Patent Publication Number: US-9842107-B2

Title: Methods, systems, and products for language preferences

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. application Ser. No. 14/827,278 filed Aug. 15, 2015 and since issued as U.S. Pat. No. 9,507,770, which is a continuation of U.S. application Ser. No. 13/669,500 filed Nov. 6, 2012 and since issued as U.S. Pat. No. 9,137,314, with all applications incorporated herein by reference in their entireties. 
    
    
     BACKGROUND 
     Video and audio processing require intensive operations. Processors and memory may be taxed and even overwhelmed when executing image and audio instructions. Indeed, in today&#39;s mobile environment, video and audio processing can waste limited processing and battery resources. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
       The features, aspects, and advantages of the exemplary embodiments are better understood when the following Detailed Description is read with reference to the accompanying drawings, wherein: 
         FIGS. 1-3  are simplified schematics illustrating an environment in which exemplary embodiments may be implemented; 
         FIGS. 4-5  are more detailed block diagrams illustrating the operating environment, according to exemplary embodiments; 
         FIGS. 6-7  are schematics illustrating personalized feedback, according to exemplary embodiments; 
         FIGS. 8-9  are schematics illustrating language capabilities, according to exemplary embodiments; 
         FIG. 10  is another detailed schematic illustrating the operating environment, according to exemplary embodiments; 
         FIGS. 11-12  are schematics illustrating position coordinates, according to exemplary embodiments; 
         FIGS. 13-14  are schematics illustrating social interactions, according to exemplary embodiments; 
         FIGS. 15-16  are flowcharts illustrating a method for voice control, according to exemplary embodiments; 
         FIG. 17  is a schematic illustrating a processor-controlled device; according to exemplary embodiments; 
         FIG. 18  depicts still more operating environments for additional aspects of the exemplary embodiments; 
         FIGS. 19-21  are schematics further illustrating various client devices for presenting personalized feedback, according to exemplary embodiments; and 
         FIG. 22  is a block diagram further illustrating the client device, according to exemplary embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     The exemplary embodiments will now be described more fully hereinafter with reference to the accompanying drawings. The exemplary embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. These embodiments are provided so that this disclosure will be thorough and complete and will fully convey the exemplary embodiments to those of ordinary skill in the art. Moreover, all statements herein reciting embodiments, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future (i.e., any elements developed that perform the same function, regardless of structure). 
     Thus, for example, it will be appreciated by those of ordinary skill in the art that the diagrams, schematics, illustrations, and the like represent conceptual views or processes illustrating the exemplary embodiments. The functions of the various elements shown in the figures may be provided through the use of dedicated hardware as well as hardware capable of executing associated software. Those of ordinary skill in the art further understand that the exemplary hardware, software, processes, methods, and/or operating systems described herein are for illustrative purposes and, thus, are not intended to be limited to any particular named manufacturer. 
     As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless expressly stated otherwise. It will be further understood that the terms “includes,” “comprises,” “including,” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being “connected” or “coupled” to another element, it can be directly connected or coupled to the other element or intervening elements may be present. Furthermore, “connected” or “coupled” as used herein may include wirelessly connected or coupled. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items. 
     It will also be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first device could be termed a second device, and, similarly, a second device could be termed a first device without departing from the teachings of the disclosure. 
       FIGS. 1-3  are simplified schematics illustrating an environment in which exemplary embodiments may be implemented.  FIG. 1  illustrates a client device  20  communicating with a server  22  via a communications network  24 . The client device  20  is illustrated as a smart phone  26 , but the client device  20  may be a computer, a set-top receiver, or any other processor-controlled device. Whatever the client device  20 , the client device  20  interfaces with a vision system  28 , an audio input system  30 , and an audio output system  32 . The client device  20  uses the vision system  28  to scan a physical space  34  and to obtain a stream  36  of image data. The stream  36  of image data is analyzed to aim the audio input system  30  and/or the audio output system  32 . That is, the client device  20  aligns its audio input system  30  and/or its audio output system  32  to people and/or objects recognized in the physical space  34 . 
