Patent Publication Number: US-9843899-B2

Title: Information processing apparatus and method to organize devices

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a U.S. National Phase of International Patent Application No. PCT/JP2015/057946 filed on Mar. 17, 2015, which claims priority benefit of Japanese Patent Application No. JP 2014-132852 filed in the Japan Patent Office on Jun. 27, 2014. Each of the above-referenced applications is hereby incorporated herein by reference in its entirety. 
     TECHNICAL FIELD 
     The present invention relates to information processing apparatuses, information processing methods, and programs. 
     BACKGROUND ART 
     Patent Literature 1 discloses a technology for suppressing calculation cost to select an item by classifying users and items based on item use logs of the users. For example, many technologies for performing operation of aggregation, analysis, classification, or the like on data of a user specified by a user account, data of a log related to items registered in a database, or the like have already been proposed. 
     CITATION LIST 
     Patent Literature 
     Patent Literature 1: JP2013-164704A 
     SUMMARY OF INVENTION 
     Technical Problem 
     In recent years, various devices have intelligent functions. For example, not only information processing terminals such as a personal computer, a smartphone, and a tablet, various devices such as home appliances including an air conditioner, a refrigerator, and the like, a car, and a vending machine each of which includes a communication function, an information processing function, a sensing function, or the like, have become more and more popular. Such a device includes the communication function. However, unlike the information processing terminal, a user is not always identified by a login operation or the like. For example, like the vending machine, some device is configured to be used by many and unspecified users. 
     In a way similar to the information processing terminal, such a device can be used as a means for acquiring a device usage log and the like as statistical information of a user, or a means for outputting information to a user, for example. However, as described above, such a device does not identify a user (even in the case where the device is used by the specific user), or is configured to be used by many and unspecified users. Therefore, it is difficult to organize and use devices. 
     Accordingly, the present disclosure proposes a novel and improved information processing apparatus, information processing method, and program that are capable of utilizing various devices more effectively by organizing such devices. 
     Solution to Problem 
     According to the present disclosure, there is provided an information processing apparatus including: a device log acquisition unit configured to acquire a time-series device log including information indicating a position of each of devices from each of the devices; a grouping unit configured to classify the devices into at least one group on the basis of the device logs and a preset condition of the positions; and a relation analysis unit configured to analyze a relation between the devices in each of the at least one group on the basis of the device logs. 
     According to the present disclosure, there is provided an information processing method including: acquiring a time-series device log including information indicating a position of each of devices from each of the devices; classifying, by a processor, the devices into at least one group on the basis of the device logs and a preset condition of the positions; and analyzing a relation between the devices in each of the at least one group on the basis of the device logs. 
     According to the present disclosure, there is provided a program for causing a computer to achieve: a function of classifying devices into at least one group on the basis of a preset condition of a position of each of the devices and a time-series device log that has been acquired from each of the devices and that includes information indicating the position of each of the devices; and a function of analyzing a relation between the devices in each of the at least one group on the basis of the device logs. 
     Advantageous Effects of Invention 
     As described above, according to the present disclosure, it is possible to utilize various devices more effectively by organizing such devices. 
     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 illustrating a schematic configuration of a system according to a first embodiment of the present disclosure. 
         FIG. 2  is a block diagram schematically illustrating a functional configuration of a server according to an embodiment of the present disclosure. 
         FIG. 3  is a flowchart illustrating an example of a process performed by a correlation determination unit according to the first embodiment of the present disclosure. 
         FIG. 4A  is a diagram illustrating an example of a device log DB according to the first embodiment of the present disclosure. 
         FIG. 4B  is a diagram illustrating an example of a device log DB according to the first embodiment of the present disclosure. 
         FIG. 4C  is a diagram illustrating an example of a device log DB according to the first embodiment of the present disclosure. 
         FIG. 5  is a diagram illustrating an example of a correlation condition DB according to the first embodiment of the present disclosure. 
         FIG. 6  is a diagram illustrating an example of a correlation score DB according to the first embodiment of the present disclosure. 
         FIG. 7  is a block diagram schematically illustrating a functional configuration of a server according to a second embodiment of the present disclosure. 
         FIG. 8  is a flowchart illustrating an example of a grouping process performed on all devices according to the second embodiment of the present disclosure. 
         FIG. 9  is a flowchart illustrating an example of a grouping process performed on an additional device according to the second embodiment of the present disclosure. 
         FIG. 10  is a diagram illustrating an example of a group DB according to the second embodiment of the present disclosure. 
         FIG. 11  is a flowchart illustrating an example of a grouping process according to a third embodiment of the present disclosure. 
         FIG. 12  is a diagram illustrating an example of a device log DB according to the third embodiment of the present disclosure. 
         FIG. 13  is a block diagram illustrating a hardware configuration example of an information processing apparatus according to an embodiment of the present disclosure. 
     
    
    
     DESCRIPTION OF EMBODIMENT(S) 
     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. 
     Note that the description is given in the following order.
     1. First Embodiment   1-1. System configuration   1-2. Functional configuration of server   1-3. Workflow of process   1-4. Example of data   1-5. Specific example of correlation determination   2. Second Embodiment   3. Third Embodiment   4. Hardware Configuration   5. Supplement
 
1. First Embodiment
 
(1-1. System Configuration)
   

