 23 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/331,886, Jun. 19, 2014, 18 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/363,689, Sep. 15, 2014, 21 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/488,046, Jul. 23, 2014, 12 pages. |
“Non-Final Office Action”, U.S. Appl. No. 14/108,008, Aug. 14, 2014, 16 pages. |
“Final Office Action”, U.S. Appl. No. 12/794,406, Jun. 4, 2014, 14 pages. |
“Foreign Office Action”, CN Application No. 201110159923.8, May 22, 2014, 10 pages. |
“Foreign Office Action”, CN Application No. 201110159923.8, Sep. 2, 2013, 13 pages. |
“Non-Final Office Action”, U.S. Appl. No. 12/794,406, Sep. 6, 2013, 13 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/114,359, Oct. 20, 2014, 7 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/441,228, Oct. 2, 2014, 18 pages. |
“Notice of Allowance”, U.S. Appl. No. 13/482,867, Sep. 30, 2014, 17 pages. |
“Summons to Attend Oral Proceedings”, EP Application No. 12194891.3, Sep. 17, 2014, 7 Pages. |
“Summons to Attend Oral Proceedings”, EP Application No. 12195349.1, Sep. 17, 2014, 7 Pages. |
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20130268954 A1 | Oct 2013 | US |