Action acquisition device, action acquisition method, and recording medium

Abstract

[Problem] With the prior art, it is not possible to estimate an action of a user by using, e.g., position information associated with time. [Solution] An action acquisition device 3 includes: a time acquisition unit 331 for acquiring a time; a position acquisition unit 332 for acquiring position information associated with the time; an action estimation unit 335 for using two or more pieces of action source information each including the position information associated with the time to acquire action information specifying an action of a user in a time slot defined by the time of each of the two or more pieces of action source information; and an action output unit 341 for outputting the action information in the time slot. With the action acquisition device 3, it is possible to estimate an action of a user by using position information or the like associated with a time.

Description

Behavior acquisition device, behavior acquisition method, and recording medium

[0001] The present invention relates to a behavior acquisition device or the like that acquires and outputs behavior information and time periods of a user using location information or the like of the user.

[0002] Conventionally, there has been a behavior determination system that determines the sleep and other behavior of a resident using energy consumption data (see Patent Document 1).

[0003] Patent No. 6470497

[0004] However, conventional techniques have not been able to properly estimate user behavior.

[0005] The behavior acquisition device of the first invention is a behavior acquisition device that includes a time acquisition unit that acquires time, a location acquisition unit that acquires location information corresponding to time, a behavior estimation unit that uses two or more pieces of behavior source information including location information corresponding to time to acquire behavior information that identifies the user's behavior in a time period identified by the time contained in each of the two or more pieces of behavior source information, and a behavior output unit that outputs the behavior information in the time period.

[0006] With this configuration, the user's behavior can be estimated using location information associated with time.

[0007] Furthermore, compared to the first invention, the behavior acquisition device of the second invention further includes an activity acquisition unit that acquires activity data of the user corresponding to the time, or a vital acquisition unit that acquires one or more types of vital data of the user corresponding to the time, and the behavior estimation unit is a behavior acquisition device that acquires behavior information for a time period using location information corresponding to the time and two or more types of behavior source information including the activity data or one or more types of vital data.

[0008] With this configuration, the user's behavior can be estimated using the location information and the user's physical data, such as activity data or vital data.

[0009] Furthermore, compared to the first invention, the behavior acquisition device of the third invention further includes an activity acquisition unit that acquires activity data of the user corresponding to the time, and a vital acquisition unit that acquires one or more types of vital data of the user corresponding to the time, and the behavior estimation unit is a behavior acquisition device that acquires behavior information for a time period using two or more pieces of behavior source information including location information, activity data, and one or more types of vital data corresponding to the time.

[0010] With this configuration, the user's behavior can be estimated using the location information and the user's physical data.

[0011] Furthermore, the behavior acquisition device of the fourth invention is a behavior acquisition device according to any one of the first to third inventions, further comprising an emotion estimation unit that acquires emotion information relating to the user's emotion during a time period using behavior source information, and the behavior estimation unit also uses the emotion information to acquire behavior information during a time period.

[0012] With this configuration, the user's behavior can be estimated with higher accuracy by also using emotion information.

[0013] Furthermore, the behavior acquisition device of the fifth invention is a behavior acquisition device in which, compared to any one of the first to third inventions, the behavior estimation unit acquires behavior information for a time period by also using one or more pieces of past behavior information including immediately previous behavior information.

[0014] With this configuration, the user's behavior can be estimated with higher accuracy by also using past behavior information.

[0015] Furthermore, the behavior acquisition device of the sixth invention is the behavior acquisition device of the first invention, further comprising an emotion estimation unit that acquires emotion information regarding the user's emotion in a time period using the behavior information or the behavior source information, and an emotion output unit that outputs the emotion information.

[0016] With this configuration, it is possible to estimate the user's emotions during the action.

[0017] Furthermore, the behavior acquisition device of the seventh invention is a behavior acquisition device in which, compared to the sixth invention, the emotion estimation unit acquires emotion information for a time period using learning information from a learning management unit in which learning information based on two or more teacher data having one or more pieces of behavior source information or behavior information and emotion information is stored, and the behavior information acquired by the behavior estimation unit or the behavior source information from which the behavior information was acquired.

[0018] With this configuration, the user's emotions during the action can be estimated with higher accuracy.

[0019] Furthermore, the behavior acquisition device of the eighth invention is a behavior acquisition device in which, compared to any one of the first to seventh inventions, the behavior estimation unit acquires behavior information for a time period using learning information from a learning management unit in which learning information based on two or more teacher data having one or more pieces of behavior source information and behavior information is stored, and two or more pieces of behavior source information including location information and activity data corresponding to time.

[0020] With this configuration, it is possible to estimate the user's behavior with higher accuracy using past records.

[0021] Furthermore, the behavior acquisition device of the ninth invention is a behavior acquisition device in which, compared to the seventh invention, the behavior information contained in at least one of the two or more teacher data is behavior information input by a user.

[0022] With this configuration, it is possible to estimate the user's behavior with higher accuracy using past records.

[0023] In addition, the behavior acquisition device of the tenth invention is a behavior acquisition device in which, compared to the ninth invention, the behavior information contained in at least two or more of the two or more teacher data is behavior information for each of two or more users.

[0024] With this configuration, the past records of two or more users can be used to estimate the user's behavior with higher accuracy.

[0025] Furthermore, compared to the first invention, the behavior acquisition device of the eleventh invention further includes a location acquisition unit that refers to a map management unit in which a map having location information corresponding to location information is stored, and acquires location information corresponding to the location information acquired by the location acquisition unit, and the behavior output unit is a behavior acquisition device that outputs the location information acquired by the location acquisition unit when the behavior estimation unit is unable to acquire the behavior information.

[0026] With this configuration, when the user's behavior cannot be estimated, the location where the user was can be output.

[0027] Furthermore, the behavior acquisition device of the twelfth invention, compared to the first invention, comprises a receiving unit that receives radio waves including a device identifier that identifies the communication device from each of three or more communication devices, an intensity acquisition unit that acquires a time series of radio wave intensity for each of the three or more communication devices, and a type determination unit that uses the time series of radio wave intensity acquired by the intensity acquisition unit to determine whether each of the three or more communication devices is a fixed terminal that is fixed or a mobile terminal that is moving, and the location acquisition unit is a behavior acquisition device that acquires location information indoors using the radio wave intensities of the three or more communication devices that the type determination unit has determined to be fixed terminals.

[0028] With this configuration, the user's behavior can be estimated using indoor position information and the user's activity data.

[0029] Furthermore, the behavior acquisition device of the thirteenth invention, compared to the first invention, further comprises a storage unit that associates the behavior information acquired by the behavior estimation unit with a time period and accumulates the behavior information associated with each of two or more time periods in a behavior management unit, in which the behavior information is stored, and is capable of determining whether it is confirmed behavior information or not, and the behavior output unit outputs two or more pieces of behavior information so that confirmed behavior information and unconfirmed behavior information can be visually distinguished from each other.

[0030] With this configuration, the estimated behavior information and the confirmed behavior information can be easily confirmed.

[0031] According to the behavior acquisition device of the present invention, the behavior of the user can be estimated using the location information and the activity data of the user.

[0032] FIG. 1 is a conceptual diagram of an information system A including a location information production device 1 in embodiment 1; FIG. 2 is a block diagram of the same location information production device 1; FIG. 3 is a flowchart explaining an example of the operation of the same location information production device 1; FIG. 4 is a flowchart explaining an example of the simultaneous series intensity acquisition process; FIG. 5 is a flowchart explaining an example of the same fixed information acquisition process; FIG. 6 is a flowchart explaining an example of the same type determination process; FIG. 7 is a diagram showing a simultaneous series radio wave intensity management table; FIG. 8 is a diagram showing the same location information management table; FIG. 9 is a conceptual diagram of an information system C in embodiment 2; a flowchart illustrating an example of the first behavior estimation process; a flowchart illustrating an example of the second behavior estimation process; a flowchart illustrating an example of the third behavior estimation process; a flowchart illustrating an example of the first emotion estimation process; a flowchart illustrating an example of the second emotion estimation process; a flowchart illustrating an example of the third emotion estimation process; a flowchart illustrating an example of the output configuration process; a flowchart illustrating an example of the first behavior learning process; a flowchart illustrating an example of the second behavior learning process; a flowchart illustrating an example of the first emotion learning process; a flowchart illustrating an example of the second emotion learning process; a diagram illustrating the behavior source management table; a diagram illustrating the behavior / emotion management table; a diagram illustrating the time period information management table; a diagram illustrating an output example; a conceptual diagram of an information system E in embodiment 4; a block diagram of the information system E; a block diagram of the behavior acquisition device 5; a flowchart illustrating an operation example of the behavior acquisition device 5; a flowchart illustrating an operation example of the terminal device 6; a block diagram of a computer system in the above embodiments.

[0033] Hereinafter, embodiments of a behavior acquisition device and the like will be described with reference to the drawings. Note that components with the same reference numerals in the embodiments perform similar operations, and therefore repeated description may be omitted.

[0034] (First Embodiment) In this embodiment, a location information production device will be described. The location information production device is a device that acquires location information of a specific location, which will be described later.

[0035] In this specification, information X being associated with information Y means that information Y can be obtained from information X, or information X can be obtained from information Y, and the method of association is not important. Information X and information Y may be linked, may exist in the same buffer, information X may be included in information Y, or information Y may be included in information X, etc.

[0036] Furthermore, in this specification, selecting or determining information Z means obtaining information Z, obtaining a pointer to information Z, obtaining the ID of information Z, setting a flag on information Z, etc., and it is sufficient if information Z can be accessed.

[0037] 1 is a conceptual diagram of an information system A including a location information production device 1 according to this embodiment. The information system A has the location information production device 1 and three or more communication devices B.

[0038] Each of the three or more communication devices B is a device that transmits radio waves to other devices such as the location information production device 1. The communication device B transmits to the other devices a device identifier that identifies the communication device B. The communication device B is, for example, a Wi-Fi router or a communication device that uses BLE (Bluetooth Low Energy), but the type is not critical.

[0039] 2 is a block diagram of the location information production device 1 according to the present embodiment. The location information production device 1 includes a storage unit 11, a reception unit 12, a receiving unit 13, and a processing unit 14. The reception unit 12 includes a position reception unit 121. The processing unit 14 includes an intensity acquisition unit 141, a type determination unit 142, and an accumulation unit 143.

[0040] The receiving unit 12 receives various instructions and information. The various instructions and information include, for example, position information, which will be described later. The instructions and information can be input using any means, such as a touch panel, a keyboard, a mouse, or a menu screen.

[0041] The position receiving unit 121 receives position information of specific locations. The position receiving unit 121 typically receives position information of three or more specific locations. The position receiving unit 121 receives, for example, position information input by a user. The position receiving unit 121 reads the position information from the storage unit 11, for example.

[0042] The specific location is a specific indoor location, but may also be a specific outdoor location. Here, location information is information that identifies a location indoors or outdoors. The location information is, for example, three-dimensional coordinate values ​​(x, y, z) that indicate a relative indoor or outdoor location, but may also be two-dimensional coordinate values ​​(x, y). The origin of the coordinate values ​​used to identify a relative indoor or outdoor location does not matter. The specific outdoor location is preferably a location where GPS signals are difficult to reach, such as among high-rise buildings or in a forest, but this does not matter. The location information may also be information that allows a person to recognize a location (for example, a character string) or an ID. Such location information may also be, for example, a label such as "living room," "workroom," "conference room," "east side of the library," or "toy section of a department store."

[0043] The location receiving unit 121 may generate a unique ID. Such a unique ID may be considered to be a label and location information.

[0044] The position receiving unit 121 does not need to receive position information. In this case, the position receiving unit 121 is unnecessary.

[0045] The receiving unit 13 receives, at a specific location, radio waves including the device identifiers from each of three or more communication devices B. The receiving unit 13 usually receives radio waves including the device identifiers from each of three or more communication devices B consecutively.

[0046] The device identifier is information that identifies the communication device B. The device identifier is, for example, the ID or name of the communication device B. Reception of radio waves can be considered as reception of information.

[0047] The processing unit 14 performs various types of processing, such as processing performed by an intensity acquisition unit 141, a type determination unit 142, and a storage unit 143.

[0048] The intensity acquisition unit 141 acquires the intensity of radio waves received from each of the three or more communication devices B. The intensity acquisition unit 141 acquires the radio wave intensity in pairs with the device identifier of the communication device B. The intensity acquisition unit 141 acquires radio wave intensity in a time series. The time series radio wave intensity means two or more radio wave intensities that are consecutive in time. It goes without saying that being consecutive in time may include a time interval.

[0049] The type determination unit 142 determines whether each of the three or more communication devices B is a fixed terminal or a mobile terminal, using the time-series radio wave intensity acquired by the intensity acquisition unit 141. A fixed terminal is a communication device that is installed at a fixed location. A mobile terminal is a communication device that is not installed at a fixed location and is mobile.

[0050] The type determination unit 142, for example, acquires the degree of variation of two or more temporally consecutive radio wave intensities in a time series paired with a device identifier, and if the degree of variation is equal to or greater than a threshold, determines that the communication device B identified by the device identifier is a mobile terminal.Furthermore, the type determination unit 142, for example, acquires the degree of variation of two or more temporally consecutive radio wave intensities in a time series paired with a device identifier, and if the degree of variation is equal to or less than a threshold, determines that the communication device B identified by the device identifier is a fixed terminal.

[0051] The degree of variation is information indicating the degree of variation or change in radio wave strength over time, and is, for example, a number based on variance, standard deviation, or difference (for example, the sum of the difference between two consecutive radio wave strengths among three or more consecutive radio wave strengths).

[0052] The type determination unit 142 determines that the communication device B identified by a certain device identifier is a mobile terminal, for example, if the number of radio wave intensities obtained in a specified time period and the number of consecutive radio wave intensities in a time series paired with a certain device identifier is below or less than a threshold value.

[0053] The storage unit 143 configures and stores location information having a device identifier and radio wave intensity of the communication device B that the type determination unit 142 has determined to be a fixed terminal. It is preferable that the storage unit 143 configures and stores location information having a device identifier and radio wave intensity for each of the three or more communication devices B.

[0054] The accumulation unit 143, for example, constructs and accumulates location information including a device identifier, radio wave intensity, and location information of a specific location of a communication device B that the type determination unit 142 has determined to be a fixed terminal. It is preferable that the accumulation unit 143 constructs and accumulates location information including a device identifier, radio wave intensity, and location information of a specific location for each of three or more communication devices B. For example, the accumulation unit 143 accumulates the location information in the storage unit 11, but it may also accumulate the location information in another device. It is preferable that the location information includes location information, but it does not have to include location information. The location information may also be comprised of only a device identifier and radio wave intensity.

[0055] The radio wave intensity stored in the storage unit 143 is usually a representative value of the time series radio wave intensity of the radio wave from the communication device B. The representative value is, for example, a median, an average, a maximum value, or a minimum value.

[0056] The storage unit 11 is preferably a non-volatile recording medium, but may also be a volatile recording medium.

[0057] There is no restriction on the process by which information is stored in the storage unit 11. For example, information may be stored in the storage unit 11 via a recording medium, information transmitted via a communication line or the like may be stored in the storage unit 11, or information input via an input device may be stored in the storage unit 11.

[0058] The receiving unit 12 and the position receiving unit 121 can be realized by a device driver for an input means such as a touch panel or a keyboard, or control software for a menu screen.

[0059] The receiving unit 13 is usually realized by a wireless or wired communication means.

