Patent Publication Number: US-2018040317-A1

Title: Information processing device, information processing method, and program

Description:
TECHNICAL FIELD 
     The present disclosure relates to an information processing device, an information processing method, and a program. 
     BACKGROUND ART 
     Detecting communication such as conversations occurring between users is useful, for example, to guess the relationship between the users. As technology therefor, for example, Patent Literature 1 proposes the technology of extracting a conversation group on the basis of the similarity between speech feature values such as frequency components extracted from the sound information transmitted from the terminal devices of respective users. This makes it possible to analyze conversations irregularly occurring between unspecified people. 
     CITATION LIST 
     Patent Literature 
     Patent Literature 1: JP 2012-155374A 
     DISCLOSURE OF INVENTION 
     Technical Problem 
     However, it is not necessarily easy for the technology, for example, as described in Patent Literature 1 to detect a short conversation between users or detect that a conversation begins in real time in order to detect a conversation on the basis of aggregated speech feature values such as frequency components. Further, for example, in a case where there are a large number of users that can be candidates for conversation groups or users are found in noisy environments, it can be difficult to robustly detect conversations. 
     The present disclosure then proposes a novel and improved information processing device, information processing method, and program that uses feature values extracted from speech data and makes it possible to more robustly detect conversations between users in a variety of phases. 
     Solution to Problem 
     According to the present disclosure, there is provided an information processing device including: a communication determination unit configured to determine, on the basis of a feature value extracted from speech data including at least a sound of speech of a user, whether communication occurs between users including the user, the feature value indicating an interaction between the users. 
     Further, according to the present disclosure, there is provided an information processing method including, by a processor: determining, on the basis of a feature value extracted from speech data including at least a sound of speech of a user, whether communication occurs between users including the user, the feature value indicating an interaction between the users. 
     Further, according to the present disclosure, there is provided a program for causing a computer to execute: a function of determining, on the basis of a feature value extracted from speech data including at least a sound of speech of a user, whether communication occurs between users including the user, the feature value indicating an interaction between the users. 
     Advantageous Effects of Invention 
     As described above, according to the present disclosure, it is possible to use feature values extracted from speech data and more robustly detect conversations between users in a variety of phases. 
     Note that the effects described above are not necessarily limitative. With or in the place of the above effects, there may be achieved any one of the effects described in this specification or other effects that may be grasped from this specification. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a diagram for schematically describing detection of a conversation in a first embodiment of the present disclosure. 
         FIG. 2  is a diagram illustrating a configuration example of a system according to the first embodiment of the present disclosure. 
         FIG. 3  is a diagram illustrating a functional component example of the system according to the first embodiment of the present disclosure. 
         FIG. 4  is a diagram for describing detection of an action in the first embodiment of the present disclosure. 
         FIG. 5  is a diagram for describing determination about whether a conversation occurs in the first embodiment of the present disclosure. 
         FIG. 6  is a diagram illustrating an example in which a state of a conversation occurring between users is expressed in a chronological order in the first embodiment of the present disclosure. 
         FIG. 7  is a diagram illustrating an example in which a state of a conversation occurring between users is expressed in a chronological order in the first embodiment of the present disclosure. 
         FIG. 8  is a diagram illustrating an example in which a state of a conversation occurring between users is expressed in a chronological order in the first embodiment of the present disclosure. 
         FIG. 9  is a diagram for describing optimization of a conversation graph structure in the first embodiment of the present disclosure. 
         FIG. 10  is a diagram for describing extension of a feature value in the first embodiment of the present disclosure. 
         FIG. 11  is a diagram for describing a use example of information obtained from detection of a conversation in the first embodiment of the present disclosure. 
         FIG. 12  is a diagram for describing a first use example of information obtained from detection of a conversation in the first embodiment of the present disclosure. 
         FIG. 13  is a diagram for describing the first use example of information obtained from detection of a conversation in the first embodiment of the present disclosure. 
         FIG. 14  is a diagram for describing the first use example of information obtained from detection of a conversation in the first embodiment of the present disclosure. 
         FIG. 15  is a diagram for describing a second use example of information obtained from detection of a conversation in the first embodiment of the present disclosure. 
         FIG. 16  is a diagram for describing the second use example of information obtained from detection of a conversation in the first embodiment of the present disclosure. 
         FIG. 17  is a diagram for describing a third use example of information obtained from detection of a conversation in the first embodiment of the present disclosure. 
         FIG. 18  is a diagram for describing a sixth use example of information obtained from detection of a conversation in the first embodiment of the present disclosure. 
         FIG. 19  is a diagram for describing the sixth use example of information obtained from detection of a conversation in the first embodiment of the present disclosure. 
         FIG. 20  is a diagram for schematically describing a transfer of a GNSS positioning right in a second embodiment of the present disclosure. 
         FIG. 21  is a diagram describing a use example of a GNSS positioning right in the second embodiment of the present disclosure. 
         FIG. 22  is a block diagram illustrating a system configuration according to the second embodiment of the present disclosure. 
         FIG. 23  is a block diagram illustrating another system configuration according to the second embodiment of the present disclosure. 
         FIG. 24  is a block diagram illustrating another system configuration according to the second embodiment of the present disclosure. 
         FIG. 25  is a flowchart illustrating an operation according to an application example of the second embodiment of the present disclosure. 
         FIG. 26  is a block diagram illustrating a hardware configuration example of an information processing device according to an embodiment of the present disclosure. 
     
    
    
     MODE(S) FOR CARRYING OUT THE INVENTION 
     Hereinafter, (a) preferred embodiment(s) of the present disclosure will be described in detail with reference to the appended drawings. In this specification and the appended drawings, structural elements that have substantially the same function and structure are denoted with the same reference numerals, and repeated explanation of these structural elements is omitted. 
     Hereinafter, the description will be made in the following order. 
     1. First Embodiment 
     1-1. Overview and System Configuration 
     1-2. Example of Processing for Detecting Conversation 
     1-3. Applied Information Generation Example 
     1-4. Supplemental Information on First Embodiment 
     2. Second Embodiment 
     2-1. Overview and System Configuration 
     2-2. Application Example 
     2-3. Supplemental Information on Second Embodiment 
     3. Hardware Configuration 
     1. First Embodiment 
     1-1. Overview and System Configuration 
       FIG. 1  is a diagram for schematically describing detection of a conversation in an embodiment of the present disclosure.  FIG. 1  illustrates processes of specifying users having conversations from among users in the present embodiment in (a) to (c). First, as illustrated in (a), users other than a target user are divided into candidate users and other users to determine whether conversations with the target user occur. The candidate users are indicated, for example, through GNSS positioning, Wi-Fi positioning, or the like as users positioned near the target user. If the other users or users who are hardly estimated to have conversations because of a physical constraint are removed from targets of the following detection processing, it is possible to reduce the processing amount and improve the accuracy of detection. 
     Next, as illustrated in (b), sensor data is acquired for both the target user and the candidate users. More specifically, the sensor data includes sensor data such as speech data acquired by microphones (sound sensors), and acceleration indicating motions of users. As illustrated in (c), it is determined whether conversations occur between the target user and the candidate users, on the basis of feature values that are extracted from these kinds of sensor data and indicate interactions between the users. The target user can be categorized into a common conversation group along with candidate users determined to have the conversations. 
       FIG. 2  is a diagram illustrating a schematic configuration example of a system according an embodiment of the present disclosure.  FIG. 2  illustrates that a system  10  includes a wearable terminal  100  (eyewear  100   a  and wristwear  100   b ), a mobile terminal  200 , and a server  300 . Devices included in the system  10  can be implemented, for example, by the hardware configuration of an information processing device described below. 
     The wearable terminal  100  is worn by each user. The wearable terminal  100  includes, for example, a microphone (sound sensor), and acquires speech data including a sound of speech of the user. Further, the wearable terminal  100  may include other sensors such as an acceleration sensor and a gyro sensor, and acquire sensor data such as acceleration indicating a motion of the user. For example, the eyewear  100   a  can be capable of acquiring sensor data indicating the acceleration or the angular velocity corresponding to a nod of a user. Further, for example, the wristwear  100   b  can be capable of acquiring sensor data indicating the acceleration or the angular velocity corresponding to a movement of a user&#39;s hand, a biological indicator such as a pulse, or the like. Further, the wearable terminal  100  may use information generated through information processing according to the present embodiment described below for presentation to a user. More specifically, the wearable terminal  100  may include output devices such as a display and a speaker, and present information to a user from these output devices in the form of images and sounds. Additionally, although the wearable terminal  100  and the mobile terminal  200  are separately shown in the illustrated example, the function of the wearable terminal  100  may be included in the mobile terminal  200  in another example. In this case, the mobile terminal  200  acquires sensor data by using a microphone, an acceleration sensor, a gyro sensor, or the like, and presents information generated through information processing to a user. 
     The mobile terminal  200  is carried by each user. The mobile terminal  200  relays communication between the wearable terminal  100  and the server  300  in the illustrated example. More specifically, for example, the wearable terminal  100  communicates with the mobile terminal  200  as wireless communication such as Bluetooth (registered trademark), while the mobile terminal  200  communicates with the server  300  as network communication such as the Internet. Here, the mobile terminal  200  may process information received from the wearable terminal  100  as necessary, and then transmit the processed information to the server  300 . For example, the mobile terminal  200  may analyze sensor data including speech data received from the wearable terminal  100 , and extract an intermediate feature value. Alternatively, the mobile terminal  200  may transfer sensor data received from the wearable terminal  100  to the server  300  with no processing. In such a case, for example, the system  10  does not necessarily have to include the mobile terminal  200  as long as network communication is possible between the wearable terminal  100  and the server  300 . Further, the mobile terminal  200  may use information generated through information processing according to the present embodiment described below for presentation to a user instead of or in combination with the wearable terminal  100 . 