     Here, though, analysis is cloud-based. The client device  20  uploads the stream  36  of image data to the server  22  for analysis. The server  22  then analyzes the stream  36  of image data to recognize people and objects of interest. As the reader may know, image recognition may require significant processing and memory resources. Exemplary embodiments thus relieve the client device  20  of these intensive operations. The client device  20  routes, sends, or forwards the stream  36  of image data to the server  22  for analysis. The server  22  analyzes the stream  36  of image data and recognizes people and/or objects of interest. 
     The server  22  generates instructions for the client device  20 . Once the server  22  recognizes the people or objects of interest, the server  22  may then instruct the client device  20  where to aim the audio input system  30 . As  FIG. 2  illustrates, for example, the server  22  may determine a position  38  of a recognized person in a room under surveillance. The server  22  may then send the position  38  of the person back to the client device  20 . When the client device  20  receives the position  38 , the client device  20  may then point the audio input system  30  toward the position  38  of the recognized person. The audio input system  30  thus receives audio from the direction of the position  38  determined by the server  22 . The client device  20  may thus capture a stream  40  of audio data from the direction of the position  38  of the recognized person. 
       FIG. 3  illustrates more network reliance. The stream  40  of audio data may be analyzed for its semantic content, such as spoken commands and phrases. Audio processing, though, may also require significant processing and memory resources. Exemplary embodiments may thus again relieve the client device  20  of these intensive operations. The client device  20  may thus upload the stream  40  of audio data to the server  22  for analysis. The server  22  analyzes the stream  40  of audio data to determine any semantic content. The server  22  may then generate audible feedback  42 . The feedback  42  may be an answer, phrase, or response to the semantic content contained within the stream  40  of audio data. Whatever the feedback  42 , the server  22  sends the feedback  42  to the client device  20 . 
     As  FIG. 3  illustrates, the client device  20  may beam the feedback  42  to the recognized person. Because the server  22  has determined the position  38  of the recognized person, the client device  20  may aim the feedback  42  to the same position  38 . The client device  20  may thus aim its audio output system  32  to the same position  38  within the physical space  34 . The client device  20  may thus instruct the audio output system  32  to point toward the position  38  of the recognized person. The client device  20  may thus align the feedback  42  beam of sound in the direction of the position  38 . 
     Exemplary embodiments thus establish personalized, bi-directional, web-based communication. Conventional natural speech systems require intensive processing capabilities that can bog down modern mobile devices. Exemplary embodiments, though, offload intensive video and audio processing to the web-based server  22 . Mobile client devices, such as smart phones and tablet computers, may thus provide natural, synthesized speech without powerful hardware componentry and intensive battery consumption. The client device  20  merely aims its audio input system  30  and/or its audio output system  32  according to the position  38  calculated by the server  22 . 
       FIGS. 4-5  are more detailed block diagrams illustrating the operating environment, according to exemplary embodiments. Here the client device  20  has a processor  50  (e.g., “μP”), application specific integrated circuit (ASIC), or other component that executes a client-side algorithm  52  stored in a memory  54 . The server  22  may also have a processor  56  (e.g., “μP”), application specific integrated circuit (ASIC), or other component that executes a server-side algorithm  58  stored in a memory  60 . The client-side algorithm  52  and the server-side algorithm  58  are programming, code, or instructions that cooperate in a client-server relationship to provide personalized and synthesized bi-directional communication. The client-side algorithm  52  instructs the processor  50  to receive the stream  36  of image data from the vision system  28 . The stream  36  of image data may comprise still images and/or video of the physical space  34 . The client-side algorithm  52  instructs the processor  50  to route the stream  36  of image data into and along the communications network  24  to the network address associated with the server  22 . 
     The server  22  analyzes the stream  36  of image data. When the server  22  receives the stream  36  of image data, the server-side algorithm  58  instructs the server  22  to perform an image analysis  70 . The image analysis  70  is executed to recognize one or more persons and/or objects in the stream  36  of image data. Any image analysis  70  may be performed, such as facial recognition  72  of a face in the stream  36  of image data. Regardless, once a person (or object) of interest is recognized, the server  22  may then determine the position  38  of the recognized person relative to the physical space  34  shown in the stream  36  of image data. The server  22  sends the position  38  to the network address associated with the client device  20 , and the client device  20  further refines the vision system  28  to the position  38  of the recognized person. 