       FIG. 1  is a diagram illustrating a schematic configuration of a system according to a first embodiment of the present disclosure. With reference to  FIG. 1 , a system  10  according to the embodiment includes devices  100  and a server  200 . As an example of the devices  100 ,  FIG. 1  illustrates a smartphone  100   a , a tablet  100   b , a laptop personal computer (PC)  100   c , a wearable device  100   d , a car-mounted device  100   e , a television  100   f , a game console  100   g , an air conditioner  100   h , a light  100   i , and a kitchen appliance  100   j.    
     The example of the devices  100  is not limited thereto, and the devices  100  may include various other devices. For example, the devices  100  may include a mobile device other than the smartphone, tablet, and laptop PC. In addition to the illustrated eyewear, the devices  100  may include other wearable devices such as a contact lens type terminal, a watch type terminal, a bracelet type terminal, a ring type terminal, a headset, a terminal attached to clothing, a terminal integrated into clothing, a terminal attached to shoes, a terminal integrated into shoes, and a necklace type terminal. Specifically, the car-mounted device  100   e  may be a car navigation system or a rear seat entertainment system. The devices  100  are not limited to the television, game consoles, air conditioner, or light. The devices  100  may include every kind of consumer electronics (CE) devices having communication functions and information processing functions. 
     The devices  100  may include a device shared by many and unspecified users. For example, the devices  100  may include a public display, a terminal device used for an order system in a restaurant or the like, and a vending machine. In addition, sometimes the mobile device or the car-mounted device may be rented or mounted on a rental car, and used by many and unspecified users. 
     In the above described environment where there are various types of devices, one user often uses two or more of the devices  100 . For example, the user may search for information on a movie by using the smartphone  100   a  during watching the movie on the television  100   f . Alternatively, the user may ride on a vehicle equipped with the car-mounted device  100   e  while holding the tablet  100   b . Alternatively, the user may switch on/off the air conditioner  100   h , switch on/off the light  100   i , or make a dish by using the kitchen appliance  100   j  while wearing the wearable device  100   d . Alternatively, the user may search for walkthroughs of a game by using the laptop PC  100   c  while playing the game on the game console  100   g.    
     In such cases, if it becomes possible to associate the two or more of the devices  100  with each other as devices  100  used by one user, such association helps acquiring various long-term logs of the user and helps selecting an optimum output device for providing the user with information, for example. However, such association of the devices  100  is possible only in a limited case, and in most cases it is difficult to associate the devices  100  with each other. 
     For example, when one user has logged in to services using the same user account via two or more of the devices  100 , these devices  100  can be associated with each other. However, the devices  100  not always use the same user account. In addition, in a device  100  for using a service that does not require login or a device  100  without a function for performing a login operation, the user does not perform login at all. 
     On the other hand, for example, two or more devices  100  can be associated with each other in the case where these devices  100  identify users by analyzing images acquired by their cameras and the identified users are the same parson. However, the devices  100  do not always have the cameras, and the images of the user are not always acquired. In addition, the image analysis to identify a user requires high processing load, and it is difficult to enhance its accuracy. 
     On the other hand, for example, two or more devices  100  can be associated with each other in the case where network addresses such as IP addresses used for communication between these devices  100  are acquired and the network addresses are the same. In such a way, it is possible to associate devices  100  that are connected to each other via Wi-Fi or the like in a house, for example. However, a mobile device that performs communication via a mobile network such as a mobile telephone network even in the house has a different network address from a CE device that performs communication via a local area network (LAN) and a fixed line through a router. Therefore, it is difficult to associate the mobile device with the CE device. 
     According to the first embodiment (to be described later) of the present disclosure, for example, it is possible to easily evaluate a relation between devices  100  that are used by the same user by associating the devices  100  with each other in a way different from the above described examples. 
     (1-2. Functional Configuration of Server) 
       FIG. 2  is a block diagram schematically illustrating a functional configuration of a server according to an embodiment of the present disclosure. With reference to  FIG. 2 , a server  200  includes a device log acquisition unit  210 , a device log DB  220 , a correlation determination unit  230 , a correlation condition DB  240 , and a correlation score DB  250 . 
     The server  200  is realized by one or a plurality of server apparatuses on a network. Each of the server apparatuses is realized by a hardware configuration of an information processing apparatus (to be described later). For example, the device log acquisition unit  210  is realized by a communication apparatus, the device log DB  220 , the correlation condition DB  240 , and the correlation score DB  250  are realized by memory or a storage, and the correlation determination unit  230  is realized by a processor such as a CPU. In the case where the server  200  is realized by the plurality of server apparatus, each of the server apparatuses realizes one of the illustrated functional configurations. Alternatively, it is also possible that one functional configuration is distributed to the plurality of server apparatus. 
     The device log acquisition unit  210  acquires device logs provided by the devices  100  illustrated in  FIG. 1 . For example, the device log may be a time-series log including information on a position of each device and information on a state caused by behavior of a user related to each device. For example, the device log acquisition unit  210  is transmitted from a device periodically or when a certain event occurs. In the embodiment, it is assumed that unique IDs (device ID and user ID) are assigned to the devices and users who use the devices. The logs acquired by the device log acquisition unit  21  are stored in the device log DB  220 . A specific example of data stored in the device log DB  220  is described later. 
     The correlation determination unit  230  determines a correlation between at least two devices that provide the device logs. The correlation determination unit  230  determines a correlation between devices on the basis of preset conditions of states of the devices, and stores the result in the correlation score DB  250 . The preset conditions are registered in the correlation condition DB  240 . In the embodiment, the correlation condition DB  240  defines correlation conditions mainly in terms of whether or not respective devices are used by a same user. In the embodiment, a plus score is calculated for a combination of devices that are highly possible to have been used by a same user, and a minus score is calculated for a combination of devices that are highly possible to have been used by different users. A specific example of data stored in the correlation condition DB  240  and the correlation score DB  250  is described later. 
     (1-3. Workflow of Process) 
       FIG. 3  is a flowchart illustrating an example of a process performed by the correlation determination unit according to the first embodiment of the present disclosure. With reference to  FIG. 3 , the correlation determination unit  230  first performs a loop process for each combination of devices on log data stored in the device log DB  220  (S 101 ). Here, the correlation determination unit  230  acquires logs of the devices in the combination (S 103 ). The acquired log may include a series of logs of each device that have been acquired in chronological order. It is assumed that the log includes at least time and positional information. 
     Next, the correlation determination unit  230  determines whether or not positions of the devices are in proximity at any point in time in accordance with the logs (S 105 ), and calculates a correlation score in processes in subsequent Steps S 107  to S 109  only for a combination of devices that are in proximity at any point in time. Conversely, in the illustrated example, a correlation score is not calculated for a combination of devices that are not in proximity at every point in time. Such a combination of devices is treated as a combination for which it is impossible to determine whether or not devices are correlated (whether or not the devices have been used by a same user), for example. In addition, in this determination, it is only necessary that positions of devices are in proximity at least at a certain time. The positions may be different at a time other than the certain time. 
     In the case where it has been determined that the positions are in proximity at any point in time in the step S 105 , the correlation determination unit  230  further determines whether or not a condition indicated by logs of the devices is registered in the correlation condition DB  240  (S 107 ). A specific example of the determination processes is described later in addition to a detailed example of the correlation condition DB  240 . Here, in the case where it has been determined that the condition is not registered, a correlation score is not calculated in a way similar to the case where the positions are not in proximity, and the combination of the devices is treated as a combination for which it is impossible to determine whether or not devices are correlated (whether or not the devices have been used by a same user), for example. 
     In the case where it has been determined that the condition indicated by the logs of the devices is registered in the correlation condition DB  240  in the step S 107 , the correlation determination unit  230  updates the correlation score of the devices recorded in the correlation score DB  250 , on the basis of information associated with the registered condition (S 109 ). Here, as described later, correlation scores according to the embodiment include a positive score indicating a high correlation between devices and a negative score indicating a low correlation between devices. Accordingly, in the illustrated example, the correlation determination unit  230  determines level of a correlation of a combination of devices that are in proximity at least at any point in time, in accordance with the conditions registered in the correlation condition DB  240 . On the other hand, the correlation determination unit  230  does not actively determine a correlation between devices other than the above described devices, that is, devices that are not in proximity at every point in time or devices that do not satisfy the conditions registered in the correlation condition DB  240 . 
     (1-4. Example of Data) 
       FIG. 4A  to  FIG. 4C  are each a diagram illustrating an example of the device log DB according to the first embodiment of the present disclosure. With reference to  FIG. 4A  to  FIG. 4C , records in the device log DB  220  may include fields of time  220   a , device ID  220   b , device type  220   c , state  220   d , detailed state  220   e , and position  220   f .  FIG. 4A  to  FIG. 4C  will be referred to again for describing correlation determination in an example of the correlation condition DB  240  (to be described later). 
     The time  220   a  is a timestamp indicating time when a log has been acquired. The device ID  220   b  is an ID for identifying a device that has provided the log. The device type  220   c  is the type of the device that has provided the log (the device type may be separately defined in association with the device ID). 
     The state  220   d  is a state of the device indicated by the device ID  220   b  at the time  220   a . With reference to the illustrated example, the state  220   d  may indicate a function of the device such as “video playback”, “web browsing”, or the like. Alternatively, the state  220   d  may indicate that the device has been switched on/off, simply indicate that the device has been operated, or indicate that the device has moved, for example. 