     The server  300  is implemented by one or more information processing devices on a network, and provides a service to each user. For example, the server  300  extracts feature values from sensor data collected from the wearable terminal  100  of each user via the mobile terminal  200 , and determines on the basis of the feature values whether a conversation occurs between users. The server  300  may generate information expressing the situation in which a conversation occurs between users, for example, on the basis of a result of the determination. This information may be used to display a screen for allowing, for example, a user (who can be a user not participating in a conversation or a user whose conversation is not a target of detection) to grasp, in real time, the situation in which a conversation occurs, or accumulated as a log. The information accumulated as a log may be, for example, referred to the above-described user afterwards, or a graph structure that expresses the relationship between users may be specified on the basis of the information accumulated as a log. Additionally, these kinds of processing may be executed, for example, by the mobile terminal  200  serving as a host between the wearable terminal  100  and the mobile terminal  200  of each user. In this case, the system  10  does not necessarily have to include the server  300 . 
       FIG. 3  is a diagram illustrating a functional component example of a system according an embodiment of the present disclosure.  FIG. 3  illustrates that the system  10  includes a sensing unit  11 , an action detection unit  12 , a candidate selection unit  13 , a feature value extraction unit  14 , a conversation determination unit  15 , a score calculation unit  16 , and a grouping unit  17  as functional components. Additionally, the sensing unit  11  is implemented by a sensor such as the microphone (sound sensor), the acceleration sensor, and/or the gyro sensor included in the wearable terminal  100  described above with reference to  FIG. 2 . The action detection unit  12 , the candidate selection unit  13 , the feature value extraction unit  14 , the conversation determination unit  15 , the score calculation unit  16 , and the grouping unit  17  are implemented in the server  300 , the mobile terminal  200 , and/or the server  300  by a processor such as a CPU operating in accordance with a program. That is, the functional components implemented by the above-described processor may be integrated and implemented by any of the information processing devices included in the system  10 , or distributed and implemented by information processing devices. Each functional component will be further described below. 
     The sensing unit  11  includes a sensor such as a microphone (sound sensor) that acquires speech data as an input into the system  10 , an acceleration sensor or a gyro sensor that acquires sensor data such as acceleration indicating a motion of a user as an input into the system  10 . Moreover, the sensing unit  11  includes a GNSS receiver or a wireless communication device for Wi-Fi or the like that acquires positional information of a user. The sensing unit  11  is implemented in the wearable terminal  100  such as the eyewear  100   a  and the wristwear  100   b  as illustrated, for example, in  FIG. 2 . However, in a case where the function of the wearable terminal  100  is included in the mobile terminal  200  as described above, the sensing unit  11  is implemented in the mobile terminal  200 . 
     The action detection unit  12  detects, from sensor data (that can include speech data) acquired by the sensing unit  11 , an action of each user that provides sensor data. More specifically, for example, the action detection unit  12  detects a user&#39;s speech action from speech data. Here, the action detection unit  12  does not necessarily have to detect a feature of voice in speech or a speech content in the present embodiment. That is, the action detection unit  12  may simply detect whether a user speaks at certain time. In a case where the action detection unit  12  can additionally detect a feature of voice, a speech content, or the like, the action detection unit  12  may also detect them. Further, for example, the action detection unit  12  detects an action such as a nod of a user or a movement (gesture) of a user&#39;s hand from sensor data of acceleration or angular velocity. Moreover, for example, the action detection unit  12  may also detect a psychological action of a user from sensor data of a biological indicator such as the pulse of the user. 
     The candidate selection unit  13  detects the positional relationship between users who each provide sensor data, from sensor data acquired by the sensing unit  11 , and selects users whose positional relationship satisfies a predetermined condition as candidates for users included in a conversation group. More specifically, the candidate selection unit  13  selects, as a candidate user, another user who is positioned near a target user, which is indicated through GNSS positioning, Wi-Fi positioning, or the like. Additionally, positional information of each user does not necessarily have to be available for the candidate selection unit  13  to select a candidate user. For example, if a terminal device (such as the wearable terminal  100  or the mobile terminal  200 ) of each user is directly communicable through wireless communication such as Bluetooth (registered trademark), the candidate selection unit  13  may recognize that these users are approaching. Alternatively, the candidate selection unit  13  may select a candidate user on the basis of behavior information of each user. More specifically, for example, the candidate selection unit  13  may acquire a user&#39;s behavior recognition result (such as work or a meeting in the office) associated with a position, and select another user whose behavior recognition result common to that of a target user is acquired as a candidate user. Further, for example, the candidate selection unit  13  may acquire a user&#39;s schedule (such as work or a meeting in the office similarly to a behavior recognition result) associated with a position, and select another user whose schedule common to that of a target user is acquired as a candidate user. 
     The feature value extraction unit  14  extracts the mutual relationship of actions detected by the action detection unit  12  between each candidate user extracted by the candidate selection unit  13  and the target user: a feature value indicating an interaction. Such a feature value is extracted on the basis of the temporal relationship between actions in the present embodiment. 
     For example, the feature value extraction unit  14  extracts, from speech data including a sound of speech of a user, a feature value indicating an interaction between users including the user. More specifically, the users include a first user and a second user, and the feature value extraction unit  14  extracts a feature value on the basis of the temporal relationship between a sound of speech of the first user (who can be a target user) and a sound of speech of the second user (who can be a candidate user). This feature can indicate that the first user exchanges speech with  16  the second user. For example, in a case where the first user converses with the second user, it is unlikely that speech sections of the first user overlap much with speech sections of the second user. The speech sections of the respective users should alternately occur. 
     Additionally, speech data acquired by the sensing unit  11  may separately include first speech data including a sound of speech of the first user, and second speech data including a sound of speech of the second user in the above-described example. Alternatively, speech data acquired by the sensing unit  11  may include a single piece of speech data including a sound of speech of the first user and a sound of speech of the second user (a sound of speech of still another user may be included in the single piece of speech data or different speech data). Additionally, in a case a single piece of speech data includes sounds of speech of users, processing of separating the sounds of speech of the respective users can be executed, for example, on the basis of a speaker recognition result or the like. 
     Further, for example, the feature value extraction unit  14  may extract a feature value between the first user and the second user on the basis of the temporal relationship between a sound of speech of each user included in the speech data provided from the user, and a motion or a biological indicator indicated by the sensor data provided from each user in the same way. That is, for example, the feature value extraction unit  14  may extract a feature value on the basis of the relationship between a sound of speech of the first user included in the speech data provided from the first user, and a motion or a biological indicator indicated by the sensor data provided from the second user. Further, the feature value extraction unit  14  may not only extract a feature value between a target user and a candidate user, but also extract a feature value between candidate users. 
     The conversation determination unit  15  determines on the basis of a feature value extracted by the feature value extraction unit  14  whether a conversation occurs between users. Since the candidate selection unit  13  is included in the present embodiment, the conversation determination unit  15  determines whether a conversation occurs between users selected on the basis of the positional relationship between users (all the users who are processing targets) from the users. As already described for the candidate selection unit  13 , users who are determination targets may be selected on the basis of behavior information of each user. More specifically, for example, in a case where the occurrence probability of conversations calculated on the basis of a feature value extracted between the first user and the second user exceeds a predetermined threshold, the conversation determination unit  15  determines that a conversation occurs between the first user and the second user. The conversation determination unit  15  can specify a candidate user who has a conversation with a target user by calculating occurrence probability on the basis of a feature value extracted by the feature value extraction unit  14  between the target user and the candidate user. Moreover, the conversation determination unit  15  can specify a conversation that occurs between candidate users by calculating occurrence probability on the basis of a feature value extracted by the feature value extraction unit  14  between candidate users. Specifying conversations occurring not only between a target user and a candidate user, but also between candidate users make it is possible to grasp the situation of conversations occurring around the target user. 
     The score calculation unit  16  calculates a score between users on the basis of a conversation occurrence history based on the determination of the conversation determination unit  15 . For example, the score calculation unit  16  may calculate a score by integrating times for which conversations are occurring between users within a predetermined period of time. Alternatively, the score calculation unit  16  may calculate a score on the basis of the frequency of occurrence of conversations occurring between users for a predetermined time or more within a predetermined period of time. Further, for example, in a case where it is determined that a conversation occurs between users, the score calculation unit  16  may refer to the occurrence probability of conversations calculated by the conversation determination unit  15 , and calculate a higher score between users who are determined to have conversations with a higher occurrence probability. Moreover, for example, in a case where the action detection unit  12  can detect a feature of a user&#39;s voice, a speech content, and the like, the score calculation unit  16  may estimate the degree to which a conversation is active, on the basis of them, and calculate a higher score between users having a more active conversation. 
     The grouping unit  17  groups users on the basis of a score calculated by the score calculation unit  16 . There can be a variety of grouping expressions. For example, the grouping unit  17  categorizes users whose mutual scores exceed a threshold into a common group. Further, the grouping unit  17  may specify a graph structure expressing the relationship between users. The graph structure may be defined separately from a group, or a group may be defined in accordance with the presence or absence, or the strength of a link of the graph structure. Additionally, information based on a determination result of the conversation determination unit  15  in the present embodiment may be generated not only by the grouping unit  17 , but may be generated in a variety of forms. Such another example will be described below. 
     1-2. Example of Processing for Detecting Conversation 
       FIG. 4  is a diagram for describing detection of an action in an embodiment of the present disclosure. The wearable terminal  100   c  includes a headset  110  and a motion sensor  120  for detecting a nod of a user in the example illustrated in  FIG. 4 . The headset  110  includes a microphone  112 , and acquires speech data. The motion sensor  120  includes a gyro sensor  122  and an acceleration sensor  124 , and acquires sensor data of angular velocity and acceleration. Here, the above-described action detection unit  12  in the system  10  uses it as a start condition that energy subjected speech data extraction exceeds a threshold, and uses it as an end condition that the energy stays below the threshold for a predetermined time or more. The action detection unit  12  can then hereby detect a speech section of a user. Meanwhile, the action detection unit  12  can detect a section in which a nod of a user occurs by removing a section in which acceleration is distributed much from a section in which the predetermined frequency of angular velocity is large. 
