JP2020095293A - Human relationship estimation device - Google Patents

Abstract

To provide a human relationship estimation device capable of accurately estimating a human relationship between users.SOLUTION: A human relationship estimation device comprises: a detection unit 21 for detecting that two users are in proximity to each other; a location profile estimation unit 23 for acquiring information indicating attributes that are different from each other and that are associated with each user with respect to a position where the proximity is detected by the detection unit; and a relationship estimation unit 24 for estimating human relationships between the users based on the attributes different from each other that are associated respectively with the users as indicated by the information acquired by the location profile estimation unit 23.SELECTED DRAWING: Figure 2

Description

The present invention relates to a human relationship estimation device.

Patent Document 1 discloses a technique for estimating what kind of human relationship users of mobile terminals have. In this technique, the area is defined according to the purpose of the user based on the registered content in advance. Then, the human relationship between the users is determined based on in which area the users exist simultaneously. For example, when users are in the home area at the same time, it is estimated that the relationship between the users is a family.

JP, 2010-165097, A

In the conventional technology, even if users exist in an area at the same time, it is difficult to estimate the human relationship between the users when, for example, the definitions of the users in the area are different.

It is an object of one aspect of the present invention to provide a human relationship estimation device that can accurately estimate human relationships between users.

The human relationship estimation device according to one aspect of the present invention includes a detection unit that detects that two users are in proximity to each other, and a position in which proximity is detected by the detection unit, which is associated with each user. A position attribute information acquisition unit that acquires information indicating different attributes, and a person between users based on a combination of different attributes that are associated with each user and that are indicated by the information acquired by the position attribute information acquisition unit. A relationship estimation unit that estimates a relationship.

In this human relationship estimation device, human relationships are estimated based on combinations of different attributes. Therefore, even humans having different position attributes detected by the detection unit can estimate human relationships. it can. Further, based on the combination of attributes, more cases can be classified than the types of attributes, and more detailed estimation can be performed. Therefore, the human relationship between the users can be accurately estimated.

According to one aspect of the present invention, it is possible to provide a human relationship estimation device that can accurately estimate human relationships between users.

It is a conceptual diagram of a human relationship estimation system using a human relationship estimation device according to an embodiment. It is a functional block diagram of a human relationship estimation system. It is a figure which shows an example of the processing result of human relationship estimation. It is a figure which shows profile information notionally. It is a sequence which shows the process (periodic estimation) performed by a human relationship estimation system. It is a sequence which shows the process (human relation estimation) performed by the human relation estimation system. It is a figure which shows the hardware constitutions of a portable terminal and a server device.

Hereinafter, embodiments according to the present invention will be specifically described with reference to the drawings. For convenience, the substantially same elements are denoted by the same reference numerals, and the description thereof may be omitted.

FIG. 1 is a conceptual diagram of a human relationship estimation system using a human relationship estimation device according to an embodiment. In the present embodiment, the human relationship estimation system 3 includes a plurality of mobile terminals 1 and a human relationship estimation server (human relationship estimation device) 2. In this embodiment, the human relationship estimation system 3 includes a plurality of mobile terminals 1. However, FIG. 1 shows only two mobile terminals that are close to each other. In the following description, the mobile terminal 1a and the mobile terminal 1b may be referred to as other terminal and own terminal, respectively.

The mobile terminal 1 is a device carried and used by a user. The mobile terminal 1 is specifically an information processing terminal such as a smartphone, a mobile phone, a tablet terminal, a PDA (Personal Digital Assistant), or a personal computer. The mobile terminal 1 has a function of connecting to a network such as a mobile communication network to perform wireless communication. The mobile terminal 1 includes hardware such as a CPU (Central Processing Unit), a memory, and a communication module.

The human relationship estimation server 2 is an information processing terminal such as a server computer. The human relationship estimation server 2 is composed of hardware such as a CPU, a memory, and a communication module. In the human-relationship estimation system 3, the mobile terminal 1 and the human-relationship estimation server 2 can communicate with each other via a network and can exchange information with each other.

FIG. 2 is a functional block diagram of the mobile terminal and the human relationship estimation server. As shown in FIG. 2, the mobile terminal 1b includes at least the sensor data acquisition unit 11. The mobile terminal 1a also has the same functional block configuration as the mobile terminal 1b.