     As  FIG. 5  illustrates, the client device  20  also sends the stream  40  of audio data. The client device  20  interfaces with its audio input system  30  to generate the stream  40  of audio data. As the position  38  is known, the client device  20  may also align the audio input system  30  to the same position  38 . The position  38  may thus serve as positional feedback when aiming the audio input system  30 . When the stream  40  of audio data is generated, the client-side algorithm  52  instructs the processor  50  to route the stream  40  of audio data into and along the communications network  24  to the network address associated with the server  22 . 
     The server  22  analyzes the stream  40  of audio data. When the server  22  receives the stream  40  of audio data, the server-side algorithm  58  instructs the server  22  to perform a speech analysis  80 . The speech analysis  80  is executed to recognize the semantic content contained within the stream  40  of audio data. While any speech analysis  80  may be performed, speech-to-text translation may be preferred due to its availability and inexpensive cost. Regardless, once the semantic content is determined, the feedback  42  is generated. The server  22  routes the feedback  42  into and along the communications network  24  to the network address associated with the client device  20 . 
     The client device  20  then aims its audio output system  32 . When the feedback  42  is received, the client device  20  also aligns its audio output system  32  to the position  38  determined by the server  22 . The client device  20  thus points a beam of sound to the position  38  of the recognized person, thus providing the personalized audio feedback  42 . 
     Exemplary embodiments isolate the feedback  42 . Because the audio feedback  42  is beamed to the position  38  of the recognized person, the audio feedback  42  is personal and unheard by others in the same physical space  34 . That is, even if a crowd of people mingle in a room, exemplary embodiments may narrowly beam the audio feedback  42  to only the location of the recognized person. The recognized person, for example, may ask questions to the client device  20 , and the client device  20  aims an audible answer back to the recognized person, without sending audio cues to the crowd. As the recognized person moves about the room, exemplary embodiments may track the movements, listen in, and provide the personalized feedback  42  based on audible interactions with the client device  20 . Exemplary embodiments thus establish a bidirectional communication channel that follows the movements of the recognized person. 
       FIGS. 6-7  are schematics further illustrating the personalized feedback  42  in a crowd, according to exemplary embodiments. Here exemplary embodiments may be applied to multiple, recognized people in a crowd. That is, as the vision system  28  pans a room, the server  22  may recognize multiple people in the physical space  34 . The server  22  may thus determine multiple positions  38 , with each position  38  (illustrated as P N ) corresponding to each recognized person in the physical space  34 . The client device  20  may thus be instructed to aim its audio input system  30  to capture multiple streams  40  of audio data. The audio input system  30 , for example, may have an array of microphones, with each individual microphone individually aimed to the respective position  38  of each recognized person. The client device  20  may thus send or route separate streams  40  of audio data to the server  22  for analysis. The server  22  thus performs the speech analysis  80  for each stream  40  of audio data. 
     As  FIG. 7  illustrates, the feedback  42  is generated. Because the multiple streams  40  of audio data were received, the server  22  may generate multiple streams of the audio feedback  42 . Each feedback  42  is responsive to the semantic content of each corresponding stream  40  of audio data. The server  22  may thus send multiple streams of the audio feedback  42  to the client device  20 . If the audio output system  32  includes multiple directional speakers, then the client device  20  may dedicate each speaker to one of the recognized people. The client device  20  may thus instruct the audio output system  32  to aim each dedicated speaker to the direction of the position  38  of each recognized person. The audio output system  32  thus outputs one of the multiple streams of the audio feedback  42  to each recognized person. As the recognized people mingle about the physical space  34 , exemplary embodiments may provide personal feedback  42  that is unheard by others in the crowd. 
       FIGS. 8-9  are schematics illustrating language capabilities, according to exemplary embodiments. As the audio output system  32  may output different streams of the audio feedback  42 ,  FIGS. 8-9  illustrate how exemplary embodiments may beam preferred languages to recognized users. Exemplary embodiments, in other words, may narrowly beam different audio feedback  42  to each recognized person, such as each person&#39;s preferred language. 
     Suppose, for example, multiple people watch a movie in the same media room. As  FIG. 8  illustrates, the client device  20  interfaces with a display device  90  to project or display the movie to the physical space  34 . As the display device  90  presents the movie or other content, the vision system  28  scans the physical space  34 . The client device  20  sends or routes the stream  36  of image data to the server  22  (as earlier paragraphs explained), along with content information  92 . The content information  92  describes the content being displayed by the display device  90 , such as a title of a movie or other program being displayed. The server-side algorithm  58  performs or invokes the image analysis  70  and determines the positions  38  of the recognized users within the physical space  34 , as earlier paragraphs explained. The server-side algorithm  58  shares the positions  38  with the client device  20 , also as earlier paragraphs explained. 