     Next, the extraction of feature values in an embodiment of the present disclosure will be described. The feature value extraction unit  14  in the system  10  calculates a feature value indicating an interaction between the first user and the second user in the present embodiment. The feature value extraction unit  14  extracts a positive feature value for an interaction between users, for example, on the basis of the following events. That is, in a case where the following events frequently occur, feature values indicating interactions between users can be higher. 
     Exchange of speech (the speech of the first user and the speech of the second user alternately occur) 
     Nod of a non-speaker during speech 
     Nod of a non-speaker within a short speech period of time 
     Concurrent nods of a speaker and a non-speaker 
     Speech during speech of a speaker+a response of a nod 
     Meanwhile, the feature value extraction unit  14  calculates a negative feature value for an interaction between users, for example, on the basis of the following events. That is, in a case where the following events frequently occur, feature values indicating interactions between users can be lower. 
     Coincidence of speech sections (the speech of the first user and the speech of the second user concurrently occur) 
     No reaction of a non-speaker to speech 
     For example, the feature value extraction unit  14  calculates feature values based on the above-described events in a predetermined cycle (100 Hz as an example). The conversation determination unit  15  inputs the calculated feature values into a determination device in a predetermined cycle (which may be longer than the cycle for calculating the feature values, and 0.2 Hz as an example. In this case, the feature values may be treated as an average for every 30 s). The determination device may be, for example, a binary determination device, and determines whether the first user is likely or unlikely to converse with the second user. Such a determination device is generated, for example, through machine learning. For example, a support vector machine (SVM) can be used as a technique of machine learning, but a variety of known techniques in addition to this example can also be used. Further, any determination device can be used in the present embodiment as long as an output thereof enables the following determination. More specifically, the determination device may be a binary determination device, or a determination device that outputs probability. Further, the determination device does not necessarily have to be generated through machine learning. 
       FIG. 5  is a diagram for describing determination about whether a conversation occurs in an embodiment of the present disclosure. The conversation determination unit  15  of the system  10  calculates occurrence probability in accordance with an output of the determination device, and determines on the basis of the occurrence probability whether a conversation occurs in the example illustrated in  FIG. 5 . More specifically, in a case where the above-described determination device is a binary determination device, the conversation determination unit  15  increases occurrence probability in a case where an output of the determination device is positive (a conversation is likely to occur), and decreases occurrence probability in a case where the output is negative (a conversation is unlikely to occur). Further, in a case where the above-described determination device outputs probability, the conversation determination unit  15  may vary the score of occurrence probability in accordance with the magnitude of the probability output from the determination device. The determination device makes an output at 0.2 Hz, and the occurrence probability is updated every 5 s in the illustrated example. The conversation determination unit  15  determines that a conversation occurs between users in a section in which the occurrence probability consecutively added/subtracted in this way exceeds a predetermined threshold. 
       FIGS. 6 to 8  are diagrams each illustrating an example in which the state of a conversation occurring between users is expressed in a chronological order in an embodiment of the present disclosure. The states of conversations between users may be output in a chronological order on the basis of a conversation occurrence determination of the conversation determination unit  15 , for example, as illustrated in  FIG. 5  separately from statistical processing performed by the above-described score calculation unit  16  or grouping unit  17  in the present embodiment. The wearable terminal  100  or the mobile terminal  200  shown, for example, in the example of  FIG. 1  may present such an output to a user involved in the conversations, a user of the system  10  who is not involved in the conversations, or another user who does not use the system  10 , but has a viewing right. In this case, the processor of the wearable terminal  100 , the mobile terminal  200 , or the server  300  can implement a display control unit that displays a screen for presenting detected conversations in a chronological order. 
       FIG. 6  is an example in which the states of conversations occurring between two users are expressed. The occurrence states of conversations between a user U 1  and a user U 2  are represented by a link L 1  and a link L 2  in the illustrated example. The widths of the link L 1  and the link L 2  change in accordance with the occurrence probability of conversations between the user U 1  and the user U 2  which is calculated, for example, as shown in the example illustrated in  FIG. 5 . That is, the link L 1  indicates that the occurrence probability of conversations between the user U 1  and the user U 2  is low in the illustrated example. For example, in a case where the occurrence probability of conversations is greater than 0, but does not reach a predetermined threshold, the width of the link L 1  can be shown as the narrowest Meanwhile, as the occurrence probability of conversations exceeds the threshold and increases, the link L 2  having greater width can be shown. 
     As already described, the occurrence probability of conversations is used in determination using a threshold, for example, as illustrated in  FIG. 5  to determine whether a conversation occurs in the present embodiment. Further, in a case where the occurrence probability of conversations is calculated on the basis of feature values as exemplified above, the higher occurrence probability of conversations can indicate that conversations are exchanged or non-speakers nod more frequently. It is thus possible to interpret the occurrence probability of conversations, for example, as a continuous value representing the activity of conversations, and use the occurrence probability of conversations to change the above-described display form. 
       FIG. 7  is an example in which the states of conversations occurring among three users are expressed. As described above, feature values are extracted for a pair of users (first user and second user) included in users who are targets, and it is determined whether a conversation occurs, in accordance with the occurrence probability of conversations which is calculated further on the basis of the feature values in the present embodiment. In a case of three users, feature values are extracted and conversations are determined for the three respective pairs (= 3 C 2 ). As results of such extraction and determination, the wide links L 2  are displayed between the user U 1  and the user U 2 , and between the user U 2  and a user U 3  in the example illustrated in  FIG. 7 . The links L 2  show that conversations actively occur between these users similarly to the example illustrated in  FIG. 6 . Meanwhile, a link L 3  having middle width is displayed between the user U 3  and the user U 1 . The link L 3  shows that conversations also occur between the user U 3  and the user U 1 , but the conversations are not so active. It is guessed from these kinds of display that conversations among the three of the users U 1  to U 3  proceed with the user U 2  serving as a hub. 
     Meanwhile, in a case where a user U 4  who is not involved in the conversations passes by the users U 1  to U 3 , the position of the user U 4  approaches the users U 1  to U 3 . Accordingly, the user U 4  can be treated as a candidate user, but feature values that are extracted by the feature value extraction unit  14  and indicate interactions between users do not become positive for the occurrence of conversations as described above. Thus, the occurrence probability of conversations calculated by the conversation determination unit  15  is not also high. The occurrence probability of conversations does not therefore exceed the threshold, although the narrow links L 1  can be displayed between the user U 1  and the user U 4 , and between the user U 3  and the user U 4 , for example, as illustrated. Accordingly, the displayed links do not have greater width. Once the user U 4  goes further, the links L 1  also disappear. 
       FIG. 8  is an example in which the states of conversations occurring among four users are expressed. Feature values are also extracted for a pair of users included in users who are targets, and it is determined whether a conversation occurs, in accordance with the occurrence probability of conversations which is calculated further on the basis of the feature values in this example. As results of such extraction and determination, the wide links L 2  are displayed between the user U 1  and the user U 2 , and between the user U 3  and a user U 4  in the example illustrated in  FIG. 8 . The links L 2  show that conversations actively occur between these users similarly to the example that has already been described. Meanwhile, the narrow links L 1  are displayed between the other combinations of the four users. Similarly to the examples that have already been described, the links L 1  also show that conversations hardly occur between these users. It is guessed from these kinds of display that the users U 1  to U 4  gather in the same place to have conversations, but actually conversations proceed separately in the pair of the user U 1  and the user U 2  (subgroup) and the pair of the user U 3  and the user U 4  (subgroup). 
     For example, conversations are detected on the basis of feature values such as frequency components of speech data acquired by the wearable terminal  100  of each of the users U 1  to U 4 , it is possible to categorize the users U 1  to U 4  into a single conversation group because the speech data provided from each of the users U 1  to U 4  can indicate similar feature values, but it is difficult to guess in what combinations of them the conversations proceed in the above-described example illustrated  FIG. 8 . In contrast, it is possible in the present embodiment to accurately specify combinations of users in which conversations actually occur because the feature value extraction unit  14  extracts feature values indicating interactions between the users. 
       FIG. 9  is a diagram for describing the optimization of a conversation graph structure in an embodiment of the present disclosure. Categorizing users estimated to have conversations into a common conversation group, the conversation determination unit  15  of the system  10  optimizes a graph structure showing the occurrence situations of conversations between users in accordance with a rule set in advance for the graph structure in the example illustrated in  FIG. 9 . More specifically, a link between the user U 2  and a user U 5  is optimized and disconnected in the original graph structure including the users U 1  to U 7 , and a conversation group including the users U 1  to U 4  is separated from a conversation group including the users U 5  to U 7  in the illustrated example. 
     For example, the conversation determination unit  15  minimizes the energy of the generated graph structure, thereby performing the above-described optimization (rule of minimizing energy). Further, the conversation determination unit  15  may also optimize the graph structure in accordance with a rule based on the common knowledge that it is a single person who serves as a hub of conversations, for example, like the user U 2  in the example of  FIG. 7 . Further, for example, in optimizing the graph structure, the conversation determination unit  15  may use the speed of other users&#39; reactions to a certain user&#39;s speech to determine whether to maintain links (a link between a speaker and a non-speaker who speedily reacts to the speech is easier to maintain). Further, for example, in a case where the wearable terminal  100  can detect the direction of each user&#39;s face, the conversation determination unit  15  may use the direction of each user&#39;s face to determine whether to maintain links (a link between a speaker and a person who is spoken to and turns his or her face to the speaker is easier to maintain). 
       FIG. 10  is a diagram for describing the extension of a feature value in an embodiment of the present disclosure. Although the speech and nodding of users are used as an action for extracting feature values for detecting conversations between the users in the example described above, for example, with reference to  FIG. 4 , feature values may be extracted on the basis of further various actions as illustrated in  FIG. 10  in another example.  FIG. 10  exemplifies, as sensors that can be used to extract such feature values, a microphone  112 , a motion sensor  120  (that can include the gyro sensor  122  and the acceleration sensor  124  exemplified in  FIG. 4 ), a geomagnetic sensor  126 , and a biological sensor  128 . These sensors are included, for example, in the wearable terminal  100  or the mobile terminal  200 . Illustrated actions  130  to  140  will be each described below. 