The sensor data acquisition unit 11 detects that the two mobile terminals 1a and 1b are close to each other. In the present embodiment, the sensor data acquisition unit 11 of the one mobile terminal 1b detects the other mobile terminal 1a in proximity. The state in which the mobile terminals 1a and 1b are close to each other means that the mobile terminal 1a exists within a predetermined range centered on the mobile terminal 1b. For example, both users of the mobile terminals 1a and 1b are acting together. It is in a state where it can be determined that As an example, when the mobile terminal 1a exists within a range of several meters centering on the mobile terminal 1b, it can be determined that the two mobile terminals 1a and 1b are close to each other. Specifically, the sensor data acquisition unit 11 uses a communication module to detect radio waves of short-distance wireless communication such as Bluetooth (registered trademark) and WiFi (registered trademark) emitted from the mobile terminal 1a, and The proximity of the terminal 1a is detected. In addition, the sensor data acquisition unit 11 may detect the proximity of the mobile terminal 1a by using a microphone, a BLE (Bluetooth Low Energy) Peripheral mode, an LTE (Long Term Evolution) Direct technology, or the like.

The sensor data acquisition unit 11 of the mobile terminal 1b acquires information about the mobile terminal 1a or the user of the mobile terminal 1a (hereinafter, referred to as “proximity terminal information”) when detecting the mobile terminal 1a in proximity. For example, the radio wave of the short-distance wireless communication emitted from the mobile terminal 1a includes identification information of the mobile terminal 1a (for example, MAC address), identification information of the user of the mobile terminal 1a (for example, user ID), and the like. There is. The sensor data acquisition unit 11 of the mobile terminal 1b can acquire the identification information included in the detected radio waves as the proximity terminal information. The sensor data acquisition unit 11 can output the acquired proximity terminal information to the human relationship estimation server 2 and the context estimation unit 13 of the own terminal 1b.

In addition, the sensor data acquisition unit 11 can acquire data from various sensors provided in the terminal 1b. The data acquired by the sensor data acquisition unit 11 includes the position information of the own terminal 1b. The position information may be information indicating latitude and longitude by GPS (Global Positioning System). Further, the position information may be information based on identification information of the short-range wireless communication device received from a fixed-range short-range wireless communication device (for example, an access point) such as WiFi. Further, the sensor data acquisition unit 11 can also acquire data indicating the time when each data was acquired, as a part of the sensor data. The sensor data acquisition unit 11 can regularly detect sensor data. The sensor data acquisition unit 11 outputs the detected sensor data to the human relationship estimation server 2 together with the identification information of the own terminal (hereinafter referred to as “own terminal information”).

The human relationship estimation server 2 includes a detection unit 21, a sensor data storage unit 22, a location profile estimation unit (position attribute information acquisition unit) 23, and a relation estimation unit 24. The detection unit 21 receives the self terminal information, the proximity terminal information, and the sensor data from the mobile terminal 1b. In the present embodiment, the detection unit 21 detects that the user of the own terminal 1b and the user of the other terminal 1a approach each other by acquiring the proximity terminal information. Further, the detection unit 21 can detect information on the length of time that the users are close to each other based on the data indicating the time when the proximity terminal information is acquired. The data received by the detection unit 21 may be output to the sensor data storage unit 22. Further, among the data detected by the detection unit 21, at least information on the length of time that the users are close to each other may be output to the relationship estimation unit 24.

The sensor data storage unit 22 stores own terminal information, proximity terminal information, and sensor data received from the mobile terminal 1b. Further, the sensor data storage unit 22 can output the stored own terminal information, proximity terminal information, and sensor data to the location profile estimation unit 23. The sensor data storage unit 22 also receives and stores the sensor data in the other mobile terminal 1a from the other mobile terminal 1a. The sensor data received from each mobile terminal 1 is stored in the sensor data storage unit 22 in association with the identification information of each mobile terminal 1.

The location profile estimation unit 23 acquires, as the location profile, information indicating an attribute (meaning) associated with each position for each user. For example, the location profile estimation unit 23 acquires information indicating the history of the position of each user, and estimates the location profile based on the history. The meaning of each position for each user may differ depending on the time. Therefore, in the present embodiment, the meaning of the date and time for the user is included in the location profile.

The location profile estimation unit 23 estimates (derives) information in which the location information and the meaning of the location information are associated as a location profile. For example, the location profile estimation unit 23 associates meaning information such as “home”, “workplace”, “business trip”, “eating out”, “play” with the position information of the place where the user stayed. When the user is estimated to be a student, “workplace” may be meaning as “school”.

As an example, in estimating the location profile, WiFi identification information acquired from the mobile terminal, information indicating latitude and longitude by GPS, and the like are used as position information. For example, the location profile estimation unit 23 extracts the position information detected in each time zone of each day of the week (hereinafter referred to as “date and time information”) based on the acquired position information and the like. By this processing, the date and time information and the position information are associated with each other. The location profile estimation unit 23 uses a statistical method to add meaning to the location based on the location information associated with the date and time information.