     As  FIG. 9  illustrates, the server  22  may then determine preferred language tracks. When the server-side algorithm  58  recognizes a user, the server-side algorithm  58  may query for the user&#39;s corresponding profile  94 . The server  22  queries a database  96  of profiles for an identity of the recognized user. The database  96  of profiles is illustrated as being locally stored in the server  22 , but the database  96  of profiles may be remotely stored and accessed from any network location within the communications network  24 . Regardless, as each user is recognized, the server  22  may associate the recognized face with the corresponding name. The server  22  queries the database  96  of profiles for each recognized user&#39;s name, and the server  22  retrieves the corresponding profile  94 . Each user&#39;s corresponding profile  94  may store a language preference  98 . The server-side algorithm  58  retrieves each recognized user&#39;s language preference  98 . The server-side algorithm  58  may then send a language instruction  100  to a database  102  of content. The language instruction  100  instructs the database  102  of content to retrieve a language track  104  associated with the content information  92 . If the recognized user prefers German, for example, then the database  102  of content retrieves a German language track  104 . The language instruction  100  also instructs the database  102  of content to route the language track  104  to the network address associated with the client device  20 . 
     The client device  20  thus aims the user&#39;s preferred language. As this disclosure explains, the server  22  has determined the respective positions  38  of the recognized users. The client device  20  may thus instruct the audio output system  32  to dedicate and aim its output devices to the respective positions  38  of each recognized user. That is, as the display device  90  displays the movie, the audio output system  32  beams each user&#39;s preferred language track  104  to their respective position  38 . The recognized user thus enjoys the movie according to her language preference  98 . 
     Exemplary embodiments are especially helpful in multi-language environments. As multiple people view the movie, exemplary embodiments may beam different language tracks to different people. The image analysis  70  may be used to recognize several different people within a presentation space of the display device  90 . Each different, recognized person may have a different language preference  98 . One person may prefer an English language track  104 , another person may prefer a German language track  104 , and yet another person may prefer a Spanish language track  104 . As the server  22  consults each recognized person&#39;s profile  94 , the server  22  may set-up each person&#39;s different language track  104 . The multiple language tracks  104  may be streamed to the client device  20 , and the audio output system  32  beams each person&#39;s preferred language track  104  to their respective positions  38 . The multiple people thus enjoy the same, common visual content, but each person may enjoy a different, but personal, language track  104 . Because each person&#39;s language track  104  is isolated to their respective position  38 , the other language tracks  104  are unheard by the other viewers in the same physical space  34 . 
     Exemplary embodiments may be applied to other scenarios. As the users view the content on the display device  90 , suppose one of the users wishes to call a friend. Even though separate channels have been established with the server  22 , one of the users may audibly utter a command to “call” to a “name.” The client device  20  and the server  22  may cooperate to initiate the call, while the client device  20  continues receiving and displaying content on the display device  90 . So, in parallel, one of the users may speak commands to change the displayed content (such as “change channel”), while the other viewing user converses over the established call. The client device  20  and the server  22  may thus also cooperate to suppress cross-talk, thus reducing or eliminating the channel change commands from compromising the other user&#39;s call (and vice versa). Further, the client device  20  and the server  22  also cooperate to project or beam different audio to each user, thus isolating the call from the other&#39;s commands. Exemplary embodiments, in other words, directionally deliver each person&#39;s personal audio without mixing. 
     Input audio may thus be received. As the users enjoy the movie, exemplary embodiments may still interpret their speech. As this disclosure explains, the client device  20  may also aim the audio input system  30 . As the recognized users enjoy the content and their respective language track  104 , the client device  20  may also aim the audio input system (illustrated as reference numeral  30 ) to the different positions  38  of the viewers. As this disclosure explained, the audio input system  30  may have individual microphones that are individually aimed to the position  38  of each recognized viewer. The client device  20  may thus receive and forward the separate streams  40  of audio data to the server  22  for analysis, as  FIG. 6  illustrates. Any viewer may thus issue a spoken command that is recognized and executed. The server  22  may also generate the audio feedback  42  in response to any command or question in any stream  40  of audio data. The feedback  42  may also be aligned to the position  38  of the speaker, thus ensuring the feedback  42  is unheard by other viewers in the physical space  34 . 