     As described in the example illustrated in  FIG. 4 , a nod  130  is detected, for example, on the basis of sensor data acquired by the motion sensor  120  installed in a terminal device such as the eyewear  100   a  worn on the head of a user. Moreover, the nod  130  may be detected by using sensor data of the geomagnetic sensor  126  that is similarly installed in the terminal device. 
     A speech section  132  is detected on the basis of speech data including sounds of speech of a user acquired by the microphone  112  as described in the example illustrated in  FIG. 4 . It may be possible to further detect a speech content  134  from the speech data. In this case, for example, it is possible to detect conversations occurring between users on the basis of the topic commonality of the conversations in addition to the states of interactions between the users indicated by the temporal relationship with the speech section  132 . 
     A body direction  136  is detected, for example, by using sensor data acquired by the geomagnetic sensor  126 . As described above with reference to  FIG. 9 , for example, a non-speaker facing a speaker, and a speaker facing a non-speaker can be positive elements for detecting the occurrence of a conversation in between. 
     A gesture  138  is detected, for example, by using sensor data acquired by the motion sensor  120  or the geomagnetic sensor  126 . For example, similarly to a nod in the example described with reference to  FIG. 4 , the gesture  138  can be used as an element indicating an interaction between users by specifying the temporal relationship with a speech section. 
     A pulse  140  is detected, for example, by using the biological sensor  128 . For example, in a case where, when users have an active conversation, the pulse  140  also looks likely to increase, it can be possible to estimate whether the state of the pulse matches the state of a conversation between users, or whether users are conversing with each other (e.g., if another action or a feature value indicates an active conversation, but the pulse  140  does not increase, it is possible that the users are not actually conversing with each other). 
     In a case where the above-described detection results of actions are used, feature values indicating interactions between users can be higher, for example, in a case where the occurrence frequency of the following events is high. 
     Reaction of a non-speaker in the form of gestures at the end of speech of a speaker 
     Words included in speech have commonality 
     Speech contents have commonality, and an answer matches the speech contents 
     Body directions of a speaker and a non-speaker intersect each other 
     Actions of walking, eating, or the like are common 
     Changes of a speaker and a non-speaker in pulse are correlated 
     Further, the conversation determination unit  15  may consider the context of user behaviors or methods for using specified conversation groups in categorizing users into conversation groups. For example, in a case where a private image of a user is shared between the specified conversation groups, it is possible to prevent the image from being shared with an inappropriate user, by setting a higher threshold for determining that a conversation occurs between users. Further, for example, setting a lower threshold makes it possible to categorize those who converse with a user into a conversation group without showing them in a party or the like, where participants are very likely to converse with each other in a wide area. Moreover, for example, a higher threshold may be set in the daytime, when a user is in the crowd in the city or the like in many cases, to prevent false detection, while a lower threshold may be set in the nighttime in many cases, when a user is in a less crowded place such as homes. 
     1-3. Applied Information Generation Example 
       FIG. 11  is a diagram for describing a use example of information obtained from detection of a conversation in an embodiment of the present disclosure. For example, a result obtained by the conversation determination unit  15  determining whether a conversation occurs is used by the score calculation unit  16  and the grouping unit  17  in the example described above with reference to  FIG. 3 , but a use example of information in the present embodiment is not limited to such an example. Information can be used in other various ways.  FIG. 11  illustrates a UI generation unit  171  to a topic recommendation unit  183  as functional components for such use. These functional components are implemented as software in a terminal device or a server, for example, by using a processor and a memory or a storage. The following further describes information provided by these functional components. 
     First Example 
     The UI generation unit  171  may provide a user interface that displays the states of conversations between users in a chronological order in the form of a graph, for example, as described above with respect to  FIGS. 6 to 8 . Further, for example, the UI generation unit  171  may provide a user interface that displays the states of conversations in real time in the form of a list as described above. Further, the UI generation unit  171  makes it possible to use the states of conversations displayed in real time in that way in another application. For example, the UI generation unit  171  may cooperate with a link function  172  to social media to make it possible to share data such as images among users belonging to a common conversation group, or support users in concurrently play a game. 
     For example, in a case where the states of conversations between users detected as described above are used for various kinds of use, ad hoc conversation group recognition between terminal devices as illustrated in  FIG. 12  can make it possible to recognize a conversation group similar to an actual conversation occurrence situation. 
     Terminal devices  100   x  and  100   y  (that are only have to be terminal devices each of which are used by a user, and may be, for example, the wearable terminal  100  or the mobile terminal  200  in the example of  FIG. 2 . The same applies to the following example) each includes the sensing unit  11 , the action detection unit  12 , the candidate selection unit  13 , the feature value extraction unit  14 , the conversation determination unit  15 , a communication unit  31 , a display unit  32 , and the UI generation unit  171  in the example illustrated in  FIG. 12 . Additionally, the communication unit  31  is implemented by a communication device of Bluetooth (registered trademark) or the like included in each of the terminal devices  100   x  and  100   y . The display unit  32  is implemented by a display such as an LCD included in each of the terminal devices  100   x  and  100   y . The corresponding functional components of the terminal device  100   x  and the terminal device  100   y  cooperate with each other to specify that the users of the respective terminal devices belong to a common conversation group in the illustrated example. 
     More specifically, the candidate selection unit  13  selects a candidate user on the basis of positional information acquired by the sensing unit  11 , and positional information acquired by the sensing unit  11  of the other user in the illustrated example. The users of the terminal devices  100   x  and  100   y  are then selected as each other&#39;s candidate user. Next, the action detection unit  12  specifies a section in which an action such as speech or nodding occurs, on the basis of the sensor data acquired by the sensing unit  11 . Moreover, the feature value extraction unit  14  shares information such as the section specified by the action detection unit  12  of each terminal device via the communication unit  31 , and extracts a feature value indicating an interaction between the users of the terminal devices  100   x  and  100   y . The conversation determination unit  15  determines on the basis of the extracted feature value whether a conversation occurs between the users of the terminal devices  100   x  and  100   y . The UI generation unit  171  generates a user interface such as the above-described graph or list in accordance with a result of the determination, and presents the generated user interface to each user via the display unit  32 . 
       FIGS. 13 and 14  are diagrams each illustrating an example of a user interface provided in the above-described first example. In these examples, there is a screen displayed on a display  210  of the mobile terminal  200  as a user interface (e.g., there may also be a similar screen displayed on the wearable terminal  100 ). A conversation group of users is displayed in screens  2100   a  and  2100   b  in the form of a graph in the example illustrated in  FIG. 13 . For example, a user can transition between the screen  2100   a  and the screen  2100   b  through an operation of zooming in/out. Only other users estimated to be more likely to belong to the same conversation group are displayed in the screen  2100   a , for example, by a threshold for relatively high occurrence probability. Meanwhile, other users estimated to belong to the same conversation group are displayed more widely in the screen  2100   b , for example, by a threshold for relatively low occurrence probability. A user can correct a recognition result of conversation groups, for example, by operating the icons of other users displayed in the screens  2100   a  and  2100   b  via a touch panel or the like. For example, a user can remove another user represented as an icon from a conversation group by performing an operation of swiping the icon of the other user to the outsides of the screens  2100   a  and  2100   b.    
     A conversation group of users is displayed in a screen  2100   c  in the form of a list in the example illustrated in  FIG. 14 . For example, users having the higher occurrence probability of conversations which is calculated by the conversation determination unit  15  may be displayed in the list. The display order of the list can thus dynamically change. The maximum number of users included in a conversation group can be limited by setting the number of users displayed in the list in advance. Further, it is also possible to remove another user displayed in the list in the screen  2100   c  from a conversation group, for example, by performing an operation of swiping the other user to the outside of the screen  2100   c.    
     Second Example 
     The history of a person with whom a user has conversations is output onto a time line by a log output unit  175  and the link function  172  to social media in a second example.  FIG. 15  illustrates functional components for such an output (additionally, it is a terminal device that estimates conversations in the illustrated example, but a server may also estimate conversations). A terminal device  100   z  includes the sensing unit  11 , the action detection unit  12 , the candidate selection unit  13 , the feature value extraction unit  14 , the conversation determination unit  15 , the communication unit  31 , the log output unit  175 , and the function  172  for linking to social media in the example illustrated in  FIG. 15 . The log output unit  175  outputs a log including at least any of information of a person with whom at least one user included in users (all the users who are processing targets) converses, or information of a conversation with a person who converses, on the basis of a conversation occurrence history based on determination of the conversation determination unit  15 . The log generated by the log output unit  175  is output onto the time line of social media presented to the at least one user via the function  172  for linking to social media (the log generated by the log output unit  175  may also be output to a time line that is not related to the social media in another example). Further, a speech recognition unit  34  and a topic recognition unit  35  are implemented as software in the server  300   z.    
     Functional components as described above can recommend, as a friend, another user in social media who, for example, has conversations (which can be determined on the basis of conversation time or high conversation probability) to some extent. This eliminates the necessity to take the trouble of registering another user with whom a user has conversations, as a friend in social media. Further, logs based on conversation occurrence histories can also be referred to in an application for social media or the like. Information such as topics of conversations recognized through processing of the speech recognition unit  34  and the topic recognition unit  35 , information of places where conversations occur, images, or the like may be then added to the logs. For example, if conversation logs are filtered and displayed in accordance with topics or persons with whom a user has conversations, the conversation logs are useful as a tool for assisting the user in memory or a means for recording memories. 
       FIG. 16  is a diagram illustrating an example of a user interface provided in the above-described second example. In this example, there is a screen displayed on the display  210  of the mobile terminal  200  as a user interface (e.g., there may also be a similar screen displayed on the wearable terminal  100 ). A date  2101  and a list of other users on a time line with whom a user converses on that day are displayed in a screen  2100   d  in the example illustrated in  FIG. 16 . The list can include, for example, a name  2103  and a conversation time  2105  of another user. The user can display a conversation history screen  2100   e  of any of users listed on the screen  2100   d , for example, by selecting the user. The screen  2100   e  includes the name  2103  and the conversation time  2105  of another user, an icon  2107  for making friends with the user in social media, and a past conversation history  2109  with the user. A conversation topic  2111  may be displayed in the past conversation history in addition to the date when a conversation occur, and the conversation time. 