[0060] The processing unit 14, intensity acquisition unit 141, type determination unit 142, and storage unit 143 can usually be realized by a processor, memory, etc. The processing procedures of the processing unit 14, etc. are usually realized by software, and the software is recorded on a recording medium such as a ROM. However, they may also be realized by hardware (dedicated circuit). The processor may be a CPU, MPU, GPU, etc., and the type does not matter.

[0061] Next, an example of the operation of the location information production device 1 will be described with reference to the flowchart of FIG.

[0062] (Step S301) The location receiving unit 121 determines whether or not location information of a specific location has been received. If location information has been received, the process proceeds to step S302, and if not, the process returns to step S301.

[0063] (Step S302) The accumulation unit 143 acquires the location information accepted in step S301.

[0064] (Step S303) The processing unit 14 and the like perform a time-series intensity acquisition process. The time-series intensity acquisition process is a process for acquiring the time-series radio wave intensity of radio waves from each of the three or more communication devices B. An example of the time-series intensity acquisition process will be described with reference to the flowchart of FIG. 4.

[0065] (Step S304) The processing unit 14 and the like perform fixed information acquisition processing. Return to step S301. The fixed information acquisition processing is processing for acquiring the strength of radio waves from the fixed terminal at the point identified by the location information accepted in step S301. An example of the fixed information acquisition processing will be described with reference to the flowchart of FIG. 5.

[0066] In the flowchart of Figure 3, it is preferable that a user holding the location information production device 1 moves to each of three or more specific locations, and the location information production device 1 accepts location information for each of the three or more specific locations and repeatedly performs the processes from S301 to S304.

[0067] In the flowchart of FIG. 3, the process ends when the power is turned off or an interrupt occurs to end the process.

[0068] Next, an example of the time-series intensity acquisition process in step S303 will be described with reference to the flowchart of FIG.

[0069] (Step S401) The receiving unit 13 determines whether or not it has received a radio wave from any of the communication devices B. If it has received a radio wave, the process proceeds to step S402, and if it has not received a radio wave, the process returns to step S401.

[0070] (Step S402) The storage unit 143 acquires the device identifier corresponding to the radio wave received in step S401.

[0071] (Step S403) The intensity acquisition unit 141 acquires the intensity of the radio wave received in step S401.

[0072] (Step S404) The accumulation unit 143 adds the radio wave intensity acquired in step S403 to a buffer (not shown) in association with the device identifier acquired in step S402.

[0073] (Step S405) The accumulation unit 143 determines whether the accumulation condition of the location information is met. If the accumulation condition is met, the process returns to the upper process, and if the accumulation condition is not met, the process returns to step S401. The accumulation condition may be, for example, that a threshold time or more has elapsed since the location information of the specific location was received, or that a threshold or more of radio wave intensity values ​​paired with three or more device identifiers have been accumulated.

[0074] Next, an example of the fixed information acquisition process in step S304 will be described with reference to the flowchart of FIG.

[0075] (Step S501) The type determining unit 142 assigns 1 to a counter i.

[0076] (Step S502) The type determination unit 142 determines whether the i-th device identifier exists in a buffer (not shown). If the i-th device identifier exists, the process proceeds to step S503; if the i-th device identifier does not exist, the process returns to the upper level process.

[0077] (Step S503) The type determination unit 142 determines the type of the communication device B identified by the i-th device identifier. An example of such type determination processing will be described with reference to the flowchart of FIG.

[0078] (Step S504) If the determination result in step S503 is a "fixed terminal", the process proceeds to step S505, and if it is a "mobile terminal", the process proceeds to step S508.

[0079] (Step S505) The accumulation unit 143 acquires two or more radio wave intensities paired with the i-th device identifier from a buffer (not shown).

[0080] (Step S506) The accumulation unit 143 acquires two or more representative values ​​of radio field strength.

[0081] (Step S507) The accumulation unit 143 accumulates in the storage unit 11 a pair of the i-th device identifier and the representative value of the radio wave intensity acquired in step S506 in association with the location information accepted in step S301.

[0082] (Step S508) The type determination unit 142 increments the counter i by 1. The process returns to step S502.

[0083] In the flowchart of FIG. 5, the accumulation unit 143 may acquire the latest signal strength in step S506 instead of the representative value of two or more signal strengths.

[0084] Next, an example of the type determination process in step S503 will be described with reference to the flowchart in FIG.

[0085] (Step S601) The type determination unit 142 acquires two or more radio wave intensities paired with the i-th device identifier in step S502 from a buffer (not shown).

[0086] (Step S602) The type determination unit 142 acquires the degree of variation in the two or more radio wave intensities acquired in step S601.

[0087] (Step S603) The type determination unit 142 determines whether the degree of variation acquired in step S602 is equal to or less than a threshold value. If the degree of variation is equal to or less than the threshold value, the process proceeds to step S604. If the degree of variation is equal to or greater than the threshold value, the process proceeds to step S605.

[0088] (Step S604) The type determination unit 142 determines the type of communication device B as "fixed terminal." The process returns to the upper level process.

[0089] (Step S605) The type determination unit 142 determines the type of communication device B as "mobile terminal", and returns to the upper level processing.

[0090] A specific example of the operation of the location information production device 1 in this embodiment will be described below. Here, the accumulation condition is that a predetermined time (e.g., three minutes) has elapsed since the location information of a specific location was accepted.

[0091] For example, assume that user A is indoors (for example, at home or in a department store that user A frequently visits). Then, user A sends location information (x １ , y １ ) is input.

[0092] Next, the location reception unit 121 of the location information production device 1 receives the location information (x １ , y １ Next, the storage unit 143 receives the received position information (x １ , y １ ) is acquired in a buffer not shown.

[0093] Then, the receiving unit 13 receives radio waves including device identifiers from three or more communication devices B for a predetermined time (for example, three minutes). Then, the storage unit 143 acquires the device identifiers included in the received radio waves. Furthermore, the intensity acquisition unit 141 acquires the intensity of the received radio waves. Next, the storage unit 143 associates the acquired device identifiers with the acquired radio wave intensity and adds the acquired radio wave intensity to a buffer (not shown). As a result, a time-series radio wave intensity management table shown in FIG. 7 is configured in the buffer (not shown). The time-series radio wave intensity management table shown in FIG. 7 includes location information (x １ , y １ ) is a table for a specific location indicated by

[0094] The time-series radio wave strength management table is a table that manages the time-series radio wave strength for each communication device B. The time-series radio wave strength management table is a table that manages two or more records each having an "ID," "device identifier," and "time-series radio wave strength." "ID" is information that identifies a record. "Time-series radio wave strength" is radio wave strength that is continuous over time. "R １１ " "R １２ " "R ２１ " etc. indicates radio wave strength.

[0095] After the management table of FIG. 7 is constructed, the location information (x １ , y １ Since a predetermined time (for example, three minutes) has elapsed since the request was accepted, the accumulation unit 143 determines that the accumulation condition for the location information is met.

[0096] Next, the type determination unit 142 acquires the degree of variation in time-series radio wave intensity of each record in Fig. 7 according to the operation of the flowchart in Fig. 6, and determines whether each communication device B is a fixed terminal or a mobile terminal. Then, the type determination unit 142 determines that the communication devices B identified by the device identifiers "device 1, device 3, device 4, device 6, ..." are fixed terminals, and determines that the communication devices B identified by the device identifiers "device 2, device 5, ..." are mobile terminals.

[0097] Next, the storage unit 143 generates radio wave intensity information by pairing the device identifier of the communication device B, which is a fixed terminal, with the representative value of the radio wave intensity. Then, the storage unit 143 stores each of the plurality of pieces of radio wave intensity information together with the position information (x１ , y １ ) and stores the information. This process creates a record with "ID=1" in the location information management table of FIG. 8. The storage unit 143 may store only the device identifier and radio wave intensity. In this case, each record in FIG. 8 does not have location information. In this case, the location receiving unit 121 does not need to receive location information of a specific location.

[0098] The location information management table is a table for managing location information. The location information management table stores multiple records that correspond to location information and have an "ID," "device identifier," and "signal strength information." The "signal strength information" has a "device identifier" and "signal strength."

[0099] By the above process, the position information (x １ , y １ ) is stored as location information for specific location 1.

[0100] User A holds the location information production device 1 and sends location information (x ２ , y ２ ) and does the same as above. As a result, the point information production device 1 creates and stores a record for "ID=2" in the point information management table of FIG. 8. Furthermore, user A moves to one or more specific points including specific point 3 with the point information production device 1, and does the same as above. As a result, the point information production device 1 creates and stores a record (not shown) for "ID=3" in the point information management table of FIG. 8.

[0101] As described above, according to this embodiment, it is possible to obtain point information for acquiring the indoor position of a terminal device. In other words, according to this embodiment, it is possible to generate three or more pieces of point information for acquiring the indoor position of a terminal device.

[0102] The processing in this embodiment may be implemented by software. This software may be distributed by software download or the like. Furthermore, this software may be recorded on a recording medium such as a CD-ROM and distributed. This also applies to other embodiments in this specification. The software implementing the location information production device 1 in this embodiment is the following program. That is, this program causes a computer to function as: a location reception unit that receives location information of a specific location; a reception unit that receives radio waves from three or more communication devices at the specific location, each of which includes a device identifier that identifies the communication device; an intensity acquisition unit that acquires a time series of radio wave intensity for each of the three or more communication devices; a type determination unit that uses the time series of radio wave intensity acquired by the intensity acquisition unit to determine whether each of the three or more communication devices is a fixed terminal, i.e., a fixed communication device, or a mobile terminal, i.e., a mobile communication device; and a storage unit that configures and stores location information including the device identifiers and radio wave intensities of each of the three or more communication devices that the type determination unit determines to be a fixed terminal, and the location information of the specific location.

[0103] (Embodiment 2) In this embodiment, we will describe a terminal device that receives radio waves from three or more communication devices B, determines the type of each communication device B using the time-series radio wave strength, and obtains and outputs a terminal position indicating the location of the terminal device indoors using the radio wave strength from only communication devices B whose type is ``fixed terminal.''

[0104] In this embodiment, a terminal device will be described that determines whether the terminal device is moving or stationary, and acquires and outputs the terminal position using the determination result.

[0105] 9 is a conceptual diagram of an information system C according to this embodiment. The information system C comprises one or more terminal devices 2 and three or more communication devices B.

[0106] The terminal device 2 is a terminal capable of acquiring indoor position information. The terminal device 2 may be, for example, a smartphone, a tablet terminal, a smartwatch, a so-called personal computer, or the like, and the type of the terminal device 2 is not important.

[0107] 10 is a block diagram of the terminal device 2 in this embodiment. The terminal device 2 includes a storage unit 21, a receiving unit 22, a processing unit 23, and an output unit 24. The storage unit 21 includes a point information storage unit 211. The processing unit 23 includes an intensity acquisition unit 231, a type determination unit 232, a movement determination unit 233, and a position acquisition unit 234. The position acquisition unit 234 includes intensity acquisition means 2341, point determination means 2342, and position acquisition means 2343. The output unit 24 includes a position output unit 241.

[0108] Various types of information are stored in the storage unit 21. The various types of information include, for example, location information, which will be described later.

[0109] Three or more pieces of spot information are stored in the spot information storage unit 211. It is preferable that the three or more pieces of spot information in the spot information storage unit 211 are information accumulated by the spot information production device 1.

[0110] Each of the three or more pieces of point information in the point information storage unit 211 has, for example, location information of a specific point, a device identifier, and radio wave intensity. It is preferable that three or more pieces of radio wave intensity information correspond to each of the three or more pieces of location information. The radio wave intensity information has a device identifier and radio wave intensity. The three or more pieces of radio wave intensity information may form a radio wave intensity vector. The radio wave intensity vector is a vector using radio wave intensity information, and has, for example, a structure of (radio wave intensity of device identifier 1, radio wave intensity of device identifier 2, radio wave intensity of device identifier 3, ..., radio wave intensity of device identifier n). The point information storage unit 211 stores, for example, a point information management table having the structure shown in FIG. 8.

[0111] The terminal device 2 does not necessarily have to have the location information storage unit 211. In such a case, the terminal device 2 refers to the location information storage unit 211 of an external device (not shown) and acquires the terminal position, which will be described later.

[0112] The receiving unit 22 receives radio waves including a device identifier that identifies the communication device B from each of the three or more communication devices B. The receiving unit 22 typically performs the same function as the receiving unit 13 described above. The receiving unit 22 typically receives radio waves including a device identifier from each of the three or more communication devices B. The receiving unit 22 typically receives radio waves continuously.

[0113] The processing unit 23 performs various types of processing, such as processing performed by an intensity acquisition unit 231, a type determination unit 232, a movement determination unit 233, and a position acquisition unit 234.

[0114] The intensity acquisition unit 231 acquires time-series radio wave intensity for each of the three or more communication devices B. The intensity acquisition unit 231 acquires radio wave intensity based on the radio waves received by the receiving unit 22. The intensity acquisition unit 231 performs the same function as the intensity acquisition unit 141 described above.

[0115] The type determination unit 232 determines whether each of the three or more communication devices B is a fixed terminal or a mobile terminal, using the time-series radio wave intensity acquired by the intensity acquisition unit 231. The type determination unit 232 performs the same function as the type determination unit 142 described above.

[0116] The movement determination unit 233 determines whether the terminal device 2 is moving or stopped, and acquires a movement determination result that is the result of the determination. The movement determination result is, for example, "moving" or "stopped."

[0117] The movement determination unit 233 acquires, for example, sensor information of the terminal device 2 and acquires a movement determination result using the sensor information. The sensor information is, for example, acceleration measured by a gyro and time-series position information.

[0118] For example, if the acceleration measured by the gyro is "0" or less than a threshold, the movement determination unit 233 obtains the movement determination result as "stopped." For example, if the acceleration measured by the gyro is equal to or greater than the threshold, the movement determination unit 233 obtains the movement determination result as "moving."

[0119] The movement determination unit 233, for example, uses the time-series radio wave strength of each of three or more communication devices B acquired by the strength acquisition unit 231 to determine that one or more communication devices B is stopped if there is no change in the time-series radio wave strength of one or more communication devices B, and acquires the movement determination result of ``stopped.''

[0120] The location acquisition unit 234 acquires the radio wave strength of three or more communication devices B that the type determination unit 232 has determined to be fixed terminals, and uses the radio wave strength of the fixed terminals to acquire the terminal location, which is the location information of the terminal device 2 indoors.

[0121] The location acquisition unit 234 acquires, for example, the radio wave intensity of three or more communication devices B that the type determination unit 232 has determined to be fixed terminals, and uses the three or more radio wave intensities to refer to three or more pieces of location information in the location information storage unit 211 and acquires the terminal location using the fingerprint method.

[0122] It is preferable that the location acquisition unit 234 acquires location information using the movement determination result. For example, it is preferable that the location acquisition unit 234 acquires the terminal location only when the movement determination result is "stopped."

[0123] The location acquisition unit 234 may acquire the terminal location using location information including the device identifier only when the device identifier corresponding to the radio waves received by the receiving unit 22 is included in the location information in the location information storage unit 211. This is because the location information in the location information storage unit 211 is location information of a fixed terminal.

[0124] The intensity acquisition unit 2341 acquires the radio wave intensity of three or more communication devices that the type determination unit 232 has determined to be fixed terminals, in association with the device identifiers.

[0125] The location determination unit 2342 determines, from the location information in the location information storage unit 211, one or more pieces of location information that satisfy the radio wave intensity and similarity condition associated with each of the three or more device identifiers acquired by the intensity acquisition unit 2341.