       FIG. 10  is another detailed schematic illustrating the operating environment, according to exemplary embodiments.  FIG. 10  illustrates the server  22  receiving the stream  36  of image data and the stream  40  of audio data. The server  22  performs the image analysis  70  to recognize one or more people from the stream  36  of image data. When a particular face is recognized, the server  22  performs a position analysis  110 . The server  22  thus determines the position  38  of the recognized face from the stream  36  of image data. 
     The client device  20  responds to the position  38 . Once the position  38  of the recognized face is determined, the server  22  may send the position  38  to the client device  20 . The client device  20  may use the position  38  to orient the vision system  28  and the audio input system  30 . That is, the client device  20  aims or aligns its cameras and microphones according to the position  38  determined by the server  22 . As the recognized face moves, the position  38  may be repeatedly determined as feedback to the client device  20 . The client device  20  is thus able to train its cameras and microphones to the roving, recognized face. 
     The server  22  also generates the feedback  42 . When the server  22  receives the stream  40  of audio data, the server  22  calls or invokes the speech analysis  80 . The speech analysis  80  provides a real-time interaction with any recognized user. The server  22  may process the stream  40  of audio data to suppress all but the recognized user&#39;s audio input. If multiple people are recognized, exemplary embodiments may simultaneously track and listen to all the recognized users present in the room, through multiple instantiations applied as one instance per individual user. The speech analysis  80  may perform a speech-to-text translation to convert the stream  40  of audio data into text. The server  22  may then send or feed the text to a dialogue manager  112 . The dialogue manager  112  analyzes the text for recognized commands, phrases, and other semantic content. The dialogue manager  112  generates the acoustic feedback  42 . The dialogue manager  112  may perform a text-to-speech translation that converts the acoustic feedback  42  into speech. However the feedback  42  is obtained, the feedback  42  is routed back to the client device  20  for directional delivery. The client device  20  pinpoints its audio output system  32  to the position  38  of the recognized user, thus delivering the personalized feedback  42  to the user. Because the feedback  42  directed to the position  38 , though, the feedback  42  remains mostly inaudible to other users in the same room. The feedback  42  is based on audio signals that are largely inaudible to the rest of the users because of narrow (highly-directive) beaming of audio. The server  22  may thus deliver audio content that may be different for the individual users, as dictated by their personal profile  94 . 
       FIGS. 11-12  are schematics illustrating position coordinates, according to exemplary embodiments. Here the server  22  may calculate coordinates  120  for aiming the audio input system  30  and the audio output system  32 . As the above paragraphs explained, the server  22  may use the position analysis  110  to determine the position  38  of the recognized person within the physical space  34 . The position  38  of the user, though, may need to be related to a location of audio input system  30  and the audio output system  32 . As the reader may understand, the vision system  28 , the audio input system  30 , and the audio output system  32  may have different installation locations. A media theater, for example, may have directional microphones and speakers dispersed throughout the physical space  34  for optimum audio effects. The position  38  of the recognized user, then, may need to be vectorized for alignment of the audio input system  30  and the audio output system  32 . 
       FIG. 11  thus illustrates vectorizations. When the server  22  performs the position analysis  110 , the position  38  of the recognized user may be expressed as a position vector  122  having both direction and depth within the physical space  34 . The position  38  of the recognized user, in other words, may be expressed as a three-dimensional vector in a coordinate system. Once the position vector  122  is determined, the server  22  may consult the database  96  of profiles for the profile  94  of the client device  20 . The profile  94  of the client device  20  stores information describing installation coordinates  124  associated with the client device  20 . The profile  94 , for example, may describe the installation locations of the audio input system  30  and the audio output system  32  relative to the installation location of the vision system  28  within the physical space  34 . Each microphone in the audio input system  30 , for example, may have its own associated installation coordinates  124 . Each speaker in the audio output system  32  may also have its own associated installation coordinates  124 . A camera of the vision system  28  may, likewise, have its own associated installation coordinates  124 . So, when the server  22  determines the position vector  122  associated with the recognized user, the server  22  may also relate the position vector  122  to the installation coordinates  124 . 