     Third Example 
     It is possible in a third example to make an action on a person with whom conversations are not necessarily exchanged, for example, on social media in the above-described second example. As described above, the feature value extraction unit  14  can not only extract feature values on the basis of the relationship between the respective sounds of speech of users, but also extract feature values on the basis of the temporal relationship between sounds of speech of one user and an action (such as a motion or a biological indicator) other than speech of the other user in the present embodiment. If this is used, for example, a sporadically conversing person recognition unit  173  can recognize not only another user with whom a user exchanges speech and converses, but also another user who shows some reactions to the speech of the user or another user whose speech an action of the user is pointed to and display him or her on a time line provided by the log output unit  175 . The user can make an action  174  on the other user (who is not an acquaintance in many cases) in cloud computing on the basis of this. For example, an avatar of the other user is just visible at this time in the action in cloud computing because of privacy protection, but personal information does not necessarily have to be exchanged. 
       FIG. 17  is a diagram illustrating a functional component for such an output described above. A terminal device  100   w  includes the sensing unit  11 , the action detection unit  12 , the candidate selection unit  13 , the feature value extraction unit  14 , the conversation determination unit  15 , the communication unit  31 , the log output unit  175 , and a post-process unit  36  in the example illustrated in  FIG. 16 . 
     Additionally, the post-process unit  36  corresponds to the above-described sporadically conversing person recognition unit  173 , and the action  174  in cloud computing. For example, the post-process unit  36  is implemented as software by a processor included in the terminal device  100   w  operating in accordance with a program. 
     The log output unit  175  outputs, as a log, a result obtained by generating a conversation group in the illustrated example. The post-process unit  36  specifies another user for whom communication including a conversation of a predetermined time or less or speech of only one user is detected in the log. Moreover, the post-process unit  36  can extract another user whom the user temporarily meets and make an action on such a user in cloud computing by removing users who have already been friends in social media from among the specified users. 
     Fourth Example 
     The topic recommendation unit  183  illustrated in  FIG. 11  provides a topic, thereby supporting communication between users in a fourth example. For example, the topic recommendation unit  183  estimates the degree to which conversations are active from the tempo of the conversations indicated by a feature value extracted by the feature value extraction unit  14 , and accordingly recommends a topic. More specifically, in a case where the occurrence probability of conversations calculated by the conversation determination unit  15  tends to decrease or the separately estimated degree to which conversations are active (e.g., estimated on the basis of the tempo of speech of the user or the voice volume) tends to decrease, the topic recommendation unit  183  may determine that the user needs a new topic and recommend a topic different from the old topic. Further, for example, in a case where the occurrence probability of conversations tends to increase or the degree to which conversations are active tends to increase, the topic recommendation unit  183  may determine that the current topic continues and provide information on the current topic to the user. 
     Further, as another example, the topic recommendation unit  183  may provide a topic to the user in accordance with a log output by the log output unit  175  or an intimacy degree calculated by an intimacy degree graph generation unit  177  described below. More specifically, for example, in a case where the user converses with a person with whom the user constantly converses (person having a large number of logs of conversations with the user), or a person having a high intimacy degree, the topic recommendation unit  183  may determine that a new topic is provided in a case where the conversations are inactive as described above because the conversations are supposed to be active in theory. Meanwhile, in a case where the user converses with a person with whom the user does not converse much (person having few logs of conversations with the users) or a person having a low intimacy degree, the topic recommendation unit  183  may refrain from providing new topics although the conversations are estimated to be inactive as described above because conversations are not necessary in particular in some cases. 
     Fifth Example 
     The intimacy degree graph generation unit  177  illustrated in  FIG. 11  generates a graph indicating the intimacy degree between users on the basis of the log output by the log output unit  175  in a fifth example. The intimacy degree graph generated here can also be a graph structure expressing the relationship between users on the basis of the occurrence probability of conversations. Thus, the intimacy degree graph generation unit  177  can also be a relationship graph specification unit that specifies such a graph structure. The intimacy degree graph generation unit  177  generates a strong link in an intimacy degree graph between users having, for example, a high conversation frequency or a long total conversation time, which is showed, for example, by a log of the log output unit  175 . Further, the intimacy degree graph generation unit  177  may estimate an intimacy degree on the basis of the quantity or the types of reactions extracted by the action detection unit  12  or the feature value extraction unit  14 , and generate a strong link between users having a high intimacy degree. More specifically, the intimacy degree graph generation unit  177  may change the strength of the link between users in the intimacy degree graph in accordance with whether a certain user frequently speaks or only nods in conversations with another user. Further, the intimacy degree graph generation unit  177  may provide a label (such as a parent, a brother, a boss, a coworker, a friend, or a boyfriend/girlfriend) to the link between users, for example, on the basis of information (such as a profile of a user) acquired from the outside. Similarly, the intimacy degree graph generation unit  177  may provide a label (such as a family, a company, colleagues, a circle) to a group generated on the intimacy degree graph. 
     As an example, the intimacy degree graph generation unit  177  may calculate an intimacy degree C. with another user by using an equation such as the following expression 1. Additionally, it is assumed that each conversation occurring between a user and another user is provided with an index i. t now  represents the current time. t past   _   i  represents the time at which an i-th conversation with another user occurs (older conversations thus have less influence on the intimacy degree in the expression 1). duration i  represents the total time of the i-th conversation. speak i  represents a speaking time in the i-th conversation. nod i  represents a nodding time in the i-th conversation (thus, as a speaking time increases as compared with a nodding time, the intimacy degree also increases in the expression 1). positive i  and negative i  represent a user&#39;s emotion (positive and negative. If the positive emotion is stronger, the intimacy degree has a plus value in the expression 1, while if the negative emotion is stronger, the intimacy degree has a minus value) estimated on the basis of biological information or the like about another user with whom the user has the i-th conversation. 
     
       
         
           
             
               
                 
                   
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     Sixth Example 
     A desire-to-share graph generation unit  179  illustrated in  FIG. 11  applies a filter generated by an adaptation graph generation unit  181  to the intimacy degree graph generated by the intimacy degree graph generation unit  177  in a sixth example, thereby setting the range within which a user shares content. The desire-to-share graph generation unit  179  is an example of a sharing user specification unit that, in a phase in which at least one user included in users (all the users who are processing targets) shares the information, specifies the other user who shares the information by applying a filter related to the information to be shared to a graph structure expressing the relationship between users. Much content such as images, sounds, materials, and life logs closely focusing on the lives of individual users has been acquired in recent years. Accordingly, it can be useful to automatically set the sharing range for such content or automatically narrow down candidates for the sharing range. 
       FIG. 18  is a diagram for schematically describing the generation of a desire-to-share graph in the present embodiment.  FIG. 18  illustrates that a desire-to-share graph G 2  is obtained by applying a filter F to a group intimacy degree graph G 1 . Here, the group intimacy degree graph G 1  is generated, for example, by a group intimacy degree graph generation unit  178  illustrated in  FIG. 11 . The group intimacy degree graph G 1  is generated, for example, by integrating, for a given user group (including users), intimacy degree graphs generated by the intimacy degree graph generation unit  177  for the respective individual users. 
     Further, the filter F corresponds to an adaptation graph generated by the adaptation graph generation unit  181 , and the filter F related to information to be shared is selected. A graph of interests is selected from graphs of places, interests, groups, and the like, and the filter F corresponding thereto is applied in the illustrated example. As illustrated in  FIG. 11 , the adaptation graph generation unit  181  generates an adaptation graph that provides the appropriate filter F, on the basis of context (in what situation and what type of content is shared) at the time of sharing content recognized by the context recognition unit  180  or a profile  182  of a user. 
     As a result, the positional relationship between other users included in the graph changes in the desire-to-share graph G 2  as compared with the group intimacy degree graph G 1 . A certain user has a link strengthened by applying the filter F, while another user has a link weakened by applying the filter F (the strength of links are expressed in the form of the distances from the center of the graph in the illustrated example). As a result, in a case where content is shared with another user whose link has strength exceeding a predetermined threshold (or such a user is treated as a candidate for a sharing destination of content), it is possible to set the more appropriate sharing destination or a candidate therefor corresponding to the type of content or the context in which content is shared than a case where a sharing destination or a candidate therefor is decided simply by using the group intimacy degree graph G 1 . 
     Here, a more specific example will be used to describe an example of dynamically selecting the adaptation graph from which the filter F originates. For example, in a case where a user goes on a trip, the adaptation graph corresponding to the attribute of places may be selected and the link to another user at the current position (trip destination) of a user may be strengthened (filter configured on the basis of the positional relationship between users included in the graph structure). Further, for example, in a case where a user is at work, the adaptation graph corresponding to work may be selected and the link to another user (such as a coworker) having a working relationship may be strengthened (filter configured on the basis of a group to which a user included in the graph structure belongs). Further, for example, in a case where a user is playing or watching a sport, the adaptation graph corresponding to interests may be selected and the link to another user interested in the sport may be strengthened (filter configured on the basis of what a user included in the graph structure is interested in). Further, for example, in a case where a user is participating in a party (social gathering) in which anyone can participate, an adaptation graph (filter configured on the basis of behavior information of a user included in the graph structure) may be selected to strengthen the link to another user having nothing to do at that time. Further, for example, in a case where a user is confronted with something unknown and has trouble, an adaptation graph (filter configured on the basis of the knowledge of a user included in the graph structure) may be selected to strengthen the link to another user who has the knowledge. 
     Additionally, adaptation graphs may be combined to configure the filter F. Further, it may be selectable to use no adaptation graph (apply substantially no filter F). As described above, the adaptation graph generation unit  181  automatically (e.g., rule-based) select an adaptation graph on the basis of the recognized context, the profile of a user, or the like. The adaptation graph generation unit  181  may be, however, capable of presenting selectable adaptation graphs to a user in the form of lists, tabs, or the like, and then selecting an adaptation graph in accordance with the selection of the user. In this case, the adaptation graph generation unit  181  may be configured to select an adaptation graph in accordance with the selection of a user and learn a selection criterion of an adaptation graph (based on the context of the situation of the user, the type of content to be shared, or the like) on the basis of selection results of the user at the initial stage, and then automatically select an adaptation graph. 