More specifically, the location profile estimation unit 23 performs clustering on the history of location information to extract a set of location information that are locationally close to each other. At this time, the WiFi identification information may be converted into information indicating the latitude and the longitude according to a correspondence table of the identification information stored in advance and the latitude and the longitude. The location profile estimation unit 23 acquires each of the extracted sets of position information as “base”. The location profile estimation unit 23 derives the location data of the user at the acquired base. The location data may be, for example, data on the date and time of stay of the user at each base. Then, the location profile estimation unit 23 estimates the meaning of the user for each base. For this estimation, for example, the in-service day rate indicating how many days each site is visited during a predetermined period (for example, the past half year) may be used. For example, even if the site is visited only once on a certain day, or if the site is visited a plurality of times on a certain day, the number of days in service is "1". The in-service day rate is derived based on the in-service data. As an example, the base having the first ranked number of days in the service area may be defined as “home”. In addition, a base where the number of days spent in the area is ranked second to tenth, the frequency of stays per week is one day or longer, and the average length of stay on the day of visit is 200 minutes or longer is defined as "workplace". May be attached.

Further, the location profile estimation unit 23 estimates whether each day of the week is a work day or a holiday based on the day of the week in which the area estimated as the “workplace” is located. Further, the location profile estimation unit 23 assigns meanings to other bases based on the estimated results of work days and holidays, time zone information for staying at the base, and the like. In this case, for example, a base where the day-to-day stay on weekdays is equal to or greater than a predetermined value (for example, 0.3) may be referred to as a “business trip”. Here, weekdays are the days of the working day for the user, and the stay day rate is the ratio of the number of days the user stays on weekdays. A base whose average stay time during the day is a predetermined time (for example, 20 minutes) or less, and whose average stay time at night is a predetermined time (for example, 60 minutes) or more may be defined as "dining out". Good. In addition, a base where the average stay time is a predetermined time (for example, 30 minutes) or more and does not correspond to “business trip” and “eating out” may be referred to as “play”. A method of giving meaning to the position information for the user may be performed by another known method. Note that in estimating the location profile, map information in which the position information in the area (facility) and the category information are associated may be referred to. The category information is, for example, information indicating the characteristics of the area, and examples thereof include “commercial facility”, “restaurant”, “entertainment facility”, “office district”, and the like.

The location profile estimation unit 23 can store the estimated location profile and output it to the relationship estimation unit 24 in response to a request from the relationship estimation unit 24. For example, when the detection unit 21 detects the mobile terminals 1a and 1b that are close to each other, the location profile estimation unit 23 causes the location profile estimation unit 23 to associate the location where the proximity is detected by the detection unit 21 with each user. To get. The acquired location profile may be different between users or may be the same. The location profile estimation unit 23 outputs the information of the location profile associated with the mobile terminals 1a and 1b to the relationship estimation unit 24.

The relationship estimation unit 24 estimates a human relationship profile, which is a human relationship between users, based on the combination of attributes associated with each user, which is indicated by the location profile. The combination of attributes includes the case where they are the same as each other and the case where they are different from each other. The human relationship profile is profile information related to human relationships between users, and includes relationship information indicating relationships between users and intimacy information indicating intimacy between users. As an example, in estimating the human relationship profile, proximity terminal information of the mobile terminal 1a in proximity acquired from the mobile terminal 1b, location profile data estimated by the location profile estimation unit 23, and the like are used.

For example, the relationship estimation unit 24 extracts the proximity terminal information detected at each site estimated as the location profile. The relationship estimating unit 24 determines the relationship between the user of the own terminal 1b and the user of the other terminal 1a based on the combination of the position attributes associated with each user as “family”, “friend”, “colleague”, It is categorized into “work related persons”, “acquaintances”, etc., and acquired as relationship information.

As an example, the relationship estimation unit 24 may store in advance the association between the combination of the position attributes and the human relationship between the users in order to estimate the relationship information. In this case, the human relationship between the users may be associated with both the combination of attributes at different positions and the combination of attributes at the same positions.

For example, it is estimated that the location profile for the base where the own terminal 1b and the other terminal 1a are close to each other is "home" for the user of the own terminal 1b and "home" for the user of the other terminal 1a. In this case, the user of the other terminal 1a may be estimated as the “family” of the own terminal 1b. As an example, the mobile terminals 1a and 1b in which the position information of “home” estimated as the location profile match and are close to each other for a predetermined ratio (for example, 50%) or more of the stay time at “home” Users may be estimated as "family".