[0126] The point determination means 2342, for example, acquires a first radio wave intensity vector, which is a vector whose elements are the radio wave intensities paired with each of the three or more device identifiers contained in the point information. The point determination means 2342 also acquires a second radio wave intensity vector, which is a vector whose elements are the radio wave intensities corresponding to each of the three or more device identifiers acquired by the intensity acquisition means 2341. The point determination means 2342, for example, acquires the similarity between the two radio wave intensity vectors, and if the similarity is equal to or greater than a threshold, acquires point information corresponding to the first radio wave intensity vector.

[0127] The position acquisition means 2343 acquires the position information contained in the one or more pieces of position information determined by the position determination means 2342, and acquires the terminal position using the one or more pieces of position information.

[0128] The output unit 24 outputs various types of information, such as the terminal position and an indoor map.

[0129] Here, output is a concept that includes displaying on a display, projection using a projector, printing on a printer, sound output, transmission to an external device, storage on a recording medium, and delivery of processing results to other processing devices or other programs.

[0130] The position output unit 241 outputs the terminal position acquired by the position acquisition unit 234. The position output unit 241 displays, for example, on an indoor map, a design that clearly indicates the position identified by the terminal position.

[0131] The storage unit 21 and the location information storage unit 211 are preferably non-volatile recording media, but may also be realized as volatile recording media.

[0132] There is no restriction on the process by which information is stored in the storage unit 21 etc. For example, information may be stored in the storage unit 21 etc. via a recording medium, information transmitted via a communication line etc. may be stored in the storage unit 21 etc., or information input via an input device may be stored in the storage unit 21 etc.

[0133] The receiving unit 22 is usually realized by a wireless or wired communication means.

[0134] The processing unit 23, intensity acquisition unit 231, type determination unit 232, movement determination unit 233, position acquisition unit 234, intensity acquisition means 2341, point determination means 2342, and position acquisition means 2343 can typically be realized by a processor, memory, or the like. The processing procedures of the processing unit 23, etc. are typically realized by software, and the software is recorded on a recording medium such as a ROM. However, they may also be realized by hardware (dedicated circuitry). The processor may be a CPU, MPU, GPU, or the like, and the type does not matter.

[0135] The output unit 24 and the position output unit 241 can be realized by, for example, driver software for an output device such as a display or a speaker, or driver software for an output device and the output device.

[0136] Next, a first operation example of the terminal device 2 will be described with reference to the flowchart of FIG.

[0137] (Step S1101) The movement determining unit 233 determines whether the terminal device 2 is moving or stopped. An example of such movement determining processing will be described with reference to the flowchart of FIG.

[0138] (Step S1102) If the determination result in step S1101 is "stopped", the process proceeds to step S1103, and if it is "moving", the process returns to step S1101.

[0139] (Step S1103) The intensity acquisition unit 2341 performs a time-series intensity acquisition process. An example of the time-series intensity acquisition process has been described with reference to the flowchart in FIG.

[0140] (Step S1104) The type determination unit 232, the location determination unit 2342, etc. perform fixed information acquisition processing. An example of the fixed information acquisition processing has been described with reference to the flowchart of FIG.

[0141] (Step S1105) The location acquisition unit 2343 performs location estimation processing to acquire the terminal location. An example of the location estimation processing will be described with reference to the flowchart in FIG.

[0142] (Step S1106) The position output unit 241 outputs the terminal position acquired in step S1105. The process returns to step S1101.

[0143] In the flowchart of FIG. 11, the process ends when the power is turned off or an interrupt occurs to end the process.

[0144] Next, an example of the movement determination process in step S1101 will be described with reference to the flowchart in FIG.

[0145] (Step S1201) The movement determining unit 233 acquires a sensor value (for example, acceleration) of the terminal device 2 and temporarily stores the sensor value in a buffer (not shown).

[0146] (Step S1202) The movement determination unit 233 determines whether or not to perform a movement determination using the sensor values ​​in a buffer (not shown). If a movement determination is to be performed, the process proceeds to step S1203, and if a movement determination is not to be performed, the process returns to step S1201. Note that the movement determination unit 233 may always perform a movement determination, or may perform a movement determination after, for example, a predetermined number of sensor values ​​or more have been accumulated in the buffer, or after a predetermined time has elapsed since the sensor values ​​were acquired.

[0147] (Step S1203) The movement determination unit 233 uses one or more sensor values ​​in a buffer (not shown) to determine whether the terminal device 2 is moving or stopped. If it is stopped, the process proceeds to step S1204, and if it is moving, the process proceeds to step S1205.

[0148] (Step S1204) The movement determination unit 233 determines that the movement is "stopped." The process proceeds to step S1206.

[0149] (Step S1205) The movement determining unit 233 determines that the movement determination result is "on the move."

[0150] (Step S1206) The movement determination unit 233 clears a buffer (not shown) and returns to the upper level processing.

[0151] Next, an example of the position estimation process in step S1105 will be described with reference to the flowchart in FIG.

[0152] (Step S1301) The location acquisition unit 2343 acquires three or more pieces of radio wave intensity information (pairs of device identifier and radio wave intensity) of the terminal device 2.

[0153] (Step S1302) The location acquisition unit 2343 vectorizes three or more pieces of radio wave intensity information to acquire a radio wave intensity vector. The radio wave intensity vector is, for example, (radio wave intensity of device identifier 1, radio wave intensity of device identifier 2, radio wave intensity of device identifier 3, ..., radio wave intensity of device identifier n).

[0154] (Step S1303) The position obtaining unit 2343 assigns 1 to the counter i.

[0155] (Step S1304) The position acquisition unit 2343 determines whether or not the i-th point information exists in the point information storage unit 211. If the i-th point information exists, the process proceeds to step S1305; if not, the process proceeds to step S1309.

[0156] (Step S1305) The position acquiring unit 2343 acquires the i-th radio wave intensity vector included in the i-th point information from the point information storage unit 211.

[0157] (Step S1306) The position acquisition unit 2343 acquires the similarity between the radio wave intensity vector acquired in step S1302 and the i-th radio wave intensity vector acquired in step S1305. Next, the position acquisition unit 2343 determines whether the similarity satisfies a similarity condition (for example, whether the similarity is equal to or greater than a threshold). If the similarity satisfies the similarity condition, the process proceeds to step S1307; if not, the process proceeds to step S1308.

[0158] (Step S1307) The location obtaining unit 2343 obtains the location information and the similarity included in the i-th point information, and stores them in a buffer (not shown).

[0159] (Step S1308) The position acquisition unit 2343 increments the counter i by 1. The process returns to step S1304.

[0160] (Step S1309) The location acquisition unit 2343 uses three or more sets of location information and similarity accumulated in a buffer (not shown) to acquire a terminal location that identifies the location of the terminal device 2. The process returns to the upper level process.

[0161] Next, a second operation example of the terminal device 2 will be described with reference to the flowchart of FIG.

[0162] (Step S1401) The intensity acquisition unit 2341 performs a time-series intensity acquisition process. An example of the time-series intensity acquisition process has been described with reference to the flowchart in FIG.

[0163] (Step S1402) The location determination unit 2342 performs fixed information acquisition processing. An example of the fixed information acquisition processing has been described with reference to the flowchart in FIG.

[0164] (Step S1403) The location acquisition unit 2343 performs location estimation processing to acquire the terminal location. An example of the location estimation processing has been described with reference to the flowchart in FIG.

[0165] (Step S1404) The location output unit 241 outputs the terminal location acquired in step S1403. The process returns to step S1401.

[0166] In the flowchart of FIG. 14, the process ends when the power is turned off or an interrupt occurs to end the process.

[0167] Next, a second example of the type determination process in the fixed information acquisition process in step S1402 in the flowchart of Fig. 14 will be described using the flowchart of Fig. 15. Note that the first example of the type determination process was described using the flowchart of Fig. 6.

[0168] (Step S1501) The type determination unit 232 acquires the device identifier of communication device B, the type of which is to be determined.

[0169] (Step S1502) The type determination unit 232 determines whether the device identifier acquired in step S1501 exists in any of the location information stored in the location information storage unit 211. If the device identifier exists in any of the location information, the process proceeds to step S1503; if the device identifier does not exist, the process proceeds to step S1504.

[0170] (Step S1503) The type determination unit 232 determines the type as "fixed terminal." The process returns to the upper level process.

[0171] (Step S1504) The type determination unit 232 determines the type as "mobile terminal." The process returns to the upper level process.

[0172] A specific example of the operation of the terminal device 2 in this embodiment will be described below.

[0173] Assume that user B, holding his / her terminal device 2, enters an indoor location identified by a location identifier (P). The receiving unit 22 of the terminal device 2 then transmits a location information request having the location identifier (P) to an external device (not shown) and receives the location information management table shown in Fig. 8 from the external device. The processing unit 23 then temporarily stores the location information management table in the location information storage unit 211.

[0174] Then, the terminal device 2 operates as follows, in accordance with the processes from steps S1103 to S1106 in FIG. 11 or the processes in the flowchart in FIG.

[0175] That is, the intensity acquisition means 2341 performs the time-series intensity acquisition process described using the flowchart of FIG. 4, acquires the radio wave intensity of each of three or more communication devices B at point X where the terminal device 2 is located, and constructs a time-series radio wave intensity management table having the structure shown in FIG. 7.

[0176] Next, the strength acquisition means 2341 performs a time-series strength acquisition process to acquire the time-series radio wave strength of each communication device B that can receive radio waves at point X, and creates a time-series radio wave strength management table with the structure shown in Figure 7.

[0177] Next, the type determination unit 142 refers to the time-series radio wave intensity management table and determines whether each communication device B is a "fixed terminal" or a "mobile terminal" by the type determination process described using the flowchart of Figure 4.

[0178] Next, the location determination means 2342 acquires the radio wave strength of "Device 1," "Device 3," "Device 4," "Device 6," ..., which are determined to be fixed terminals. Then, the location determination means 2342 acquires the radio wave strength vector "(Radio wave strength of Device 1, Radio wave strength of Device 3, Radio wave strength of Device 4, Radio wave strength of Device 6, ...) = (P1, P3, P4, P6, ...)." Note that the radio wave strength of communication device B acquired by the location determination means 2342 here may be a representative value of two or more radio wave strengths, or may be a single radio wave strength such as the most recent radio wave strength of communication device B.

[0179] Next, the location acquisition unit 2343 performs the location estimation process described using the flowchart in FIG. 13, and calculates the similarity between the radio wave intensity vector at point X and the radio wave intensity vector (vector configured by radio wave intensity information) of each record in FIG. 8. Next, the location acquisition unit 2343 calculates the similarity between the radio wave intensity vector at point X and the radio wave intensity vector of each record in FIG. 8 (for example, the radio wave intensity vector (S) of "ID=1" in FIG. 8) that satisfies the similarity condition "similarity>=threshold value". １１ , S １２ , S １３ , ...), the radio wave intensity vector (S ２１ , S ２２ , S ２３ Next, the location acquisition unit 2343 determines a pair of location information and similarity (for example, "(x １ , y １ ), D.S. １ " "(x ２ , y ２ ), D.S. ２ Next, the position acquisition unit 2343 acquires the indoor position of point X (x １ ×DS １ / Sum of similarities + x ２ ×DS ２ / Sum of similarities +..., y １ ×DS １ / Sum of similarities + y ２ ×DS ２ / sum of similarities + ...). The sum of similarities is calculated by "DS １ +DS ２ +...".

[0180] Next, the position output unit 241 outputs the indoor or outdoor map stored in the storage unit 21, and places a pattern on the map at the position indicated by the position information (terminal position) acquired by the position acquisition means 2343.

[0181] As described above, according to this embodiment, the position of the terminal device 2 indoors or outdoors can be easily acquired.

[0182] The processing in this embodiment may be implemented by software. This software may be distributed by software download or the like. Furthermore, this software may be recorded on a recording medium such as a CD-ROM and distributed. This also applies to other embodiments in this specification. The software implementing the terminal device 2 in this embodiment is the following program. That is, this program causes a computer to function as a receiver that receives radio waves from three or more communication devices, each of which includes a device identifier that identifies the communication device; an intensity acquirer that acquires a time series of radio wave intensity for each of the three or more communication devices; a type determiner that uses the time series of radio wave intensity acquired by the intensity acquirer to determine whether each of the three or more communication devices is a fixed terminal or a mobile terminal; a location acquirer that acquires a terminal location, which is location information for the terminal device, using the radio wave intensity of the three or more communication devices that the type determiner determines to be fixed terminals; and a location outputter that outputs the terminal location acquired by the location acquirer.

[0183] (Embodiment 3) In this embodiment, a behavior acquisition device is described that acquires user behavior information and a time period using user location information corresponding to a time, and outputs the behavior information associated with the time period. Note that it is preferable that the behavior acquisition device also acquires user behavior information and a time period using the user's activity data and vital data. Furthermore, the location information used when acquiring the behavior information may be indoor location information, etc., acquired by the terminal device 2 described in embodiment 2.

[0184] In this embodiment, a behavior acquisition device that acquires behavior information using past record information will be described. Note that the past record information may be information based on input from one or more users.

[0185] In this embodiment, a behavior acquisition device that also acquires and outputs emotional information of a user will be described. In this embodiment, it is preferable to acquire emotional information using past recorded information.

[0186] In this embodiment, the behavior acquisition device is a terminal. However, as will be explained in embodiment 4, the behavior acquisition device may also be a server. In other words, the processes of acquiring behavioral information and emotional information, which will be described later, may be performed by the user's terminal or the server.

[0187] In addition, in this embodiment, a behavior acquisition device that acquires and outputs location information using a map when behavior information cannot be acquired will be described.

[0188] Furthermore, in this embodiment, a behavior acquisition device that displays estimated behavior information and confirmed behavior information in a manner that visually distinguishes them will be described.

[0189] 16 is a conceptual diagram of an information system D according to this embodiment. The information system D includes one or more behavior acquisition devices 3, a server device 4, and three or more communication devices B.

[0190] The behavior acquisition device 3 is a terminal. The behavior acquisition device 3 is a device that acquires and outputs behavior information. The behavior acquisition device 3 may be, for example, a smartphone, a tablet terminal, a smartwatch, a so-called personal computer, or the like, and its type does not matter.

[0191] The server device 4 is, for example, a device that stores sets of behavior source information and behavior information for two or more users, each set for each time period. The server device 4 is, for example, a device that stores learning information (described later) and provides the learning information to the behavior acquisition device 3. The server device 4 is, for example, a cloud server or an ASP server, but the type is not important.

[0192] 17 is a block diagram of an information system D according to this embodiment. FIG. 18 is a block diagram of a behavior acquisition device 3.

[0193] The behavior acquisition device 3 includes a storage unit 31, a receiving unit 32, a processing unit 33, and an output unit 34. The storage unit 31 includes a learning management unit 311, a map management unit 312, and a behavior management unit 313. Note that the behavior management unit 313 may be present in an external device (not shown). The processing unit 33 includes an intensity acquisition unit 231, a type determination unit 232, a time acquisition unit 331, a position acquisition unit 332, an activity acquisition unit 333, a vital sign acquisition unit 334, a behavior estimation unit 335, an emotion estimation unit 336, a location acquisition unit 337, an accumulation unit 338, and a configuration unit 339. The output unit 34 includes a behavior output unit 341 and an emotion output unit 342.