       FIG. 19  is a diagram for describing the dynamic correction of an intimacy degree graph in the present embodiment. A group intimacy degree graph G 3  for a user A and a user B is positionally corrected by the comment “C should have come, too” made by the user A in conversations between the user A and the user B to generate an intimacy degree graph G 4  in which the links between a user C and the users A and B are strengthened (the position of the user C is moved closer to the center of the graph) in the illustrated example. At this time, for example, in a case where there is a rule to maintain the total strength of links constant in an intimacy degree graph, the link to the user C is strengthened and the links to the other users (users D to F) are weakened all the more (the positions are moved farther from the center of the graph). 
     The link to the user C is strengthened in the illustrated example because the user A mentions the name of the user C in the actual speech. However, the similar processing is also possible, for example, in a case where the name of the user C is included in sentences input by the user A (or the user B) when the user A and the user B have an on-line chat. The above-described example can also be an example in which the group intimacy degree graph generation unit  178  temporarily corrects an intimacy degree graph (graph structure expressing the relationship between users) specified on the basis of the occurrence histories of conversations between the user A and another user (including the user C) within a certain period of time (first period of time) in a case where the name of the user C is included in the contents sent by the user A in conversations (which may be actual conversations or virtual conversations such as on-line chats) occurring between the user A and another user (user B in the above-described example) within the most recent second period of time shorter than the first period of time. More specifically, the group intimacy degree graph generation unit  178  temporarily strengthens the relationship between the user A and the user C in a group intimacy degree graph in this example. As a similar example, the group intimacy degree graph generation unit  178  may temporarily strengthen the link to another user to whom the user casts the line of sight in the intimacy degree graph. 
     If content is shared as described in the above-described sixth example, the desire-to-share graph (G 3  illustrated in  FIG. 18 ) is presented to a user, for example, as an image. The sharing range (range R illustrated in G 3  of  FIG. 18 ) may be superimposed on the desire-to-share graph. Moreover, not only the desire-to-share graph, but a group intimacy degree graph or an adaptation graph may also be displayed. First, automatically generated candidates are presented to the user, the user corrects the candidates, and the sharing range R is then finally decided in a certain example. The user can correct the sharing range R, for example, by increasing/decreasing the size of a figure such as a circle representing the sharing range R, including another user displayed as an icon in the sharing range R, or removing another user displayed as an icon from the sharing range R. The desire-to-share graph generation unit  179  may additionally learn the intention of the user regarding the sharing range from a correction result of the sharing range R of the user, and the desire-to-share graph generation unit  179  may then automatically set the appropriate sharing range R. 
     If content is shared in the above-described configuration, for example, a user with whom content is shared can be satisfied very much by selectively sharing content of another really intimate user or content in which the user can be interested when content is shared. Further, if content (such as watching sports games live) that is experienced by a certain user in real time is shared with another user in a remote place in real time, the experience can be shared. 
     1-4. Supplemental Information on First Embodiment 
     The embodiments of the present disclosure may include, for example, an information processing device as described above, a system, an information processing method executed by the information processing device or the system, a program for causing the information processing device to function, and a non-transitory tangible medium having the program recorded thereon. 
     Additionally, conversations can be detected between users in the system in the description of the above-described embodiment. However, the conversations detected between users are not necessarily limited in the above-described embodiment to conversations in which the related users all speak as already described. For example, a case can also be detected where only a part of the users speaks, and the other users make an action such as nodding in accordance with the speech. It can be thus the occurrence of communication (conversations are a type of communication) between users that can be detected in an embodiment of the present disclosure in addition to a case where such a case is detected separately from conversations in another embodiment. The conversation determination unit can be thus an example of a communication determination unit. 
     2. Second Embodiment 
     2-1. Overview and System Configuration 
     The embodiment has been described above in which it is determined whether a conversation occurs between a target user and a candidate user, on the basis of a feature value indicating an interaction between the users. The following describes a second embodiment, which is an application example of the above-described first embodiment. A system in which positioning information is transferred between users will be described in the second embodiment. 
     GNSS positioning consumes much power. It is desirable to enable GNSS positioning with less power in a terminal such as the mobile terminal  100  or the wearable terminal  200  including a small battery. The following then describes an embodiment in which positioning information is transferred between users. 
       FIG. 20  is a diagram illustrating the overview of a GNSS positioning method in the second embodiment. As illustrated in A of  FIG. 20 , the wearable terminal  100  of a pendant type or a glasses type or a wristwatch type, or the like is used in the second embodiment, thereby forming a group among users.  FIG. 20  describes a case where the three users from U 1  to U 3  form a group. Additionally, the users include at least one accompanying person who accompanies a specific user. The relationship between the specific user and the accompanying person is not, however, important. The other user (the other users if there are some users) as viewed from one user is treated as an accompanying person. Further, a method for recognizing accompanying persons included in the group will be described below. 
     Next, as illustrated in B of  FIG. 20 , GNSS positioning rights (which are schematically represented as key figures in B of  FIG. 20 ) for GNSS positioning are transferred between users in the formed group. Next, as illustrated in C of  FIG. 3 , the wearable terminal  100  of the user U 1  having a GNSS positioning right performs GNSS positioning, and the wearable terminals  100  of the other users (U 2  and U 3 ) having no GNSS positioning rights receive positioning information from the wearable terminal  100  of the user U 1 , which performs GNSS positioning, and share the positioning information. The received positioning information is then used as a representative value (information such as latitude and longitude, or the like) of the group. 
     Additionally, the above-described GNSS positioning rights may be transferred at predetermined time intervals. Further, in a case where the remaining battery level of each wearable terminal  100  is recognized, the GNSS positioning right may be transferred to the wearable terminal  100  having a higher remaining battery level. If GNSS positioning is performed by the wearable terminal  100  having a higher remaining battery level in this way, it is possible to smooth the remaining battery levels of the terminals in the group. Further, as illustrated in  FIG. 21 , the GNSS positioning right may be transferred via the operation screen displayed on the mobile terminal  200  of a user. 
     In  FIG. 21 , three users are recognized in a group. It is then shown that “Ms. BBB” currently has a GNSS positioning right. That is, a check indicating that Ms. BBB has a GNSS positioning right is displayed in the check box next to the name of Ms. BBB in the display screen. When a user wants to transfer a GNSS positioning right to another user, the user transfers a GNSS positioning right to the other user by checking the check box displayed next to the name of the user to whom the user wants to transfer a GNSS positioning right. 
       FIG. 22  is a diagram illustrating the system configuration of a system that performs the above-described operation. The system according to the present embodiment includes a server  300   m  and wearable terminals  100   m  and  100   n . Additionally, the number of wearable terminals  100  is not limited to two. 
     The server  300   m  includes a communication unit  37 , an accompanying person recognition unit  38 , and a GNSS positioning decision unit  39 . The communication unit  37  communicates with each of the wearable terminals  100   m  and  100   n . Further, the accompanying person recognition unit  38  groups accompanying persons on the basis of information sent from each of the wearable terminals  100   m  and  100   n . Further, the GNSS positioning decision unit  39  decides to which user a GNSS positioning right is provided in a group recognized by the accompanying person recognition unit  38 . 
     Further, the wearable terminals  100   m  and  100   n  includes the communication unit  31 , the display unit  32 , the sensing unit  11 , an accompanying person recognition unit  40 , a GNSS positioning unit  41 , a GNSS control unit  42 , and a virtual GNSS positioning unit  43 . Here, the communication unit  31  communicates with the server  300   m . Further, the display unit  32  displays information such as information on users belonging to a group. Additionally, the communication unit  31  is implemented by communication devices of Bluetooth (registered trademark), Wi-Fi, or the like included in the respective wearable terminals  100   m  and  100   n  as described above. 
     Further, the sensing unit  11  may include a microphone, an acceleration sensor, and/or a gyro sensor as described above, and further include an imaging unit such as a camera. Further, the accompanying person recognition unit  40  receives information from the sensing unit  11  and the communication unit  31 , and transmits the received information to the accompanying person recognition unit  38  of the server  300   m  via the communication unit  31 . Further, the accompanying person recognition unit  40  receives information of an accompanying person recognized by the accompanying person recognition unit  38  of the server  300   m . Additionally, this information of an accompanying person may also be displayed on the display unit  32 , and the displayed information of an accompanying person may be corrected by a user. 
     The GNSS positioning unit  41  receives GNSS signals from a GNSS satellite for positioning. The virtual GNSS positioning unit  43  uses positioning information received from another terminal to determine the position of an own terminal. Next, the GNSS control unit  42  switches whether to turn on the GNSS positioning unit  41  or the virtual GNSS positioning unit  43 , on the basis of a GNSS positioning right generated by the GNSS positioning decision unit  39  of the server  300   m . Further, as described above with reference to  FIG. 21 , in a case where a GNSS positioning right is manually changed, the GNSS control unit  42  recognizes that a GNSS positioning right is manually changed, and switches whether to turn on the GNSS positioning unit  41  or the virtual GNSS positioning unit  43 . 
     The operation of the above-described configuration will be specifically described below. The accompanying person recognition units  40  of the wearable terminals  100   m  and  100   n  receive the following information from the sensing unit  11  or the GNSS control unit  42  or the communication unit  31 . 
     (1) Positioning information generated by the GNSS positioning unit  41  or the virtual GNSS positioning unit  43 
 
(2) Terminal identification information (ID) of Bluetooth (registered trademark) or Wi-Fi of the other terminal that is received
 
(3) Sounds received by a microphone
 
(4) Information of captured images taken by a camera
 
     The accompanying person recognition units  40  of the wearable terminals  100   m  and  100   n  transmit the information described above in (1) to (4) to the accompanying person recognition unit  38  of the server  300   m . The accompanying person recognition unit  38  of the server  300   m , which receives the information, then determines the distance to each wearable terminal  100 , for example, from the positioning information in (1). If the distance is a predetermined distance or less, the user who possesses the wearable terminal  100  may be recognized as an accompanying person. 