Further, it is estimated that the location profile of the base where the own terminal 1b and the other terminal 1a are close to each other is "home" for the user of the own terminal 1b and "play" for the user of the other terminal 1a. If so, the user of the other terminal 1a may be estimated as a “friend” for the user of the own terminal 1b.

In addition, it is estimated that the location profile for the base where the own terminal 1b and the other terminal 1a are close to each other is the “workplace” for the user of the own terminal 1b, and the location is the “workplace” for the user of the other terminal 1a. If so, the user of the other terminal 1a may be estimated as a “colleague” for the user of the own terminal 1b. As an example, the estimated position information of the “workplace” matches, and the time (hereinafter referred to as “encounter time”) when the own terminal 1b and the other terminal 1a are close to each other in one week at the “workplace” is When it is a predetermined time (for example, 50 minutes) or more, the users of the mobile terminals 1a and 1b may be estimated as “colleagues”.

Further, another user having the maximum encounter time of one day or more for a predetermined time (for example, 30 minutes) may be estimated as an “acquaintance”. Further, among users who correspond to “acquaintances”, users who are other than colleagues and have encountered at the “workplace” of the user of the terminal 1b or other terminal 1a may be estimated as “work-related persons”. .. For example, it is presumed that the location profile for the base where the own terminal 1b and the other terminal 1a are close to each other is the “workplace” for the user of the own terminal 1b and the “business trip destination” for the user of the other terminal 1a. If so, the user of the other terminal 1a may be estimated as a “work-related person” for the user of the own terminal 1b. As an example, the process of the relationship estimation unit 24 when the relationship between the cram school teacher and the students attending the cram school is estimated will be described. For example, in the location profile of the cram school teacher, the cram school position is estimated as the “workplace”. On the other hand, in the student location profile, the location of the cram school is estimated as the “business trip destination”. The relationship estimating unit 24 estimates the relationship between the two as a “work-related person” based on a combination of different attributes of “workplace” and “claimee”.

If other users correspond to a plurality of “family”, “friends”, “colleagues”, “work related persons” and “acquaintances”, “family”> “friends”> “colleagues”> “work relations” The relationship may be estimated according to the priority order of “person”>“acquaintance”.

Further, the relationship estimation unit 24 may estimate the degree of intimacy between the user of the own terminal 1b and the user of the other terminal 1a based on the time information detected by the detection unit 21. For example, the relationship estimation unit 24 estimates the intimacy degree between the user of the own terminal 1b and the user of the other terminal 1a by relative evaluation, with all the users whose relationships with the user of the own terminal 1b are estimated as a population. You can As an example, for the evaluation of intimacy, the encounter rate with the encounter partner, the average encounter time per day, the number of encounter bases, and the like may be used. The encounter rate with the encounter partner may be, for example, ((number of encounter days in a predetermined period)/(number of days in a predetermined period)). The “number of encounter days” is the number of days when it is detected that the own terminal 1b and the other terminal 1a are close to each other. The average encounter time per day may be, for example, ((first day encounter time+...+nth day encounter time)÷(number of encounter days)). The number of bases encountered may be the number of bases encountered with the encounter partner in a predetermined period (for example, the last half year). The higher the value of the encounter rate with the encounter partner, the average daily encounter time, and the number of encounter bases, the higher the degree of intimacy is evaluated. The degree of intimacy may be represented as a numerical value in a range where the lowest value is 0 and the highest value is 100.

Further, the relationship estimation unit 24 may generate graph data for displaying the human relationship as a graph based on the estimated human relationship between the users. In this case, the relationship estimation unit 24 may extract a plurality of users for each estimated human relationship and generate graph data regarding the plurality of extracted users. The relationship estimation unit 24 extracts, for example, a plurality of users estimated to have the same “workplace”. In this case, the extracted relationships between the users belong to “colleagues” or “friends”.

The relationship estimation unit 24 generates graph data using the extracted users as nodes and the relationships between the users as edges. FIG. 3 is an image diagram for explaining a state in which the generated graph data is displayed. For example, in the example of FIG. 3, the relationship as a “friend” between a plurality of users having the same “workplace” is graphed.

Moreover, the relationship estimation unit 24 may cluster the plurality of extracted users based on the degree of intimacy. For example, the relationship estimation unit 24 extracts the intimacy degree between users who are in a “friend” relationship and divides all users into a plurality of groups. As described above, since the intimacy between users is derived for each user, one user is different from the other user. In this case, for example, the relationship estimation unit 24 normalizes all the estimated intimacy degrees. In the normalization process, for example, the relationship estimation unit 24 derives an average value of the degree of intimacy with one user as seen from one user and the degree of intimacy with one user as seen from the other user, and this average is calculated. The value is the intimacy degree between one user and the other user. The relationship estimation unit 24 forms a group boundary when the intimacy degree between the users connected by the edges is smaller than a preset threshold value. By this processing, the extracted plurality of users can be divided into groups according to human relationships. In the illustrated example, a plurality of users A to J are divided into a group α composed of users B to G, a group β composed of users H to J, and an isolated user A not belonging to any group. Has been.