     The detailed state  220   e  indicates details of the state indicated by the state  220   d . For example, like the illustrated example, the detailed state  220   e  may indicate the title of the video in the case where the state  220   d  is a “video playback” state. In the case where the state  220   d  is a “web browsing” state, the detailed state  220   e  may indicate the title of the web page. 
     The position  220   f  is a position of the device indicated by the device ID  220   b  at the time  220   a . For example, the position  220   f  may be positional information acquired by each device providing positioning using a global navigation satellite system (GNSS) such as the GPS. Alternatively, the position  220   f  may be a position that the user has input in advance as a position where the device is fixedly installed (in this case, positional information provided by each device is a fixed value). 
       FIG. 5  is a diagram illustrating an example of the correlation condition DB according to the first embodiment of the present disclosure. With reference to  FIG. 5 , records in the correlation condition DB  240  may include fields of state ( 1 )  240   a , detailed state ( 1 )  204   b , state ( 2 )  240   c , detailed state ( 2 )  240   d , temporal relationship  240   e , detailed condition  240   f , and correlation  240   g.    
     The states  240   a  and  240   c  designates states of devices in the combination of which a correlation is determined. For example, in the case where positions of two devices are in proximity at any point in time and one of the devices in the state  240   a  and the other of the devices is in the state  240   c , it may be possible to determine a correlation between these devices. 
     With regard to the state  240   a  and  240   c  of the respective devices, the detailed states  240   b  and  240   d  designate information used in determination of the detailed condition  240   f  (to be described later). Therefore, depending on the detailed condition  240   f , sometimes at least any of the detailed states  240   b  and  240   d  is not set. In addition, the detailed state  240   b  and  240   b  are not necessarily in proximity to the detailed state  220   e  in the device log DB  220 . 
     The temporal relationship  240   e  indicates a temporal relationship between the states  240   a  and  240   c  of the respective devices. The illustrated example includes two types of the temporal relationship: “synchronization” and “switchover”. The “synchronization” indicates that the states  240   a  and  240   c  of the devices are caused at the same time in parallel. The “switchover” indicates that the states  240   a  and  240   c  of the devices are caused alternately. A specific example of the temporal relationship will be described later. 
     The detailed condition  240   f  indicates an additional determination condition in the case where the states  240   a  and  240   c  are caused in the devices in the combination and the temporal relationship  240   e  is satisfied. For example, in a record  240 - 1 , the detailed condition  240   f  is that, in the case where one of the devices is in the “video playback” state (state  240   a ) and the other of the devices is in the “web browsing” state (state  240   c ), the video title (detailed state  240   b ) of video played back in the “video playback” state corresponds to the web page title (detailed state  240   d ) of a web page browsed in the “web browsing state”. 
     The correlation  240   g  indicates an estimated correlation between the devices in the combination in the case where a condition indicated in each record is satisfied. The illustrated example includes two types of the correlation: “YES” and “NO”. “YES” indicates that the devices are highly possible to have been used by a same user, and a correlation between the device is high. “NO” indicates that the devices are highly possible to have been used by different users, and a correlation between the devices is low. 
     As described above, according to the embodiment, the correlation determination unit  230  estimates a correlation of devices in a combination indicated by the correlation  240   g  in the case where the condition defined in the correlation condition DB  240  is satisfied. In the case where the conditions are not satisfied, the correlation determination unit  230  does not estimate the correlation (according to the above example, the correlation determination unit  230  determines that the correlation is neither YES or NO). 
       FIG. 6  is a diagram illustrating an example of the correlation score DB according to the first embodiment of the present disclosure. With reference to  FIG. 6 , records in the correlation score DB  250  may include fields of device ID ( 1 )  250   a , device ID ( 2 )  250   b , and correlation score  250   c.    
     According to the embodiment, the correlation determination unit  230  represents a correlation between devices determined in the above described process, as a correlation score. For example, the correlation determination unit  230  may add a predetermined correlation score with regard to a combination of devices in the case where a condition defined in a certain record in the correlation condition DB  240  are satisfied, and the correlation  240   g  of the certain record indicates that the correlation between the devices is high (“YES” in the example in  FIG. 5 ). On the other hand, in the case where the correlation  240   g  indicates that the correlation between the devices is low (“NO” in the example in  FIG. 5 ), the correlation determination unit  230  may subtract the predetermined correlation score with regard to the combination of the devices. 
     The device IDs  250   a  and  250   b  in the correlation score DB  250  of the illustrated example indicate devices in a combination for which a correlation score have been calculated. The correlation score  250   c  indicates a correlation score calculated for a combination of devices. For example, a record  250 - 1  indicates that a correlation score “+0.75” has been calculated for a combination of a device of ID “000001” and a device of ID “000002”. Although this score is the positive correlation score like a record  250 - 2 , the sore of the record  250 - 1  is higher than the record  250 - 2 . 
     For example, in the case where a combination of certain devices satisfies a plurality of conditions defined in the correlation condition DB  240  and all the conditions indicate that the correlation between the certain devices is high, the correlation determination unit  230  may integrate correlation scores corresponding to the respective conditions with regard to the certain devices. In this case, the correlation score  250   c  becomes higher as the combination of devices satisfies more conditions (indicating that the correlation between the devices is high). Alternatively, the condition defined in the correlation condition DB  240  does not have to correspond to a binary correlation (YES or NO) as illustrated in  FIG. 5 , but may correspond to a gradual correlation according to strength of the estimated correlation. According to such a configuration, it is possible to represent a gradual correlation of each combination of devices in the embodiment. 
     On the other hand, a record  250 - 3  in the correlation score DB  250  of the illustrated example indicates that a correlation score “−0.50” has been calculated for a combination of the device of ID “000001” and a device of ID “000004”. As described above, the negative correlation score is calculated in the case where the devices are highly possible to have been used by different users, and a correlation between the devices is low. 
     (1-5. Specific Example of Correlation Determination) 
     Next, with reference to the examples of a device log DB in  FIG. 4A  to  FIG. 4C  and the example of a correlation condition DB in  FIG. 5 , a specific example of correlation determination) according to the embodiment will be described. 
     As a first example, an example of two device (TV and smartphone) will be described with reference to  FIG. 4A . Records  220 - 1  and  220 - 2  in the device log DB  220  illustrated in  FIG. 4A  indicate that the TV and the smartphone have been in the same position (home) at the same time (2014/3/24 21:00:00). Therefore, the combination of the TV and the smartphone in the records  220 - 1  and  220 - 2  goes through the determination in S 105  in  FIG. 3  and proceeds to determination based on the condition defined in the correlation condition DB  240 . 
     At the above described time, the TV is in the “video playback” state, and the smartphone is in the “web browsing” state. Therefore, the states of these devices correspond to a “video playback” state  240   a  and a “web browsing” state  240   c  in the record  240 - 1  in the correlation condition DB  240  illustrated in  FIG. 5 . In addition, since the states of these devices are simultaneously caused at the above described time, the temporal relationship  240   e  “synchronization” in the record  240 - 1  is satisfied. 
     Subsequently, in order to determine whether a detailed condition  240   f  “titles correspond to each other” in the record  240 - 1  is satisfied, the correlation determination unit  230  acquires information recorded in detailed states  220   e  in the records  220 - 1  and  220 - 2 . In the illustrated example, the detailed state  220   e  in the record  220 - 1  indicates that the title of the video that is being played back is “XXXXX”. In addition, the detailed state  220   e  in the record  220 - 2  indicates that the title of the web page that is being browsed is “XXXXX fan page”. For example, the correlation determination unit  230  checks whether character strings of the titles match with each other, and determines that the titles correspond to each other and the detailed condition  240   f  in the record  240 - 1  is satisfied. 
     As a result of the above described determination, as shown in the correlation  240   g  in the record  240 - 1 , the correlation determination unit  230  estimates that it is highly possible that the TV and the smartphone of the records  220 - 1  and  220 - 2  are used by the same user and the correlation between the devices are high. The record  240 - 1  in the correlation condition DB  240  corresponds to the state in which the user is browsing information on the video by using a second device (smartphone here) while playing back the video on a first device (TV here), for example. 
     As described above, the correlation determination unit  230  determines that the correlation between the first device and the second device is high in the case where the first device provides the content of the first type, the second device provides the content of the second type that is different from the first type at the same time, and the content provided by these devices has a commonality. The combination of the types of the content is not limited to the video and the web page like the above described example. Combinations of any kinds of content are possible such as music content, a TV program, and an electronic book. 
     As a second example, an example of two device (TV and tablet) will be described with reference to  FIG. 4B . Records  220 - 3  and  220 - 5  in the device log DB  220  illustrated in  FIG. 4B  indicate that the TV and the tablet have been in the same position (home) at the same time (2014/3/24 21:00:00). Therefore, the combination of the TV and the tablet in the records  220 - 3  to  220 - 6  goes through the determination in S 105  in  FIG. 3  and proceeds to determination based on the condition defined in the correlation condition DB  240 . 
     The records  220 - 4  and  220 - 6  indicate that the devices are in different positions at another point in time (2014/3/24 21:30:00). However, as described above, it is only necessary that the devices are in proximity at any point in time in the determination in S 105 , the devices can be at different position at another point in time. Therefore, as described above, the combination of the TV and the tablet illustrated in  FIG. 4B  goes through the determination in S 105  and proceeds to subsequent determination. 
     The record  220 - 3  indicates a state of the TV among the records  220 - 3  to  220 - 6 . The record  220 - 3  indicates that the TV has been playing back the video at 21:00. The record  220 - 4  indicates that a state of the TV has not been detected at 21:30 (it may be possible that the TV has been powered off). On the other hand, the record  220 - 6  indicates a state of the tablet. The record  220 - 6  indicates that the tablet has been playing back the video at 21:30. The record  220 - 5  indicates that a state of the tablet has not been detected at 21:00 (it may be possible that the tablet has not provided any functions). 