       FIG. 11  also illustrates vector intersections  126 . Once the position vector  122  is determined, the server  22  may calculate an input vector  128  associated with a microphone in the audio input system  30 . The input vector  128  describes a direction in which the corresponding microphone is pointed to obtain the stream  40  of audio data. As  FIG. 11  graphically illustrates, the input vector  128  intersects the position vector  122  at the determined position  38  of the recognized person. The server  22  may thus calculate the input vector  128  from the installation coordinates  124  of the microphone to the current position  38  of the recognized user. 
     The server  22  may thus send an input alignment command  130  to the client device  20 . The input alignment command  130  routes along the communications network (illustrated as reference numeral  24  in  FIG. 1 ) to the network address associated with the client device  20 . The input alignment command  130  instructs the client device  20  to aim the corresponding microphone in a direction of the input vector  128 . A motor control mechanism may thus move and aim the microphone to the direction of the input vector  128 , thus obtaining audio that is converted to the stream  40  of audio data. The client device  20  forwards the stream  40  of audio data to the server  22  for analysis, as this disclosure explains. 
     As  FIG. 12  illustrates, the server  22  similarly aligns the audio output system  32 . Once the position vector  122  is determined, the server  22  may consult the database  96  of profiles for the installation coordinates  124  associated with the audio output system  32 . The server  22 , for example, retrieves the installation coordinates  124  associated with a speaker of the audio output system  32 . Knowing the position vector  122 , the server  22  determines an output vector  140  from the speaker&#39;s installation coordinates  124  to the current position  38  of the recognized user. The output vector  140  thus describes a direction in which the corresponding speaker is pointed to beam the feedback  42  to the current position  38  of the recognized person. The output vector  140  intersects the position vector  122  at the determined position  38  of the recognized person. 
     The server  22  may thus send an output alignment command  142 . The output alignment command  142  instructs the client device  20  to aim the corresponding speaker in a direction of the output vector  140 . The output alignment command  142  routes along the communications network  24  to the network address associated with the client device  20 . A motor control mechanism thus aims the speaker to the direction of the output vector  140 , thus directing the feedback  42  to the determined position  38  of the recognized person. 
       FIGS. 13-14  are schematics illustrating social interactions, according to exemplary embodiments. Here exemplary embodiments anticipate social interactions as a recognized user moves in a crowd. As a recognized user moves in a crowd, exemplary embodiments may predict when the recognized user will converse with another person in the room. If interaction is predicted, exemplary embodiments may stimulate social memories and provide personalized names and social connections. That is, as two people converge, the feedback  42  may provide the name of the approaching person, along with employment and family information. The feedback  42 , in other words, helps jog memories and helps avoid embarrassing social situations. 
     The server  22  may thus determine a trajectory for the recognized person. As this disclosure explains, exemplary embodiments may track the movements of one or more recognized persons. As each person&#39;s movement is tracked, the server  22  may determine a trajectory vector  150  associated with recognized person&#39;s movements. As the recognized people mingle, some trajectory vectors  150  will intersect.  FIG. 13 , for example, illustrates two trajectory vectors  152  and  154 . Each trajectory vector  152  and  154  is associated with a different, recognized person within the stream  36  of image data of the physical space  34 . As each recognized person moves, their respective direction may be linearly projected at a rate of movement. As the trajectory vectors  152  and  154  are determined, the server  22  may extrapolate their movement to determine that the two trajectory vectors  152  and  154  will intersect in the future. If the two trajectory vectors  152  and  154  are projected to intersect within some threshold period of time, the server  22  may infer the two corresponding people will socially interact at the vector intersection  126 . 
     The server  22  may thus facilitate social interactions. When the two trajectory vectors  152  and  154  are projected to intersect, the server  22  may retrieve social information  156  associated with each respective person. The server  22  may again query the database  96  of profiles for the profile  94  associated with each recognized person. The server  22  then queries each respective profile  94  for each person&#39;s social information  156 . Each person&#39;s social information  156 , for example, may include their name, their spouse&#39;s name, and their children&#39;s names. The server  22  then sends the social information  156  to the client device  20  as the feedback  42 . The client device  20  aims the social information  156  to the current position  38  of the respective person, as this disclosure explains. 