Further, the relationship estimation unit 24 may derive the degree centrality, the proximity centrality, and the mediation centrality for each user. The degree centrality is an index of the number of connections with other users, and the larger the number, the more acquaintances there are. In the present embodiment, the relationship estimation unit 24 derives the number of edges of each user in the generated graph data as degree centrality. That is, the degree centrality is derived by the following equation. For example, in the example of FIG. 3, the degree centrality of the user indicated as “D” is high, and “D” is estimated to be the leader existence of the group α.
User degree centrality = number of edges with other users extracted

Proximity centrality is an index indicating the degree of overall interaction between the extracted plurality of users, and the larger the value, the deeper the overall interaction. In the present embodiment, (100-intimacy) is defined as the "distance (heart distance)" between each user. The relationship estimation unit 24 derives the reciprocal of the “distance” as the proximity centrality so that the smaller the sum of the “distances” to all other users, the higher the value. That is, the proximity centrality is derived by the following formula. For example, when the number of connections with other users is relatively large and the degree of intimacy with other users who have connections is high, the proximity centrality is high. Conversely, when the number of connections with other users is relatively small and the degree of intimacy with other users who have connections is low, the proximity centrality is low. Note that the extracted other user in the following formula is not only another user who is directly connected to the user by the edge, but also another user who is indirectly connected by the edge through the user and the other user. including.
Proximity centrality of user=(number of extracted other users)/(sum of shortest distances between other extracted users)

The intermediary centrality is an index of the height of a role (hub) as a relay role (window) among a plurality of extracted users, and a higher number indicates a higher role as a relay role. In the present embodiment, the relationship estimation unit 24 derives the mediation centrality based on the following formula so that the higher the number of routes passing through the node indicated by the user, the higher the value. For example, in the example of FIG. 3, it is estimated that the user indicated by “F” is the relay role between the group α and the group β.
Intermediary centrality of user = (shortest number of routes through user) / (shortest number of routes between other users)

Note that the scope of derivation for degree centrality, proximity centrality, and intermediary centrality can be changed according to human relations, such as "colleagues" only, "family" only, or "colleagues + family". Good.

The human relationship profile estimated by the relationship estimation unit 24 may be stored as profile information together with the location profile. An example of profile information is shown in FIG. As shown in FIG. 4, the profile information includes a user ID and a location profile and a personal relationship profile associated with the user ID. As shown in the figure, in the location profile information, the position information of the latitude and the longitude and the identification information of the WiFi that are observed are associated with each other for the home, the workplace, and each base. In the illustrated example, only one latitude and longitude information is shown for each base, but since the base is a set of position information, the location profile information includes a plurality of latitude and longitude information for each base. Can have. The human relationship profile includes degree centrality, proximity centrality, and mediation centrality of the user (self) of the own terminal 1b. Order centrality, proximity centrality, and intermediary centrality may be generated for each of the user's relationships. Further, in the human relationship profile, information of other users (relationship, intimacy degree, centrality in the illustrated example) related to the user is associated with each user ID. The profile information may include known WiFi identification information acquired by the mobile terminal 1b in the past.

Next, the operation of the human relationship estimation server 2 will be described with reference to the sequence shown in FIG. FIG. 5 shows a process in which the human relationship estimation server 2 periodically estimates a place profile. First, the mobile terminals 1a and 1b periodically acquire sensor data by their respective sensor data acquisition units 11 (steps S1 and S2). For example, the sensor data acquisition unit 11 of the mobile terminals 1a and 1b may acquire sensor data every 5 minutes. In this case, the mobile terminals 1a and 1b may store the data acquired within a predetermined period. Next, the mobile terminals 1a and 1b transmit the acquired data to the human relationship estimation server 2 (steps S3 and S4). For example, the mobile terminals 1a and 1b may transmit the acquired and held sensor data to the human relationship estimation server 2 every predetermined time (for example, 2 hours) longer than the data acquisition interval. The human relationship estimation server 2 stores the sensor data transmitted from the mobile terminals 1a and 1b in association with each piece of identification information of the mobile terminals 1a and 1b (steps S5 and S6). Next, the human relationship estimation server 2 estimates the attributes of the users of the mobile terminals 1a and 1b based on the accumulated sensor data of the mobile terminals 1a and 1b (step S7). For example, the attribute estimation may be performed every predetermined time longer than the data transmission interval by the mobile terminals 1a and 1b. As an example, attribute estimation may be performed once a day. As described above, the human relationship estimation server 2 estimates the location profile. In this way, the location profile of the user may be regularly estimated between the mobile terminals 1a and 1b and the human relationship estimation server 2.