     Since the TV is in the “video playback” state and the tablet is also in the “video playback” state in this case, it may be possible that the combination of the TV and the tablet in the records  220 - 3  and  220 - 6  satisfies a condition in a record  240 - 2  or  240 - 3  in the correlation condition DB  240  illustrated in  FIG. 5 . The temporal relationship  240   e  in the record  240 - 2  is “switchover”. The “switchover” indicates that the states  240   a  and  240   c  of the devices are caused alternately. On the other hand, the temporal relationship  240   e  in the record  240 - 3  is “synchronization”. The “video playback” state common in the records  220 - 3  and  220 - 6  has been caused in the TV at 21:00 and caused in the tablet at 21:30. Therefore, the temporal relationship  240   e  of “switchover” defined in the record  240 - 2  is satisfied. 
     Subsequently, in order to determine whether a detailed condition  240   f  “titles correspond to each other” in the record  240 - 2  is satisfied, the correlation determination unit  230  acquires information recorded in detailed states  220   e  in the records  220 - 3  and  220 - 6 . In the illustrated example, the detailed state  220   e  in the record  220 - 3  indicates that the title of the video that has been played back is “XXXXX”. In addition, the detailed state  220   e  in the record  220 - 6  indicates that the title of the video that has been played back is “XXXXX”. For example, the correlation determination unit  230  checks whether character strings of the titles match with each other, and determines that the titles correspond to each other and the detailed condition  240   f  in the record  240 - 2  is satisfied. 
     As a result of the above described determination, as shown in the correlation  240   g  in the record  240 - 2 , the correlation determination unit  230  estimates that it is highly possible that the TV and the tablet of the records  220 - 3  and  220 - 6  have been used by the same user and the correlation between the devices is high. The record  240 - 2  in the correlation condition DB  240  corresponds to the state in which a user has played back the video by using a first device (TV here), and the user has gone from one room to another room and has changed the first device to a second device (tablet here) to continue playback of the video, for example. 
     As described above, the correlation determination unit  230  determines that the correlation between the first device and the second device is high in the case where the first device provides the content of the first type, the second device provides the content of the first type alternately with the first device, and the content provided by these devices has a commonality. The types of the content are not limited to the video like the above described example. Any kinds of content is possible such as music content, a web page, a TV program, and an electronic book. 
     As a third example, an example of two devices (TV and smartphone) will be described with reference to  FIG. 4C . Records  220 - 7  and  220 - 8  in the device log DB  220  illustrated in  FIG. 4C  indicate that the TV and the tablet have been in the same position (home) at the same time (2014/3/24 21:00:00). Therefore, the combination of the TV and the smartphone in the records  220 - 7  and  220 - 8  goes through the determination in S 105  in  FIG. 3  and proceeds to determination based on the condition defined in the correlation condition DB  240 . 
     At the above described time, the TV is in the “video playback” state, and the smartphone is also in the “video playback” state. Therefore, it may be possible that the states of these devices correspond to a condition in a record  240 - 2  or the record  240 - 3  in the correlation condition DB  240  illustrated in  FIG. 5 . In addition, since the states of these devices have been simultaneously caused at the above described time, the temporal relationship  240   e  “switchover” in the record  240 - 2  is not satisfied but the temporal relationship  240   e  “synchronization” in the record  240 - 3  is satisfied. 
     In the record  240 - 3 , the detailed condition  240   f  is not set. In other words, unlike the two examples described above, the condition defined in the record  240 - 3  is satisfied when the two devices (TV and smartphone) have been in the same position at the same time and are playing back video (titles of video do not matter). In this case, as shown in the correlation  240   g  in the record  240 - 3 , the correlation determination unit  230  estimates that it is highly possible that the TV and the smartphone of the records  220 - 7  and  220 - 8  have been used by different users and the correlation between the devices is low. The record  240 - 3  in the correlation condition DB  240  corresponds to the state in which a first device (TV here) and a second device (smartphone here) are used by different users who are in proximity (for example, users live in the same house) to play back video, for example. 
     As described above, the correlation determination unit  230  determines that the correlation between the first device and the second device is low in the case where the first device provides the content of the first type, and the second device also provides content of the first type at the same time. The combination of the types of the content is not limited to the video and the web page like the above described example. Combinations of any kinds of content are possible such as music content, a TV program, and an electronic book. 
     Next, with reference to the other examples of the correlation condition DB illustrated in  FIG. 5 , the specific example of correlation estimation will be described. 
     In a way similar to the record  240 - 3  in the third example described above, the record  240 - 4  in the correlation condition DB  240  estimates that it is highly possible that two devices are used by different users and the correlation between the two devices is low in the case where the two devices are playing back music in synchronization. For example, the record  240 - 3  corresponds to a state in which a first device and a second device are used by different users who are in proximity to play back music. In a way similar to the records  240 - 3  and  240 - 4 , the condition that the correlation between two devices is estimated to be low in the case where the two devices are playing back music in synchronization may be set with regard to content other than the video and the music. 
     A record  240 - 5  in the correlation condition DB  240  defines a condition that a correlation between two devices is estimated to be low if movement trajectories detected by the two devices are different in the case where the two devices detect movement of users at the same time by continuously acquiring positional information, for example. The devices moving along different movement trajectories at the same time are highly possible to have been used by different users. 
     A record  240 - 6  in the correlation condition DB  240  defines a condition that a correlation between two devices is estimated to be high if the title of a game that is being played with a first device corresponds to the title of a web page that is being browsed with a second device in the case where the first device provides a game function and the second device provides a web browsing function at the same time. In this case, for example, it is estimated that a user is playing the game with the first device (for example, game console) while referring to walkthroughs of the game with the second device (for example, tablet). 
     A record  240 - 7  in the correlation condition DB  240  defines a condition that a correlation between devices is estimated to be low if a second device (in any state) is detecting some user operation in the case where a first device is detecting that a state (behavior) of the user is a sleep state. When the devices are used by the same user, one device does not detect the user operation while the other device is detecting the sleep state. 
     A record  240 - 8  in the correlation condition DB  240  defines a condition that a correlation between two devices is estimated to be high when a second device (that is carried by a user and moves along with the user) finishes moving and an end point of the movement of the second device is in proximity to the installation site of a first device that has been fixedly installed in the case where the first device is turned on. For example, when a light or an air conditioner is turned on, a device moving toward the installation site of the light or the air conditioner is highly possible to have been carried or worn by a user arrived at the installation site (house, office, or the like). 
     A record  240 - 9  in the correlation condition DB  240  defines a condition that a correlation between two devices is estimated to be high when a second device (that is carried by a user and moves along with the user) starts moving and a starting point of the movement of the second device is in proximity to the installation site of a first device that has been fixedly installed in the case where the first device is turned off. For example, when a light or an air conditioner is turned off, a device starting to move away from the installation site of the light or the air conditioner is highly possible to have been carried or worn by a user left from the installation site (house, office, or the like). 
     A record  240 - 10  in the correlation condition DB  240  defines a condition that a correlation between devices is estimated to be high when a second device detects that a state (behavior) of a user is a wake-up state in the case where a first device is turned on. For example, if a user wakes up when the light or the air conditioner is turned on, it is highly possible that the light or the air conditioner in his/her home has been turned on by the user as behavior after waking up. 
     A record  240 - 11  in the correlation condition DB  240  defines a condition that a correlation between devices is estimated to be high when a second device detects that a user goes to bed as a state (behavior) of the user in the case where a first device is turned on. For example, if a user goes to bed when the light or the air conditioner is turned off, it is highly possible that the light or the air conditioner in his/her home has been turned off by the user as behavior before going to bed. 
     A record  240 - 12  in the correlation condition DB  240  defines a condition that a correlation between two devices is estimated to be high when a device (second device) is providing the web browsing function and a recipe in a web page corresponds to a type of operation of a kitchen appliance (first device), in the case where the operation on the kitchen appliance is detected. This condition also indicates that the second device provides an instruction (recipe) about user operation of a predetermined pattern in the case where the user operation of the predetermined pattern on the first device is detected. For example, it is highly possible that a smartphone and a microwave oven are used by the same user when the microwave oven starts heating for three minutes while the smartphone is displaying a web page of a recipe including a step “heating it in a microwave for three minutes”. 
     A record  240 - 13  in the correlation condition DB  240  defines a condition that a correlation between devices is estimated to be high when a destination of navigation provided by a second device is in proximity to an end point of a moving trajectory in the case where a first device is detecting movement of a vehicle. For example, this condition is satisfied in the case where the first device is a car-mounted device, the second device is a smartphone, and a user uses the navigation in the smartphone while riding a car equipped with the car-mounted device. 
     A record  240 - 14  in the correlation condition DB  240  defines a condition that a correlation between devices is estimated to be high when a state of a user detected by a second device is a sitting state and duration of the sitting corresponds to duration of movement of a vehicle in the case where a first device detects the movement. In this case, sitting posture is posture of the user on a vehicle. According to the type or the state of the vehicle, standing posture or other posture may be detected. For example, a mobile device detects a sitting state of a user when a first device is a car-mounted device, a second device is the mobile device, and the user is traveling while sitting in a car equipped with the car-mounted device. 
     As described above, in the embodiment, the correlation condition DB  240  defines the conditions that a correlation between devices is estimated to be high or low. Therefore, a correlation between devices cannot be determined be high or low when the devices do not satisfy any condition defined in the correlation condition DB  240 . The correlation determination unit  230  does not estimate a correlation between such devices and leaves it as it is. For example, the correlation cannot be determined to be high or low in the case where a first device detects that a state (behavior) of a user is an exercise state and a second device (in any state) detects user operation. This is because sometimes the user operates the device while exercising. Since the correlation in such a case is treated as “unknown” according to the embodiment, it is possible to prevent erroneous estimation and therefore it is possible to improve reliability of the estimation result. 