[0194] The server device 4 includes a server storage unit 41 , a server receiving unit 42 , a server processing unit 43 , and a server transmitting unit 44 .

[0195] The behavior acquisition device 3 accepts, for example, output instructions, confirmation instructions, and information input. An output instruction is an instruction to output output information, which will be described later. An output instruction usually includes period information. The period information is information that specifies the period for outputting behavioral information, etc. A confirmation instruction is an instruction to confirm estimated behavioral information or emotional information. When the estimated behavioral information or emotional information is incorrect, the information input is information for changing the behavioral information or emotional information. The information input is updated behavioral information or updated emotional information.

[0196] Various types of information are stored in the storage unit 31 constituting the behavior acquisition device 3. The various types of information include, for example, learning information (described later), a map (described later), behavior information (described later), location information, a calendar template, behavior information associated with two or more behavior conditions, and emotion information associated with two or more emotion conditions.

[0197] The calendar template is information indicating the template of the calendar to be output. The calendar template may be, for example, an ICS file, a file written in HTML, or a file written in XML, but the data structure is not important.

[0198] An action condition is a condition for acquiring action information. An action condition is a condition using two or more pieces of action source information. An action condition is associated with action information. For example, an action condition is "location information = office AND 8:00 <= time <= 19:00", and the action information associated with this action condition is "work". For example, an action condition is "location information = kitchen AND activity data = standing AND 7:00 <= time <= 8:00", and the action information associated with this action condition is "cooking". For example, an action condition is "location information = park AND activity data = standing AND 120 <= heart rate", and the action information associated with this action condition is "running".

[0199] Emotion conditions are conditions for acquiring emotion information. Emotion conditions are conditions using two or more pieces of emotion source information. Emotion conditions are associated with emotion information. For example, an emotion condition is "location information = office AND 8:00 <= time <= 19:00", and the emotion information associated with this behavioral condition is "positive". For example, an emotion condition is "location information = kitchen AND activity data = standing AND 7:00 <= time <= 8:00", and the emotion information associated with this behavioral condition is "positive". For example, an activity condition is "location information = park AND activity data = standing AND 120 <= heart rate", and the emotion information associated with this behavioral condition is "negative".

[0200] The learning management unit 311 stores learning information. The learning information is information based on two or more pieces of teacher data. The learning information in the learning management unit 311 is, for example, behavioral learning information and emotional learning information. Each of the two or more pieces of learning information in the learning management unit 311 may correspond to a different user attribute value condition. A user attribute value condition is a condition related to one or two or more user attribute values.

[0201] The user attribute value is a user's attribute value, such as, but not limited to, occupation, family structure, single or married status, gender, age, age group, morning or night type, and residential area.

[0202] The behavior learning information is information based on two or more behavior teacher data. The behavior learning information is, for example, a behavior learning model or a behavior correspondence table. The emotion learning information is information based on two or more emotion teacher data. The emotion learning information is, for example, an emotion learning model or an emotion correspondence table.

[0203] The behavior instructor data includes, for example, one or more pieces of behavior source information and behavior information. The emotion instructor data includes, for example, one or more pieces of behavior source information, behavior information, and emotion information.

[0204] The emotion teacher data has, for example, one or more pieces of action source information or action information, and emotion information. The emotion teacher data has, for example, one or more pieces of action source information, action information, and emotion information. In the emotion teacher data, one or more pieces of action source information, action information, or one or more pieces of action source information and action information are explanatory variables, and the emotion information is a target variable.

[0205] The behavioral information is information that identifies the user's behavior, such as "work," "watching TV," "walking," "running," "gym," "bath," and "sleep."

[0206] Emotional information is information about the user's emotions. Emotional information is, for example, positive (e.g., "1") or negative (e.g., "0"). Emotional information is, for example, the degree of positivity or negativity. Emotional information is, for example, joy (e.g., "1"), anger (e.g., "2"), sadness (e.g., "3"), and happiness (e.g., "4").

[0207] Action source information is information that serves as a source for acquiring action information. Action source information includes location information. Action source information preferably includes activity data or one or more types of vital data. Action source information may also include emotional information. Action source information may also include one or more pieces of past action information. Past action information typically includes immediately preceding action information. Action source information may also include one or more pieces of future schedule information for the user. Schedule information is, for example, information stored in a calendar server (e.g., a Google Calendar (registered trademark) server) not shown. Action source information may also include the elapsed time since the user arrived at the same location information. Action source information may also include one or more user attribute values.

[0208] Location information is information that specifies the location of the behavior acquisition device 3. Location information is, for example, (latitude, longitude), (latitude, longitude, altitude), a three-dimensional relative position indoors (x, y, z), or a two-dimensional relative position indoors (x, y), or location information. Location information is information that expresses the meaning of a location. Location information is, for example, indoor location information or outdoor location information. Indoor location information is information that specifies an indoor location. Examples of indoor location information are "living room," "kitchen," "workroom," and "office." Outdoor location information is information that specifies an outdoor location. Examples of outdoor location information are "ABC Station," "library," "izakaya," and "point A."

[0209] Physical data is information about the user's body, such as activity data and vital data.

[0210] The activity data is information that identifies the activity of the user. For example, the activity data is "standing" or "on the ground (e.g., sitting)."

[0211] Vital data is information that can be obtained from a user's biological data. Vital data may also be referred to as biological information. Examples of vital data include heart rate, heart rate variability, blood pressure (systolic and / or diastolic blood pressure), respiratory rate, and body temperature per unit time (e.g., 1 minute or 30 seconds). Learning information is, for example, a learning model or a correspondence table. A learning model is information constructed through machine learning learning processing using two or more pieces of training data, and is information used in machine learning prediction processing. A learning model may also be referred to as a learner, classifier, classification model, etc. Machine learning algorithms include deep learning, random forest, decision tree, SVM, etc. Furthermore, various machine learning functions, such as the TensorFlow (registered trademark) library, the random forest module of the R language, and TinySVM, as well as various existing libraries, can be used for machine learning. Furthermore, when the learning information is a learning model, one or more behavior source information of the training data is an explanatory variable, and the behavior information is a target variable.

[0212] The learning model here is, for example, a behavioral learning model or an emotion learning model. The behavioral learning model is a learning model for acquiring behavioral information, and is information acquired through a machine learning learning process using behavioral teacher data. The behavioral teacher data has one or more pieces of behavioral source information and behavioral information.

[0213] The emotion learning model is a learning model for acquiring emotion information, and is information acquired through machine learning learning processing using emotion training data. The emotion training data has one or more pieces of emotion source information and emotion information.

[0214] The correspondence table is an action correspondence table or an emotion correspondence table. An action correspondence table is a table for acquiring action information. An action correspondence table has two or more pieces of action correspondence information. Action correspondence information is information showing the correspondence between one or more pieces of action source information and action information. Note that one or more pieces of action source information have, for example, a vector structure. Such a vector is called an action source vector. An action source vector is a vector that has one or more pieces of action source information as elements. An emotion correspondence table has two or more pieces of emotion correspondence information. Emotion correspondence information is information showing the correspondence between one or more pieces of emotion source information and emotion information. One or more pieces of emotion source information have, for example, a vector structure. Such a vector is called an emotion source vector. An emotion source vector is a vector that has one or more pieces of emotion source information as elements.

[0215] The map management unit 312 stores a map. The map has location information corresponding to one or more pieces of location information. The map is in, for example, the KIWI format, but the structure is not critical.

[0216] The behavior management unit 313 stores behavior information associated with two or more time periods. The behavior information here is, for example, information acquired by the behavior estimation unit 335. The behavior information is information acquired by the behavior estimation unit 335 that has been changed by the user.

[0217] It is preferable that the behavioral information here can be determined whether it is confirmed behavioral information. For example, a confirmation flag is associated with the behavioral information. The confirmation flag is a flag that indicates that the behavioral information is confirmed. Whether it is confirmed behavioral information can be determined, for example, when the storage areas for confirmed behavioral information and unconfirmed behavioral information are different. Any other method can be used to determine whether it is confirmed behavioral information.

[0218] The receiving unit 32 receives radio waves including a device identifier that identifies the communication device B from one or more communication devices B. The receiving unit 32 typically receives radio waves from three or more communication devices B. The receiving unit 32 has the same functions as the receiving unit 22.

[0219] The processing unit 33 performs various types of processing, such as processing performed by the intensity acquisition unit 231, the type determination unit 232, the time acquisition unit 331, and the like.

[0220] The time acquisition unit 331 acquires the time. The time acquisition unit 331 acquires the time, for example, from a clock (not shown). The time acquisition unit 331 receives the time, for example, from the server device 4 or a device (not shown). The time may be in hours, minutes, and seconds, or may be in hours and minutes. The time may include one or more pieces of information from "year," "month," and "day." The time acquisition unit 331 may also acquire the day of the week. The day of the week may be considered to be information included in the acquired time.

[0221] The position acquisition unit 332 acquires position information. The position information is associated with time. The position acquisition unit 332 usually acquires position information in association with the time acquired by the time acquisition unit 331. The position acquisition unit 332 may perform the same processing as the position acquisition unit 234. In particular, when the behavior acquisition device 3 is located indoors or when a GPS signal cannot be received, the position acquisition unit 332 preferably performs the same processing as the position acquisition unit 234. The position acquisition unit 332 preferably includes a GPS receiver. The position acquisition unit 332 acquires position information using, for example, a GPS receiver. Such position information is absolute position information. The position information acquired by the position acquisition unit 332 may be either outdoor or indoor position information.

[0222] It is preferable that the location acquisition unit 332 acquires indoor location information using the radio wave intensity of the communication device B that the type determination unit 232 has determined to be a fixed terminal. The radio wave intensity used here is preferably the radio wave intensity of three or more communication devices B, but may be the radio wave intensity of one or two or more communication devices B.

[0223] The activity acquisition unit 333 acquires activity data of the user associated with a time. The activity acquisition unit 333 usually acquires the activity data associated with the time acquired by the time acquisition unit 331. The process of acquiring activity data is a well-known technique.

[0224] The vital sign acquisition unit 334 acquires one or more types of vital sign data of the user associated with a time. The vital sign acquisition unit 334 typically acquires one or more types of vital sign data associated with the time acquired by the time acquisition unit 331. The process of acquiring vital sign data is a known technique.

[0225] The behavior estimation unit 335 uses two or more pieces of behavior source information including location information associated with time to acquire behavior information that identifies the user's behavior during a time period identified by the time included in each of the two or more pieces of behavior source information. The behavior source information preferably also includes activity data. Furthermore, the behavior source information preferably also includes one or more types of vital data.

[0226] The behavior estimation unit 335 detects a behavior condition that matches two or more pieces of behavior source information including, for example, location information associated with a time, and acquires behavior information paired with the behavior condition from the storage unit 31 .

[0227] For example, if the user is sitting at a desk indoors at home at 1:15 PM, the behavior estimation unit 335 acquires behavior information "work." For example, if the user is sitting indoors in the living room at home at 8:17 PM, the behavior estimation unit 335 acquires behavior information "watching TV." For example, if the user acquires location information "izakaya" at 8:17 PM, the behavior estimation unit 335 acquires behavior information "drinking party."

[0228] The behavior estimation unit 335 may acquire behavior information for a time period using the behavior learning information of the learning management unit 311 and two or more pieces of behavior source information including location information associated with time. Below, the processing of the behavior estimation unit 335 when the learning information is a learning model and when it is a correspondence table will be described.

[0229] The behavior estimation unit 335 may acquire one or more user attribute values, acquire behavior learning information paired with a user attribute value condition that matches the one or more user attribute values ​​from the learning management unit 311, and acquire behavior information using the behavior learning information. (1) When the behavior learning information is a behavior learning model

[0230] The behavior estimation unit 335 acquires the learning model from the learning management unit 311. The behavior estimation unit 335 also acquires a time and one or more pieces of behavior source information associated with the time. Next, the behavior estimation unit 335 provides the time, the behavior source information, and the learning model to a module that performs machine learning prediction processing, executes the module, and acquires behavior information.

[0231] If the score output by the module is equal to or less than the threshold, the behavior estimation unit 335 does not need to acquire the behavior information. (2) When the behavior learning information is a behavior correspondence table

[0232] The behavior estimation unit 335 acquires a time and behavior source information associated with the time. Next, the behavior estimation unit 335 acquires a behavior source vector whose elements are the time and the behavior source information. Next, the behavior estimation unit 335 calculates the similarity between the behavior source vector and the behavior source vectors included in each of two or more pieces of behavior correspondence information included in the behavior correspondence table. Next, the behavior estimation unit 335 acquires, from the behavior correspondence table, behavior information paired with the behavior source vector having the highest similarity. Note that even if the similarity is highest, the behavior estimation unit 335 does not need to acquire the behavior information if the similarity is equal to or less than a threshold value.

[0233] The emotion estimation unit 336 acquires emotion information related to the emotion of the user in a time period using the behavior information or the behavior source information.

[0234] The emotion estimation unit 336 detects an emotion condition in which two or more pieces of emotion source information including, for example, location information associated with a time match, and acquires emotion information paired with the emotion condition from the storage unit 31 .

[0235] For example, when the user is sitting at a desk indoors at home at 1:15 PM, the emotion deduction unit 336 acquires emotion information "positive." When the user is sitting indoors in the living room of home at 8:17 PM, the emotion deduction unit 336 acquires emotion information "positive." When the user acquires location information "izakaya" at 8:17 PM, the emotion deduction unit 336 acquires emotion information "positive."

[0236] The emotion deduction unit 336 acquires emotion information for a time period using the emotion learning information of the learning management unit 311 and the behavioral information acquired by the behavior deduction unit 335 or one or more pieces of behavior source information that were the basis for acquiring the behavioral information. Note that the behavioral information or the one or more pieces of behavior source information are referred to as emotion source information here because they are used to acquire emotion information.

[0237] The emotion deduction unit 336 may acquire one or more user attribute values, acquire emotion learning information paired with user attribute value conditions that match the one or more user attribute values ​​from the learning management unit 311, and use the emotion learning information to acquire emotion information.

[0238] The emotion estimation unit 336 will now explain the processing of the behavior estimation unit 335 when the emotion learning information is an emotion learning model and when it is an emotion correspondence table. (1) When the emotion learning information is an emotion learning model

[0239] The emotion deduction unit 336 acquires the emotion learning model from the learning management unit 311. The emotion deduction unit 336 also acquires one or more pieces of behavior source information or behavior information associated with the time. Next, the emotion deduction unit 336 provides the acquired one or more pieces of behavior source information or behavior information and the emotion learning model to a module that performs machine learning prediction processing, executes the module, and acquires emotion information.

[0240] If the score output by the module is equal to or less than the threshold, the emotion estimation unit 336 does not need to acquire emotion information. (2) When the emotion learning information is an emotion correspondence table

[0241] The emotion deduction unit 336 acquires one or more pieces of action source information or action information associated with the time. Next, the emotion deduction unit 336 acquires an emotion source vector whose elements are the one or more pieces of action source information or action information. Next, the emotion deduction unit 336 calculates the similarity between the emotion source vector and the emotion source vectors included in each of two or more pieces of correspondence information included in the emotion correspondence table. Next, the emotion deduction unit 336 acquires, from the emotion correspondence table, emotion information paired with the emotion source vector with the highest similarity. Note that even if the similarity is highest, the emotion deduction unit 336 does not need to acquire emotion information if the similarity is equal to or less than a threshold value.

[0242] The location acquisition unit 337 refers to the map in the map management unit 312 and acquires location information corresponding to the location information acquired by the location acquisition unit 332. The location information in this case is usually absolute location information (e.g., (latitude, longitude)).