Next, an example of a process in which the human relationship estimation server 2 estimates a human relationship profile will be described with reference to the sequence shown in FIG. First, the proximity terminal information is transmitted to the mobile terminal 1b from the mobile terminal 1a of another user who is in proximity to the mobile terminal 1b of the user (step S21). The proximity terminal information may be transmitted by BLE, for example.

Next, the mobile terminal 1b transmits the self-terminal information and the sensor data together with the received proximity terminal information to the human relationship estimation server (step S22). When the proximity terminal information, the own terminal information, and the sensor data transmitted from the mobile terminal 1b are acquired by the human relationship estimation server 2 (step S23), the human relationship estimation server estimates the human relationship profile described above (step S23). S24). The estimated human relationship profile may be stored in the human relationship estimation server 2. In this embodiment, for example, the administrator 5 of the human relationship estimation server 2 can browse the human relationship profile (step S25). The administrator 5 may display, for example, data based on the human relationship profile (including the graph shown in FIG. 3, for example) on a display device such as a display connected to the human relationship estimation server 2.

In the human relationship estimation server 2 described above, the human relationship is estimated based on the combination of the attributes (location profile) of each user associated with the bases where the users stay at the same time. In other words, not only when the position attributes are the same, but also when the combinations are different from each other, it is possible to estimate the human relationship between the users. Further, based on the combination of attributes, more cases can be classified than the types of attributes, and more detailed estimation can be performed. Therefore, the human relationship between the users can be accurately estimated.

In addition, the relationship estimation unit estimates the human relationship between the users based on the association of the attribute combination and the human relationship between the users stored in advance. Since the association is stored in advance, the processing load of the human relation estimation by the relation estimation unit is reduced. Further, the correspondence relationship between the attribute combination and the human relationship can be easily updated.

In addition, the location profile estimation unit acquires information indicating the history of the position for each user, and based on the history, acquires the location profile associated with the position for each user. In this case, it is not necessary to register the address etc. in advance. Further, by accumulating the position history, the amount of information and the accuracy of the estimated location profile are increased, and more accurate human relationship estimation is performed.

In addition, the relationship estimation unit estimates the degree of intimacy between the users based on the information on the length of time detected by the detection unit. In this case, the degree of intimacy can be estimated according to the length of time the users are close to each other at the base, and the behavior of the user is easily reflected in the degree of intimacy.

Further, the relationship estimation unit extracts a plurality of users for each estimated human relationship and clusters the extracted plurality of users based on the degree of intimacy. According to this configuration, since the human relationship between the users is analyzed based on the degree of intimacy, the human relationship can be estimated with higher accuracy.

Further, when the degree centrality, proximity centrality, and intermediary centrality of the user are relatively low, there are few acquaintances of the user, and they are not good friends as a whole (low sociability) and connect groups. It can be seen that the role of the hub is also low. As described above, it is possible to accurately estimate an isolated user or a user with high sociality, as compared with the case where the intimacy between users is individually referred to.

Further, in the above-described embodiment, the detection unit 21 may acquire the cell ID that identifies the base station to which the mobile terminal 1 is connected by mobile communication. In this case, the cell ID may be used as the position information.

In addition, the detection unit 21 may acquire the usage status of the application installed in the mobile terminal 1 as the data of the sensor used for estimating the profile. In this case, the hobby preference may be estimated based on the usage status of the application. For example, the degree of coincidence of the hobby preference may be added to the intimacy parameter. In addition, the detection unit 21 may acquire the web browsing history of the mobile terminal, the remaining battery level, the acceleration sensor information, and the like for use in estimating the profile.

Note that the block diagrams used in the description of the above embodiments show blocks of functional units. These functional blocks (components) are realized by an arbitrary combination of hardware and/or software. Further, the means for realizing each functional block is not particularly limited. That is, each functional block may be realized by one device that is physically and/or logically coupled, or may be directly and/or indirectly connected to two or more devices that are physically and/or logically separated. (For example, wired and/or wireless), and may be realized by a plurality of these devices.

For example, the mobile terminal 1, the human relationship estimation server 2 and the like according to the embodiment of the present invention may function as a computer that performs the processing of the data sharing determination device according to the present embodiment. FIG. 7 is a diagram showing an example of a hardware configuration of the mobile terminal 1 and the human relationship estimation server 2 according to this embodiment. The mobile terminal 1 and the human relationship estimation server 2 described above are physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, and the like. Good.