     In addition to the above described examples, there are various examples of the condition for estimating a correlation between devices. For example, a correlation between a public display and a mobile device is estimated to be high in the case where environment conditions such as temperature, humidity, brightness, and sound are satisfied, and the mobile device has detected that the user is in a stop state. Information indicating the environment conditions such as temperature, humidity, brightness, and sound is information indicating states caused by behavior of a user related to each device since the environment conditions change when the user goes to a specific place. 
     For example, when a correlation between a mobile device and a terminal device used for an order system in an restaurant or the like is estimated, the correlation between the mobile device and the terminal device is estimated to be high in the case where a picture of a menu ordered through the terminal device is posted on social media via the mobile device. 
     (2. Second Embodiment) 
     Next, a second embodiment of the present disclosure will be described. A schematic configuration of a system according to the embodiment is similar to the system  10  described with reference to  FIG. 1 . Therefore, repeated description is omitted. 
       FIG. 7  is a block diagram schematically illustrating a functional configuration of a server according to the second embodiment of the present disclosure. With reference to  FIG. 7 , a server  400  includes the device log acquisition unit  210 , the device log DB  220 , the correlation determination unit  230 , the correlation condition DB  240 , the correlation score DB  250 , a grouping unit  410 , an area DB  420 , and a group DB  430 . 
     In a way similar to the server  200  according to the first embodiment, the server  400  is realized by one or a plurality of server apparatuses on a network. Each of the server apparatuses is realized by a hardware configuration of an information processing apparatus (to be described later). For example, the device log acquisition unit  210  is realized by a communication apparatus, the device log DB  220 , the correlation condition DB  240 , the correlation score DB  250 , the area DB  420 , and the group DB  430  are realized by memory or a storage, and the correlation determination unit  230  and the grouping unit  410  are realized by a processor such as a CPU. In the case where the server  400  is realized by the plurality of server apparatuses, each of the server apparatuses realizes one of the illustrated functional configurations. Alternatively, it is also possible that one functional configuration is distributed to the plurality of server apparatus. 
     Next, the functional configuration of the server  400  will be described. With regard to the device log acquisition unit  210 , the device log DB  220 , the correlation determination unit  230 , the correlation condition DB  240 , the correlation score DB  250 , repeated description similar to the first embodiment is omitted. 
     The grouping unit  410  performs grouping on the devices  100  illustrated in  FIG. 1  on the basis of data stored in the device log DB  220 . More specifically, the grouping unit  410  performs grouping on the devices  100  on the basis of a relation between positions of the devices  100  indicated by logs stored in the device log DB  220 , and a geographical area defined in the area DB  420  (positional conditions set in advance). For example, the area DB  420  defines areas corresponding to address areas. In this case, the grouping unit  410  converts positions of the devices  100  indicated by the logs or the like to addresses by using an external service or the like, and performs grouping on the devices  100  on the basis of the addresses. As the conversion from the positional information to the addresses, known technologies may be used such as JP 2008-89815A and JP 2011-43626A. The grouping unit  410  stores a result of the grouping in the group DB  430 . 
     As described above, in the case where the devices  100  are subjected to the grouping process on the basis of the predefined areas, the grouping unit  410  performs grouping on the devices  100  under a condition that positions of the devices are in a common area at any point of time. The devices  100  that are in the common area at any point of time may be classified into the same group. In such a way, the grouping unit  410  classifies the devices into groups depending on areas. 
     In the embodiment, the correlation determination unit  230  determines a correlation between at least two devices that provide the device logs in a way similar to the first embodiment. However, in the server  400  according to the second embodiment, the correlation determination unit  230  refers to the group DB  430  and determines the correlation between the devices in the group. As described above, the group into which the devices are classified corresponds to the geographical area defined in the area DB  420 , for example, the address area. The correlation determination unit  230  narrows down analysis targets to the devices in such a geographical area. Therefore, it is possible to reduce a calculation amount in comparison with the case where all the devices are treated as the analysis targets. 
     For example, in the case where the number of devices serving as the analysis targets is N and a relation between devices are determined for each combination of devices, calculation has to be performed N 2  times for all the combinations. Quadratic functional increase in the number of times of the calculation occurs as N increases. Therefore, in respect of reduction in the calculation amount, it is effective to limit the analysis targets to the devices in the same geographical area as described above. 
       FIG. 8  is a flowchart illustrating an example of a grouping process performed on all devices according to the second embodiment of the present disclosure. With reference to  FIG. 8 , the grouping unit  410  performs a loop process for each area defined in the area DB  420  (S 201 ), and groups devices on an area basis (S 203 ). More specifically, for example, the grouping unit  410  groups the devices by checking whether an address area converted from the positional information of each device stored in the device log DB  220  matches with the address areas defined in the area DB  420 . 
     A device in different positions according to time such as the mobile device may be included in a plurality of device groups of a plurality of areas. In this case, the positional information used for grouping the devices may be limited to information on a position where stay over a predetermined time has been detected. 
     The grouping unit  410  performs the grouping process on all the devices as an initial process or as batch processing performed periodically. 
       FIG. 9  is a flowchart illustrating an example of a grouping process performed on an additional device according to the second embodiment of the present disclosure. With reference to  FIG. 9 , the grouping unit  410  performs a loop process for each device of which the device log acquisition unit  210  has newly acquired a log, in other words, for each device that has been newly added, for example (S 205 ). Here, the grouping unit  410  determines whether or not the new device is included in the areas of the existing groups (S 207 ). More specifically, for example, the grouping unit  410  checks whether an address area converted from the positional information of the new device matches with the address areas corresponding to the groups recorded in the group DB  430 . 
     In the case where it has been determined that the new device is included in any of the areas of the existing groups (YES in S 207 ), the grouping unit  410  adds the new device in the existing group (S 209 ). On the other hand, in the case where the new device is not included in any of the areas of the existing groups (NO), the new device does not belong to any group in the illustrated example. In another example, the grouping unit  410  may additionally create a group including the new device in the case where the new device is not included in any of the areas of the existing groups. 
     The grouping unit  410  may perform the grouping process on the additional device periodically or every time a device is added after the grouping process illustrated in  FIG. 9  is performed on all the devices at least once. 
       FIG. 10  is a diagram illustrating an example of the group DB according to the second embodiment of the present disclosure. With reference to  FIG. 10 , records in the group DB  430  may include fields of group ID  430   a , group detail  430   b , and device ID  430   c.    
     The group ID  430   a  is an ID for identifying a group. The group ID  430   a  may be associated with an ID for defining an area in the area DB  420 . The group detail  430   b  describes detailed information of a group. In the illustrated example, the group detail  430   b  describes an address of an address area corresponding to a group. The group detail  430   b  is not necessary in the case where there is other information associating the group ID with the area defined in the area DB  420 . For example, the group detail  430   b  that describes the address may be used for notifying a user of a group to which a device belongs. The device ID  430   c  indicates the device that belongs to the group. 
     The means for recording a group to which each device belongs is not limited to the group DB  430  in the above example. For example, the device log DB  220  may record a group to which each device belongs at a time of acquiring a log. Alternatively, for example, the area DB  420  may record a device that belongs to a group corresponding to each area. Alternatively, a device DB may be provided in addition to the device log DB  220  to record a group to which each device belongs. 
     (3. Third Embodiment) 
     Next, a third embodiment of the present disclosure will be described. In the third embodiment, additional grouping is performed on a mobile device in addition to the grouping process according to the second embodiment. Configuration other than the additional grouping in the third embodiment is similar to the second embodiment. Therefore, repeated description will be omitted. 
       FIG. 11  is a flowchart illustrating an example of a grouping process according to the third embodiment of the present disclosure. With reference to  FIG. 11 , the grouping unit  410  performs a loop process for each group based on the area generated by a grouping process after the grouping process like the second embodiment is performed on all the devices at least once (S 301 ). Here, the grouping unit  410  extracts mobile devices in each group and performs the loop process for each of the mobile devices (S 303 ). In addition, the grouping unit  410  performs the loop process with the other device in the group for each of the mobile devices (S 305 ). 
     In the loop process, the grouping unit  410  determines whether or not the other device is operated when proximity of the other device to the mobile device is detected (S 307 ). More specifically, for example, the grouping unit  410  specifies time when the mobile device has come closer to the other device on the basis of the device log, and determines whether or not a user operation performed on the other device at that time is recorded in the device log. In the case where the other device is operated when the mobile device comes closer to the other device (YES), the grouping unit  410  adds the mobile device and the other device in a group based on an operation history (S 309 ). The grouping unit  410  stores information on the group based on the operation history in the group DB  430 . 
     According to the third embodiment, the group based on the operation history may be treated similar to the group based on the area according to the second embodiment. In other words, when determining a correlation between devices, the correlation determination unit  230  refers to the group DB  430  and determines the correlation between the devices in the group based on the operation history. The group based on the operation history may be coexistent with the group based on the area. According to the third embodiment, devices included in groups based on areas are further classified into subgroups based on operation histories according to a relation with each mobile device in the groups (sometimes one device belongs to a plurality of groups based on the operation histories). In the embodiment, a target of determination of a correlation between devices other than mobile devices may also be devices in a group based on an area, for example. 