[0243] It is preferable that the location acquisition unit 337 acquires location information only for a time period during which the behavior estimation unit 335 did not acquire behavior information.

[0244] The accumulation unit 338 accumulates the behavior information acquired by the behavior estimation unit 335 in the behavior management unit 313 in association with the time period.

[0245] The storage unit 338 may store the emotion information acquired by the emotion estimation unit 336 in the behavior management unit 313 in association with a time period.

[0246] The composition unit 339 composes information to be output using the behavior information of the behavior management unit 313. The composition unit 339 composes information to be output using, for example, emotion information of the behavior management unit 313.

[0247] The composition unit 339, for example, composes output information having behavioral information paired with each time period in an area specified by each of two or more time periods on a calendar. It is preferable that the two or more pieces of behavioral information are arranged in the output information so that confirmed behavioral information and unconfirmed behavioral information are visually distinguishable. It is preferable that the composition unit 339 composes output information that visually indicates emotion information corresponding to each of two or more time periods. It is preferable that the composition unit 339 composes output information that visually indicates time periods in which emotion information is "positive" and time periods in which emotion information is "negative." It is preferable that the composition unit 339 composes output information so that time periods in which emotion information is "positive" and time periods in which emotion information is "negative" have different background colors, for example.

[0248] The output unit 34 outputs various types of information, such as behavior information, emotion information, and location information.

[0249] Here, output usually means display on a display, but it may also be a concept that includes projection using a projector, printing on a printer, transmission to an external device, storage on a recording medium, and handing over the processing results to other processing devices or other programs.

[0250] The behavior output unit 341 outputs behavior information for one or more time periods.

[0251] It is preferable that the behavior output unit 341 outputs the location acquired by the location acquisition unit 337 when the behavior estimation unit 335 cannot acquire behavior information.

[0252] It is preferable that the behavior output unit 341 outputs two or more pieces of behavior information so that the confirmed behavior information and the unconfirmed behavior information can be visually distinguished.

[0253] The emotion output unit 342 outputs emotion information. For example, the emotion output unit 342 outputs emotion information acquired by the emotion estimation unit 336. It is preferable that the emotion output unit 342 outputs emotion information for one or more time periods.

[0254] Various types of information are stored in the server storage unit 41 constituting the server device 4. The various types of information include, for example, the above-mentioned learning information, behavior source information and time corresponding to each of two or more user identifiers, two or more pieces of behavior teacher data, and two or more pieces of emotion teacher data.

[0255] The server receiving unit 42 receives various instructions and information, such as instructions to transmit information, including learning information and behavior source information.

[0256] The server processing unit 43 performs various types of processing, such as learning processing, including behavior learning processing and emotion learning processing.

[0257] The behavioral learning process is a process of acquiring a behavioral learning model using two or more behavioral teacher data. The server processing unit 43, for example, provides two or more behavioral teacher data to a machine learning learning processing module, executes the module, acquires a behavioral learning model, and stores it in the server storage unit 41. For example, the server processing unit 43 provides two or more positive examples, which are teacher data including behavioral information, and two or more negative examples, which are teacher data not including behavioral information, to the machine learning learning processing module for each of two or more behavioral information candidates, executes the module, acquires a behavioral learning model for each behavioral information candidate, and stores it in the server storage unit 41 in association with the behavioral information. The server processing unit 43, for example, acquires a behavior correspondence table, which is a table containing two or more behavioral teacher data as records, and stores it in the server storage unit 41.

[0258] The emotion learning process is a process of acquiring an emotion learning model using two or more pieces of emotion teacher data. The server processing unit 43, for example, provides two or more pieces of emotion teacher data to a machine learning learning processing module, executes the module, acquires an emotion learning model, and stores it in the server storage unit 41. The server processing unit 43, for example, provides two or more positive examples that are teacher data including emotion information and two or more negative examples that are teacher data not including emotion information to the machine learning learning processing module for each of two or more emotion information candidates, executes the module, acquires an emotion learning model for each emotion information candidate, and stores it in the server storage unit 41 in association with the emotion information. The server processing unit 43, for example, acquires an emotion correspondence table, which is a table in which each of two or more pieces of emotion teacher data has a record, and stores it in the server storage unit 41.

[0259] The server transmitting unit 44 transmits various types of information, such as a behavior learning model, an emotion learning model, a behavior correspondence table, and an emotion correspondence table.

[0260] The storage unit 31, learning management unit 311, map management unit 312, behavior management unit 313, and server storage unit 41 are preferably non-volatile recording media, but may also be realized as volatile recording media.

[0261] There is no restriction on the process by which information is stored in the storage unit 31 etc. For example, information may be stored in the storage unit 31 etc. via a recording medium, information transmitted via a communication line etc. may be stored in the storage unit 31 etc., or information input via an input device may be stored in the storage unit 31 etc.

[0262] The receiving unit 32, the server receiving unit 42, and the server transmitting unit 44 are typically realized by wireless or wired communication means.

[0263] The processing unit 33, the time acquisition unit 331, the position acquisition unit 332, the activity acquisition unit 333, the vital sign acquisition unit 334, the behavior estimation unit 335, the emotion estimation unit 336, the location acquisition unit 337, the storage unit 338, the configuration unit 339, and the server processing unit 43 can typically be realized by a processor, a memory, or the like. The processing procedures of the processing unit 33 and the like are typically realized by software, and the software is recorded on a recording medium such as a ROM. However, they may also be realized by hardware (dedicated circuits). The processor may be a CPU, an MPU, a GPU, or the like, and the type does not matter.

[0264] The output unit 34, the behavior output unit 341, and the emotion output unit 342 may or may not include output devices such as a display, a speaker, etc. The output unit 34 may be realized by driver software for an output device, or by a combination of driver software for an output device and the output device, etc.

[0265] Next, an example of the operation of the behavior acquisition device 3 constituting the information system D will be described with reference to the flowchart of FIG.

[0266] (Step S1901) The processing unit 33 determines whether or not to acquire information. If the information is to be acquired, the process proceeds to step S1902, and if the information is not to be acquired, the process proceeds to step S1916. The processing unit 33 may always determine to acquire information, or may determine to acquire information when a flag indicating that the information is to be acquired is stored in the storage unit 31. The conditions for such a determination are not important.

[0267] (Step S1902) The time acquisition unit 331 acquires the time from a clock (not shown). Here, the time acquisition unit 331 may also acquire the day of the week.

[0268] (Step S1903) The processing unit 33 acquires one or more pieces of action source information. An example of such action source acquisition processing will be described with reference to the flowchart of FIG.

[0269] (Step S1904) The behavior estimation unit 335 estimates behavior information that identifies the user's behavior, using one or more pieces of behavior source information acquired in step S1903. An example of such behavior estimation processing will be described with reference to the flowcharts of FIGS.

[0270] (Step S1905) The location acquisition unit 337 determines whether or not the behavior information was acquired in step S1904. If the behavior information was acquired, the process proceeds to step S1907, and if the behavior information was not acquired, the process proceeds to step S1906.

[0271] (Step S1906) The location acquisition unit 337 refers to the map in the map management unit 312 and acquires location information corresponding to the position information acquired in step S1903. Note that there may be times when location information cannot be acquired.

[0272] (Step S1907) The storage unit 338 determines whether the behavior information stored in a buffer (not shown) most recently matches the behavior information acquired in step S1904. If they match, the process proceeds to step S1908, and if they do not match, the process proceeds to step S1912.

[0273] (Step S1908) The accumulation unit 338 associates the acquired behavior information and the like with the time acquired in step S1902, and accumulates them in a buffer (not shown).

[0274] (Step S1909) The emotion estimation unit 336 performs a process of estimating emotion information. An example of the emotion estimation process will be described with reference to the flowcharts of FIGS. 25 to 27.

[0275] (Step S1910) Accumulation unit 338 determines whether or not emotion information was acquired in step S1909. If emotion information was acquired, the process proceeds to step S1911, and if emotion information was not acquired, the process returns to step S1901.

[0276] (Step S1911) Storage unit 338 stores the emotion information acquired in step S1909 in a buffer (not shown) in association with the time acquired in step S1902. The process returns to step S1901.

[0277] (Step S1912) The accumulation unit 338 accumulates the time acquired in step S1902 and the acquired behavior information and the like in a buffer (not shown) in association with each other.

[0278] (Step S1913) The accumulation unit 338 acquires the immediately preceding behavior information and the like.

[0279] (Step S1914) The accumulation unit 338 acquires a time period specified by two or more times associated with the immediately preceding behavior information or the like.

[0280] (Step S1915) The storage unit 338 associates the time period acquired in step S1914 with the immediately preceding behavior information, etc. acquired in step S1913, and stores them in the behavior management unit 313. Note that here, the storage unit 338 may also associate the time period with the immediately preceding behavior information, etc., and store them in association with the user identifier. In such a case, it is preferable that the storage destination be the server device 4.

[0281] (Step S1916) The behavior acquisition device 3 determines whether or not an output instruction has been accepted. If an output instruction has been accepted, the process proceeds to step S1917, and if not, the process proceeds to step S1919.

[0282] (Step S1917) The composition unit 339 composes output information using the behavior information and the like of the behavior management unit 313. An example of such output composition processing will be described with reference to the flowchart of FIG.

[0283] (Step S1918) The output unit 34 outputs the output information constructed in step S1917. The process returns to step S1901.

[0284] (Step S1919) The behavior acquisition device 3 determines whether or not input of information has been accepted for the output information being output. If input of information has been accepted, the process proceeds to step S1920, and if input of information has not been accepted, the process proceeds to step S1923.

[0285] (Step S1920) The processing unit 33 determines whether the information received in step S1919 is a confirmation instruction for the estimated behavioral information or the estimated emotion information. If it is a confirmation instruction, the processing unit 33 proceeds to step S1921, and if it is not a confirmation instruction, the processing unit 33 proceeds to step S1922.

[0286] (Step S1921) The accumulation unit 338 performs processing to confirm the behavioral information or emotion information corresponding to the confirmation instruction. Return to step S1901. Note that this processing is, for example, processing to associate a confirmation flag with the behavioral information corresponding to the confirmation instruction or the emotion information corresponding to the confirmation instruction.

[0287] (Step S1922) The storage unit 338 stores the input information. Return to step S1901. The input information is, for example, correct behavioral information or correct emotional information. The storage unit 338 then updates the estimated behavioral information or emotional information corresponding to the input information to the input behavioral information or emotional information. The storage unit 338 also performs processing to confirm the behavioral information or emotional information.

[0288] (Step S1923) The behavior acquisition device 3 determines whether or not a learning instruction has been received. If a learning instruction has been received, the process proceeds to step S1924, and if not, the process returns to step S1901.

[0289] (Step S1924) A learning unit (not shown) or a learning device (not shown) of the behavior acquisition device 3 uses two or more pieces of behavioral teacher data including behavior information, etc. to configure behavioral learning information and store it in the learning management unit 311. An example of such behavioral learning processing will be described using the flowcharts of Figures 29 and 30.

[0290] (Step S1925) A learning unit (not shown) or a learning device (not shown) of the behavior acquisition device 3 uses two or more pieces of emotion teacher data including emotion information, etc. to configure emotion learning information and store it in the learning management unit 311. Return to step S1901. An example of such emotion learning processing will be described using the flowcharts in FIGS. 31 and 32.

[0291] In the flowchart of FIG. 19, the process ends when the power is turned off or an interrupt occurs to end the process.

[0292] Next, an example of the action source acquisition process in step S1903 will be described with reference to the flowchart in FIG.

[0293] (Step S2001) The processing unit 33 determines whether or not the receiving unit 32 has acquired a GPS signal. If the receiving unit 32 has acquired a GPS signal, the process proceeds to step S2002. If the receiving unit 32 has not acquired a GPS signal, the process proceeds to step S2003.

[0294] (Step S2002) The position acquisition unit 332 acquires absolute position information based on the GPS signal received by the receiving unit 32.

[0295] (Step S2003) The position acquisition unit 332 acquires position information. An example of such position estimation processing will be described with reference to the flowchart of FIG.

[0296] (Step S2004) The activity acquisition unit 333 acquires activity data of the user.

[0297] (Step S2005) The vital sign acquisition unit 334 acquires one or more types of vital sign data of the user.

[0298] (Step S2006) The behavior estimation unit 335 acquires one or more pieces of past behavior information. Note that the one or more pieces of past behavior information include behavior information immediately preceding in time.

[0299] (Step S2007) The behavior estimation unit 335 determines whether or not to use emotion information for the behavior estimation process. If emotion information is to be used, the process proceeds to step S2008, and if emotion information is not to be used, the process proceeds to step S2009. Note that whether or not emotion information is to be used for the behavior estimation process is usually determined in advance.

[0300] (Step S2008) The emotion estimation unit 336 acquires emotion information. An example of the emotion estimation process will be described with reference to the flowcharts in FIGS. 25 to 27.

[0301] (Step S2009) The activity inferrer 335 acquires the elapsed time since the start of the new activity identified by the new activity information.

[0302] (Step S2010) The behavior estimation unit 335 uses two or more types of behavior source information including time and location information to configure information to be used in the behavior estimation process. The process returns to the upper level process. Here, the configured information is typically a collection of behavior source information, for example, a behavior source vector having two or more pieces of behavior source information as elements. The two or more types of behavior source information are, for example, two or more types of information selected from the group consisting of time, day of the week, location information, activity data, vital data, past behavior information, emotion information, and elapsed time.

[0303] In the flowchart of FIG. 20, even if a GPS signal is acquired, the position acquisition unit 332 may acquire position information by a position estimation process which will be described using the flowchart of FIG.

[0304] 20, the behavior estimation unit 335 may acquire one or more user attribute values ​​and acquire behavior source information including the one or more user attribute values. The user attribute values ​​may be information stored in the storage unit 31 or may be information input by the user. Next, an example of the position estimation process of step S2003 will be described using the flowchart of FIG. 21. In the flowchart of FIG. 21, descriptions of steps that are the same as those in FIG. 13 will be omitted. The position estimation process of step S2003 may be the same process as that in the flowchart of FIG. 13.

[0305] (Step S2101) The position acquisition unit 332 acquires the similarity between two radio wave intensity vectors, associates the similarity with the i-th point information, and temporarily stores it in a buffer (not shown). Then, the process proceeds to step S1308.

[0306] (Step S2102) The location acquisition unit 332 acquires location information included in the location information paired with the highest similarity. The process returns to the upper level process. The acquired location information is the terminal location.

[0307] Next, an example of the first behavior estimation process in step S1904 will be described with reference to the flowchart in Fig. 22. The flowchart in Fig. 22 illustrates a process for estimating behavior information through machine learning prediction processing using one behavior learning model. In other words, the flowchart in Fig. 22 illustrates a process for estimating behavior information through machine learning multi-value classification prediction processing.

[0308] (Step S2201) The behavior estimation unit 335 acquires two or more types of behavior source information (for example, behavior source vectors) acquired in step S1903.

[0309] (Step S2202) The behavior estimation unit 335 acquires a behavior learning model from the learning management unit 311.

[0310] (Step S2203) The behavior estimation unit 335 provides the two or more types of behavior source information and the behavior learning model acquired in step S2201 to a machine learning prediction processing module, and executes the module.

[0311] (Step S2204) The behavior estimation unit 335 acquires estimated behavior information and a score, which are the execution results in step S2203.