In the following description, the word “device” can be read as a circuit, a device, a unit, or the like. The hardware configurations of the mobile terminal 1 and the human relationship estimation server 2 may be configured to include one or a plurality of the respective devices illustrated in the figure, or may be configured not to include some devices.

Each function in the mobile terminal 1 and the human relationship estimation server 2 causes a predetermined software (program) to be loaded on hardware such as the processor 1001 and the memory 1002, so that the processor 1001 performs calculation and communication by the communication device 1004. It is realized by controlling reading and/or writing of data in the memory 1002 and the storage 1003.

The processor 1001 operates an operating system to control the entire computer, for example. The processor 1001 may be configured by a central processing unit (CPU) including an interface with peripheral devices, a control device, a calculation device, a register, and the like. For example, the functional units of the mobile terminal 1 and the human relationship estimation server 2 may be realized by the processor 1001.

Further, the processor 1001 reads a program (program code), a software module and data from the storage 1003 and/or the communication device 1004 into the memory 1002, and executes various processes according to these. As the program, a program that causes a computer to execute at least part of the operations described in the above-described embodiments is used. For example, each functional unit of the mobile terminal 1 and the human relationship estimation server 2 may be realized by a control program stored in the memory 1002 and operated by the processor 1001, and may be realized similarly for other functional blocks. . Although it has been described that the various processes described above are executed by one processor 1001, they may be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The program may be transmitted from the network via an electric communication line.

The memory 1002 is a computer-readable recording medium, and is configured by at least one of a ROM (Read Only Memory), an EPROM (Erasable Programmable ROM), an EEPROM (Electrically Erasable Programmable ROM), a RAM (Random Access Memory), and the like. May be done. The memory 1002 may be called a register, a cache, a main memory (main storage device), or the like. The memory 1002 may store an executable program (program code), a software module, etc. for executing the method according to the embodiment of the present invention.

The storage 1003 is a computer-readable recording medium, for example, an optical disc such as a CD-ROM (Compact Disc ROM), a hard disc drive, a flexible disc, a magneto-optical disc (for example, a compact disc, a digital versatile disc, a Blu-ray disc). (Registered trademark) disk), smart card, flash memory (for example, card, stick, key drive), floppy (registered trademark) disk, magnetic strip, and the like. The storage 1003 may be called an auxiliary storage device. The storage medium described above may be, for example, a database including the memory 1002 and/or the storage 1003, a server, or another appropriate medium.

The communication device 1004 is hardware (transmission/reception device) for performing communication between computers via a wired and/or wireless network, and is also called, for example, a network device, a network controller, a network card, a communication module, or the like. For example, the sensor data acquisition unit 11, the detection unit 21, the relationship estimation unit 24, and the like described above may be realized by including the communication device 1004.

The input device 1005 is an input device (for example, a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that receives an input from the outside. The output device 1006 is an output device (for example, a display, a speaker, an LED lamp, etc.) that performs output to the outside. The input device 1005 and the output device 1006 may be integrated (for example, a touch panel).

Further, each device such as the processor 1001 and the memory 1002 is connected by a bus 1007 for communicating information. The bus 1007 may be configured by a single bus or may be configured by different buses among devices.

The mobile terminal 1 and the human relationship estimation server 2 include a microprocessor, a digital signal processor (DSP), an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), an FPGA (Field Programmable Gate Array), and the like. The hardware may be included, and the hardware may implement some or all of the functional blocks. For example, processor 1001 may be implemented with at least one of these hardware.

Although the present embodiment has been described above in detail, it is obvious to those skilled in the art that the present embodiment is not limited to the embodiment described in this specification. The present embodiment can be implemented as modified and changed modes without departing from the spirit and scope of the present invention defined by the description of the claims. Therefore, the description of the present specification is for the purpose of exemplifying explanation, and does not have any restrictive meaning to the present embodiment.

Each aspect/embodiment described in this specification is LTE (Long Term Evolution), LTE-A (LTE-Advanced), SUPER 3G, IMT-Advanced, 4G, 5G, FRA (Future Radio Access), W-CDMA. (Registered trademark), GSM (registered trademark), CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20, UWB (Ultra-WideBand), It may be applied to a system using Bluetooth (registered trademark), other appropriate system, and/or a next-generation system extended based on these.

As long as there is no contradiction, the order of the processing procedure, sequence, flowchart, etc. of each aspect/embodiment described in this specification may be changed. For example, the methods described herein present elements of the various steps in a sample order, and are not limited to the specific order presented.