     The social information  156  helps jog memories. As two people converge, the server  22  can provide the personal, isolated feedback  42  of the approaching social interaction. The feedback  42  is preferably beamed for hearing just prior to the actual intersection  126 , thus audibly providing names and other important social information  156  prior to interaction. Each person is thus audibly, but privately, informed of the other person&#39;s name and other social information  156 . Social interaction may thus commence with less awkward moments of memory loss. 
       FIG. 14  illustrates social connectivity  160 . When the two trajectory vectors  152  and  154  are projected to intersect, the server  22  may also determine how the converging two people are socially connected. The social information  156  may be expansive and describe an employment history, clubs, memberships, sports, activities, and any other affiliations. The social information  156 , in other words, may be as minimal or expansive as the person wishes to reveal. The server  22 , for example, may determine that the converging people work for the same employer, attend the same church, or share a passion for some sports team. The server  22  may thus compare each person&#39;s social information  156  and determine matching entries. Any matches between their social information  156  helps determine how the two people are socially connected. A comparison of the social information  156 , for example, may reveal that the two people may have a common current or past employer. Their children may have a common school or team affiliation. The server  22  may even submit queries to Internet search engines to further determine the social connectivity  160  between the converging people. The server  22  may then send the social connectivity  160  to the client device  20  for beaming to the respective user. 
       FIGS. 15-16  are flowcharts illustrating a method for voice control, according to exemplary embodiments. The vision system  28  generates the stream  36  of image data (Block  200 ). The stream  36  of image data is sent to the server  22  for analysis (Block  202 ). The server  22  performs the image analysis  70  to recognize a user (Block  204 ). The profile  94  associated with the recognized user is retrieved (Block  206 ). The server  22  performs the position analysis  110  to determine the position  38  of the user from the stream  36  of image data (Block  208 ). The position  38  is sent to the client device  22  (Block  210 ). The client device  22  instructs the audio input system  30  to align a microphone to the position  38  (Block  212 ). The audio input system  30  generates the stream  40  of audio data (Block  214 ). 
     The flowchart continues with  FIG. 16 . The stream  40  of audio data is sent to the server  22  for analysis (Block  216 ). The server  22  converts the stream  40  of audio data to text (Block  218 ). The server  22  performs the speech analysis  80  to determine the semantic content (Block  220 ). The server  22  determines a dialogue response to the semantic content of the text (Block  222 ). The server  22  converts the dialogue response to a speech signal (Block  224 ). The server  22  sends the speech signal to the client device  22  as the audio feedback  42  (Block  226 ). The client device  22  instructs the audio output system  32  to align a speaker to the position  38  (Block  228 ). 
       FIG. 17  is a schematic illustrating still more exemplary embodiments.  FIG. 17  is a more detailed diagram illustrating a processor-controlled device  300 . As earlier paragraphs explained, the device-side algorithm  52  and/or the server-side algorithm  58  may operate in any processor-controlled device.  FIG. 17 , then, illustrates the device-side algorithm  52  and/or the server-side algorithm  58  stored in a memory subsystem of the processor-controlled device  300 . One or more processors communicate with the memory subsystem and execute either or both applications. Because the processor-controlled device  300  is well-known to those of ordinary skill in the art, no further explanation is needed. 
       FIG. 18  depicts still more operating environments for additional aspects of the exemplary embodiments.  FIG. 18  illustrates that the exemplary embodiments may alternatively or additionally operate within other processor-controlled devices  300 .  FIG. 18 , for example, illustrates that the device-side algorithm  52  and/or the server-side algorithm  58  may entirely or partially operate within a set-top box (“STB”) ( 302 ), a personal/digital video recorder (PVR/DVR)  304 , personal digital assistant (PDA)  306 , a Global Positioning System (GPS) device  308 , an interactive television  310 , an Internet Protocol (IP) phone  312 , a pager  314 , a cellular/satellite phone  316 , or any computer system, communications device, or any processor-controlled device utilizing a digital signal processor (DP/DSP)  318 . The processor-controlled device  300  may also include watches, radios, vehicle electronics, clocks, printers, gateways, mobile/implantable medical devices, and other apparatuses and systems. Because the architecture and operating principles of the various processor-controlled devices  300  are well known, the hardware and software componentry of the various processor-controlled devices  300  are not further shown and described. 