[0312] (Step S2205) The behavior estimation unit 335 determines whether the score acquired in step S2204 is equal to or greater than a threshold. If it is equal to or greater than the threshold, the process proceeds to step S2206, and if it is less than the threshold, the process proceeds to step S2207.

[0313] (Step S2206) The behavior estimation unit 335 acquires the behavior information acquired in step S2204 as behavior information to be output, and returns to the upper-level processing.

[0314] (Step S2207) The behavior estimation unit 335 acquires the behavior information of “empty.” The process returns to the upper level process.

[0315] In the flowchart of FIG. 22, the processes from step S2205 to step S2207 do not have to be performed.

[0316] Next, an example of the second behavior estimation process in step S1904 will be described with reference to the flowchart in Fig. 23. The flowchart in Fig. 23 illustrates a process for estimating behavioral information through machine learning prediction processing using a behavioral learning model for each of two or more behavioral information candidates. In other words, the flowchart in Fig. 22 illustrates a process for estimating behavioral information through machine learning binary classification prediction processing.

[0317] (Step S2301) The behavior estimation unit 335 acquires two or more types of behavior source information acquired in step S1903.

[0318] (Step S2302) The behavior estimation unit 335 assigns 1 to a counter i.

[0319] (Step S2303) The behavior estimation unit 335 determines whether or not a candidate for the i-th behavior information exists, by referring to the learning management unit 311. If a candidate for the i-th behavior information exists, the process proceeds to step S2304; if not, the process proceeds to step S2309.

[0320] (Step S2304) The behavior estimation unit 335 acquires the i-th behavior learning model paired with the i-th behavior information candidate from the learning management unit 311.

[0321] (Step S2305) The behavior estimation unit 335 provides the two or more types of behavior source information acquired in step S2301 and the i-th behavior learning model acquired in step S2304 to a prediction processing module that performs binary classification in machine learning, and executes the module.

[0322] (Step S2306) The behavior estimation unit 335 determines whether the execution result in step S2305 is “true.” If “true,” the process proceeds to step S2307, and if “false,” the process proceeds to step S2308.

[0323] (Step S2307) The behavior estimation unit 335 temporarily stores the score, which is part of the execution result in step S2305, in a buffer (not shown) in association with the i-th behavior information candidate.

[0324] (Step S2308) The behavior estimation unit 335 increments the counter i by 1. The process returns to step S2303.

[0325] (Step S2309) The behavior estimation unit 335 acquires the maximum score and determines whether the score is equal to or greater than a threshold. If the maximum score is equal to or greater than the threshold, the process proceeds to step S2310. If the maximum score is less than the threshold, the process proceeds to step S2311.

[0326] (Step S2310) The behavior estimation unit 335 selects the behavior information paired with the maximum score and returns to the upper process.

[0327] (Step S2311) The behavior estimation unit 335 acquires the behavior information of “empty.” The process returns to the upper level process.

[0328] In the flowchart of FIG. 23, the processes from step S2308 to step S2311 do not have to be performed.

[0329] Next, an example of the third behavior estimation process in step S1904 will be described with reference to the flowchart in Fig. 24. The flowchart in Fig. 24 illustrates a process for estimating behavior information using a behavior correspondence table.

[0330] (Step S2401) The behavior estimation unit 335 acquires two or more types of behavior source information acquired in step S1903. The two or more types of behavior source information here are behavior source vectors.

[0331] (Step S2402) The behavior estimation unit 335 assigns 1 to a counter i.

[0332] (Step S2403) The behavior estimation unit 335 determines whether or not the i-th behavior correspondence information exists in the behavior correspondence table of the learning management unit 311. If the i-th behavior correspondence information exists, the process proceeds to step S2404; if not, the process proceeds to step S2407.

[0333] (Step S2404) The behavior estimation unit 335 acquires the i-th behavior source vector included in the i-th behavior correspondence information.

[0334] (Step S2405) The behavior estimation unit 335 obtains the similarity between the behavior origin vector obtained in step S2403 and the behavior origin vector obtained in step S2404, and associates it with the i-th behavior correspondence information.

[0335] (Step S2406) The behavior estimation unit 335 increments the counter i by 1. The process returns to step S2403.

[0336] (Step S2407) The behavior estimation unit 335 acquires the maximum similarity.

[0337] (Step S2408) The behavior estimation unit 335 determines whether the maximum similarity acquired in step S2407 is equal to or greater than a threshold. If it is equal to or greater than the threshold, the process proceeds to step S2409, and if it is less than the threshold, the process proceeds to step S2410.

[0338] (Step S2409) The behavior estimation unit 335 acquires behavior information associated with the i-th behavior correspondence information paired with the maximum similarity, and returns to the upper-level process.

[0339] (Step S2410) The behavior estimation unit 335 acquires the behavior information of “empty.” The process returns to the upper level process.

[0340] In the flowchart of FIG. 24, the processes from step S2408 to step S2411 do not have to be performed.

[0341] Next, an example of the first emotion estimation process in step S1909 will be described using the flowchart in Fig. 25. The flowchart in Fig. 25 illustrates a process for estimating emotion information through machine learning prediction processing using one emotion learning model. In other words, the flowchart in Fig. 25 illustrates a process for estimating emotion information through machine learning multi-value classification prediction processing.

[0342] (Step S2501) The emotion deduction unit 336 acquires two or more types of behavior source information acquired in step S1903. Here, each of the two or more types of behavior source information is emotion source information. Furthermore, the two or more types of emotion source information are, for example, emotion source vectors.

[0343] (Step S2502) The emotion estimation unit 336 acquires an emotion learning model from the learning management unit 311.

[0344] (Step S2503) The emotion estimation unit 336 provides the two or more types of emotion source information and the emotion learning model acquired in step S2501 to a machine learning prediction processing module and executes the module.

[0345] (Step S2504) The emotion estimation unit 336 acquires the estimated emotion information and score, which are the execution results in step S2503.

[0346] (Step S2505) The feeling estimation unit 336 determines whether the score acquired in step S2504 is equal to or greater than a threshold. If it is equal to or greater than the threshold, the process proceeds to step S2506, and if it is less than the threshold, the process proceeds to step S2507.

[0347] (Step S2506) The emotion deduction unit 336 acquires the emotion information acquired in step S2504 as emotion information to be output, and returns to the upper-level processing.

[0348] (Step S2507) The emotion deduction unit 336 acquires emotion information of “sky.” The process returns to the upper level process.

[0349] In the flowchart of FIG. 25, the processes from step S2505 to step S2507 do not have to be performed.

[0350] Next, an example of the second emotion estimation process in step S1909 will be described using the flowchart in Fig. 26. The flowchart in Fig. 26 illustrates a process for estimating emotion information through machine learning prediction processing using an emotion learning model for each of two or more emotion information candidates. In other words, the flowchart in Fig. 26 illustrates a process for estimating emotion information through machine learning binary classification prediction processing.

[0351] (Step S2601) The emotion estimation unit 336 acquires two or more types of emotion source information acquired in step S1903.

[0352] (Step S2602) The feeling estimation unit 336 assigns 1 to a counter i.

[0353] (Step S2603) The emotion deduction unit 336 determines whether or not a candidate for the i-th emotion information exists, by referring to the learning management unit 311. If a candidate for the i-th emotion information exists, the process proceeds to step S2604; if not, the process proceeds to step S2609.

[0354] (Step S2604) The emotion estimation unit 336 acquires the i-th emotion learning model paired with the i-th emotion information candidate from the learning management unit 311.

[0355] (Step S2605) The emotion estimation unit 336 provides the two or more types of emotion source information acquired in step S2601 and the i-th emotion learning model acquired in step S2604 to a prediction processing module that performs binary classification in machine learning, and executes the module.

[0356] (Step S2606) The feeling estimation unit 336 determines whether the execution result in step S2605 is “true.” If “true,” the process proceeds to step S2607, and if “false,” the process proceeds to step S2608.

[0357] (Step S2607) The emotion estimation unit 336 temporarily accumulates the score, which is part of the execution result in step S2605, in a buffer (not shown), in association with the i-th emotion information candidate.

[0358] (Step S2608) The feeling estimation unit 336 increments the counter i by 1. The process returns to step S2603.

[0359] (Step S2609) The feeling estimation unit 336 acquires the maximum score and determines whether the score is equal to or greater than a threshold. If the maximum score is equal to or greater than the threshold, the process proceeds to step S2610. If the maximum score is less than the threshold, the process proceeds to step S2611.

[0360] (Step S2610) The emotion estimation unit 336 selects emotion information paired with the maximum score and returns to the upper level process.

[0361] (Step S2611) The emotion deduction unit 336 acquires emotion information of “sky.” The process returns to the upper level process.

[0362] In the flowchart of FIG. 26, the processes from step S2608 to step S2611 do not have to be performed.

[0363] Next, an example of the third emotion estimation process in step S1909 will be described using the flowchart in Fig. 27. The flowchart in Fig. 27 illustrates a process for estimating emotion information using an emotion correspondence table.

[0364] (Step S2701) The emotion estimation unit 336 acquires two or more types of emotion source information acquired in step S1903. The two or more types of emotion source information here are emotion source vectors.

[0365] (Step S2702) The feeling estimation unit 336 assigns 1 to a counter i.

[0366] (Step S2703) The emotion deduction unit 336 determines whether the i-th emotion correspondence information exists in the emotion correspondence table of the learning management unit 311. If the i-th emotion correspondence information exists, the process proceeds to step S2704; if not, the process proceeds to step S2707.

[0367] (Step S2704) The emotion estimation unit 336 acquires the i-th emotion source vector included in the i-th emotion correspondence information.

[0368] (Step S2705) The emotion estimation unit 336 obtains the similarity between the emotion source vector obtained in step S2703 and the emotion source vector obtained in step S2704, and associates it with the i-th emotion correspondence information.

[0369] (Step S2706) The feeling estimation unit 336 increments the counter i by 1. The process returns to step S2703.

[0370] (Step S2707) The feeling estimation unit 336 obtains the maximum similarity.

[0371] (Step S2708) The feeling estimation unit 336 determines whether the maximum similarity acquired in step S2707 is equal to or greater than a threshold. If it is equal to or greater than the threshold, the process proceeds to step S2709, and if it is less than the threshold, the process proceeds to step S2710.

[0372] (Step S2709) The emotion estimation unit 336 acquires emotion information associated with the i-th emotion association information paired with the highest similarity, and returns to the upper-level process.

[0373] (Step S2710) The emotion deduction unit 336 acquires emotion information of “sky.” The process returns to the upper level process.

[0374] In the flowchart of FIG. 27, the processes from step S2708 to step S2711 do not have to be performed.

[0375] Next, an example of the output configuration process in step S1917 will be described with reference to the flowchart in FIG.

[0376] (Step S2801) The configuration unit 339 acquires a template of a calendar from the storage unit 31.

[0377] (Step S2802) The configuration unit 339 assigns 1 to the counter i.

[0378] (Step S2803) The configuration unit 339 determines whether or not the i-th time period exists stored in the behavior management unit 313. If the i-th time period exists, the process proceeds to step S2804; if not, the process returns to the upper level processing. The behavior management unit 313 stores behavior information and emotion information in association with each of one or more time periods.

[0379] (Step S2804) The configuration unit 339 determines whether the i-th time period stored in the behavior management unit 313 is included in the period covered by the calendar template acquired in step S2801. If it is included, the process proceeds to step S2805; if it is not included, the process proceeds to step S2809.

[0380] (Step S2805) The configuration unit 339 acquires the behavior information paired with the i-th time period from the behavior management unit 313.

[0381] (Step S2806) The configuration unit 339 acquires emotion information paired with the i-th time period from the behavior management unit 313. Note that emotion information does not necessarily have to be acquired here.

[0382] (Step S2807) The composition unit 339 composes time zone information, which is information to be arranged in the i-th time zone of the calendar, is information that can identify the behavioral information acquired in step S2805, and is information that can identify the emotion information acquired in step S2806.

[0383] (Step S2808) The composition unit 339 arranges the time period information composed in step S2807 at the position in the calendar specified by the i-th time period.

[0384] (Step S2809) The configuration unit 339 increments the counter i by 1. The process returns to step S2803.

[0385] Next, an example of the first behavior learning process in step S1924 will be described using the flowchart in Fig. 29. Note that the behavior learning process is performed, for example, by a learning unit (not shown). The learning unit may be a learning device different from the behavior acquisition device 3. The first behavior learning process is a process of acquiring a behavior learning model for multi-value classification.

[0386] (Step S2901) The learning unit assigns 1 to a counter i.

[0387] (Step S2902) The learning unit determines whether or not the i-th behavior information, etc. exists in the behavior management unit 313. If the i-th behavior information, etc. exists, the process proceeds to step S2903, and if not, the process proceeds to step S2905.

[0388] (Step S2903) The learning unit uses the i-th behavior information, etc. to construct behavior teacher data and appends it to a buffer (not shown). Note that the behavior teacher data is usually information that uses two or more types of behavior source information as explanatory variables and behavior information as a target variable.

[0389] (Step S2904) The learning unit increments the counter i by 1. The process returns to step S2902.

[0390] (Step S2905) The learning unit provides two or more pieces of behavioral teacher data stored in a buffer (not shown) to a machine learning learning processing module, executes the module, and acquires a behavioral learning model.

[0391] (Step S2906) The learning unit accumulates the behavior learning model acquired in step S2905 in the learning management unit 311.

[0392] In the flowchart of Figure 29, the learning unit may not perform the learning process of steps S2905 and S2906, but may instead store in the learning management unit 311 a behavior correspondence table in a buffer not shown, with two or more behavior teacher data records (behavior correspondence information).

[0393] Next, an example of the second behavior learning process in step S1924 will be described with reference to the flowchart in Fig. 30. The second behavior learning process is a process for acquiring a binary classification behavior learning model for each of two or more behavior information candidates.

[0394] (Step S3001) The learning unit assigns 1 to a counter i.

[0395] (Step S3002) The learning unit determines whether or not the i-th type of behavioral information exists. If the i-th type of behavioral information exists, the process proceeds to step S3003; if not, the process returns to the upper level process.

[0396] (Step S3003) The learning unit acquires the i-th type of behavior information.

[0397] (Step S3004) The learning unit 313 acquires two or more positive examples, which are teacher data in the behavior management unit 313 and are behavior teacher data including the i-th type of behavior information.

[0398] (Step S3005) The learning unit acquires two or more negative examples, which are teacher data in the behavior management unit 313 and are behavior teacher data that do not include the i-th type of behavior information.

[0399] (Step S3006) The learning unit provides the two or more positive examples obtained in step S3004 and the two or more negative examples obtained in step S3005 to a machine learning learning processing module, executes the module, and obtains a behavioral learning model.

[0400] (Step S3007) The learning unit stores the behavior learning model acquired in step S3006 in the learning management unit 311 in pairs with the i-th type of behavior information.

[0401] (Step S3008) The learning unit increments the counter i by 1. The process returns to step S3002.

[0402] Next, an example of the first emotion learning process in step S1925 will be described using the flowchart in Fig. 31. Note that the behavior learning process is performed, for example, by a learning unit (not shown). The learning unit may be a learning device different from the behavior acquisition device 3. The first emotion learning process is a process for acquiring a multi-value classification emotion learning model.

[0403] (Step S3101) The learning unit assigns 1 to a counter i.

[0404] (Step S3102) The learning unit determines whether or not the i-th emotion information, etc. exists in the behavior management unit 313. If the i-th emotion information, etc. exists, the process proceeds to step S3103, and if not, the process proceeds to step S3105.