The input/output information and the like may be stored in a specific place (for example, a memory) or may be managed by a management table. Information that is input/output can be overwritten, updated, or added. The output information and the like may be deleted. The input information and the like may be transmitted to another device.

The determination may be performed by a value represented by 1 bit (whether 0 or 1), may be performed by a Boolean value (Boolean: true or false), and may be performed by comparing numerical values (for example, a predetermined value). (Comparison with the value).

The aspects/embodiments described in the present specification may be used alone, in combination, or may be switched according to execution. Further, the notification of the predetermined information (for example, the notification of “being X”) is not limited to the explicit notification, but is performed implicitly (for example, the notification of the predetermined information is not performed). Good.

Software, whether called software, firmware, middleware, microcode, hardware description language, or any other name, instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules. , Application, software application, software package, routine, subroutine, object, executable, thread of execution, procedure, function, etc. should be construed broadly.

Further, software, instructions, etc. may be transmitted and received via a transmission medium. For example, the software may use a wired technology such as coaxial cable, fiber optic cable, twisted pair and digital subscriber line (DSL) and/or wireless technology such as infrared, wireless and microwave to websites, servers, or other When transmitted from a remote source, these wireline and/or wireless technologies are included within the definition of transmission medium.

The information, signals, etc. described herein may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description include voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any of these. May be represented by a combination of

Note that the terms described in this specification and/or the terms necessary for understanding this specification may be replaced with terms having the same or similar meanings.

As used herein, the terms “system” and “network” are used interchangeably.

The information, parameters, and the like described in this specification may be represented by absolute values, relative values from predetermined values, or may be represented by other corresponding information. ..

Mobile communication terminals are defined by those skilled in the art as subscriber stations, mobile units, subscriber units, wireless units, remote units, mobile devices, wireless devices, wireless communication devices, remote devices, mobile subscriber stations, access terminals, mobile terminals, It may also be referred to as a wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other suitable term.

As used herein, the terms "determining" and "determining" can encompass a wide variety of actions. "Judgment", "decision" means, for example, calculating, computing, processing, deriving, investigating, looking up (e.g., a table, database or another). (Search in data structure), ascertaining that it is regarded as "judgment" and "decision" can be included. In addition, "decision" and "decision" include receiving (eg, receiving information), transmitting (eg, transmitting information), input (input), output (output), access (accessing) (for example, accessing data in a memory) may be regarded as “judging” and “deciding”. In addition, "judgment" and "decision" are considered to be "judgment" and "decision" when things such as resolving, selecting, choosing, establishing, establishing, and comparing are done. May be included. That is, the “judgment” and “decision” may include considering some action as “judgment” and “decision”.

As used herein, the phrase "based on" does not mean "based only on," unless expressly specified otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

As long as the terms “include”, “including”, and variations thereof are used in the present specification and claims, these terms are the same as the term “comprising”. It is intended to be comprehensive. Furthermore, the term “or” as used in the specification or claims is not intended to be an exclusive OR. In the present specification, a plurality of devices are also included unless the context or technology clearly indicates that only one device exists.

Throughout this disclosure, the plural includes the plural unless the context clearly indicates the singular.

1a(1)... Other terminal (mobile terminal), 1b(1)... Own terminal (mobile terminal), 2... Human relationship estimation server (human relationship estimation device), 21... Detection section, 23... Location profile estimation section (position) Attribute information acquisition unit), 24... Relationship estimation unit.

Claims (5)

A detection unit that detects that two users are close to each other;
A position attribute information acquisition unit that acquires information indicating different attributes, which is associated for each user with respect to the position where proximity is detected by the detection unit;
A human being, comprising: a relationship estimation unit that estimates a human relationship between the users based on a combination of different attributes that are associated with each other of the user and that is indicated by the information acquired by the position attribute information acquisition unit. Relationship estimation device. The human relationship estimating device according to claim 1, wherein the relationship estimating unit estimates the human relationship between the users based on a pre-stored association between the combination of the attributes and the human relationship between the users. .. The position attribute information acquisition unit acquires information indicating a history of the position for each user, estimates the attribute associated with the position for each user based on the history, and calculates the attribute. The human relationship estimation device according to claim 1 or 2, which acquires information indicating The detection unit detects information on the length of time that the users are close to each other,
The human relationship according to any one of claims 1 to 3, wherein the relationship estimation unit further estimates the degree of intimacy between the users based on the information on the length of time detected by the detection unit. Estimator. The human relationship estimation device according to claim 4, wherein the relationship estimation unit extracts a plurality of the users for each of the estimated human relationships and clusters the extracted plurality of users based on the intimacy degree.

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