     Further, with respect to the terminal identification information in (2), the accompanying person recognition unit  38  of the server  300   m  may recognize, as an accompanying person, the user who possesses the wearable terminal  100  whose terminal identification information is observes on a long-term basis. That is, in a case where the wearable terminal  100  having terminal identification information A observes the wearable terminal  100  of terminal identification information B on a long-term basis, the user who possesses the wearable terminal  100  having the terminal identification information B is identified as an accompanying person. 
     Further, the accompanying person recognition unit  38  of the server  300   m  may perform environmental sound matching on the basis of the sound information in (3), and recognize the user of a wearable terminal having similar sound information as an accompanying person. Further, the accompanying person recognition unit  38  of the server  300   m  may recognize, on the basis of the image information in (4), a person recognized in captured images within a predetermined period of time as an accompanying person. Person data (such as face image data) used for image recognition may be then stored in each of the wearable terminals  100   m  and  100   n , and the accompanying person recognition units  40  of the wearable terminals  100   m  and  100   n  may transmit the person data to the server  300   m.    
     Further, the above-described accompanying person recognition unit  38  of the server  300   m  may recognize an accompanying person on the basis of an action such as a user&#39;s nod or hand movement (gesture) described in the first embodiment, or a feature value indicating an interaction between users (i.e., accompanying persons) which is based on the sounds of speech between the users. Further, various kinds of information in (1) to (4) and various kinds of information of an interaction between users user may be integrated to recognize an accompanying person. If an accompanying person is selected on the basis of the above-described various kinds of information, the recognition method corresponding to the conditions of the wearable terminals  100   m  and  100   n  is selected. For example, when a camera is activated, information of captured images of the camera may be used to recognize an accompanying person. Further, when a microphone is activated, sound information may be used to recognize an accompanying person. Further, the integration and use of some kinds of information make it possible to more accurately identify an accompanying person. As described above, various kinds of information in (1) to (4) and various kinds of information of an interaction between users can be an example of accompanying person recognition information used to recognize an accompanying person. 
     The above describes the example in which an accompanying person is recognized in the accompanying person recognition unit  40  of the server  300   m  via the server  300   m . An accompanying person may be, however, recognized through communication between the respective wearable terminals  100   m  and  100   n .  FIG. 23  is a diagram illustrating the configuration of an example in which an accompanying person is recognized in each of the wearable terminals  100   m  and  100   n . The accompanying person recognition unit  40  in  FIG. 23  has the function of the accompanying person recognition unit  38  of the server  300   m  in  FIG. 22 . Further, the GNSS control unit  42  in  FIG. 23  has the function of the GNSS positioning decision unit  39  of the server in  FIG. 22 . Further, the mobile terminal  200  configured in a similar way may form a similar system. 
     The above describes the example in which GNSS positioning rights are transferred among grouped users. The following describes an example in which positioning information of a device such as a vehicle including a sufficiently large power source and capable of GNSS positioning is used. 
       FIG. 24  is a block diagram illustrating a vehicle  400  including a GNSS positioning unit  45 , and the wearable terminal  100  that uses positioning information measured by the vehicle  400 . The wearable terminal  100  and the vehicle  400  illustrated in  FIG. 24  are associated with each other through communication established by the communication units  31  and  44 . The wearable terminal  100  and the vehicle  400  illustrated in  FIG. 24  may be associated with each other, for example, through pairing of Bluetooth (registered trademark) or the like. 
     The GNSS control unit  42  of the wearable terminal  100  associated with the vehicle  400  powers off the GNSS positioning unit  41 . The GNSS control unit  42  then acquires positioning information measured by the GNSS positioning unit  45  of the vehicle  400  via the communication unit  31 . The GNSS control unit  42  turns on the virtual GNSS positioning unit  43 , and recognizes the position of an own terminal by using the acquired control information. Once the association of the wearable terminal  100  with the vehicle  400  is released, the wearable terminal  100  turns on the GNSS positioning unit  41  of the wearable terminal  100  and performs positioning by itself. 
     In a case where a device such as the vehicle  400  including a sufficient power source is associated with the wearable terminal  100  in this way, the wearable terminal  100  uses positioning information measured by the device including a sufficient power source. This reduces the power consumption of the wearable terminal  100 . 
     2-2. Application Example 
     The above describes the example of the system that uses positioning information measured by another device. The following describes an application example of the system. Positioning information is shared between terminals positioned adjacent to each other in the application example. This application example is effective in a situation in which a large amount of terminals crowd a limited area such as a shopping mall. 
       FIG. 25  is a flowchart illustrating the operation of the application example. First, in S 100 , the wearable terminal  100  scans adjacent terminals by using the communication unit  31  of Bluetooth (registered trademark) or the like. At this time, near-field communication such as Bluetooth Low Energy allows the communication unit  31  of the wearable terminal  100  to detect terminals within a radius of some meters. 
     Next, in S 102 , the wearable terminal  100  determines the number of adjacent terminals scanned in S 100 . Next, in S 106 , the wearable terminal  100  performs intermittent positioning described below in detail on the basis of the number of adjacent terminals determined in S 102 . 
     Next, the wearable terminal  100  determines in S 108  whether to receive positioning information from another terminal. Here, in a case where no positioning information is acquired from another terminal, the processing proceeds to S 112  and the wearable terminal  100  performs GNSS positioning by itself. If the wearable terminal receives positioning information from another terminal in S 108 , the processing proceeds to S 110  and the wearable terminal uses the positioning information received from the other terminal to recognize the position of an own terminal. The processing then returns to S 100 , and the above-described processing is repeated. 
     The above describes the operation of the application example of the second embodiment. The following describes the intermittent positioning in S 106  of  FIG. 25  in more detail. As described above, in a case where the wearable terminal  100  receives positioning information from another terminal, the wearable terminal that receives the positioning information does not have to perform positioning by itself. The wearable terminal  100  can therefore intermittently perform positioning in the above-described system. 
     Further, as described above, in a case where the wearable terminal  100  intermittently performs positioning, the intermittence rate may be changed in the accordance with the number of adjacent terminals determined in S 102 . It is assumed that the number of adjacent terminals determined in S 102  is, for example, ten, and each performs positioning at an intermittence rate of 90%. Here, an intermittence rate of 90% means that the GNSS positioning unit  41  is turned on, for example, for only one second every ten seconds. 
     The probability that the nine terminals other than the own terminal do not perform positioning is 0.9̂10≈0.35 (35%) in the above-described situation. Here, the probability that the terminals other than the own terminal do not perform positioning for three straight seconds is 0.35̂3≈0.039 (3.9%). This probability is very low. That is, there is a very high probability that the wearable terminal  100  can receive positioning information from another terminal at at least approximately 3-second intervals. The wearable terminal  100  can therefore acquire positioning information with sufficient accuracy in the above-described system while maintaining an intermittence rate of 90%. 
     As understood from the above description, a wearable terminal  199  can increase the intermittence rate if more adjacent terminals are detected, while the wearable terminal  100  has to decrease the intermittence rate if fewer adjacent terminals are detected. Intermittently operating the GNSS positioning unit  41  in this way allows the wearable terminal  100  to save power. Further, GNSS positioning may be executed by being complemented with past positioning information in the GNSS positioning method for intermittent positioning. At this time, if the past positioning information is too old, complementation can be impossible. Meanwhile, the use of the above-described system makes it possible to acquire positioning information from another terminal in spite of the increased intermittence rate. Accordingly, positioning information is appropriately complemented. 
     2-3. Supplemental Information of Second Embodiment 
     The embodiments of the present disclosure may include, for example, an information processing device as described above, a system, an information processing method executed by the information processing device or the system, a program for causing the information processing device to function, and a non-transitory tangible medium having the program recorded thereon. 
     Additionally, the example in which an accompanying person is recognized from various kinds of information detected by the wearable terminal  100  has been described in the above-described embodiment. An accompanying person may be, however, recognized by using a dedicated application that registers a user as an accompanying person in advance. Further, an accompanying person may be recognized by using a group function of an existing social network service (SNS). 
     3. Hardware Configuration 
     Next, the hardware configuration of the information processing device according to the embodiment of the present disclosure will be described with reference to  FIG. 26 .  FIG. 26  is a block diagram illustrating a hardware configuration example of the information processing device according to the  6  embodiment of the present disclosure. 
     The information processing device  900  includes a central processing unit (CPU)  901 , read only memory (ROM)  903 , and random access memory (RAM)  905 . 
     Further, the information processing device  900  may include a host bus  907 , a bridge  909 , an external bus  911 , an interface  913 , an input device  915 , an output device  917 , a storage device  919 , a drive  921 , a connection port  923 , and a communication device  925 . Moreover, the information processing device  900  may include an imaging device  933  and a sensor  935  as necessary. The information processing device  900  may include a processing circuit such as a digital signal processor (DSP), an application specific integrated circuit (ASIC), or a field-programmable gate array (FPGA) instead of or in combination with the CPU  901 . 
     The CPU  901  functions as an operation processing device and a control device, and controls all or some of the operations in the information processing device  900  in accordance with a variety of programs recorded on the ROM  903 , the RAM  905 , the storage device  919 , or a removable recording medium  927 . The ROM  903  stores a program, an operation parameter, and the like which are used by the CPU  901 . The RAM  905  primarily stores a program which is used in the execution of the CPU  901  and a parameter which is appropriately modified in the execution. The CPU  901 , the ROM  903 , and the RAM  905  are connected to each other by the host bus  907  including an internal bus such as a CPU bus. Moreover, the host bus  907  is connected to the external bus  911  such as a peripheral component interconnect/interface (PCI) bus via the bridge  909 . 
     The input device  915  is a device which is operated by a user, such as a mouse, a keyboard, a touch panel, a button, a switch, and a lever. The input device  915  may be, for example, a remote control device using infrared light or other radio waves, or may be an external connection device  929  such as a mobile phone operable in response to the operation of the information processing device  900 . The input device  915  includes an input control circuit which generates an input signal on the basis of information input by a user and outputs the input signal to the CPU  901 . By operating the input device  915 , a user inputs various types of data to the information processing device  900  or requires a processing operation. 