     In the case where a correlation between the mobile device and the other devices is determined in terms of whether or not the mobile device and the other devices are used by the same user, it is unlikely that a device that is not operated when the device is in proximity to the mobile device that has been estimated to be carried by the user has a high correlation with the mobile device. Therefore, when determining the correlation with the mobile device, it is reasonable to limit an analysis area to a group based on the operation history. In the embodiment, the devices in the group based on the area are further classified into groups based on the operation histories. Thereby, it is possible to break down the group and reduce a calculation amount while maintaining an accuracy of determination. 
       FIG. 12  is a diagram illustrating an example of the device log DB according to the third embodiment of the present disclosure. With reference to  FIG. 12 , the device log DB  220  includes a record  220 - 9  of a smartphone, a record  220 - 10  of a TV, and a record  220 - 11  of an air conditioner. 
     The record  220 - 9  and record  220 - 10  indicate that the smartphone and the TV have been in proximity at a certain point of time (2014/1/1 10:00) (the smartphone and the TV are in home), and the TV has been operated (turned on) at that time. In this case, the grouping unit  410  according to the embodiment may perform a process to classify the smartphone (mobile device) in the record  220 - 9  and the TV (another device) in the record  220 - 10  into the same group on an operation history basis. 
     On the other hand, the record  220 - 9  and the record  220 - 11  indicate that although the smart phone and the air conditioner have been in proximity (the air conditioner has been estimated to be in the home at 2014/1/1 10:00 since the air conditioner has been fixedly installed), the air conditioner has not been operated at that time and the air conditioner has been operated (turned on) after the smartphone has gotten away from the air conditioner. In this case, the grouping unit  410  according to the embodiment does not perform a process to classify the smartphone (mobile device) in the record  220 - 9  and the air conditioner (another device) in the record  220 - 11  into the same group on an operation history basis. 
     (Modification) 
     In the above described embodiments, the devices that has classified into groups on an area basis are further subjected to grouping on an operation history basis. However, in another example, all devices may be subjected to grouping on an operation history basis before the devices are classified into groups on the area basis. In this case, the devices are first subjected to grouping based on a relation with each mobile device. For example, in the case where a TV in a home and a PC in an office are operated when a common mobile device comes closer to the TV or the PC, the TV and the PC may be classified into a same group. In addition, as necessary, the group on an operation history basis may be broken down into subgroups on an area basis. 
     According to the first to third embodiments of the present disclosure, it is possible to estimate a correlation between various devices such as a mobile device, a wearable device, a car-mounted device, and a CE device. In addition, by organizing the devices according to their correlations, it is possible to acquire more detailed profile of preference and behavior patterns of users, and it is possible to select an optimum device to present information to a user. 
     In addition, for example, when a correlation between a device shared by many and unspecified users such as a public display and a device dedicated to an individual user such as a mobile device is estimated, it is possible to individually track effects of information (for example, advertisement) provided by the shared device on behavior of the user. In addition, for example, information output via the shared device may be changed according to the user in the case where the shared device is temporally occupied by the user or in the case where it is estimated that a rate of the number of users having a specific attribute among the users using the shared device is high. 
     In the first to third embodiments, the correlation is estimated in terms of whether or not the devices are used by the same user. However, the embodiments of the present disclosure are not limited thereto. For example, the correlation between the devices may be estimated in terms of whether or not the devices are used by users having the same attribute such as age, job, or sex. In addition, the grouping process according to the second and third embodiments is not limited to the case of determining a correlation between devices. The grouping process may be useful for reducing a calculation amount also in any case of analyzing a correlation of a combination of certain devices in a device group. In this respect, it can be said that the correlation determination unit  230  according to the above described embodiments is a relation analysis unit that analyzes a relation between devices on the basis of device logs. 
     (4. Hardware Configuration) 
     Next, with reference to  FIG. 13 , a hardware configuration of an information processing apparatus according to an embodiment of the present disclosure is explained.  FIG. 13  is a block diagram illustrating a hardware configuration example of an information processing apparatus according to the embodiment of the present disclosure. An illustrated information processing apparatus  900  may achieve the server apparatus according to the embodiments of the present disclosure, for example. 
     The information processing apparatus  900  includes a central processing unit (CPU)  901 , read only memory (ROM)  903 , and random access memory (RAM)  905 . In addition, the information processing apparatus  900  may include a host bus  907 , a bridge  909 , an external bus  911 , an interface  913 , an input apparatus  915 , an output apparatus  917 , a storage apparatus  919 , a drive  921 , a connection port  923 , and a communication apparatus  925 . Moreover, the information processing apparatus  900  may include an imaging apparatus  933 , and a sensor  935 , as necessary. The information processing apparatus  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), alternatively or in addition to the CPU  901 . 
     The CPU  901  serves as an arithmetic processing apparatus and a control apparatus, and controls the overall operation or a part of the operation of the information processing apparatus  900  according to various programs recorded in the ROM  903 , the RAM  905 , the storage apparatus  919 , or a removable recording medium  927 . The ROM  903  stores programs, operation parameters, and the like used by the CPU  901 . The RAM  905  transiently stores programs used when the CPU  901  is executed, and various parameters that change as appropriate when executing such programs. The CPU  901 , the ROM  903 , and the RAM  905  are connected with each other via the host bus  907  configured from an internal bus such as a CPU bus or the like. 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 apparatus  915  is a device operated by a user such as a mouse, a keyboard, a touch panel, a button, a switch, and a lever. The input apparatus  915  may be a remote control device that uses, for example, infrared radiation and another type of radiowave. Alternatively, the input apparatus  915  may be an external connection apparatus  929  such as a mobile phone that corresponds to an operation of the information processing apparatus  900 . The input apparatus  915  includes an input control circuit that generates input signals on the basis of information which is input by a user to output the generated input signals to the CPU  901 . A user inputs various types of data to the information processing apparatus  900  and instructs the information processing apparatus  900  to perform a processing operation by operating the input apparatus  915 . 
     The output apparatus  917  includes an apparatus that can report acquired information to a user visually, audibly, or haptically. The output apparatus  917  may be, for example, a display device such as a liquid crystal display (LCD) or an organic electro-luminescence (EL) display, an audio output apparatus such as a speaker or a headphone, or a vibrator. The output apparatus  917  outputs a result obtained through a process performed by the information processing apparatus  900 , in the form of video such as text and an image, sounds such as voice and audio sounds, or vibration. 
     The storage apparatus  919  is an apparatus for data storage that is an example of a storage unit of the information processing apparatus  900 . The storage apparatus  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 apparatus  919  stores therein the programs and various data executed by the CPU  901 , various data acquired from an 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 built in or externally attached to the information processing apparatus  900 . The drive  921  reads out information recorded on the mounted removable recording medium  927 , and outputs the information to the RAM  905 . The drive  921  writes the record into the mounted removable recording medium  927 . 
     The connection port  923  is a port used to connect devices to the information processing apparatus  900 . The connection port  923  may include 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 data between the information processing apparatus  900  and the external connection device  929 . 
     The communication apparatus  925  is a communication interface including, for example, a communication device for connection to a communication network  931 . The communication apparatus  925  may be, for example, a communication card for a local area network (LAN), Bluetooth (registered trademark), Wi-Fi, or a wireless USB (WUSB). The communication apparatus  925  may also be, for example, a router for optical communication, a router for asymmetric digital subscriber line (ADSL), or a modem for various types of communication. For example, the communication apparatus  925  transmits and receives signals in the Internet or transits signals to and receives signals from another communication device by using a predetermined protocol such as TCP/IP. The communication network  931  to which the communication apparatus  925  connects is a network established through wired or wireless connection. The communication network  931  may include, for example, the Internet, a home LAN, infrared communication, radio communication, or satellite communication. 
     The imaging apparatus  933  is an apparatus that captures an image of a real space by using an image sensor such as a charge coupled device (CCD) and a complementary metal oxide semiconductor (CMOS), and various members such as a lens for controlling image formation of a subject image onto the image sensor, and generates the captured image. The imaging apparatus  933  may capture a still image or a moving image. 
     The sensor  935  is various sensors such as an acceleration sensor, an angular velocity sensor, a geomagnetic sensor, an illuminance sensor, a temperature sensor, a barometric sensor, and a sound sensor (microphone). The sensor  935  acquires information regarding a state of the information processing apparatus  900  such as a posture of a housing of the information processing apparatus  900 , and information regarding an environment surrounding the information processing apparatus  900  such as luminous intensity and noise around the information processing apparatus  900 . The sensor  935  may include a global positioning system (GPS) receiver that receives GPS signals to measure latitude, longitude, and altitude of the apparatus. 
     The example of the hardware configuration of the information processing apparatus  900  has been described. Each of the structural elements described above may be configured by using a general purpose component or may be configured by hardware specialized for the function of each of the structural elements. The configuration may be changed as necessary in accordance with the state of the art at the time of working of the present disclosure. 
     (5. Supplement) 
     The embodiments of the present disclosure may include, for example, the above-described information processing apparatus (for example, server), the above-described system, the information processing method executed by the information processing apparatus or the system, a program for causing the information processing apparatus to exhibits its function, and a non-transitory physical medium having the program stored therein. 
     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 based on the description of this specification. 
     Additionally, the present technology may also be configured as below.
     (1)   