[0405] (Step S3103) The learning unit uses the i-th emotion information, etc. to construct emotion teacher data and adds it to a buffer (not shown). Note that the emotion teacher data is information that uses two or more types of emotion source information as explanatory variables and emotion information as a target variable.

[0406] (Step S3104) The learning unit increments the counter i by 1. The process returns to step S3102.

[0407] (Step S3105) The learning unit provides two or more pieces of emotion teacher data stored in a buffer (not shown) to a machine learning learning processing module, executes the module, and acquires an emotion learning model.

[0408] (Step S3106) The learning unit accumulates the emotion learning model acquired in step S3105 in the learning management unit 311.

[0409] In the flowchart of FIG. 31 , the learning unit may store an emotion correspondence table in a buffer (not shown) in the learning management unit 311, with two or more emotion teacher data records (emotion correspondence information) instead of performing the learning processes in steps S3105 and S3106.

[0410] Next, an example of the second emotion learning process of step S1925 will be described using the flowchart in Fig. 32. The second emotion learning process is a process for acquiring a binary classification emotion learning model for each of two or more emotion information candidates.

[0411] (Step S3201) The learning unit assigns 1 to a counter i.

[0412] (Step S3202) The learning unit determines whether or not the i-th type of emotion information exists. If the i-th type of emotion information exists, the process proceeds to step S3203; if not, the process returns to the upper level processing.

[0413] (Step S3203) The learning unit acquires emotion information of the i-th type.

[0414] (Step S3204) The learning unit acquires two or more positive examples, which are emotion teacher data in the behavior management unit 313 and which are emotion teacher data including emotion information of the i-th type.

[0415] (Step S3205) The learning unit acquires two or more negative examples, which are emotion teacher data in the behavior management unit 313 and which are emotion teacher data that do not include emotion information of the i-th type.

[0416] (Step S3206) The learning unit provides the two or more positive examples acquired in step S3204 and the two or more negative examples acquired in step S3205 to a machine learning learning processing module, executes the module, and acquires an emotion learning model.

[0417] (Step S3207) The learning unit stores the emotion learning model acquired in step S3206 in learning management unit 311, paired with the i-th type of emotion information.

[0418] (Step S3208) The learning unit increments the counter i by 1. The process returns to step S3202.

[0419] A specific example of the operation of the information system D in this embodiment will be described below. Currently, the storage unit 31 of the server device 4 stores a behavior learning model acquired by a machine learning learning process using a large amount of teacher data including behavior source information of one or more users. The storage unit 31 also stores an emotion learning model acquired by a machine learning learning process using a large amount of teacher data including emotion source information of one or more users.

[0420] Also, the user "U １ In the storage unit 31 of the behavior acquisition device 3, which is a terminal (e.g., a smart watch) held by "," a large number of behavior source information at each of two or more consecutive times acquired by the processing unit 33 through the above-mentioned processing is stored in the behavior source management table shown in Figure 33.

[0421] The activity source management table (Figure 33) is a record that has an "ID," "time information," "physical data," "location information," and "elapsed time." "ID" is information that identifies a record. "Time information" is information that specifies the time, and in this case, it has "date," "time of day," and "day of the week." "Physical data" has "activity data" and "vital data." "Vital data" has "heart rate," "blood pressure (systolic)," "blood pressure (diastolic)," and "body temperature." "Heart rate" is the heart rate per unit of time (here, "1 minute"). "Blood pressure (systolic)" is the systolic blood pressure, and "blood pressure (diastolic)" is the diastolic blood pressure. "Location information" is, for example, location information obtained by the processing described in embodiment 2. "Elapsed time" is the time that has elapsed since the same activity began.

[0422] And the user "U １ " inputs an output instruction to the behavior acquisition device 3. Then, the behavior acquisition device 3 accepts the output instruction.

[0423] Next, the behavior estimation unit 335 accesses, for example, the server device 4, receives the behavior learning model and the emotion learning model from the server device 4, and stores the behavior learning model and the emotion learning model in the learning management unit 311.

[0424] Next, the behavior estimation unit 335 constructs a behavior source vector having time information, body data, location information, elapsed time, etc. for each record in FIG. 33 by, for example, the process described using the flowchart in FIG. 22. Next, the behavior estimation unit 335 acquires a behavior learning model from the learning management unit 311. Next, the behavior estimation unit 335 provides the behavior source vector and the behavior learning model to a machine learning prediction processing module and executes the module. Then, the behavior estimation unit 335 generates behavior information "A" for the behavior source vectors from the record with "ID=1" to the record with "ID=289". １ " is acquired, and for the action source vectors from "ID=290" to "ID=N", the action information "A ２ " is acquired. Then, the storage unit 338 stores the behavior information and the behavior confirmation flag "0" for each record in the behavior management unit 313. Note that "0" for the behavior confirmation flag and emotion confirmation flag indicates that the flag is not yet confirmed, and "1" indicates that the flag is confirmed.

[0425] Furthermore, the emotion deduction unit 336 constructs an emotion source vector having time information, physical data, location information, elapsed time, behavioral information, etc. for each record in FIG. 33 by, for example, the process described using the flowchart in FIG. 25 . Next, the emotion deduction unit 336 acquires an emotion learning model from the learning management unit 311. Next, the emotion deduction unit 336 provides the emotion source vector and the emotion learning model to a machine learning prediction processing module and executes the module. Then, the emotion deduction unit 336 generates emotion information "E" for the behavior source vectors from the record with "ID=1" to the record with "ID=289". １ " is obtained, and emotion information "E" is obtained for the action source vectors from "ID=290" to "ID=N". ２ Then, the storage unit 338 stores the emotion information and the emotion confirmation flag “0” for each record in the behavior management unit 313.

[0426] As a result of the above processing, the behavior / emotion management table shown in Fig. 34 is stored in the behavior management unit 313. The behavior / emotion management table has two or more records each having an "ID," "behavior information," "behavior confirmation flag," "emotion information," and "emotion confirmation flag."

[0427] Next, the storage unit 338 stores the same behavior information (for example, "A １ ") and the time (e.g., T ００１ , ..., T ００２ , T ２８９ ) time period (e.g., "T ００１ From T ２８９ " is "TZ １ ") is acquired and stored in association with the time period and the behavioral information.

[0428] Furthermore, the storage unit 338 stores the same emotion information (for example, "E") for each record in FIG. 33 and FIG. 34. １ ") and the time (e.g., T ００１ , ..., T ００２ , T ２８９ ) time period (e.g., "T ００１ From T ２８９ " is "TZ １ ") is acquired and stored in association with the time period and emotion information. At this stage, the behavior determination flag and emotion determination flag corresponding to each time period are both "0." An example of the stored information is shown in FIG. 35. FIG. 35 is a time period information management table. The time period information management table has one or more records each having an "ID," "time period," "behavior information," "behavior determination flag," "emotion information," and "emotion determination flag."

[0429] Then, the composition unit 339 performs the processing described using the flowchart in Figure 28 using one or more sets of time zones, behavioral information, and emotion information accumulated by the accumulation unit 338, composes time zone information for each behavioral information, arranges it in a calendar template, and composes output information.

[0430] Next, the output unit 34 outputs the output information. An example of such output is shown in Fig. 36. In Fig. 36, estimated behavior information for each time period of each day on the calendar is displayed.

[0431] And the user "U １ If the activity information for each time period is correct, the user inputs a "confirmation instruction" for the displayed activity information, and if the estimated activity information is incorrect, the user inputs the correct activity information.１ " inputs a "confirmation command" for the emotion information being output if the emotion information for each time period is correct, and inputs the correct emotion information if the estimated emotion information is incorrect. The user's input changes the "behavior information," "behavior confirmation flag," "emotion information," and "emotion confirmation flag" in FIG. 35.

[0432] As described above, according to this embodiment, the user's behavior can be estimated using location information corresponding to time.

[0433] Furthermore, according to this embodiment, the user's behavior can be estimated using the location information corresponding to a time and the user's activity data corresponding to that time.

[0434] Furthermore, according to this embodiment, the user's behavior can be estimated with higher accuracy using location information corresponding to the time, the user's activity data corresponding to the time, and the user's vital data corresponding to the time.

[0435] Furthermore, according to this embodiment, it is possible to estimate the user's emotions during the action.

[0436] Furthermore, according to this embodiment, the user's behavior can be estimated with higher accuracy using past records.

[0437] Furthermore, according to this embodiment, the past records of two or more users can be used to estimate the user's behavior with higher accuracy.

[0438] Furthermore, according to this embodiment, when the user's behavior cannot be estimated, the location where the user was can be output.

[0439] Furthermore, according to this embodiment, by using indoor location information, etc., acquired by the location information acquisition method described in embodiment 2, it is possible to accurately estimate user behavior even in places where GPS signals cannot be received.

[0440] The processing in this embodiment may be realized by software. This software may be distributed by software download or the like. This software may also be recorded on a recording medium such as a CD-ROM and distributed. This also applies to other embodiments in this specification. The software that realizes the information system D in this embodiment is the following program. That is, this program causes a computer to function as a time acquisition unit that acquires time, a location acquisition unit that acquires location information associated with the time, a behavior estimation unit that uses two or more pieces of behavior source information including the location information associated with the time to acquire behavior information that identifies the user's behavior in a time period identified by the time contained in each of the two or more pieces of behavior source information, and a behavior output unit that outputs the behavior information in the time period.

[0441] (Embodiment 4) The difference between this embodiment and embodiment 3 is as follows: In other words, in this embodiment, the behavior acquisition device is a server, and estimates behavioral information and emotional information of a user using location information and the like received from the user's terminal device.

[0442] 37 is a conceptual diagram of an information system E according to this embodiment. The information system E includes a behavior acquisition device 5, one or more terminal devices 6, and one or more communication devices B.

[0443] The behavior acquisition device 5 is a server, for example, a cloud server or an ASP server, but the type does not matter. The behavior acquisition device 5 is a device that receives behavior source information such as location information from the user's terminal device 6, estimates the user's behavior information using the behavior source information, and transmits the behavior information to the terminal device 6. The behavior acquisition device 5 is a device that receives emotion source information from the user's terminal device 6, estimates the user's emotion information using the emotion source information, and transmits the behavior information to the terminal device 6.

[0444] The terminal device 6 is a terminal used by a user. The terminal device 6 may be, for example, a smartphone, a tablet device, a smartwatch, a so-called personal computer, or the like, and the type does not matter. The terminal device 6 is a terminal that transmits behavior source information and emotion source information including location information and the like to the behavior acquisition device 5, and receives and outputs behavior information and emotion information from the behavior acquisition device 5. Note that the device that transmits behavior source information and emotion source information to the behavior acquisition device 5 may be a different device from the device that receives and outputs behavior information and emotion information from the behavior acquisition device 5.

[0445] 38 is a block diagram of the information system E in this embodiment. FIG. 39 is a block diagram of the behavior acquisition device 5.

[0446] The behavior acquisition device 5 includes a storage unit 51, a receiving unit 52, a processing unit 53, and a transmitting unit 54. The storage unit 51 includes a learning management unit 311 and a behavior management unit 313. The receiving unit 52 includes a position acquisition unit 521, an activity acquisition unit 522, and a vital sign acquisition unit 523. The processing unit 53 includes a time acquisition unit 331, a behavior estimation unit 335, an emotion estimation unit 336, an accumulation unit 338, and a configuration unit 339. The transmitting unit 54 includes a behavior output unit 341 and an emotion output unit 342.

[0447] The terminal device 6 includes a terminal storage unit 61, a terminal reception unit 62, a terminal reception unit 63, a terminal processing unit 64, a terminal transmission unit 65, and a terminal output unit 66. The terminal storage unit 61 includes a map management unit 312. The device processing unit 64 includes an intensity acquisition unit 231, a type determination unit 232, a position acquisition unit 332, an activity acquisition unit 333, a vital sign acquisition unit 334, and a location acquisition unit 337.

[0448] Various types of information are stored in the storage unit 51 constituting the behavior acquisition device 5. The various types of information include, for example, the above-mentioned learning information and the above-mentioned behavior information.

[0449] The receiving unit 52 receives various instructions and information from the terminal device 6. The various instructions and information include, for example, location information, activity data, vital data, output instructions, confirmation instructions, behavior information to be corrected, and emotion information to be corrected.

[0450] It is preferable that the receiving unit 52 receives location information, activity data, vital data, etc. from the terminal device 6 all at once. It is preferable that the receiving unit 52 receives location information, etc. associated with a user identifier all at once. The user identifier is information that identifies the user who uses the terminal device 6. The user identifier is, for example, a user ID, a telephone number, an email address, or an identifier of the terminal device 6. The identifier of the terminal device 6 is, for example, an IP address.

[0451] The location acquisition unit 521 receives location information from the terminal device 6. The location information is associated with time. When the location acquisition unit 521 receives the location information, it preferably acquires the time from a clock (not shown) and associates the time with the location information. It is preferable that the location information be associated with a user identifier.

[0452] The activity acquisition unit 522 receives activity data from the terminal device 6. The activity data is associated with a time. When the activity acquisition unit 522 receives the activity data, it preferably acquires the time from a clock (not shown) and associates the time with the activity data. It is preferable that the activity data be associated with a user identifier.

[0453] The vital sign acquisition unit 523 receives one or more types of vital sign data from the terminal device 6. Such vital sign data is associated with time. When receiving vital sign data, the vital sign acquisition unit 523 preferably acquires the time from a clock (not shown) and associates the time with the vital sign data. Such vital sign data is preferably associated with a user identifier.

[0454] The processing unit 53 performs various processes, such as processes performed by the time acquisition unit 331, the behavior estimation unit 335, the emotion estimation unit 336, and the accumulation unit 338.

[0455] The transmitting unit 54 transmits various types of information to the terminal device 6. The various types of information include, for example, estimated behavior information, estimated emotion information, and output information configured by the configuration unit 339.

[0456] The behavior output unit 341 transmits the behavior information acquired by the behavior estimation unit 335 and associated with the time period to the terminal device 6 .

[0457] The emotion output unit 342 transmits emotion information acquired by the emotion estimation unit 336 and associated with a time period to the terminal device 6.

[0458] Various types of information are stored in the terminal storage unit 61 of the terminal device 6. The various types of information include, for example, location information, activity data, and vital data.

[0459] The terminal receiving unit 62 receives various instructions and information, such as an output instruction, a confirmation instruction, behavioral information that the user modifies the estimated behavioral information, and emotion information that the user modifies the estimated emotion information.

[0460] The means for inputting various instructions and information may be any means, such as a touch panel, keyboard, mouse, or menu screen.

[0461] The terminal receiving unit 63 receives various types of information from the behavior acquisition device 5. The various types of information include, for example, output information, behavior information, and emotion information.

[0462] The device processing unit 64 performs various types of processing, such as processing to change instructions, information, etc. received by the device receiving unit 62 into instructions, information, etc. with a structure to be transmitted, and processing to change information received by the device receiving unit 63 into a structure to be output.

[0463] The terminal transmitting unit 65 transmits various instructions and information, such as an output instruction, a confirmation instruction, behavior information to be changed, and emotion information to be changed.

[0464] The terminal output unit 66 outputs various types of information, such as output information, behavioral information, and emotional information.

[0465] The storage unit 51 and the terminal storage unit 61 are preferably non-volatile recording media, but may also be realized as volatile recording media.