     The output device  917  includes a device capable of notifying a user of the acquired information via senses of sight, hearing, touch, and the like. The output device  917  can be a display device such as a liquid crystal display (LCD) or an organic electro-luminescence (EL) display, a sound output device such as a speaker or headphones, a vibrator, or the like. The output device  917  outputs a result obtained by the information processing device  900  performing processing as video such as text or images, audio such as speech or sounds, vibration, or the like. 
     The storage device  919  is a device for data storage which is configured as an example of a storage unit of the information processing device  900 . The storage device  919  includes, for example, a magnetic storage device such as a hard disk drive (HDD), a semiconductor storage device, an optical storage device, or a magneto-optical storage device. The storage device  919  stores a program, for example, to be executed by the CPU  901 , various types of data, various types of data acquired from the outside, and the like. 
     The drive  921  is a reader/writer for the removable recording medium  927  such as a magnetic disk, an optical disc, a magneto-optical disk, and a semiconductor memory, and is built in the information processing device  900  or externally attached thereto. The drive  921  reads out information recorded in the removable recording medium  927  attached thereto, and outputs the read-out information to the RAM  905 . Further, the drive  921  writes record into the mounted removable recording medium  927 . 
     The connection port  923  is a port used to connect a device to the information processing device  900 . The connection port  923  may include, for example, a universal serial bus (USB) port, an IEEE1394 port, and a small computer system interface (SCSI) port. The connection port  923  may further include an RS-232C port, an optical audio terminal, a high-definition multimedia interface (HDMI) (registered trademark) port, and so on. The connection of the external connection device  929  to the connection port  923  makes it possible to exchange various types of data between the information processing device  900  and the external connection device  929 . 
     The communication device  925  is, for example, a communication interface including a communication device or the like for a connection to a communication network  931 . The communication device  925  may be, for example, a communication card for a local area network (LAN), Bluetooth (registered trademark), Wi-Fi, a wireless USB (WUSB), or the like. Further, the communication device  925  may be a router for optical communication, a router for an asymmetric digital subscriber line (ADSL), a modem for various kinds of communication, or the like. The communication device  925  transmits a signal to and receives a signal from, for example, the Internet or other communication devices on the basis of a predetermined protocol such as TCP/IP. Further, the communication network  931  connected to the communication device  925  may include a network connected in a wired or wireless manner, and is, for example, the Internet, a home LAN, infrared communication, radio wave communication, satellite communication, or the like. 
     The imaging device  933  is a device that images a real space by using an image sensor such as a complementary metal oxide semiconductor (CMOS) or a charge coupled device (CCD), and a variety of members such as a lens for controlling the formation of an object image on the image sensor, and generates a captured image. The imaging device  933  may be a device that captures a still image, and may also be a device that captures a moving image. 
     The sensor  935  includes a variety of sensors such as an acceleration sensor, an angular velocity sensor, a geomagnetic sensor, an illuminance sensor, a temperature sensor, a barometric sensor, or a sound sensor (microphone). The sensor  935  acquires information on a state of the information processing device  900 , such as the attitude of the housing of the information processing device  900 , and information on an environment around the information processing device  900 , such as the brightness and noise around the information processing device  900 . The sensor  935  may also include a global positioning system (GPS) receiver that receives GPS signals and measures the latitude, longitude, and altitude of the device. 
     The example of the hardware configuration of the information processing device  900  has been described above. Each of the above-described components may be configured with a general-purpose member, and may also be configured with hardware specialized in the function of each component. Such a configuration may also be modified as appropriate in accordance with the technological level at the time of the implementation. 
     The preferred embodiment(s) of the present disclosure has/have been described above with reference to the accompanying drawings, whilst the present disclosure is not limited to the above examples. A person skilled in the art may find various alterations and modifications within the scope of the appended claims, and it should be understood that they will naturally come under the technical scope of the present disclosure. 
     Further, the effects described in this specification are merely illustrative or exemplified effects, and are not limitative. That is, with or in the place of the above effects, the technology according to the present disclosure may achieve other effects that are clear to those skilled in the art from the description of this specification. 
     Additionally, the present technology may also be configured as below. 
     (1) 
     An information processing device including: 
     a communication determination unit configured to determine, on the basis of a feature value extracted from speech data including at least a sound of speech of a user, whether communication occurs between users including the user, the feature value indicating an interaction between the users. 
     (2) 
     The information processing device according to (1), in which 
     the users include a first user and a second user, and 
     the feature value is extracted on the basis of a temporal relationship between a sound of speech of the first user and a sound of speech of the second user which are included in the speech data. 
     (3) 
     The information processing device according to (2), in which the speech data includes first speech data including the sound of speech of the first user, and second speech data including the sound of speech of the second user. 
     (4) 
     The information processing device according to (2), in which 
     the speech data includes a single piece of speech data including the sound of speech of the first user, and the sound of speech of the second user. 
     (5) 
     The information processing device according to any one of (1) to (4), further including: 
     a feature value extraction unit configured to extract the feature value from the speech data. 
     (6) 
     The information processing device according to any one of (1) to (5), in which 
     the communication determination unit determines whether the communication occurs between users selected from the users on the basis of a positional relationship between the users. 
     (7) 
     The information processing device according to any one of (1) to (6), in which 
     the communication determination unit determines whether the communication occurs between users selected from the users on the basis of behavior information of each user. 
     (8) 
     The information processing device according to any one of (1) to (7), in which 
     the feature value is extracted further from sensor data indicating motions or biological indicators of the users. 
     (9) 
     The information processing device according to (8), in which 
     the users include a third user and a fourth user, and 
     the feature value is extracted on the basis of a relationship between a sound of speech of the third user included in the speech data, and a motion or a biological indicator of the fourth user indicated by the sensor data. 
     (10) 
     The information processing device according to any one of (1) to (9), further including: 
     a display control unit configured to display a screen for presenting the communication in a chronological order. 
     (11) 
     The information processing device according to (10), in which 
     the communication is presented in the screen in a form corresponding to occurrence probability of the communication calculated on the basis of the feature value. 
     (12) 
     The information processing device according to any one of (1) to (11), further including: 
     a log output unit configured to output, on the basis of an occurrence history of the communication, a log including at least one of information of a person with whom at least one user included in the users communicates, or information of a conversation with the person with whom the at least one user included in the users communicates. 
     (13) 
     The information processing device according to (12), in which 
     the log output unit outputs the log onto a time line presented to the at least one user. 
     (14) The information processing device according to any one of (1) to (13), further including: 
     a relationship graph specification unit configured to specify a graph structure expressing a relationship between the users on the basis of an occurrence history of the communication. 
     (15) 
     The information processing device according to (14), further including: 
     a sharing user specification unit configured to apply, in a phase in which at least one user included in the users shares information, a filter related to the shared information to the graph structure, thereby specifying another user who shares the information. 
     (16) 
     The information processing device according to (15), in which 
     the filter is configured on the basis of a positional relationship with a user included in the graph structure, a group to which a user included in the graph structure belongs, an interest of a user included in the graph structure, behavior information of a user included in the graph structure, or knowledge of a user included in the graph structure. 
     (17) 
     The information processing device according to any one of (14) to (16), in which 
     the relationship graph specification unit temporarily corrects the graph structure specified on the basis of the occurrence history of the communication within a first period of time in accordance with a content of the communication occurring within a most recent second period of time that is shorter than the first period of time. 
     (18) 
     The information processing device according to (17), in which 
     the users include a fifth user and a sixth user, and 
     in a case where a content sent by the fifth user includes a name of the sixth user in the communication occurring within the second period of time, the relationship graph specification unit temporarily strengthens a relationship between the fifth user and the sixth user in the graph structure. 
     (19) 
     An information processing method including, by a processor: 
     determining, on the basis of a feature value extracted from speech data including at least a sound of speech of a user, whether communication occurs between users including the user, the feature value indicating an interaction between the users. 
     (20) 
     A program for causing a computer to execute: 
     a function of determining, on the basis of a feature value extracted from speech data including at least a sound of speech of a user, whether communication occurs between users including the user, the feature value indicating an interaction between the users. 
     (21) 
     The information processing device according to (1), including: 
     an accompanying person recognition unit configured to recognize an accompanying person of the user on the basis of accompanying person recognition information for recognizing the accompanying person; and 
     a GNSS positioning decision unit configured to determine whether a GNSS positioning right for GNSS positioning is provided to a first information processing device or a second information processing device, the first information processing device being possessed by the user, the second information processing device being possessed by the accompanying person. 
     (22) 
     The information processing device according to (21), in which 
     the accompanying person recognition information includes any one or a combination of a feature value indicating an interaction between the user and the accompanying person, or image information captured by the first information processing device possessed by the user, or information on a distance between the first information processing device and the second information processing device, or terminal identification information sent by the first information processing device or the second information processing device. 
     (23) 
     The information processing device according to (21) or (22), in which 
     remaining battery levels of the first information processing device and the second information processing device are recognized, and an information processing device to which the GNSS positioning right is provided is decided on the basis of the remaining battery levels. 
     (24) 
     The information processing device according to any one of (21) to (23), in which 
     in a case where a vehicle that is adjacent to the first information processing device and is capable of GNSS positioning is recognized, positioning information is acquired from the vehicle. 
     (25) 
     The information processing device according to any one of (21) to (24), further including: 
     a communication unit, in which 
     a frequency at which GNSS positioning is intermittently performed is changed in accordance with a number of adjacent terminals recognized by the communication unit. 
     REFERENCE SIGNS LIST 
     
         
           10  system 
           11  sensing unit 
           12  action detection unit 
           13  candidate selection unit 
           14  feature value extraction unit 
           15  conversation determination unit 
           16  score calculation unit 
           17  grouping unit 
           38 ,  40  accompanying person recognition unit 
           39  GNSS positioning decision unit 
           100   a  eyewear 
           100   b  wristwear 
           200  mobile terminal 
           300  server