     An information processing apparatus including: 
     a device log acquisition unit configured to acquire a time-series device log including information indicating a position of each of devices from each of the devices; 
     a grouping unit configured to classify the devices into at least one group on the basis of the device logs and a preset condition of the positions; and 
     a relation analysis unit configured to analyze a relation between the devices in each of the at least one group on the basis of the device logs.
     (2)   

     The information processing apparatus according to (1), wherein 
     the condition includes a first condition that the positions of the respective devices are included in a common area at least at any point in time, and 
     the grouping unit classifies the devices into the at least one group based on the areas in accordance with the first condition.
     (3)   

     The information processing apparatus according to (2), wherein the areas correspond to address areas.
     (4)   

     The information processing apparatus according to (1), wherein 
     the devices include a mobile device and another device, 
     the condition includes a second condition that the another device is operated when the mobile device and the another device are in proximity, and 
     the grouping unit classifies the mobile device and the another device into a same group in a case where the second condition is satisfied.
     (5)   

     The information processing apparatus according to (1), wherein 
     the devices include a mobile device and another device, 
     the condition includes a first condition that the positions of the respective devices are included in a common area at least at any point in time, and a second condition that the another device is operated when the mobile device and the another device are in proximity, and 
     the grouping unit classifies the devices into a first group based on the areas in accordance with the first condition, and further classifies the mobile device and the another device in the first group into at least one subgroup in accordance with the second condition.
     (6)   