[0466] There is no restriction on the process by which information is stored in the storage unit 51 etc. For example, information may be stored in the storage unit 51 etc. via a recording medium, information transmitted via a communication line etc. may be stored in the storage unit 51 etc., or information input via an input device may be stored in the storage unit 51 etc.

[0467] The receiving unit 52, the position acquiring unit 521, the activity acquiring unit 522, the vitals acquiring unit 523, the transmitting unit 54, the behavior output unit 341, the emotion output unit 342, the terminal receiving unit 63, and the terminal transmitting unit 65 are realized, for example, by wireless or wired communication means.

[0468] The processing unit 53 and the terminal processing unit 64 can usually be realized by a processor, memory, etc. The processing procedures of the processing unit 53, etc. are usually realized by software, and the software is recorded on a recording medium such as a ROM. However, they may also be realized by hardware (dedicated circuit). The processor may be a CPU, MPU, GPU, etc., and the type does not matter.

[0469] The terminal reception unit 62 can be realized by a device driver for an input means such as a touch panel or a keyboard, or control software for a menu screen.

[0470] The terminal output unit 66 may or may not be considered to include an output device such as a display, a speaker, etc. The terminal output unit 66 may be realized by driver software for an output device, or by a combination of driver software for an output device and the output device, etc.

[0471] Next, an example of the operation of the behavior acquisition device 5 will be described using the flowchart in Fig. 40. In the flowchart in Fig. 40, the description of the same steps as in Fig. 19 will be omitted.

[0472] (Step S4001) The receiving unit 52 determines whether or not location information, etc. paired with a user identifier has been received from the terminal device 6. If location information, etc. has been received, the process proceeds to step S4002; if not, the process proceeds to step S4004. The location information, etc. is, for example, a user identifier and location information. The location information, etc. is, for example, one or more types of information selected from a user identifier and location information, activity data, and vital data.

[0473] (Step S4002) The time acquisition unit 331 acquires the time from a clock (not shown). Here, the time acquisition unit 331 may acquire the day of the week. The time usually includes the hour and minute. The time may also include one or more pieces of information from the year, month, and day.

[0474] (Step S4003) The storage unit 338 stores the location information and the time received in step S4001 in association with the user identifier in the behavior management unit 313. The process returns to step S4001.

[0475] (Step S4004) The receiving unit 52 determines whether or not an output instruction has been received from the terminal device 6. If an output instruction has been received, the process proceeds to step S4005; if not, the process proceeds to step S4009. The received output instruction is usually associated with a user identifier. The output instruction also usually includes period information specifying the period for which behavioral information is to be acquired (e.g., "from December 17, 2023 to December 23, 2023").

[0476] (Step S4005) The behavior estimation unit 335 assigns 1 to a counter i.

[0477] (Step S4006) The behavior estimation unit 335 determines whether or not the i-th behavior source information paired with the user identifier associated with the output instruction received in step S4004 exists in the behavior management unit 313. If the i-th behavior source information exists, the process proceeds to step S4007, and if not, the process proceeds to step S1917.

[0478] (Step S4007) The behavior estimation unit 335 determines whether or not behavior information corresponding to the i-th behavior source information exists. If behavior information exists, the process proceeds to step S4008, and if not, the process proceeds to step S1904. Note that if behavior information corresponding to the behavior source information exists, this usually means that behavior information has already been estimated using the behavior source information.

[0479] (Step S4008) The behavior estimation unit 335 increments the counter i by 1. The process returns to step S4006.

[0480] (Step S4009) The receiving unit 52 determines whether or not information, etc. has been received from the terminal device 6. If information, etc. has been received, the process proceeds to step S1920, and if not, the process proceeds to step S4010. Note that the information, etc. may be, for example, a confirmation instruction, behavioral information to be corrected, or emotion information to be corrected.

[0481] (Step S4010) The receiving unit 52 determines whether or not a learning instruction has been received from the terminal device 6. If a learning instruction has been received, the process proceeds to step S1924, and if not, the process returns to step S4001.

[0482] (Step S4011) The transmission unit 54 transmits the output information constructed in step S1917 to the terminal device 6. The process returns to step S4001.

[0483] In the flowchart of FIG. 40, the process ends when the power is turned off or an interrupt occurs to end the process.

[0484] Next, an example of the operation of the terminal device 6 will be described with reference to the flowchart of Fig. 41. In the flowchart of Fig. 41, the description of the same steps as in Fig. 19 will be omitted.

[0485] (Step S4101) The terminal transmitting unit 65 obtains the user identifier from the terminal storage unit 61, and transmits the position information and the like obtained in step S1903 to the behavior acquisition apparatus 5 in association with the user identifier.

[0486] (Step S4102) The terminal transmitting unit 65 transmits the output instruction accepted in step S1916 to the behavior acquisition apparatus 5 in association with the user identifier in the terminal storage unit 61. Note that the output instruction usually includes period information.

[0487] (Step S4103) The terminal receiving unit 63 determines whether or not output information has been received from the behavior acquisition apparatus 5. If output information has been received, the process proceeds to step S4104, and if not, the process returns to step S4103.

[0488] (Step S4104) The terminal processing unit 64 uses the received output information to compose output information to be output. The terminal output unit 66 outputs the output information. The process returns to step S1901.

[0489] (Step S4105) The terminal transmitting unit 65 transmits the information acquired from the information input accepted in step S1919 to the behavior acquisition apparatus 5 in association with the user identifier in the terminal storage unit 61. The process returns to step S1901.

[0490] The information may be, for example, a confirmation instruction, changed behavioral information, or changed emotion information. The confirmation instruction includes information that specifies the behavioral information to be confirmed or the emotion information to be confirmed. The changed behavioral information corresponds to information that specifies the behavioral information to be modified. The changed emotion information corresponds to information that specifies the emotion information to be modified.

[0491] In the flowchart of FIG. 41, the process ends when the power is turned off or an interrupt occurs to end the process.

[0492] As described above, according to this embodiment, the user's behavior can be estimated using location information corresponding to time.

[0493] Furthermore, according to this embodiment, the user's behavior can be estimated using the location information corresponding to a time and the user's activity data corresponding to that time.

[0494] Furthermore, according to this embodiment, the user's behavior can be estimated with higher accuracy using location information corresponding to the time, the user's activity data corresponding to the time, and the user's vital data corresponding to the time.

[0495] Furthermore, according to this embodiment, it is possible to estimate the user's emotions during the action.

[0496] Furthermore, according to this embodiment, the user's behavior can be estimated with higher accuracy using past records.

[0497] Furthermore, according to this embodiment, the past records of two or more users can be used to estimate the user's behavior with higher accuracy.

[0498] Furthermore, according to this embodiment, when the user's behavior cannot be estimated, the location where the user was can be output.

[0499] Furthermore, the software that realizes the behavior acquisition device 5 in this embodiment is the following program. That is, this program causes a computer to function as a time acquisition unit that acquires time, a location acquisition unit that acquires location information associated with the time, a behavior estimation unit that uses two or more pieces of behavior source information including the location information associated with the time to acquire behavior information that identifies the user's behavior in a time period identified by the time contained in each of the two or more pieces of behavior source information, and a behavior output unit that outputs the behavior information in the time period.

[0500] Furthermore, the devices indicated by reference numeral 4 in Fig. 16 and reference numeral 6 in Fig. 37 represent the external appearance of a computer that executes the programs described herein to realize the behavior acquisition devices and the like of the various embodiments described above. The above-described embodiments can be realized by computer hardware and a computer program executed thereon. The devices indicated by reference numeral 4 in Fig. 16 and reference numeral 6 in Fig. 37 are overview diagrams of this computer system 300, and Fig. 42 is a block diagram of the system 300.

[0501] In each of the devices 4 in FIG. 16 and 6 in FIG. 37, a computer system 300 includes a computer 301 including a CD-ROM drive, a keyboard 302, a mouse 303, and a monitor 304.

[0502] 42, the computer 301 includes, in addition to a CD-ROM drive 3012, an MPU 3013, a bus 3014 connected to the CD-ROM drive 3012 etc., a ROM 3015 for storing programs such as a boot-up program, a RAM 3016 connected to the MPU 3013 for temporarily storing instructions for application programs and providing temporary storage space, and a hard disk 3017 for storing application programs, system programs, and data. Although not shown here, the computer 301 may further include a network card for providing connection to a LAN.

[0503] A program that causes computer system 300 to execute the functions of the behavior acquisition device and the like of the above-described embodiment may be stored on CD-ROM 3101, inserted into CD-ROM drive 3012, and then transferred to hard disk 3017. Alternatively, the program may be sent to computer 301 via a network (not shown) and stored on hard disk 3017. The program is loaded into RAM 3016 when executed. The program may also be loaded directly from CD-ROM 3101 or the network.

[0504] The program does not necessarily have to include an operating system (OS) or a third-party program that causes the computer 301 to execute the functions of the behavior acquisition device of the above-described embodiment. The program only needs to include instructions that call appropriate functions (modules) in a controlled manner and achieve the desired results. How the computer system 300 operates is well known, and a detailed description thereof will be omitted.

[0505] In addition, in the above program, the steps of transmitting information and receiving information do not include processing performed by hardware, such as processing performed by a modem or interface card in the transmission step (processing that can only be performed by hardware).

[0506] The computer that executes the program may be a single computer or a plurality of computers, that is, it may perform centralized processing or distributed processing.

[0507] Furthermore, in each of the above embodiments, it goes without saying that two or more communication means present in one device may be physically realized by one medium.

[0508] Furthermore, in each of the above embodiments, each process may be realized by centralized processing in a single device, or may be realized by distributed processing in a plurality of devices.

[0509] The present invention is not limited to the above-described embodiment, and various modifications are possible, and it goes without saying that these modifications are also included within the scope of the present invention.

[0510] As described above, the behavior acquisition device according to the present invention has the effect of being able to estimate a user's behavior using location information corresponding to time, and is useful, for example, as a smart watch or smartphone carried by a user.

Claims

1. A behavior acquisition device comprising: a time acquisition unit that acquires time; a reception unit that receives radio waves including device identifiers for identifying the communication devices from each of three or more communication devices; an intensity acquisition unit that acquires time-series radio wave intensities for each of the three or more communication devices; a type determination unit that determines whether each of the three or more communication devices is a fixed terminal that is fixed or a mobile terminal that is moving, using the time-series radio wave intensities acquired by the intensity acquisition unit; a position acquisition unit that acquires position information corresponding to the time and that is indoor position information, using the radio wave intensities of the three or more communication devices determined by the type determination unit to be fixed terminals; a behavior estimation unit that acquires behavior information for identifying the behavior of a user in a time zone specified by the time included in two or more pieces of behavior source information including the position information corresponding to the time; and a behavior output unit that outputs the behavior information in the time zone.

2. The behavior acquisition device according to claim 1, further comprising an activity acquisition unit that acquires activity data of the user corresponding to the time or a vital acquisition unit that acquires one or two or more types of vital data of the user corresponding to the time, wherein the behavior estimation unit acquires the behavior information in the time zone using two or more pieces of behavior source information including the position information corresponding to the time, the activity data, or one or more types of the vital data.

3. The behavior acquisition device according to claim 1, further comprising an activity acquisition unit that acquires activity data of the user corresponding to the time and a vital acquisition unit that acquires one or two or more types of vital data of the user corresponding to the time, wherein the behavior estimation unit acquires the behavior information in the time zone using two or more pieces of behavior source information including the position information corresponding to the time, the activity data, and one or more types of the vital data.

4. The behavior acquisition device according to any one of claims 1 to 3, further comprising an emotion estimation unit that acquires emotion information regarding the emotion of the user in the time zone using the behavior source information, wherein the behavior estimation unit also uses the emotion information to acquire the behavior information in the time zone.

5. The behavior acquisition device according to any one of claims 1 to 3, wherein the behavior estimation unit also uses one or more pieces of past behavior information including the immediately preceding behavior information to acquire the behavior information in the time zone.

6. The action acquisition device according to claim 1, further comprising an emotion estimation unit that acquires emotion information regarding the user's emotion in the time period using the action information or the action source information, and an emotion output unit that outputs the emotion information.

7. The emotion estimation unit uses the learning information of the learning management unit in which learning information based on two or more pieces of teacher data having one or two or more pieces of action source information or action information and emotion information is stored, and the action information acquired by the action estimation unit or the action source information that is the source of acquiring the action information to acquire the emotion information in the time period. The action acquisition device according to claim 6.

8. The action estimation unit uses the learning information of the learning management unit in which learning information based on two or more pieces of teacher data having one or two or more pieces of action source information and action information is stored, and two or more pieces of the action source information including the position information corresponding to the time to acquire the action information in the time period. The action acquisition device according to claim 1.

9. The action information included in at least one piece of teacher data among the two or more pieces of teacher data is action information input by the user. The action acquisition device according to claim 7.

10. The action information included in at least two pieces of teacher data among the two or more pieces of teacher data is action information for two or more users. The action acquisition device according to claim 9.

11. The action acquisition device according to claim 1, further comprising a location acquisition unit that refers to a map management unit storing a map having location information corresponding to the position information, and acquires the location information corresponding to the position information acquired by the position acquisition unit, and when the action estimation unit cannot acquire the action information, the action output unit outputs the location information acquired by the location acquisition unit.

12. The action acquisition device according to claim 1, further comprising a storage unit that associates the action information acquired by the action estimation unit with the time period and stores it in an action management unit in which action information corresponding to two or more time periods is stored. The action information can be determined whether it is confirmed action information or not, and the action output unit outputs two or more pieces of action information in such a manner that the confirmed action information and the unconfirmed action information can be visually distinguished.

13. A method for acquiring an action realized by a time acquisition unit, a reception unit, an intensity acquisition unit, a type determination unit, a position acquisition unit, an action estimation unit, and an action output unit, wherein the time acquisition unit has a time acquisition step of acquiring a time; the reception unit has a reception step of receiving radio waves including device identifiers for identifying the communication device from each of three or more communication devices; the intensity acquisition unit has an intensity acquisition step of acquiring time-series radio wave intensities for each of the three or more communication devices; the type determination unit has a type determination step of determining, using the time-series radio wave intensities acquired by the intensity acquisition unit, whether each of the three or more communication devices is a fixed terminal that is fixed or a mobile terminal that is moving; the position acquisition unit has a position acquisition step of acquiring position information corresponding to the time and being indoor position information using the radio wave intensities of the three or more communication devices determined by the type determination unit to be fixed terminals; the action estimation unit has an action estimation step of acquiring action information for specifying the action of a user in a time zone specified by the time included in the two or more pieces of action source information including the position information corresponding to the time using the two or more pieces of action source information; and the action output unit has an action output step of outputting the action information in the time zone. A method for acquiring an action comprising the above steps.

14. A recording medium recording a program for causing a computer to function as a time acquisition unit for acquiring a time, a reception unit for receiving radio waves including device identifiers for identifying the communication device from each of three or more communication devices, an intensity acquisition unit for acquiring time-series radio wave intensities for each of the three or more communication devices, a type determination unit for determining, using the time-series radio wave intensities acquired by the intensity acquisition unit, whether each of the three or more communication devices is a fixed terminal that is fixed or a mobile terminal that is moving, a position acquisition unit for acquiring position information corresponding to the time and being indoor position information using the radio wave intensities of the three or more communication devices determined by the type determination unit to be fixed terminals, an action estimation unit for acquiring action information for specifying the action of a user in a time zone specified by the time included in the two or more pieces of action source information including the position information corresponding to the time using the two or more pieces of action source information, and an action output unit for outputting the action information in the time zone.

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