       FIGS. 19-21  are schematics further illustrating various client devices for presenting personalized feedback, according to exemplary embodiments.  FIG. 19  is a block diagram of a subscriber identity module  400 , while  FIGS. 20 and 21  illustrate, respectively, the subscriber identity module  400  embodied in a plug  402  and in a card  404 . As those of ordinary skill in the art recognize, the subscriber identity module  400  may be used in conjunction with the client device (illustrated as reference numeral  20  in  FIGS. 1-14 ). The subscriber identity module  400  stores user information and any portion of the device-side algorithm  52  and/or the server-side algorithm  58 . As those of ordinary skill in the art also recognize, the plug  402  and the card  404  each interface with the client device  20 . 
     As  FIG. 19  illustrates, the subscriber identity module  400  may be processor-controlled. A microprocessor  406  (μP) communicating with memory modules  408  via a data bus  410 . The memory modules  408  may include Read Only Memory (ROM)  412 , Random Access Memory (RAM) and or flash memory  414 , and Electrically Erasable-Programmable Read Only Memory (EEPROM)  416 . The subscriber identity module  400  stores some or all of the device-side algorithm  52  and/or the server-side algorithm  58  in one or more of the memory modules  408 .  FIG. 19  shows the device-side algorithm  52  and/or the server-side algorithm  58  residing in the Erasable-Programmable Read Only Memory  416 . However, either algorithm may alternatively or additionally reside in the Read Only Memory  412  and/or the Random Access/Flash Memory  414 . An Input/Output module  418  handles communication between the Subscriber Identity Module  300  and the client device. 
       FIG. 22  is a block diagram further illustrating the client device  20 , according to exemplary embodiments. Here the client device  20  may comprise a radio transceiver unit  452 , an antenna  454 , a digital baseband chipset  456 , and a man/machine interface (MMI)  458 . The transceiver unit  452  includes transmitter circuitry  460  and receiver circuitry  462  for receiving and transmitting radio-frequency (RF) signals. The transceiver unit  452  couples to the multiple input, multiple output (“MIMO”) system  58  for converting electrical current to and from electromagnetic waves. The digital baseband chipset  456  may have a digital signal processor (DSP)  464  and performs signal processing functions for audio (voice) signals and RF signals. As  FIG. 22  shows, the digital baseband chipset  456  may also include an on-board microprocessor  466  that interacts with the man/machine interface (MMI)  458 . The man/machine interface (MMI)  458  may comprise a display device  468 , a keypad  470 , and the subscriber identity module  400 . The on-board microprocessor  466  may perform TDMA, CDMA, GSM or other protocol functions and control functions. The on-board microprocessor  466  may also interface with the subscriber identity module  400  and with the device-side algorithm  52  and/or the server-side algorithm  58 . 
     Exemplary embodiments may be applied to any signaling standard. As those of ordinary skill in the art recognize,  FIGS. 19-22  may illustrate a Global System for Mobile (GSM) communications device. That is, the client device  20  may utilize the Global System for Mobile (GSM) communications signaling standard. Those of ordinary skill in the art, however, also recognize that exemplary embodiments are equally applicable to any communications device utilizing the Time Division Multiple Access signaling standard, the Code Division Multiple Access signaling standard, the “dual-mode” GSM-ANSI Interoperability Team (GAIT) signaling standard, or any variant of the GSM/CDMA/TDMA signaling standard. Exemplary embodiments may also be applied to other standards, such as the I.E.E.E. 802 family of standards, the Industrial, Scientific, and Medical band of the electromagnetic spectrum, BLUETOOTH®, WI-FI®, and any other. 
     Exemplary embodiments may be physically embodied on or in a computer-readable storage medium. This computer-readable medium, for example, may include CD-ROM, DVD, tape, cassette, floppy disk, optical disk, memory card, memory drive, and large-capacity disks. This computer-readable medium, or media, could be distributed to end-subscribers, licensees, and assignees. A computer program product comprises processor-executable instructions for personalized audible feedback, as the above paragraphs explained. 
     While the exemplary embodiments have been described with respect to various features, aspects, and embodiments, those skilled and unskilled in the art will recognize the exemplary embodiments are not so limited. Other variations, modifications, and alternative embodiments may be made without departing from the spirit and scope of the exemplary embodiments.