     The information processing apparatus according to (1), wherein 
     the devices include a mobile device and another device, 
     the condition includes a first condition that the positions of the respective devices are included in a common area at least at any point in time, and a second condition that the another device is operated when the mobile device and the another device are in proximity, and 
     the grouping unit classifies the mobile device and the another device into a first group in accordance with the second condition, and further classifies the devices in the first group into at least one subgroup based on the areas in accordance with the first condition.
     (7)   

     The information processing apparatus according to any one of (1) to (6), wherein 
     the device log further includes information indicating a state caused by behavior of a user related to each of the devices, and 
     the relation analysis unit determines a correlation between the devices in each of the at least one group on the basis of the device logs and a preset condition of the states.
     (8)   

     An information processing method including: 
     acquiring a time-series device log including information indicating a position of each of devices from each of the devices; 
     classifying, by a processor, the devices into at least one group on the basis of the device logs and a preset condition of the positions; and 
     analyzing a relation between the devices in each of the at least one group on the basis of the device logs.
     (9)   

     A program for causing a computer to achieve: 
     a function of classifying devices into at least one group on the basis of a preset condition of a position of each of the devices and a time-series device log that has been acquired from each of the devices and that includes information indicating the position of each of the devices; and 
     a function of analyzing a relation between the devices in each of the at least one group on the basis of the device logs. 
     REFERENCE SIGNS LIST 
     
         
           10  system 
           100  device 
           200 ,  400  server 
           210  device log acquisition unit 
           220  device log DB 
           230  correlation determination unit 
           240  correlation condition DB 
           250  correlation score DB 
           410  grouping unit 
           420  area DB 
           430  group DB